From 306066906aba46d42fe3e22dbaad561dc76697bc Mon Sep 17 00:00:00 2001 From: carmengg Date: Fri, 29 Sep 2023 21:01:03 +0000 Subject: [PATCH 01/31] added selecting by condition --- lectures/lesson-2-pandas-basics.qmd | 77 ++++++++++++++++++++++++++++- 1 file changed, 75 insertions(+), 2 deletions(-) diff --git a/lectures/lesson-2-pandas-basics.qmd b/lectures/lesson-2-pandas-basics.qmd index 5484582..9623ff5 100644 --- a/lectures/lesson-2-pandas-basics.qmd +++ b/lectures/lesson-2-pandas-basics.qmd @@ -110,6 +110,10 @@ print(s) ``` + + + + There's much more to say about `pandas.Series`, but this is enought to get us going. Mainly, we want to know about `pandas.Series` because they will be the columns of our `pandas.DataFrame`. ### Attributes & Methods @@ -229,6 +233,12 @@ df.head() # get column names df.columns ``` + +```{python} +# check the data types of each column +df.dtypes +``` + ```{python} # data frame's shape: output is a tuple (# rows, # columns) df.shape @@ -252,7 +262,7 @@ Since we only selected a single column the output is a `pandas.Series`. :::{.callout-note} ## `pd.DataFrame` = dictionary of columns -Remember we can think of a `pandas.DataFrame` as a dictionary of its columns? Then we can access a single column using the column name as the key, just like we would do in a dictionary. That is the syntaxi we just used: `df['column_name']`. +Remember we can think of a `pandas.DataFrame` as a dictionary of its columns? Then we can access a single column using the column name as the key, just like we would do in a dictionary. That is the we just used: `df['column_name']`. ::: In general, **selecting by label** means we want to select data from our data frame using the *names* of the columns, *not their position*. @@ -309,11 +319,74 @@ Notice two things: - the slice of the data frame we got includes both endpoints of the slice `'arctic':'antarctica'`. In other words we get the `arctic` column *and* the `antarctica` column. This is different from how slicing works in base Python and NumPy, where the end point is not included. ### Selecting rows... +Now that we are familiar with some methods for selecting columns, let's move on to selecting rows. #### ... using a condition +Selecting which rows satisfy a particular condition is, in my experience, the most usual kind of row subsetting. The general syntax for this type of selection is `df[condition_on_rows]`. For example, suppose we are intersted in all data after 1996. We can select those rows in this way: + +```{python} +# select all rows with year > 1996 +after_96 = df[df['year']>1996] +after_96 +``` + +Let's break down what is happening here. In this case the condition for our rows is `df['year']>1996`, this checks which rows have a value greater than 1996 in the year column. Let's see this explicitely: + +```{python} +# check the type of df['year']>1996 +print(type(df['year']>1996)) + +df['year']>1996 +``` + +The output is a `pandas.Series` with boolean values (`True` or `False`) indicating which rows satisfy the condition year>1996. When we pass such a series of boolean values to the selection brackets `[]` we keep only those rows with a `True` value. + +Here's another example of using a condition. Suppose we want to look at data from years 1970 to 1979. One of doing this is to use the `in` operator in our condition: + +```{python} +seventies = df[df['year'].isin(range(1970,1980))] +seventies +``` + +Let's break it down: + +- `df['year']` is the column with the year values, a `pandas.Series`, + +- in `df['year'].isin()`, we have that [`isin` is a method for the `pandas.Series`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.isin.html) and we are calling it using the dot `.`. + +- `range(1970,1980)` constructs consecutive integers from 1970 to 1979 - remember the right endopoint (1980) is not included! + +- `df['year'].isin(range(1970,1980))` is then a `pandas.Series` of boolean values indicating which rows have year equal to 1970, ..., 1979. + +- when we put `df['year'].isin(range(1970,1980))` inside the selection brackets `[]` we obtain the rows of the data frame with year equal to 1970, ..., 1979. + +#### ... using multiple conditions +We can combine multipe conditions by surrounding each one in parenthesis `()` and using the or operator `|` and the and operator `and`. + +***or* example**: + +```{python} +# select rows with +# annual_sea_level_rise<0.5 mm OR annual_sea_level_rise>1 mm + +df[ (df['annual_sea_level_rise']<0.5) | (df['annual_sea_level_rise']>0.8)] +df.head() +``` + +***and* example** + +```{python} +# select rows with cumulative_sea_level_rise>10 AND global_glacial_volume_change<-300 +df[ (df['cumulative_sea_level_rise']>10) & (df['global_glacial_volume_change']<-300)] +``` + + + #### ... by position +### Selecting rows and columns + ::: {.callout-caution} @@ -352,7 +425,7 @@ DataFrame](https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_sub -## Basic Matplotlib +## Basic Ploting ## Resources From 26044104149af7ce8a7342d438938ffe517e6337 Mon Sep 17 00:00:00 2001 From: carmengg Date: Fri, 29 Sep 2023 21:51:33 +0000 Subject: [PATCH 02/31] added iloc section --- lectures/lesson-2-pandas-basics.qmd | 46 +++++++++++++++++++---------- 1 file changed, 31 insertions(+), 15 deletions(-) diff --git a/lectures/lesson-2-pandas-basics.qmd b/lectures/lesson-2-pandas-basics.qmd index 9623ff5..edf236a 100644 --- a/lectures/lesson-2-pandas-basics.qmd +++ b/lectures/lesson-2-pandas-basics.qmd @@ -246,7 +246,7 @@ df.shape ### Selecting a single column... -#### ...by label +#### ...by column name This is the simplest case for selecting data. Suppose we are interested in the annual sea level rise. Then we can access that single column in this way: ```{python} # seelect a single column by using square brackets [] @@ -265,7 +265,7 @@ Since we only selected a single column the output is a `pandas.Series`. Remember we can think of a `pandas.DataFrame` as a dictionary of its columns? Then we can access a single column using the column name as the key, just like we would do in a dictionary. That is the we just used: `df['column_name']`. ::: -In general, **selecting by label** means we want to select data from our data frame using the *names* of the columns, *not their position*. +This is an example of **selecting by label**, which means we want to select data from our data frame using the *names* of the columns, *not their position*. #### ... with attribute syntax @@ -298,7 +298,7 @@ print((europe_asia.shape)) ``` #### ... using a slice -Yet another example of label selection! In this case we will use the `loc` function. This is a powerful function! The general syntax is +Yet another example of selecting by label! In this case we will use the `loc` function. This is a powerful function! The general syntax is ```python df.loc[ row-selection , column-selection] @@ -315,7 +315,7 @@ all_regions.head() Notice two things: -- we used the colon `:` as the `row-selection` parameters, this means "select all the rows" +- we used the colon `:` as the `row-selection` parameter, which means "select all the rows" - the slice of the data frame we got includes both endpoints of the slice `'arctic':'antarctica'`. In other words we get the `arctic` column *and* the `antarctica` column. This is different from how slicing works in base Python and NumPy, where the end point is not included. ### Selecting rows... @@ -341,7 +341,7 @@ df['year']>1996 The output is a `pandas.Series` with boolean values (`True` or `False`) indicating which rows satisfy the condition year>1996. When we pass such a series of boolean values to the selection brackets `[]` we keep only those rows with a `True` value. -Here's another example of using a condition. Suppose we want to look at data from years 1970 to 1979. One of doing this is to use the `in` operator in our condition: +Here's another example of using a condition. Suppose we want to look at data from years 1970 to 1979. One way of doing this is to use the `in` operator in our condition: ```{python} seventies = df[df['year'].isin(range(1970,1980))] @@ -367,7 +367,7 @@ We can combine multipe conditions by surrounding each one in parenthesis `()` an ```{python} # select rows with -# annual_sea_level_rise<0.5 mm OR annual_sea_level_rise>1 mm +# annual_sea_level_rise<0.5 mm OR annual_sea_level_rise>0.8 mm df[ (df['annual_sea_level_rise']<0.5) | (df['annual_sea_level_rise']>0.8)] df.head() @@ -381,23 +381,42 @@ df[ (df['cumulative_sea_level_rise']>10) & (df['global_glacial_volume_change']<- ``` +#### ... by position +All the selections we have done so far have been using labels or using a condition. Sometimes we might want to select certain rows depending on their *actual position* in the data frame. In this case we use the `iloc` method. `iloc` stands for integer-location based indexing. Let's see some examples: -#### ... by position +```{python} +# select the fifht row = index 4 +df.iloc[4] +``` + +```{python} +# select rows 23 through 30, inclduing 30 +df.iloc[23:31] +``` + +Notice since we are back to indexing by position the right endpoint of the slice (6) is not included in the ouput. + +### Selecting rows and columns simultaneously -### Selecting rows and columns +Selecting rows and columns +### Notes about `loc` and `iloc` ::: {.callout-caution} ## `iloc` vs. `loc` -At the beginning, the difference between `iloc` and `loc` can be confusing. Remember the `i` in `iloc` stands for integer-location, so this function only uses integer indexing to retrieve information from the data frames. Moreover, remember `iloc` indexing works in the same way as indexing for Python lists. +At the beginning, the difference between `iloc` and `loc` can be confusing. Remember the `i` in `iloc` stands for *integer-location*, so this function only uses integer indexing to retrieve information from the data frames in the same way as indexing for Python lists. If you want to dive deeper, this is a great discussion about the difference between `iloc` and `loc`: [Stackoverflow - How are iloc and loc different?](https://stackoverflow.com/questions/31593201/how-are-iloc-and-loc-different/31593712#31593712) + +And, as always, the documentation will provide you with more information: +[`pandas.DataFrame.loc`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.loc.html) and [`pandas.DataFrame.iloc`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.iloc.html). ::: -:::{.callout-warning collapse="true"} +:::{.callout-warning} ## Selecting a column with `iloc`? Avoid it! +We can also access columns by position using `iloc` - but it is best not to if possible. Suppose we want to access the 10th column in the data frame - then we want to select a column *by position*. In this case the 10th column is the annual sea level rise data and the 10th position corresponds to the index 9. We can select this column by position using the `iloc` method*: @@ -409,7 +428,7 @@ annual_rise_3 = df.iloc[:,9] annual_rise_3.head() ``` -Unless you are really looking for information about *the 10th column*, do not access a column by position. This is bound to break in many ways: +Unless you are *really* looking for information about *the 10th column*, do not access a column by position. This is bound to break in many ways: - it relies on a person correctly counting the position of a column. Even with a small dataset this can be prone to error. @@ -420,13 +439,10 @@ Unless you are really looking for information about *the 10th column*, do not ac ::: -This is not an exhaustive list of methods to select data, there are *so many* ways to subset the same data and [it can get quite fancy.](https://pandas.pydata.org/docs/user_guide/indexing.html#selection-by-label) For a simple take on some of these concepts, the `pandas` [Getting started tutorials - How do I select a subset of a +What is presented in this section is not an exhaustive list of methods to select data, there are *so many* ways to subset the same data and [it can get quite fancy.](https://pandas.pydata.org/docs/user_guide/indexing.html#selection-by-label) For a simple take on some of these concepts, the `pandas` [Getting started tutorials - How do I select a subset of a DataFrame](https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html) is a good reference. - -## Basic Ploting - ## Resources 📖 [Pandas documentation - User Guide](https://pandas.pydata.org/docs/user_guide/index.html) From 970508d2ca881de7e45cbe2909972655a7043a98 Mon Sep 17 00:00:00 2001 From: carmengg Date: Fri, 29 Sep 2023 23:58:01 +0000 Subject: [PATCH 03/31] added iloc and loc --- lectures/lesson-2-pandas-basics.qmd | 96 ++++++++++++++++++++++++----- 1 file changed, 80 insertions(+), 16 deletions(-) diff --git a/lectures/lesson-2-pandas-basics.qmd b/lectures/lesson-2-pandas-basics.qmd index edf236a..2332f2d 100644 --- a/lectures/lesson-2-pandas-basics.qmd +++ b/lectures/lesson-2-pandas-basics.qmd @@ -1,9 +1,7 @@ # Pandas Fundamentals ## About -In this lesson we cover the two core objects in the `pandas` library the series and the data frame. We will also learn methods to subset, select, and add data to our data frames, as well as some basic plotting with matplotlib. - -There is so much to learn about `pandas`. While we won't be able to cover every single functionality of this package in the next three lecutres, the goal is to get you started with the basic tools for data wrangling and give you a solid basis on which you can explore further. +In this lesson we cover the two core objects in the `pandas` library, the `pandas.Series` and the `pandas.DataFrame`. We will also learn methods to select data from our datasets. ## `pandas` @@ -18,6 +16,9 @@ import pandas as pd import numpy as np ``` + +There is so much to learn about `pandas`. While we won't be able to cover every single functionality of this package in the next three lecutres, the goal is to get you started with the basic tools for data wrangling and give you a solid basis on which you can explore further. + ## Series The first core data structure of pandas is the **series**. @@ -360,6 +361,30 @@ Let's break it down: - when we put `df['year'].isin(range(1970,1980))` inside the selection brackets `[]` we obtain the rows of the data frame with year equal to 1970, ..., 1979. +:::{.callout-note} +## `loc` for row selection +It is equivalent to write + +```python +# select rows with year<1965 +df[df['year'] < 1965] +``` +and +```python +# select rows with year<1965 using love +df.loc[ df['year'] <1965 , :] +``` +In the second one: + +- we are using the `df.loc[ row-selection , column-selection]` syntax + +- the `row-selection` parameter is the condition `df['year']<1965` + +- the `column-selection` parameter is a colon `:`, which indicates we want all columns for the rows we are selecting. + +We prefer the first syntax when we are selecting rows and not columns since it is simpler. +::: + #### ... using multiple conditions We can combine multipe conditions by surrounding each one in parenthesis `()` and using the or operator `|` and the and operator `and`. @@ -383,7 +408,7 @@ df[ (df['cumulative_sea_level_rise']>10) & (df['global_glacial_volume_change']<- #### ... by position -All the selections we have done so far have been using labels or using a condition. Sometimes we might want to select certain rows depending on their *actual position* in the data frame. In this case we use the `iloc` method. `iloc` stands for integer-location based indexing. Let's see some examples: +All the selections we have done so far have been using labels or using a condition. Sometimes we might want to select certain rows depending on their *actual position* in the data frame. In this case we use the `iloc` method with the syntax `df.iloc[row-indices]`. `iloc` stands for integer-location based indexing. Let's see some examples: ```{python} # select the fifht row = index 4 @@ -397,9 +422,52 @@ df.iloc[23:31] Notice since we are back to indexing by position the right endpoint of the slice (6) is not included in the ouput. -### Selecting rows and columns simultaneously +### Selecting rows and columns simultaneously... -Selecting rows and columns +Selecting rows and columns simultaneously can be done using `loc` (labels or conditions) or `iloc` (integer position). + +# ...by labels or conditions +When we want to select rows and columns simultaneously by labels or conditions we can use the method `loc` with the syntax + +```python +df.loc[ row-selection , column-selection] +``` + +specifying both paratmers: `row-selection` and `column-selection`. These parameters can be a condition (which generates a boolean array) or a subset of labels from the index or the column names. Let's see an examples: + +```{python} +# select change in glacial volume in Europe per year after 2000 +df.loc[df['year']>2000,['year','europe']] +``` +Let's break it down: + +- we are using the `df.loc[ row-selection , column-selection]` syntax + +- the `row-selection` parameter is the condition `df['year']>1990`, which is a boolean array saying which years are greater than 1990 + +- the `column-selection` parameter is `['year','europe']` which is a list with the names of the two columns we are intersted in. + +# ... by position + +When we want to select rows and columns simultaneously by position we use the `iloc` method with the syntax: +```python +df.iloc[ row-indices , column-indices] +``` + +For example, + +```{python} +# select rows 3-7 (including 7) and columns 3 and 4 +df.iloc[ 3:8, [3,4] ] +``` + +Let's break it down: + +- we are using the `df.iloc[ row-indices , column-indices]` syntax + +- the `row-indices` parameter is the slice *of integer indices* 3:8. Remember the right endpoint (8) won't be included. + +- the `column-indices` parameter is the list of integer indices 3 and 4. This means we are selecting the fourth and fifth column. ### Notes about `loc` and `iloc` @@ -414,8 +482,9 @@ And, as always, the documentation will provide you with more information: [`pandas.DataFrame.loc`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.loc.html) and [`pandas.DataFrame.iloc`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.iloc.html). ::: + :::{.callout-warning} -## Selecting a column with `iloc`? Avoid it! +## `iloc` for column selection? Avoid it! We can also access columns by position using `iloc` - but it is best not to if possible. Suppose we want to access the 10th column in the data frame - then we want to select a column *by position*. In this case the 10th column is the annual sea level rise data and the 10th position corresponds to the index 9. We can select this column by position using the `iloc` method*: @@ -432,9 +501,9 @@ Unless you are *really* looking for information about *the 10th column*, do not - it relies on a person correctly counting the position of a column. Even with a small dataset this can be prone to error. -- it is not explicit: if we wnat information about sea level rise `df.annual_sea_level_rise` or `df['annual_sea_level_rise']` are explicitely telling us we are accessing that information. `df.iloc[:,9]` is obscure and uninformative. +- it is not explicit: if we want information about sea level rise `df.annual_sea_level_rise` or `df['annual_sea_level_rise']` are explicitely telling us we are accessing that information. `df.iloc[:,9]` is obscure and uninformative. -- datastets can get updated. Maybe a new column was added before `annual_sea_level_rise`, this would change the position of the column, which would make any code depnding on `df.iloc[:,9]` invalid. Accessing by label helps reproducibility! +- datastets can get updated. Maybe a new column was added before `annual_sea_level_rise`, this would change the position of the column, which would make any code depending on `df.iloc[:,9]` invalid. Accessing by label helps reproducibility! ::: @@ -449,11 +518,6 @@ DataFrame](https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_sub 📖 [Python for Data Analysis, 3E - Getting started with pandas](https://wesmckinney.com/book/pandas-basics) +## Acknowledgements - -CONTINUE: -https://pandas.pydata.org/docs/user_guide/dsintro.html -https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html -https://wesmckinney.com/book/pandas-basics -https://pandas.pydata.org/docs/user_guide/indexing.html#selection-by-label -https://earth-env-data-science.github.io/lectures/pandas/basic_pandas.html \ No newline at end of file +The simplified glacial_loss.csv dataset was created by [Dr. Allison Horst](https://allisonhorst.github.io) as part of her course materials on environmental data science. \ No newline at end of file From 860d67b107dec3dd2b9570d1f40ae7c3b67cbff9 Mon Sep 17 00:00:00 2001 From: carmengg Date: Sat, 30 Sep 2023 00:56:53 +0000 Subject: [PATCH 04/31] all content in lesson 2 --- docs/index.html | 5 + docs/lectures/lesson-2-pandas-basics.html | 1385 ++++++++++++++++++--- docs/search.json | 33 +- lectures/lesson-2-pandas-basics.qmd | 73 +- 4 files changed, 1287 insertions(+), 209 deletions(-) diff --git a/docs/index.html b/docs/index.html index a8f4459..a8daa8b 100644 --- a/docs/index.html +++ b/docs/index.html @@ -138,6 +138,7 @@

Table of contents

  • Syllabus
  • Calendar
  • About this website
    • +
  • Contribute
    • @@ -205,6 +206,10 @@

    Calendar

    About this website

    This website is created with Quarto and is published through GitHub pages. Click here to go to the website’s GitHub repository.

    +
    +
    +

    Contribute

    +

    If you have suggestions on how to correct, improve, or expand this book, please feel free to email galaz-garcia@nceas.ucsb.edu or file a GitHub issue.

    diff --git a/docs/lectures/lesson-2-pandas-basics.html b/docs/lectures/lesson-2-pandas-basics.html index 797d10d..789cc53 100644 --- a/docs/lectures/lesson-2-pandas-basics.html +++ b/docs/lectures/lesson-2-pandas-basics.html @@ -196,8 +196,8 @@

    Table of contents

  • 2.2 pandas
  • 2.3 Series
  • 2.4 Data Frames @@ -209,9 +209,11 @@

    Table of contents

  • 2.5.1 Selecting a single column…
  • 2.5.2 Selecting multiple columns…
  • 2.5.3 Selecting rows…
    • +
  • 2.5.4 Selecting rows and columns simultaneously…
    • +
  • 2.5.5 Notes about loc and iloc
    • -
  • 2.6 Basic Matplotlib
    • -
  • 2.7 Resources
    • +
  • 2.6 Resources
    • +
  • 2.7 Acknowledgements
    • @@ -237,8 +239,7 @@

    2 

    2.1 About

    -

    In this lesson we cover the two core objects in the pandas library the series and the data frame. We will also learn methods to subset, select, and add data to our data frames, as well as some basic plotting with matplotlib.

    -

    There is so much to learn about pandas. While we won’t be able to cover every single functionality of this package in the next three lecutres, the goal is to get you started with the basic tools for data wrangling and give you a solid basis on which you can explore further.

    +

    In this lesson we cover the two core objects in the pandas library, the pandas.Series and the pandas.DataFrame. We will also learn methods to select data from our datasets.

    2.2 pandas

    @@ -250,6 +251,7 @@

    # we will also import numpy import numpy as np +

    There is so much to learn about pandas. While we won’t be able to cover every single functionality of this package in the next three lecutres, the goal is to get you started with the basic tools for data wrangling and give you a solid basis on which you can explore further.

    2.3 Series

    @@ -267,18 +269,18 @@

    print(s) 
    <class 'numpy.ndarray'>
-[ 0.26255995 -0.43578912  2.45025948  1.46437431] 
+[-1.52046387 -0.73808435 -1.27879272  0.80935007] 
 
 <class 'pandas.core.series.Series'>
-0    0.262560
-1   -0.435789
-2    2.450259
-3    1.464374
+0   -1.520464
+1   -0.738084
+2   -1.278793
+3    0.809350
 dtype: float64
    -
    -

    2.3.1 How to create a pandas.Series

    +
    +

    2.3.1 Creating a pandas.Series

    The basic method to create a pandas.Series is to call

    s = pd.Series(data, index=index)

    The data parameter can be:

    @@ -288,13 +290,11 @@

    a number

    The index parameter is a list of index labels.

    -
    -

    2.3.1.1 From a numpy array or list

    -

    To use this method we need to pass a numpy array (or a list of objects that can be converted to NumPy types) as data and a list of indices of the same length as data.

    +

    For now, we will create a pandas.Series from a numpy array or list. To use this method we need to pass a numpy array (or a list of objects that can be converted to NumPy types) as data and a list of indices of the same length as data.

    # a Series from a numpy array 
 pd.Series(np.arange(3), index=['a','b','c'])
    -
    +
    a    0
 b    1
 c    2
@@ -305,7 +305,7 @@ 
    
 
    # a Series from a list of strings with default index
 pd.Series(['EDS 220', 'EDS 222', 'EDS 223', 'EDS 242'])
    
-
    
+
    
 
    0    EDS 220
 1    EDS 222
 2    EDS 223
@@ -313,53 +313,67 @@ 
    
-
    2.3.1.2 From a dictionary
    
-
    Remember a dictionary is a set of key-value pairs. If we create a pandas.Series via a dictionary the keys will become the index and the values the corresponding data.
    
-
    
-
    # construct dictionary
-d = {'a':0, 'b':1, 'c':2}
-
-# initialize a sries using a dictionary
-pd.Series(d)
    
-
    
-
    a    0
+
    -
    -

    2.3.2 Arithmetic operations

    +
    +

    2.3.2 Simple operations

    Arithmetic operations work on series and also most NumPy functions. For example:

    -
    # define a series
-s = pd.Series([98,73,65],index=['Andrea', 'Beth', 'Carolina'])
-
-# divide each element in series by 10
-print(s /10, '\n')
-
-# take the exponential of each element in series
-print(np.exp(s), '\n')
-
-# notice this doesn't change the values of our series
-print(s)
    +
    # define a series
+s = pd.Series([98,73,65],index=['Andrea', 'Beth', 'Carolina'])
+
+# divide each element in series by 10
+print(s /10, '\n')
+
+# take the exponential of each element in series
+print(np.exp(s), '\n')
+
+# notice this doesn't change the values of our series
+print(s)
    Andrea      9.8
 Beth        7.3
@@ -377,18 +391,67 @@ 
    
 dtype: int64
    -

    There’s much more to say about pandas.Series, but this is enought to get us going. Mainly, we want to know about pandas.Series because they will be the columns of our pandas.DataFrame.

    +

    We can also produce new pandas.Series with True/False values indicating whether the elements in a series satisfy a condition or not:

    +
    +
    s > 10
    +
    +
    Andrea      True
+Beth        True
+Carolina    True
+dtype: bool
    +
    +
    +

    This kind of simple conditions on pandas.Series will be key when we are selecting data from data frames.

    +

    2.3.3 Attributes & Methods

    pandas.Series have many attributes and methods, you can see a full list in the pandas documentation. For now we will cover two examples that have to do with identifying missing values.

    -

    pandas represents a missing or NA value with NaN, which stands for not a number. Sometime

    +

    pandas represents a missing or NA value with NaN, which stands for not a number. Let’s construct a small series with some NA values:

    +
    +
    # series with NAs in it
+s = pd.Series([1, 2, np.NaN, 4, np.NaN])
    +
    +

    A pandas.Series has an attribute called hasnans that returns True if there are any NaNs:

    +
    +
    # check if series has NAs
+s.hasnans
    +
    +
    True
    +
    +
    +

    Then we might be intersted in knowing which elements in the series are NAs. We can do this using the isna method:

    +
    +
    s.isna()
    +
    +
    0    False
+1    False
+2     True
+3    False
+4     True
+dtype: bool
    +
    +
    +

    We can see the ouput is a pd.Series of boolean values indicating if an element in the row at the given index is NA (True = is NA) or not (False = not NA).

    +
    +
    +
    + +
    +
    +moving on +
    +
    +
    +

    There’s much more to say about pandas.Series, but this is enought to get us going. At this point, we mainly want to know about pandas.Series because pandas.Series are the columns of pandas.DataFrames.

    +
    +
    -
    @@ -484,18 +544,21 @@

    The Data Frame is the most used pandas object. It represents tabular data and we can think of it as a spreadhseet. Each column of a pandas.DataFrame is a pandas.Series.

    2.4.1 Creating a pandas.DataFrame

    -

    There are many ways of creating a pandas.DataFrame. Although we likely won’t be creating data frames from scratch often, I’d like to go over creating a pandas.DataFrame from a dict of pandas.Series as this will help us understand the syntax for other Data Frame methods.

    +

    There are many ways of creating a pandas.DataFrame.

    +

    We already mentioned each column of a pandas.DataFrame is a pandas.Series. In fact, the pandas.DataFrame is a dictionary of pandas.Series, with each column name being the key and the column values being the key’s value. Thus, we can create a pandas.DataFrame in this way:

    -
    -
    # initialize dictionary with columns' data 
-d = {'col_name_1' : pd.Series(np.arange(3)),
-     'col_name_2' : pd.Series([3.1, 3.2, 3.3]),
-     }
-
-# create data frame
-df = pd.DataFrame(d)
-df
    -
    +
    +
    # initialize dictionary with columns' data 
+d = {'col_name_1' : pd.Series(np.arange(3)),
+     'col_name_2' : pd.Series([3.1, 3.2, 3.3]),
+     }
+
+# create data frame
+df = pd.DataFrame(d)
+df
    +
    @@ -529,17 +592,17 @@

    -
    # print original index
-print(df.index)
-
-# change the index
-df.index = ['a','b','c']
-df
    +
    +
    # print original index
+print(df.index)
+
+# change the index
+df.index = ['a','b','c']
+df
    RangeIndex(start=0, stop=3, step=1)
    -
    +
    @@ -572,17 +635,17 @@

    -
    # print original column names
-print(df.columns)
-
-# change column names 
-df.columns = ['C1','C2']
-df
    +
    +
    # print original column names
+print(df.columns)
+
+# change column names 
+df.columns = ['C1','C2']
+df
    Index(['col_name_1', 'col_name_2'], dtype='object')
    -
    +
    @@ -635,13 +698,13 @@

    -
    # read in file
-df = pd.read_csv('data/lesson-1/glacial_loss.csv')
-
-# see the first five rows
-df.head()
    -
    +
    +
    # read in file
+df = pd.read_csv('data/lesson-1/glacial_loss.csv')
+
+# see the first five rows
+df.head()
    +
    @@ -738,40 +801,58 @@

    -
    # get column names
-df.columns
    -
    +
    +
    # get column names
+df.columns
    +
    Index(['year', 'europe', 'arctic', 'alaska', 'asia', 'north_america',
        'south_america', 'antarctica', 'global_glacial_volume_change',
        'annual_sea_level_rise', 'cumulative_sea_level_rise'],
       dtype='object')
    -
    -
    # data frame's shape: output is a tuple (# rows, # columns)
-df.shape
    -
    +
    +
    # check the data types of each column
+df.dtypes
    +
    +
    year                              int64
+europe                          float64
+arctic                          float64
+alaska                          float64
+asia                            float64
+north_america                   float64
+south_america                   float64
+antarctica                      float64
+global_glacial_volume_change    float64
+annual_sea_level_rise           float64
+cumulative_sea_level_rise       float64
+dtype: object
    +
    +
    +
    +
    # data frame's shape: output is a tuple (# rows, # columns)
+df.shape
    +
    (43, 11)

    2.5.1 Selecting a single column…

    -
    -

    2.5.1.1 …by label

    +
    +

    2.5.1.1 …by column name

    This is the simplest case for selecting data. Suppose we are interested in the annual sea level rise. Then we can access that single column in this way:

    -
    -
    # seelect a single column by using square brackets []
-annual_rise = df['annual_sea_level_rise']
-
-# check the type of the ouput
-print(type(annual_rise))
-
-annual_rise.head()
    +
    +
    # seelect a single column by using square brackets []
+annual_rise = df['annual_sea_level_rise']
+
+# check the type of the ouput
+print(type(annual_rise))
+
+annual_rise.head()
    <class 'pandas.core.series.Series'>
    -
    +
    0    0.610010
 1    0.810625
 2    0.100292
@@ -791,18 +872,18 @@
    -

    Remember we can think of a pandas.DataFrame as a dictionary of its columns? Then we can access a single column using the column name as the key, just like we would do in a dictionary. That is the syntaxi we just used: df['column_name'].

    +

    Remember we can think of a pandas.DataFrame as a dictionary of its columns? Then we can access a single column using the column name as the key, just like we would do in a dictionary. That is the we just used: df['column_name'].

    -

    In general, selecting by label means we want to select data from our data frame using the names of the columns, not their position.

    +

    This is an example of selecting by label, which means we want to select data from our data frame using the names of the columns, not their position.

    2.5.1.2 … with attribute syntax

    We can also access a single column by using attribute syntax:

    -
    -
    annual_rise_2 = df.annual_sea_level_rise
-annual_rise_2.head()
    -
    +
    +
    annual_rise_2 = df.annual_sea_level_rise
+annual_rise_2.head()
    +
    0    0.610010
 1    0.810625
 2    0.100292
@@ -818,17 +899,17 @@ 
    
 
    2.5.2.1 … using a list of column names
    
 
    This is another example of selecting by labels. We just need to pass a list with the column names to the square brackets []. For example, say we want to look at the change in glacial volume in Europe and Asia, then we can select those columns like this:
    
-
    
-
    # select columns with names "europe" and "asia"
-europe_asia = df[['europe','asia']]
    
+
    
+
    # select columns with names "europe" and "asia"
+europe_asia = df[['europe','asia']]
    
 
    
 
    Notice there are double square brackets. This is because we are passing the list of names ['europe','asia'] to the selection brakcets [].
    
-
    
-
    # check the type of the resulting selection
-print(type(europe_asia))
-
-# check the shape of the selection
-print((europe_asia.shape))
    
+
    
+
    # check the type of the resulting selection
+print(type(europe_asia))
+
+# check the shape of the selection
+print((europe_asia.shape))
    
 
    
 
    <class 'pandas.core.frame.DataFrame'>
 (43, 2)
    
@@ -837,15 +918,15 @@ 
    
 
    2.5.2.2 … using a slice
    
-
    Yet another example of label selection! In this case we will use the loc function. This is a powerful function! The general syntax is
    
-
    df.loc[ row-selection , column-selection]
    
+
    Yet another example of selecting by label! In this case we will use the loc function. This is a powerful function! The general syntax is
    
+
    df.loc[ row-selection , column-selection]
    
 
    where row-selection and column-selection are the rows and columns we want to subset from the data frame.
    
 
    Let’s start by a simple example, where we want to select a slice of columns, say the change in glacial volume per year in all regions. This corresponds to all columns between arctic and antarctica.
    
-
    
-
    # select all columns between 'arctic' and 'antarctica'
-all_regions = df.loc[:,'arctic':'antarctica']
-all_regions.head()
    
-
    
+
    
+
    # select all columns between 'arctic' and 'antarctica'
+all_regions = df.loc[:,'arctic':'antarctica']
+all_regions.head()
    
+
    
 
 
    
 
@@ -914,18 +995,979 @@ 
    
 
    Notice two things:
    
 
      
-
    • we used the colon : as the row-selection parameters, this means “select all the rows”
      • 
+
    • we used the colon : as the row-selection parameter, which means “select all the rows”
      • 
 
    • the slice of the data frame we got includes both endpoints of the slice 'arctic':'antarctica'. In other words we get the arctic column and the antarctica column. This is different from how slicing works in base Python and NumPy, where the end point is not included.
      •

    2.5.3 Selecting rows…

    +

    Now that we are familiar with some methods for selecting columns, let’s move on to selecting rows.

    2.5.3.1 … using a condition

    +

    Selecting which rows satisfy a particular condition is, in my experience, the most usual kind of row subsetting. The general syntax for this type of selection is df[condition_on_rows]. For example, suppose we are intersted in all data after 1996. We can select those rows in this way:

    +
    +
    # select all rows with year > 1996
+after_96 = df[df['year']>1996]
+after_96
    +
    + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
    361997-13.724106-24.832246-167.229145-34.406403-27.680661-38.213286-20.179090-4600.6860130.90962512.709077
    371998-13.083338-110.429302-107.879027-58.11570230.169987-3.797978-48.129928-4914.8319660.86780713.576884
    381999-8.039555-64.644068-87.714653-26.2117235.888512-8.038630-40.653001-5146.3682310.63960314.216487
    392000-17.008590-96.494055-44.445000-37.518173-29.191986-2.767698-58.873830-5435.3171750.79820215.014688
    402001-8.419109-145.415483-55.749505-35.977022-0.9261347.553503-86.774675-5764.0399310.90807415.922762
    412002-3.392361-48.718943-87.120000-36.127226-27.853498-13.484593-30.203960-6013.2255000.68835816.611120
    422003-3.392361-48.718943-67.253634-36.021991-75.066475-13.223430-30.203960-6289.6409760.76357917.374699
    +
    +
    +
    +

    Let’s break down what is happening here. In this case the condition for our rows is df['year']>1996, this checks which rows have a value greater than 1996 in the year column. Let’s see this explicitely:

    +
    +
    # check the type of df['year']>1996
+print(type(df['year']>1996))
+
+df['year']>1996
    +
    +
    <class 'pandas.core.series.Series'>
    +
    +
    +
    0     False
+1     False
+2     False
+3     False
+4     False
+5     False
+6     False
+7     False
+8     False
+9     False
+10    False
+11    False
+12    False
+13    False
+14    False
+15    False
+16    False
+17    False
+18    False
+19    False
+20    False
+21    False
+22    False
+23    False
+24    False
+25    False
+26    False
+27    False
+28    False
+29    False
+30    False
+31    False
+32    False
+33    False
+34    False
+35    False
+36     True
+37     True
+38     True
+39     True
+40     True
+41     True
+42     True
+Name: year, dtype: bool
    +
    +
    +

    The output is a pandas.Series with boolean values (True or False) indicating which rows satisfy the condition year>1996. When we pass such a series of boolean values to the selection brackets [] we keep only those rows with a True value.

    +

    Here’s another example of using a condition. Suppose we want to look at data from years 1970 to 1979. One way of doing this is to use the in operator in our condition:

    +
    +
    seventies = df[df['year'].isin(range(1970,1980))]
+seventies
    +
    + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
    91970-6.452316-24.494667-0.125296-36.12019911.61979011.6369114.400377-999.0181770.1102252.759719
    1019710.414711-42.90418928.103328-8.702938-9.9645421.061299-6.735536-1038.1044590.1079732.867692
    111972-5.144729-27.004031-22.143350-40.88335732.363730-14.968034-6.223849-1122.8855060.2342023.101894
    1219734.0810909.83944422.985188-31.432594-20.8832322.10364910.539823-1125.6777430.0077133.109607
    1319741.545615-40.126998-29.517874-43.861622-23.991402-21.3388254.419343-1279.9642870.4262063.535813
    1419757.431192-32.410467-44.094084-43.357442-30.858810-2.368842-7.775315-1434.8180370.4277733.963586
    1519763.98675321.686639-28.234725-67.292125-12.534421-19.46535819.250607-1518.1851290.2302964.193882
    1619774.891410-33.123010-5.662139-62.165684-15.9053322.654950-23.727249-1652.4534000.3709074.564788
    1719788.404591-77.561015-12.503384-22.858040-31.0976097.127708-9.140167-1791.3550220.3837064.948495
    1819793.916703-88.351684-63.938851-49.242043-12.076624-17.718503-9.578557-2030.5378480.6607265.609221
    +
    +
    +
    +

    Let’s break it down:

    +
      +

    • df['year'] is the column with the year values, a pandas.Series,

      • +

    • in df['year'].isin(), we have that isin is a method for the pandas.Series and we are calling it using the dot ..

      • +

    • range(1970,1980) constructs consecutive integers from 1970 to 1979 - remember the right endopoint (1980) is not included!

      • +

    • df['year'].isin(range(1970,1980)) is then a pandas.Series of boolean values indicating which rows have year equal to 1970, …, 1979.

      • +

    • when we put df['year'].isin(range(1970,1980)) inside the selection brackets [] we obtain the rows of the data frame with year equal to 1970, …, 1979.

      • +
    +
    +
    +
    + +
    +
    +loc for row selection +
    +
    +
    +

    It is equivalent to write

    +
    # select rows with year<1965
+df[df['year'] < 1965]
    +

    and

    +
    # select rows with year<1965 using love
+df.loc[ df['year'] <1965 , :]
    +

    In the second one:

    +
      +

    • we are using the df.loc[ row-selection , column-selection] syntax

      • +

    • the row-selection parameter is the condition df['year']<1965

      • +

    • the column-selection parameter is a colon :, which indicates we want all columns for the rows we are selecting.

      • +
    +

    We prefer the first syntax when we are selecting rows and not columns since it is simpler.

    +
    +
    -
    -

    2.5.3.2 … by position

    +
    +

    2.5.3.2 … using multiple conditions

    +

    We can combine multipe conditions by surrounding each one in parenthesis () and using the or operator | and the and operator and.

    +

    or example:

    +
    +
    # select rows with 
+# annual_sea_level_rise<0.5 mm OR annual_sea_level_rise>0.8 mm
+
+df[ (df['annual_sea_level_rise']<0.5) | (df['annual_sea_level_rise']>0.8)]
+df.head()
    +
    + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
    01961-5.128903-108.382987-18.721190-32.350759-14.359007-4.739367-35.116389-220.8235150.6100100.610010
    119625.576282-173.252450-24.324790-4.675440-2.161842-13.694367-78.222887-514.2698620.8106251.420635
    21963-10.123105-0.423751-2.047567-3.027298-27.5358813.4196333.765109-550.5756400.1002921.520927
    31964-4.50835820.0701480.477800-18.675385-2.24828620.73263314.853096-519.589859-0.0855961.435331
    4196510.62938543.695389-0.115332-18.414602-19.3987656.86210222.793484-473.112003-0.1283921.306939
    +
    +
    +
    +

    and example

    +
    +
    # select rows with cumulative_sea_level_rise>10 AND  global_glacial_volume_change<-300
+df[ (df['cumulative_sea_level_rise']>10) & (df['global_glacial_volume_change']<-300)]
    +
    + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
    32199316.685013-73.666274-43.702040-65.995130-33.151246-20.578403-20.311577-3672.5820820.67112610.145254
    3319940.741751-3.069084-59.962273-59.004710-89.506142-15.258449-8.168498-3908.9771910.65302510.798280
    341995-2.139665-58.167778-74.1417623.500155-0.699374-19.863392-25.951496-4088.0828730.49476711.293047
    351996-6.809834-4.550205-74.847017-67.4365914.867530-21.080115-11.781489-4271.4015940.50640511.799452
    361997-13.724106-24.832246-167.229145-34.406403-27.680661-38.213286-20.179090-4600.6860130.90962512.709077
    371998-13.083338-110.429302-107.879027-58.11570230.169987-3.797978-48.129928-4914.8319660.86780713.576884
    381999-8.039555-64.644068-87.714653-26.2117235.888512-8.038630-40.653001-5146.3682310.63960314.216487
    392000-17.008590-96.494055-44.445000-37.518173-29.191986-2.767698-58.873830-5435.3171750.79820215.014688
    402001-8.419109-145.415483-55.749505-35.977022-0.9261347.553503-86.774675-5764.0399310.90807415.922762
    412002-3.392361-48.718943-87.120000-36.127226-27.853498-13.484593-30.203960-6013.2255000.68835816.611120
    422003-3.392361-48.718943-67.253634-36.021991-75.066475-13.223430-30.203960-6289.6409760.76357917.374699
    +
    +
    +
    +
    +
    +

    2.5.3.3 … by position

    +

    All the selections we have done so far have been using labels or using a condition. Sometimes we might want to select certain rows depending on their actual position in the data frame. In this case we use the iloc method with the syntax df.iloc[row-indices]. iloc stands for integer-location based indexing. Let’s see some examples:

    +
    +
    # select the fifht row = index 4
+df.iloc[4]
    +
    +
    year                            1965.000000
+europe                            10.629385
+arctic                            43.695389
+alaska                            -0.115332
+asia                             -18.414602
+north_america                    -19.398765
+south_america                      6.862102
+antarctica                        22.793484
+global_glacial_volume_change    -473.112003
+annual_sea_level_rise             -0.128392
+cumulative_sea_level_rise          1.306939
+Name: 4, dtype: float64
    +
    +
    +
    +
    # select rows 23 through 30, inclduing 30
+df.iloc[23:31]
    +
    + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
    2319848.581427-5.755672-33.466092-20.528535-20.734676-8.267686-3.261011-2569.3398020.2326097.097624
    241985-5.970980-49.65108912.065473-31.571622-33.83398510.072906-13.587886-2682.8579260.3135867.411210
    251986-5.68064222.9008477.557447-18.920773-33.014743-4.65203030.482473-2684.1976320.0037017.414911
    2619878.19147712.387780-24.007862-41.121970-48.5609961.6707333.130190-2773.3255680.2462107.661120
    271988-11.117228-31.06648949.897712-21.300712-46.54543513.460422-37.986834-2858.7676210.2360287.897148
    28198914.863220-23.462392-36.112726-46.528372-57.756422-21.687470-10.044757-3041.1691310.5038728.401020
    291990-1.226009-27.484542-92.713339-35.553433-56.563056-31.077022-29.893352-3318.2203970.7653359.166355
    301991-14.391425-34.898689-8.822063-15.338299-31.458010-7.162909-35.968429-3467.6302840.4127349.579089
    +
    +
    +
    +

    Notice since we are back to indexing by position the right endpoint of the slice (6) is not included in the ouput.

    +
    +
    +
    +

    2.5.4 Selecting rows and columns simultaneously…

    +

    Selecting rows and columns simultaneously can be done using loc (labels or conditions) or iloc (integer position).

    +
    +

    2.5.4.1 …by labels or conditions

    +

    When we want to select rows and columns simultaneously by labels or conditions we can use the method loc with the syntax

    +
    df.loc[ row-selection , column-selection]
    +

    specifying both paratmers: row-selection and column-selection. These parameters can be a condition (which generates a boolean array) or a subset of labels from the index or the column names. Let’s see an examples:

    +
    +
    # select change in glacial volume in Europe per year after 2000
+df.loc[df['year']>2000,['year','europe']]
    +
    + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + +
    yeareurope
    402001-8.419109
    412002-3.392361
    422003-3.392361
    +
    +
    +
    +

    Let’s break it down:

    +
      +

    • we are using the df.loc[ row-selection , column-selection] syntax

      • +

    • the row-selection parameter is the condition df['year']>1990, which is a boolean array saying which years are greater than 1990

      • +

    • the column-selection parameter is ['year','europe'] which is a list with the names of the two columns we are intersted in.

      • +
    +
    +
    +

    2.5.4.2 … by position

    +

    When we want to select rows and columns simultaneously by position we use the iloc method with the syntax:

    +
    df.iloc[ row-indices , column-indices]
    +

    For example,

    +
    +
    # select rows 3-7 (including 7) and columns 3 and 4
+df.iloc[ 3:8, [3,4] ]
    +
    + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    alaskaasia
    30.477800-18.675385
    4-0.115332-18.414602
    50.224762-14.630284
    6-7.174030-39.013695
    7-0.6605567.879589
    +
    +
    +
    +

    Let’s break it down:

    +
      +

    • we are using the df.iloc[ row-indices , column-indices] syntax

      • +

    • the row-indices parameter is the slice of integer indices 3:8. Remember the right endpoint (8) won’t be included.

      • +

    • the column-indices parameter is the list of integer indices 3 and 4. This means we are selecting the fourth and fifth column.

      • +
    +
    +
    +
    +

    2.5.5 Notes about loc and iloc

    @@ -936,30 +1978,30 @@

    -

    At the beginning, the difference between iloc and loc can be confusing. Remember the i in iloc stands for integer-location, so this function only uses integer indexing to retrieve information from the data frames. Moreover, remember iloc indexing works in the same way as indexing for Python lists.

    +

    At the beginning, the difference between iloc and loc can be confusing. Remember the i in iloc stands for integer-location, so this function only uses integer indexing to retrieve information from the data frames in the same way as indexing for Python lists.

    If you want to dive deeper, this is a great discussion about the difference between iloc and loc: Stackoverflow - How are iloc and loc different?

    +

    And, as always, the documentation will provide you with more information: pandas.DataFrame.loc and pandas.DataFrame.iloc.

    -
    -
    -
    -

    2.6 Basic Matplotlib

    -
    -
    -

    2.7 Resources

    -

    📖 Pandas documentation - User Guide

    +
    +

    2.6 Resources

    +

    What is presented in this section is a comprehensive, but not an exhaustive list of methods to select data in pandas.DataFrames. There are so many ways to subset data to get the same result. Some of the content from this lesson is adapted from the following resources and I encourage you to read them to learn more!

    +

    📖 Pandas getting started tutorials - How to I select a subset of a DataFrame

    +

    📖 Pandas documentation - User Guide - Indexing and Selecting Data

    📖 Python for Data Analysis, 3E - Getting started with pandas

    -

    CONTINUE: https://pandas.pydata.org/docs/user_guide/dsintro.html https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html https://wesmckinney.com/book/pandas-basics https://pandas.pydata.org/docs/user_guide/indexing.html#selection-by-label https://earth-env-data-science.github.io/lectures/pandas/basic_pandas.html

    +
    +
    +

    2.7 Acknowledgements

    +

    The simplified glacial_loss.csv dataset was created by Dr. Allison Horst as part of her course materials on environmental data science.

    diff --git a/docs/search.json b/docs/search.json index b38807f..ee4dc5d 100644 --- a/docs/search.json +++ b/docs/search.json @@ -41,6 +41,13 @@ "section": "About this website", "text": "About this website\nThis website is created with Quarto and is published through GitHub pages. Click here to go to the website’s GitHub repository." }, + { + "objectID": "index.html#contribute", + "href": "index.html#contribute", + "title": "EDS 220 - Working with Environmental Datasets", + "section": "Contribute", + "text": "Contribute\nIf you have suggestions on how to correct, improve, or expand this book, please feel free to email galaz-garcia@nceas.ucsb.edu or file a GitHub issue." + }, { "objectID": "lectures/lesson-1-python-review.html#about", "href": "lectures/lesson-1-python-review.html#about", @@ -95,48 +102,48 @@ "href": "lectures/lesson-2-pandas-basics.html#about", "title": "2  Pandas Fundamentals", "section": "2.1 About", - "text": "2.1 About\nIn this lesson we cover the two core objects in the pandas library the series and the data frame. We will also learn methods to subset, select, and add data to our data frames, as well as some basic plotting with matplotlib.\nThere is so much to learn about pandas. While we won’t be able to cover every single functionality of this package in the next three lecutres, the goal is to get you started with the basic tools for data wrangling and give you a solid basis on which you can explore further." + "text": "2.1 About\nIn this lesson we cover the two core objects in the pandas library, the pandas.Series and the pandas.DataFrame. We will also learn methods to select data from our datasets." }, { "objectID": "lectures/lesson-2-pandas-basics.html#pandas", "href": "lectures/lesson-2-pandas-basics.html#pandas", "title": "2  Pandas Fundamentals", "section": "2.2 pandas", - "text": "2.2 pandas\npandas is a Python package to wrangle and analyze tabular data. It is built on top of NumPy and has become the core tool for doing data analysis in Python.\nThe convention to import it is:\n\nimport pandas as pd\n\n# we will also import numpy \nimport numpy as np" + "text": "2.2 pandas\npandas is a Python package to wrangle and analyze tabular data. It is built on top of NumPy and has become the core tool for doing data analysis in Python.\nThe convention to import it is:\n\nimport pandas as pd\n\n# we will also import numpy \nimport numpy as np\n\nThere is so much to learn about pandas. While we won’t be able to cover every single functionality of this package in the next three lecutres, the goal is to get you started with the basic tools for data wrangling and give you a solid basis on which you can explore further." }, { "objectID": "lectures/lesson-2-pandas-basics.html#series", "href": "lectures/lesson-2-pandas-basics.html#series", "title": "2  Pandas Fundamentals", "section": "2.3 Series", - "text": "2.3 Series\nThe first core data structure of pandas is the series. A series is a one-dimensional array of indexed data. A pandas.Series having an index is the main difference between a pandas.Series and a numpy array. See the difference:\n\n# a numpy array\n# np.random.randn returns values from the std normal distribution\narr = np.random.randn(4) \nprint(type(arr))\nprint(arr, \"\\n\")\n\n# a pandas series made from the previous array\ns = pd.Series(arr)\nprint(type(s))\nprint(s)\n\n\n[ 0.26255995 -0.43578912 2.45025948 1.46437431] \n\n\n0 0.262560\n1 -0.435789\n2 2.450259\n3 1.464374\ndtype: float64\n\n\n\n2.3.1 How to create a pandas.Series\nThe basic method to create a pandas.Series is to call\ns = pd.Series(data, index=index)\nThe data parameter can be:\n\na numpy array or a list\na Python dictionary\na number\n\nThe index parameter is a list of index labels.\n\n2.3.1.1 From a numpy array or list\nTo use this method we need to pass a numpy array (or a list of objects that can be converted to NumPy types) as data and a list of indices of the same length as data.\n\n# a Series from a numpy array \npd.Series(np.arange(3), index=['a','b','c'])\n\na 0\nb 1\nc 2\ndtype: int64\n\n\nThe index parameter is optional. If we don’t include it, the default is to make the index equal to [0,...,len(data)-1]. For example:\n\n# a Series from a list of strings with default index\npd.Series(['EDS 220', 'EDS 222', 'EDS 223', 'EDS 242'])\n\n0 EDS 220\n1 EDS 222\n2 EDS 223\n3 EDS 242\ndtype: object\n\n\n\n\n2.3.1.2 From a dictionary\nRemember a dictionary is a set of key-value pairs. If we create a pandas.Series via a dictionary the keys will become the index and the values the corresponding data.\n\n# construct dictionary\nd = {'a':0, 'b':1, 'c':2}\n\n# initialize a sries using a dictionary\npd.Series(d)\n\na 0\nb 1\nc 2\ndtype: int64\n\n\n\n\n2.3.1.3 From a number\nIf we only provide a number as the data for the series, we need to provide an index. The number will be repeated to match the length of the index.\n\npd.Series(3.0, index = ['A', 'B', 'C'])\n\nA 3.0\nB 3.0\nC 3.0\ndtype: float64\n\n\n\n\n\n2.3.2 Arithmetic operations\nArithmetic operations work on series and also most NumPy functions. For example:\n\n# define a series\ns = pd.Series([98,73,65],index=['Andrea', 'Beth', 'Carolina'])\n\n# divide each element in series by 10\nprint(s /10, '\\n')\n\n# take the exponential of each element in series\nprint(np.exp(s), '\\n')\n\n# notice this doesn't change the values of our series\nprint(s)\n\nAndrea 9.8\nBeth 7.3\nCarolina 6.5\ndtype: float64 \n\nAndrea 3.637971e+42\nBeth 5.052394e+31\nCarolina 1.694889e+28\ndtype: float64 \n\nAndrea 98\nBeth 73\nCarolina 65\ndtype: int64\n\n\nThere’s much more to say about pandas.Series, but this is enought to get us going. Mainly, we want to know about pandas.Series because they will be the columns of our pandas.DataFrame.\n\n\n2.3.3 Attributes & Methods\npandas.Series have many attributes and methods, you can see a full list in the pandas documentation. For now we will cover two examples that have to do with identifying missing values.\npandas represents a missing or NA value with NaN, which stands for not a number. Sometime" + "text": "2.3 Series\nThe first core data structure of pandas is the series. A series is a one-dimensional array of indexed data. A pandas.Series having an index is the main difference between a pandas.Series and a numpy array. See the difference:\n\n# a numpy array\n# np.random.randn returns values from the std normal distribution\narr = np.random.randn(4) \nprint(type(arr))\nprint(arr, \"\\n\")\n\n# a pandas series made from the previous array\ns = pd.Series(arr)\nprint(type(s))\nprint(s)\n\n\n[-1.52046387 -0.73808435 -1.27879272 0.80935007] \n\n\n0 -1.520464\n1 -0.738084\n2 -1.278793\n3 0.809350\ndtype: float64\n\n\n\n2.3.1 Creating a pandas.Series\nThe basic method to create a pandas.Series is to call\ns = pd.Series(data, index=index)\nThe data parameter can be:\n\na numpy array or a list\na Python dictionary\na number\n\nThe index parameter is a list of index labels.\nFor now, we will create a pandas.Series from a numpy array or list. To use this method we need to pass a numpy array (or a list of objects that can be converted to NumPy types) as data and a list of indices of the same length as data.\n\n# a Series from a numpy array \npd.Series(np.arange(3), index=['a','b','c'])\n\na 0\nb 1\nc 2\ndtype: int64\n\n\nThe index parameter is optional. If we don’t include it, the default is to make the index equal to [0,...,len(data)-1]. For example:\n\n# a Series from a list of strings with default index\npd.Series(['EDS 220', 'EDS 222', 'EDS 223', 'EDS 242'])\n\n0 EDS 220\n1 EDS 222\n2 EDS 223\n3 EDS 242\ndtype: object\n\n\n\n\n\n2.3.2 Simple operations\nArithmetic operations work on series and also most NumPy functions. For example:\n\n# define a series\ns = pd.Series([98,73,65],index=['Andrea', 'Beth', 'Carolina'])\n\n# divide each element in series by 10\nprint(s /10, '\\n')\n\n# take the exponential of each element in series\nprint(np.exp(s), '\\n')\n\n# notice this doesn't change the values of our series\nprint(s)\n\nAndrea 9.8\nBeth 7.3\nCarolina 6.5\ndtype: float64 \n\nAndrea 3.637971e+42\nBeth 5.052394e+31\nCarolina 1.694889e+28\ndtype: float64 \n\nAndrea 98\nBeth 73\nCarolina 65\ndtype: int64\n\n\nWe can also produce new pandas.Series with True/False values indicating whether the elements in a series satisfy a condition or not:\n\ns > 10\n\nAndrea True\nBeth True\nCarolina True\ndtype: bool\n\n\nThis kind of simple conditions on pandas.Series will be key when we are selecting data from data frames.\n\n\n\n2.3.3 Attributes & Methods\npandas.Series have many attributes and methods, you can see a full list in the pandas documentation. For now we will cover two examples that have to do with identifying missing values.\npandas represents a missing or NA value with NaN, which stands for not a number. Let’s construct a small series with some NA values:\n\n# series with NAs in it\ns = pd.Series([1, 2, np.NaN, 4, np.NaN])\n\nA pandas.Series has an attribute called hasnans that returns True if there are any NaNs:\n\n# check if series has NAs\ns.hasnans\n\nTrue\n\n\nThen we might be intersted in knowing which elements in the series are NAs. We can do this using the isna method:\n\ns.isna()\n\n0 False\n1 False\n2 True\n3 False\n4 True\ndtype: bool\n\n\nWe can see the ouput is a pd.Series of boolean values indicating if an element in the row at the given index is NA (True = is NA) or not (False = not NA).\n\n\n\n\n\n\nmoving on\n\n\n\nThere’s much more to say about pandas.Series, but this is enought to get us going. At this point, we mainly want to know about pandas.Series because pandas.Series are the columns of pandas.DataFrames." }, { "objectID": "lectures/lesson-2-pandas-basics.html#data-frames", "href": "lectures/lesson-2-pandas-basics.html#data-frames", "title": "2  Pandas Fundamentals", "section": "2.4 Data Frames", - "text": "2.4 Data Frames\nThe Data Frame is the most used pandas object. It represents tabular data and we can think of it as a spreadhseet. Each column of a pandas.DataFrame is a pandas.Series.\n\n2.4.1 Creating a pandas.DataFrame\nThere are many ways of creating a pandas.DataFrame. Although we likely won’t be creating data frames from scratch often, I’d like to go over creating a pandas.DataFrame from a dict of pandas.Series as this will help us understand the syntax for other Data Frame methods.\nWe already mentioned each column of a pandas.DataFrame is a pandas.Series. In fact, the pandas.DataFrame is a dictionary of pandas.Series, with each column name being the key and the column values being the key’s value. Thus, we can create a pandas.DataFrame in this way:\n\n# initialize dictionary with columns' data \nd = {'col_name_1' : pd.Series(np.arange(3)),\n 'col_name_2' : pd.Series([3.1, 3.2, 3.3]),\n }\n\n# create data frame\ndf = pd.DataFrame(d)\ndf\n\n\n\n\n\n \n \n \n col_name_1\n col_name_2\n \n \n \n \n 0\n 0\n 3.1\n \n \n 1\n 1\n 3.2\n \n \n 2\n 2\n 3.3\n \n \n\n\n\n\nWe can change the index and column names by changing the index and columns attributes in the data frame.\n\n# print original index\nprint(df.index)\n\n# change the index\ndf.index = ['a','b','c']\ndf\n\nRangeIndex(start=0, stop=3, step=1)\n\n\n\n\n\n\n \n \n \n col_name_1\n col_name_2\n \n \n \n \n a\n 0\n 3.1\n \n \n b\n 1\n 3.2\n \n \n c\n 2\n 3.3\n \n \n\n\n\n\n\n# print original column names\nprint(df.columns)\n\n# change column names \ndf.columns = ['C1','C2']\ndf\n\nIndex(['col_name_1', 'col_name_2'], dtype='object')\n\n\n\n\n\n\n \n \n \n C1\n C2\n \n \n \n \n a\n 0\n 3.1\n \n \n b\n 1\n 3.2\n \n \n c\n 2\n 3.3" + "text": "2.4 Data Frames\nThe Data Frame is the most used pandas object. It represents tabular data and we can think of it as a spreadhseet. Each column of a pandas.DataFrame is a pandas.Series.\n\n2.4.1 Creating a pandas.DataFrame\nThere are many ways of creating a pandas.DataFrame.\n\nWe already mentioned each column of a pandas.DataFrame is a pandas.Series. In fact, the pandas.DataFrame is a dictionary of pandas.Series, with each column name being the key and the column values being the key’s value. Thus, we can create a pandas.DataFrame in this way:\n\n# initialize dictionary with columns' data \nd = {'col_name_1' : pd.Series(np.arange(3)),\n 'col_name_2' : pd.Series([3.1, 3.2, 3.3]),\n }\n\n# create data frame\ndf = pd.DataFrame(d)\ndf\n\n\n\n\n\n \n \n \n col_name_1\n col_name_2\n \n \n \n \n 0\n 0\n 3.1\n \n \n 1\n 1\n 3.2\n \n \n 2\n 2\n 3.3\n \n \n\n\n\n\nWe can change the index and column names by changing the index and columns attributes in the data frame.\n\n# print original index\nprint(df.index)\n\n# change the index\ndf.index = ['a','b','c']\ndf\n\nRangeIndex(start=0, stop=3, step=1)\n\n\n\n\n\n\n \n \n \n col_name_1\n col_name_2\n \n \n \n \n a\n 0\n 3.1\n \n \n b\n 1\n 3.2\n \n \n c\n 2\n 3.3\n \n \n\n\n\n\n\n# print original column names\nprint(df.columns)\n\n# change column names \ndf.columns = ['C1','C2']\ndf\n\nIndex(['col_name_1', 'col_name_2'], dtype='object')\n\n\n\n\n\n\n \n \n \n C1\n C2\n \n \n \n \n a\n 0\n 3.1\n \n \n b\n 1\n 3.2\n \n \n c\n 2\n 3.3" }, { "objectID": "lectures/lesson-2-pandas-basics.html#subsetting-a-pandas.dataframe", "href": "lectures/lesson-2-pandas-basics.html#subsetting-a-pandas.dataframe", "title": "2  Pandas Fundamentals", "section": "2.5 Subsetting a pandas.DataFrame", - "text": "2.5 Subsetting a pandas.DataFrame\nLike it’s often the case when working with pandas, there are many ways in which we can subset a data frame. We will review the core methods to do this. \nFor all examples we will use simplified data (glacial_loss.csv) from the National Snow and Ice Data Center (Original dataset). The column descriptions are:\n\nyear: ​calendar year\neurope - antarctica: ​change in glacial volume (km3​ ​) in each region that year\nglobal_glacial_volume_change: ​cumulative​ global glacial volume change (km3), starting in 1961\nannual_sea_level_rise: ​annual rise in sea level (mm)\ncumulative_sea_level_rise:​ cumulative rise in sea level (mm) since 1961\n\nFirst, we read-in the file and get some baisc information about this data frame:\n\n# read in file\ndf = pd.read_csv('data/lesson-1/glacial_loss.csv')\n\n# see the first five rows\ndf.head()\n\n\n\n\n\n \n \n \n year\n europe\n arctic\n alaska\n asia\n north_america\n south_america\n antarctica\n global_glacial_volume_change\n annual_sea_level_rise\n cumulative_sea_level_rise\n \n \n \n \n 0\n 1961\n -5.128903\n -108.382987\n -18.721190\n -32.350759\n -14.359007\n -4.739367\n -35.116389\n -220.823515\n 0.610010\n 0.610010\n \n \n 1\n 1962\n 5.576282\n -173.252450\n -24.324790\n -4.675440\n -2.161842\n -13.694367\n -78.222887\n -514.269862\n 0.810625\n 1.420635\n \n \n 2\n 1963\n -10.123105\n -0.423751\n -2.047567\n -3.027298\n -27.535881\n 3.419633\n 3.765109\n -550.575640\n 0.100292\n 1.520927\n \n \n 3\n 1964\n -4.508358\n 20.070148\n 0.477800\n -18.675385\n -2.248286\n 20.732633\n 14.853096\n -519.589859\n -0.085596\n 1.435331\n \n \n 4\n 1965\n 10.629385\n 43.695389\n -0.115332\n -18.414602\n -19.398765\n 6.862102\n 22.793484\n -473.112003\n -0.128392\n 1.306939\n \n \n\n\n\n\n\n# get column names\ndf.columns\n\nIndex(['year', 'europe', 'arctic', 'alaska', 'asia', 'north_america',\n 'south_america', 'antarctica', 'global_glacial_volume_change',\n 'annual_sea_level_rise', 'cumulative_sea_level_rise'],\n dtype='object')\n\n\n\n# data frame's shape: output is a tuple (# rows, # columns)\ndf.shape\n\n(43, 11)\n\n\n\n2.5.1 Selecting a single column…\n\n2.5.1.1 …by label\nThis is the simplest case for selecting data. Suppose we are interested in the annual sea level rise. Then we can access that single column in this way:\n\n# seelect a single column by using square brackets []\nannual_rise = df['annual_sea_level_rise']\n\n# check the type of the ouput\nprint(type(annual_rise))\n\nannual_rise.head()\n\n\n\n\n0 0.610010\n1 0.810625\n2 0.100292\n3 -0.085596\n4 -0.128392\nName: annual_sea_level_rise, dtype: float64\n\n\nSince we only selected a single column the output is a pandas.Series.\n\n\n\n\n\n\npd.DataFrame = dictionary of columns\n\n\n\nRemember we can think of a pandas.DataFrame as a dictionary of its columns? Then we can access a single column using the column name as the key, just like we would do in a dictionary. That is the syntaxi we just used: df['column_name'].\n\n\nIn general, selecting by label means we want to select data from our data frame using the names of the columns, not their position.\n\n\n2.5.1.2 … with attribute syntax\nWe can also access a single column by using attribute syntax:\n\nannual_rise_2 = df.annual_sea_level_rise\nannual_rise_2.head()\n\n0 0.610010\n1 0.810625\n2 0.100292\n3 -0.085596\n4 -0.128392\nName: annual_sea_level_rise, dtype: float64\n\n\n\n\n\n2.5.2 Selecting multiple columns…\n\n2.5.2.1 … using a list of column names\nThis is another example of selecting by labels. We just need to pass a list with the column names to the square brackets []. For example, say we want to look at the change in glacial volume in Europe and Asia, then we can select those columns like this:\n\n# select columns with names \"europe\" and \"asia\"\neurope_asia = df[['europe','asia']]\n\nNotice there are double square brackets. This is because we are passing the list of names ['europe','asia'] to the selection brakcets [].\n\n# check the type of the resulting selection\nprint(type(europe_asia))\n\n# check the shape of the selection\nprint((europe_asia.shape))\n\n\n(43, 2)\n\n\n\n\n2.5.2.2 … using a slice\nYet another example of label selection! In this case we will use the loc function. This is a powerful function! The general syntax is\ndf.loc[ row-selection , column-selection]\nwhere row-selection and column-selection are the rows and columns we want to subset from the data frame.\nLet’s start by a simple example, where we want to select a slice of columns, say the change in glacial volume per year in all regions. This corresponds to all columns between arctic and antarctica.\n\n# select all columns between 'arctic' and 'antarctica'\nall_regions = df.loc[:,'arctic':'antarctica']\nall_regions.head()\n\n\n\n\n\n \n \n \n arctic\n alaska\n asia\n north_america\n south_america\n antarctica\n \n \n \n \n 0\n -108.382987\n -18.721190\n -32.350759\n -14.359007\n -4.739367\n -35.116389\n \n \n 1\n -173.252450\n -24.324790\n -4.675440\n -2.161842\n -13.694367\n -78.222887\n \n \n 2\n -0.423751\n -2.047567\n -3.027298\n -27.535881\n 3.419633\n 3.765109\n \n \n 3\n 20.070148\n 0.477800\n -18.675385\n -2.248286\n 20.732633\n 14.853096\n \n \n 4\n 43.695389\n -0.115332\n -18.414602\n -19.398765\n 6.862102\n 22.793484\n \n \n\n\n\n\nNotice two things:\n\nwe used the colon : as the row-selection parameters, this means “select all the rows”\nthe slice of the data frame we got includes both endpoints of the slice 'arctic':'antarctica'. In other words we get the arctic column and the antarctica column. This is different from how slicing works in base Python and NumPy, where the end point is not included.\n\n\n\n\n2.5.3 Selecting rows…\n\n2.5.3.1 … using a condition\n\n\n2.5.3.2 … by position\n\n\n\n\n\n\niloc vs. loc\n\n\n\nAt the beginning, the difference between iloc and loc can be confusing. Remember the i in iloc stands for integer-location, so this function only uses integer indexing to retrieve information from the data frames. Moreover, remember iloc indexing works in the same way as indexing for Python lists.\nIf you want to dive deeper, this is a great discussion about the difference between iloc and loc: Stackoverflow - How are iloc and loc different?\n\n\n\n\n\n\n\n\nSelecting a column with iloc? Avoid it!\n\n\n\n\n\nSuppose we want to access the 10th column in the data frame - then we want to select a column by position. In this case the 10th column is the annual sea level rise data and the 10th position corresponds to the index 9. We can select this column by position using the iloc method*:\n\n# select column by position using iloc\n# the syntax is iloc[rows,columns]\n# [:,9] means \"select all rows from the 10th column\"\nannual_rise_3 = df.iloc[:,9]\nannual_rise_3.head()\n\n0 0.610010\n1 0.810625\n2 0.100292\n3 -0.085596\n4 -0.128392\nName: annual_sea_level_rise, dtype: float64\n\n\nUnless you are really looking for information about the 10th column, do not access a column by position. This is bound to break in many ways:\n\nit relies on a person correctly counting the position of a column. Even with a small dataset this can be prone to error.\nit is not explicit: if we wnat information about sea level rise df.annual_sea_level_rise or df['annual_sea_level_rise'] are explicitely telling us we are accessing that information. df.iloc[:,9] is obscure and uninformative.\ndatastets can get updated. Maybe a new column was added before annual_sea_level_rise, this would change the position of the column, which would make any code depnding on df.iloc[:,9] invalid. Accessing by label helps reproducibility!\n\n\n\n\nThis is not an exhaustive list of methods to select data, there are so many ways to subset the same data and it can get quite fancy. For a simple take on some of these concepts, the pandas Getting started tutorials - How do I select a subset of a DataFrame is a good reference." + "text": "2.5 Subsetting a pandas.DataFrame\nLike it’s often the case when working with pandas, there are many ways in which we can subset a data frame. We will review the core methods to do this. \nFor all examples we will use simplified data (glacial_loss.csv) from the National Snow and Ice Data Center (Original dataset). The column descriptions are:\n\nyear: ​calendar year\neurope - antarctica: ​change in glacial volume (km3​ ​) in each region that year\nglobal_glacial_volume_change: ​cumulative​ global glacial volume change (km3), starting in 1961\nannual_sea_level_rise: ​annual rise in sea level (mm)\ncumulative_sea_level_rise:​ cumulative rise in sea level (mm) since 1961\n\nFirst, we read-in the file and get some baisc information about this data frame:\n\n# read in file\ndf = pd.read_csv('data/lesson-1/glacial_loss.csv')\n\n# see the first five rows\ndf.head()\n\n\n\n\n\n \n \n \n year\n europe\n arctic\n alaska\n asia\n north_america\n south_america\n antarctica\n global_glacial_volume_change\n annual_sea_level_rise\n cumulative_sea_level_rise\n \n \n \n \n 0\n 1961\n -5.128903\n -108.382987\n -18.721190\n -32.350759\n -14.359007\n -4.739367\n -35.116389\n -220.823515\n 0.610010\n 0.610010\n \n \n 1\n 1962\n 5.576282\n -173.252450\n -24.324790\n -4.675440\n -2.161842\n -13.694367\n -78.222887\n -514.269862\n 0.810625\n 1.420635\n \n \n 2\n 1963\n -10.123105\n -0.423751\n -2.047567\n -3.027298\n -27.535881\n 3.419633\n 3.765109\n -550.575640\n 0.100292\n 1.520927\n \n \n 3\n 1964\n -4.508358\n 20.070148\n 0.477800\n -18.675385\n -2.248286\n 20.732633\n 14.853096\n -519.589859\n -0.085596\n 1.435331\n \n \n 4\n 1965\n 10.629385\n 43.695389\n -0.115332\n -18.414602\n -19.398765\n 6.862102\n 22.793484\n -473.112003\n -0.128392\n 1.306939\n \n \n\n\n\n\n\n# get column names\ndf.columns\n\nIndex(['year', 'europe', 'arctic', 'alaska', 'asia', 'north_america',\n 'south_america', 'antarctica', 'global_glacial_volume_change',\n 'annual_sea_level_rise', 'cumulative_sea_level_rise'],\n dtype='object')\n\n\n\n# check the data types of each column\ndf.dtypes\n\nyear int64\neurope float64\narctic float64\nalaska float64\nasia float64\nnorth_america float64\nsouth_america float64\nantarctica float64\nglobal_glacial_volume_change float64\nannual_sea_level_rise float64\ncumulative_sea_level_rise float64\ndtype: object\n\n\n\n# data frame's shape: output is a tuple (# rows, # columns)\ndf.shape\n\n(43, 11)\n\n\n\n2.5.1 Selecting a single column…\n\n2.5.1.1 …by column name\nThis is the simplest case for selecting data. Suppose we are interested in the annual sea level rise. Then we can access that single column in this way:\n\n# seelect a single column by using square brackets []\nannual_rise = df['annual_sea_level_rise']\n\n# check the type of the ouput\nprint(type(annual_rise))\n\nannual_rise.head()\n\n\n\n\n0 0.610010\n1 0.810625\n2 0.100292\n3 -0.085596\n4 -0.128392\nName: annual_sea_level_rise, dtype: float64\n\n\nSince we only selected a single column the output is a pandas.Series.\n\n\n\n\n\n\npd.DataFrame = dictionary of columns\n\n\n\nRemember we can think of a pandas.DataFrame as a dictionary of its columns? Then we can access a single column using the column name as the key, just like we would do in a dictionary. That is the we just used: df['column_name'].\n\n\nThis is an example of selecting by label, which means we want to select data from our data frame using the names of the columns, not their position.\n\n\n2.5.1.2 … with attribute syntax\nWe can also access a single column by using attribute syntax:\n\nannual_rise_2 = df.annual_sea_level_rise\nannual_rise_2.head()\n\n0 0.610010\n1 0.810625\n2 0.100292\n3 -0.085596\n4 -0.128392\nName: annual_sea_level_rise, dtype: float64\n\n\n\n\n\n2.5.2 Selecting multiple columns…\n\n2.5.2.1 … using a list of column names\nThis is another example of selecting by labels. We just need to pass a list with the column names to the square brackets []. For example, say we want to look at the change in glacial volume in Europe and Asia, then we can select those columns like this:\n\n# select columns with names \"europe\" and \"asia\"\neurope_asia = df[['europe','asia']]\n\nNotice there are double square brackets. This is because we are passing the list of names ['europe','asia'] to the selection brakcets [].\n\n# check the type of the resulting selection\nprint(type(europe_asia))\n\n# check the shape of the selection\nprint((europe_asia.shape))\n\n\n(43, 2)\n\n\n\n\n2.5.2.2 … using a slice\nYet another example of selecting by label! In this case we will use the loc function. This is a powerful function! The general syntax is\ndf.loc[ row-selection , column-selection]\nwhere row-selection and column-selection are the rows and columns we want to subset from the data frame.\nLet’s start by a simple example, where we want to select a slice of columns, say the change in glacial volume per year in all regions. This corresponds to all columns between arctic and antarctica.\n\n# select all columns between 'arctic' and 'antarctica'\nall_regions = df.loc[:,'arctic':'antarctica']\nall_regions.head()\n\n\n\n\n\n \n \n \n arctic\n alaska\n asia\n north_america\n south_america\n antarctica\n \n \n \n \n 0\n -108.382987\n -18.721190\n -32.350759\n -14.359007\n -4.739367\n -35.116389\n \n \n 1\n -173.252450\n -24.324790\n -4.675440\n -2.161842\n -13.694367\n -78.222887\n \n \n 2\n -0.423751\n -2.047567\n -3.027298\n -27.535881\n 3.419633\n 3.765109\n \n \n 3\n 20.070148\n 0.477800\n -18.675385\n -2.248286\n 20.732633\n 14.853096\n \n \n 4\n 43.695389\n -0.115332\n -18.414602\n -19.398765\n 6.862102\n 22.793484\n \n \n\n\n\n\nNotice two things:\n\nwe used the colon : as the row-selection parameter, which means “select all the rows”\nthe slice of the data frame we got includes both endpoints of the slice 'arctic':'antarctica'. In other words we get the arctic column and the antarctica column. This is different from how slicing works in base Python and NumPy, where the end point is not included.\n\n\n\n\n2.5.3 Selecting rows…\nNow that we are familiar with some methods for selecting columns, let’s move on to selecting rows.\n\n2.5.3.1 … using a condition\nSelecting which rows satisfy a particular condition is, in my experience, the most usual kind of row subsetting. The general syntax for this type of selection is df[condition_on_rows]. For example, suppose we are intersted in all data after 1996. We can select those rows in this way:\n\n# select all rows with year > 1996\nafter_96 = df[df['year']>1996]\nafter_96\n\n\n\n\n\n \n \n \n year\n europe\n arctic\n alaska\n asia\n north_america\n south_america\n antarctica\n global_glacial_volume_change\n annual_sea_level_rise\n cumulative_sea_level_rise\n \n \n \n \n 36\n 1997\n -13.724106\n -24.832246\n -167.229145\n -34.406403\n -27.680661\n -38.213286\n -20.179090\n -4600.686013\n 0.909625\n 12.709077\n \n \n 37\n 1998\n -13.083338\n -110.429302\n -107.879027\n -58.115702\n 30.169987\n -3.797978\n -48.129928\n -4914.831966\n 0.867807\n 13.576884\n \n \n 38\n 1999\n -8.039555\n -64.644068\n -87.714653\n -26.211723\n 5.888512\n -8.038630\n -40.653001\n -5146.368231\n 0.639603\n 14.216487\n \n \n 39\n 2000\n -17.008590\n -96.494055\n -44.445000\n -37.518173\n -29.191986\n -2.767698\n -58.873830\n -5435.317175\n 0.798202\n 15.014688\n \n \n 40\n 2001\n -8.419109\n -145.415483\n -55.749505\n -35.977022\n -0.926134\n 7.553503\n -86.774675\n -5764.039931\n 0.908074\n 15.922762\n \n \n 41\n 2002\n -3.392361\n -48.718943\n -87.120000\n -36.127226\n -27.853498\n -13.484593\n -30.203960\n -6013.225500\n 0.688358\n 16.611120\n \n \n 42\n 2003\n -3.392361\n -48.718943\n -67.253634\n -36.021991\n -75.066475\n -13.223430\n -30.203960\n -6289.640976\n 0.763579\n 17.374699\n \n \n\n\n\n\nLet’s break down what is happening here. In this case the condition for our rows is df['year']>1996, this checks which rows have a value greater than 1996 in the year column. Let’s see this explicitely:\n\n# check the type of df['year']>1996\nprint(type(df['year']>1996))\n\ndf['year']>1996\n\n\n\n\n0 False\n1 False\n2 False\n3 False\n4 False\n5 False\n6 False\n7 False\n8 False\n9 False\n10 False\n11 False\n12 False\n13 False\n14 False\n15 False\n16 False\n17 False\n18 False\n19 False\n20 False\n21 False\n22 False\n23 False\n24 False\n25 False\n26 False\n27 False\n28 False\n29 False\n30 False\n31 False\n32 False\n33 False\n34 False\n35 False\n36 True\n37 True\n38 True\n39 True\n40 True\n41 True\n42 True\nName: year, dtype: bool\n\n\nThe output is a pandas.Series with boolean values (True or False) indicating which rows satisfy the condition year>1996. When we pass such a series of boolean values to the selection brackets [] we keep only those rows with a True value.\nHere’s another example of using a condition. Suppose we want to look at data from years 1970 to 1979. One way of doing this is to use the in operator in our condition:\n\nseventies = df[df['year'].isin(range(1970,1980))]\nseventies\n\n\n\n\n\n \n \n \n year\n europe\n arctic\n alaska\n asia\n north_america\n south_america\n antarctica\n global_glacial_volume_change\n annual_sea_level_rise\n cumulative_sea_level_rise\n \n \n \n \n 9\n 1970\n -6.452316\n -24.494667\n -0.125296\n -36.120199\n 11.619790\n 11.636911\n 4.400377\n -999.018177\n 0.110225\n 2.759719\n \n \n 10\n 1971\n 0.414711\n -42.904189\n 28.103328\n -8.702938\n -9.964542\n 1.061299\n -6.735536\n -1038.104459\n 0.107973\n 2.867692\n \n \n 11\n 1972\n -5.144729\n -27.004031\n -22.143350\n -40.883357\n 32.363730\n -14.968034\n -6.223849\n -1122.885506\n 0.234202\n 3.101894\n \n \n 12\n 1973\n 4.081090\n 9.839444\n 22.985188\n -31.432594\n -20.883232\n 2.103649\n 10.539823\n -1125.677743\n 0.007713\n 3.109607\n \n \n 13\n 1974\n 1.545615\n -40.126998\n -29.517874\n -43.861622\n -23.991402\n -21.338825\n 4.419343\n -1279.964287\n 0.426206\n 3.535813\n \n \n 14\n 1975\n 7.431192\n -32.410467\n -44.094084\n -43.357442\n -30.858810\n -2.368842\n -7.775315\n -1434.818037\n 0.427773\n 3.963586\n \n \n 15\n 1976\n 3.986753\n 21.686639\n -28.234725\n -67.292125\n -12.534421\n -19.465358\n 19.250607\n -1518.185129\n 0.230296\n 4.193882\n \n \n 16\n 1977\n 4.891410\n -33.123010\n -5.662139\n -62.165684\n -15.905332\n 2.654950\n -23.727249\n -1652.453400\n 0.370907\n 4.564788\n \n \n 17\n 1978\n 8.404591\n -77.561015\n -12.503384\n -22.858040\n -31.097609\n 7.127708\n -9.140167\n -1791.355022\n 0.383706\n 4.948495\n \n \n 18\n 1979\n 3.916703\n -88.351684\n -63.938851\n -49.242043\n -12.076624\n -17.718503\n -9.578557\n -2030.537848\n 0.660726\n 5.609221\n \n \n\n\n\n\nLet’s break it down:\n\ndf['year'] is the column with the year values, a pandas.Series,\nin df['year'].isin(), we have that isin is a method for the pandas.Series and we are calling it using the dot ..\nrange(1970,1980) constructs consecutive integers from 1970 to 1979 - remember the right endopoint (1980) is not included!\ndf['year'].isin(range(1970,1980)) is then a pandas.Series of boolean values indicating which rows have year equal to 1970, …, 1979.\nwhen we put df['year'].isin(range(1970,1980)) inside the selection brackets [] we obtain the rows of the data frame with year equal to 1970, …, 1979.\n\n\n\n\n\n\n\nloc for row selection\n\n\n\nIt is equivalent to write\n# select rows with year<1965\ndf[df['year'] < 1965]\nand\n# select rows with year<1965 using love\ndf.loc[ df['year'] <1965 , :]\nIn the second one:\n\nwe are using the df.loc[ row-selection , column-selection] syntax\nthe row-selection parameter is the condition df['year']<1965\nthe column-selection parameter is a colon :, which indicates we want all columns for the rows we are selecting.\n\nWe prefer the first syntax when we are selecting rows and not columns since it is simpler.\n\n\n\n\n2.5.3.2 … using multiple conditions\nWe can combine multipe conditions by surrounding each one in parenthesis () and using the or operator | and the and operator and.\nor example:\n\n# select rows with \n# annual_sea_level_rise<0.5 mm OR annual_sea_level_rise>0.8 mm\n\ndf[ (df['annual_sea_level_rise']<0.5) | (df['annual_sea_level_rise']>0.8)]\ndf.head()\n\n\n\n\n\n \n \n \n year\n europe\n arctic\n alaska\n asia\n north_america\n south_america\n antarctica\n global_glacial_volume_change\n annual_sea_level_rise\n cumulative_sea_level_rise\n \n \n \n \n 0\n 1961\n -5.128903\n -108.382987\n -18.721190\n -32.350759\n -14.359007\n -4.739367\n -35.116389\n -220.823515\n 0.610010\n 0.610010\n \n \n 1\n 1962\n 5.576282\n -173.252450\n -24.324790\n -4.675440\n -2.161842\n -13.694367\n -78.222887\n -514.269862\n 0.810625\n 1.420635\n \n \n 2\n 1963\n -10.123105\n -0.423751\n -2.047567\n -3.027298\n -27.535881\n 3.419633\n 3.765109\n -550.575640\n 0.100292\n 1.520927\n \n \n 3\n 1964\n -4.508358\n 20.070148\n 0.477800\n -18.675385\n -2.248286\n 20.732633\n 14.853096\n -519.589859\n -0.085596\n 1.435331\n \n \n 4\n 1965\n 10.629385\n 43.695389\n -0.115332\n -18.414602\n -19.398765\n 6.862102\n 22.793484\n -473.112003\n -0.128392\n 1.306939\n \n \n\n\n\n\nand example\n\n# select rows with cumulative_sea_level_rise>10 AND global_glacial_volume_change<-300\ndf[ (df['cumulative_sea_level_rise']>10) & (df['global_glacial_volume_change']<-300)]\n\n\n\n\n\n \n \n \n year\n europe\n arctic\n alaska\n asia\n north_america\n south_america\n antarctica\n global_glacial_volume_change\n annual_sea_level_rise\n cumulative_sea_level_rise\n \n \n \n \n 32\n 1993\n 16.685013\n -73.666274\n -43.702040\n -65.995130\n -33.151246\n -20.578403\n -20.311577\n -3672.582082\n 0.671126\n 10.145254\n \n \n 33\n 1994\n 0.741751\n -3.069084\n -59.962273\n -59.004710\n -89.506142\n -15.258449\n -8.168498\n -3908.977191\n 0.653025\n 10.798280\n \n \n 34\n 1995\n -2.139665\n -58.167778\n -74.141762\n 3.500155\n -0.699374\n -19.863392\n -25.951496\n -4088.082873\n 0.494767\n 11.293047\n \n \n 35\n 1996\n -6.809834\n -4.550205\n -74.847017\n -67.436591\n 4.867530\n -21.080115\n -11.781489\n -4271.401594\n 0.506405\n 11.799452\n \n \n 36\n 1997\n -13.724106\n -24.832246\n -167.229145\n -34.406403\n -27.680661\n -38.213286\n -20.179090\n -4600.686013\n 0.909625\n 12.709077\n \n \n 37\n 1998\n -13.083338\n -110.429302\n -107.879027\n -58.115702\n 30.169987\n -3.797978\n -48.129928\n -4914.831966\n 0.867807\n 13.576884\n \n \n 38\n 1999\n -8.039555\n -64.644068\n -87.714653\n -26.211723\n 5.888512\n -8.038630\n -40.653001\n -5146.368231\n 0.639603\n 14.216487\n \n \n 39\n 2000\n -17.008590\n -96.494055\n -44.445000\n -37.518173\n -29.191986\n -2.767698\n -58.873830\n -5435.317175\n 0.798202\n 15.014688\n \n \n 40\n 2001\n -8.419109\n -145.415483\n -55.749505\n -35.977022\n -0.926134\n 7.553503\n -86.774675\n -5764.039931\n 0.908074\n 15.922762\n \n \n 41\n 2002\n -3.392361\n -48.718943\n -87.120000\n -36.127226\n -27.853498\n -13.484593\n -30.203960\n -6013.225500\n 0.688358\n 16.611120\n \n \n 42\n 2003\n -3.392361\n -48.718943\n -67.253634\n -36.021991\n -75.066475\n -13.223430\n -30.203960\n -6289.640976\n 0.763579\n 17.374699\n \n \n\n\n\n\n\n\n2.5.3.3 … by position\nAll the selections we have done so far have been using labels or using a condition. Sometimes we might want to select certain rows depending on their actual position in the data frame. In this case we use the iloc method with the syntax df.iloc[row-indices]. iloc stands for integer-location based indexing. Let’s see some examples:\n\n# select the fifht row = index 4\ndf.iloc[4]\n\nyear 1965.000000\neurope 10.629385\narctic 43.695389\nalaska -0.115332\nasia -18.414602\nnorth_america -19.398765\nsouth_america 6.862102\nantarctica 22.793484\nglobal_glacial_volume_change -473.112003\nannual_sea_level_rise -0.128392\ncumulative_sea_level_rise 1.306939\nName: 4, dtype: float64\n\n\n\n# select rows 23 through 30, inclduing 30\ndf.iloc[23:31]\n\n\n\n\n\n \n \n \n year\n europe\n arctic\n alaska\n asia\n north_america\n south_america\n antarctica\n global_glacial_volume_change\n annual_sea_level_rise\n cumulative_sea_level_rise\n \n \n \n \n 23\n 1984\n 8.581427\n -5.755672\n -33.466092\n -20.528535\n -20.734676\n -8.267686\n -3.261011\n -2569.339802\n 0.232609\n 7.097624\n \n \n 24\n 1985\n -5.970980\n -49.651089\n 12.065473\n -31.571622\n -33.833985\n 10.072906\n -13.587886\n -2682.857926\n 0.313586\n 7.411210\n \n \n 25\n 1986\n -5.680642\n 22.900847\n 7.557447\n -18.920773\n -33.014743\n -4.652030\n 30.482473\n -2684.197632\n 0.003701\n 7.414911\n \n \n 26\n 1987\n 8.191477\n 12.387780\n -24.007862\n -41.121970\n -48.560996\n 1.670733\n 3.130190\n -2773.325568\n 0.246210\n 7.661120\n \n \n 27\n 1988\n -11.117228\n -31.066489\n 49.897712\n -21.300712\n -46.545435\n 13.460422\n -37.986834\n -2858.767621\n 0.236028\n 7.897148\n \n \n 28\n 1989\n 14.863220\n -23.462392\n -36.112726\n -46.528372\n -57.756422\n -21.687470\n -10.044757\n -3041.169131\n 0.503872\n 8.401020\n \n \n 29\n 1990\n -1.226009\n -27.484542\n -92.713339\n -35.553433\n -56.563056\n -31.077022\n -29.893352\n -3318.220397\n 0.765335\n 9.166355\n \n \n 30\n 1991\n -14.391425\n -34.898689\n -8.822063\n -15.338299\n -31.458010\n -7.162909\n -35.968429\n -3467.630284\n 0.412734\n 9.579089\n \n \n\n\n\n\nNotice since we are back to indexing by position the right endpoint of the slice (6) is not included in the ouput.\n\n\n\n2.5.4 Selecting rows and columns simultaneously…\nSelecting rows and columns simultaneously can be done using loc (labels or conditions) or iloc (integer position).\n\n2.5.4.1 …by labels or conditions\nWhen we want to select rows and columns simultaneously by labels or conditions we can use the method loc with the syntax\ndf.loc[ row-selection , column-selection]\nspecifying both paratmers: row-selection and column-selection. These parameters can be a condition (which generates a boolean array) or a subset of labels from the index or the column names. Let’s see an examples:\n\n# select change in glacial volume in Europe per year after 2000\ndf.loc[df['year']>2000,['year','europe']]\n\n\n\n\n\n \n \n \n year\n europe\n \n \n \n \n 40\n 2001\n -8.419109\n \n \n 41\n 2002\n -3.392361\n \n \n 42\n 2003\n -3.392361\n \n \n\n\n\n\nLet’s break it down:\n\nwe are using the df.loc[ row-selection , column-selection] syntax\nthe row-selection parameter is the condition df['year']>1990, which is a boolean array saying which years are greater than 1990\nthe column-selection parameter is ['year','europe'] which is a list with the names of the two columns we are intersted in.\n\n\n\n2.5.4.2 … by position\nWhen we want to select rows and columns simultaneously by position we use the iloc method with the syntax:\ndf.iloc[ row-indices , column-indices]\nFor example,\n\n# select rows 3-7 (including 7) and columns 3 and 4\ndf.iloc[ 3:8, [3,4] ]\n\n\n\n\n\n \n \n \n alaska\n asia\n \n \n \n \n 3\n 0.477800\n -18.675385\n \n \n 4\n -0.115332\n -18.414602\n \n \n 5\n 0.224762\n -14.630284\n \n \n 6\n -7.174030\n -39.013695\n \n \n 7\n -0.660556\n 7.879589\n \n \n\n\n\n\nLet’s break it down:\n\nwe are using the df.iloc[ row-indices , column-indices] syntax\nthe row-indices parameter is the slice of integer indices 3:8. Remember the right endpoint (8) won’t be included.\nthe column-indices parameter is the list of integer indices 3 and 4. This means we are selecting the fourth and fifth column.\n\n\n\n\n2.5.5 Notes about loc and iloc\n\n\n\n\n\n\niloc vs. loc\n\n\n\nAt the beginning, the difference between iloc and loc can be confusing. Remember the i in iloc stands for integer-location, so this function only uses integer indexing to retrieve information from the data frames in the same way as indexing for Python lists.\nIf you want to dive deeper, this is a great discussion about the difference between iloc and loc: Stackoverflow - How are iloc and loc different?\nAnd, as always, the documentation will provide you with more information: pandas.DataFrame.loc and pandas.DataFrame.iloc.\n\n\n\n\n\n\n\n\niloc for column selection? Avoid it!\n\n\n\nWe can also access columns by position using iloc - but it is best not to if possible.\nSuppose we want to access the 10th column in the data frame - then we want to select a column by position. In this case the 10th column is the annual sea level rise data and the 10th position corresponds to the index 9. We can select this column by position using the iloc method*:\n\n# select column by position using iloc\n# the syntax is iloc[rows,columns]\n# [:,9] means \"select all rows from the 10th column\"\nannual_rise_3 = df.iloc[:,9]\nannual_rise_3.head()\n\n0 0.610010\n1 0.810625\n2 0.100292\n3 -0.085596\n4 -0.128392\nName: annual_sea_level_rise, dtype: float64\n\n\nUnless you are really looking for information about the 10th column, do not access a column by position. This is bound to break in many ways:\n\nit relies on a person correctly counting the position of a column. Even with a small dataset this can be prone to error.\nit is not explicit: if we want information about sea level rise df.annual_sea_level_rise or df['annual_sea_level_rise'] are explicitely telling us we are accessing that information. df.iloc[:,9] is obscure and uninformative.\ndatastets can get updated. Maybe a new column was added before annual_sea_level_rise, this would change the position of the column, which would make any code depending on df.iloc[:,9] invalid. Accessing by label helps reproducibility!" }, { - "objectID": "lectures/lesson-2-pandas-basics.html#basic-matplotlib", - "href": "lectures/lesson-2-pandas-basics.html#basic-matplotlib", + "objectID": "lectures/lesson-2-pandas-basics.html#resources", + "href": "lectures/lesson-2-pandas-basics.html#resources", "title": "2  Pandas Fundamentals", - "section": "2.6 Basic Matplotlib", - "text": "2.6 Basic Matplotlib" + "section": "2.6 Resources", + "text": "2.6 Resources\nWhat is presented in this section is a comprehensive, but not an exhaustive list of methods to select data in pandas.DataFrames. There are so many ways to subset data to get the same result. Some of the content from this lesson is adapted from the following resources and I encourage you to read them to learn more!\n📖 Pandas getting started tutorials - How to I select a subset of a DataFrame\n📖 Pandas documentation - User Guide - Indexing and Selecting Data\n📖 Python for Data Analysis, 3E - Getting started with pandas" }, { - "objectID": "lectures/lesson-2-pandas-basics.html#resources", - "href": "lectures/lesson-2-pandas-basics.html#resources", + "objectID": "lectures/lesson-2-pandas-basics.html#acknowledgements", + "href": "lectures/lesson-2-pandas-basics.html#acknowledgements", "title": "2  Pandas Fundamentals", - "section": "2.7 Resources", - "text": "2.7 Resources\n📖 Pandas documentation - User Guide\n📖 Python for Data Analysis, 3E - Getting started with pandas\nCONTINUE: https://pandas.pydata.org/docs/user_guide/dsintro.html https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html https://wesmckinney.com/book/pandas-basics https://pandas.pydata.org/docs/user_guide/indexing.html#selection-by-label https://earth-env-data-science.github.io/lectures/pandas/basic_pandas.html" + "section": "2.7 Acknowledgements", + "text": "2.7 Acknowledgements\nThe simplified glacial_loss.csv dataset was created by Dr. Allison Horst as part of her course materials on environmental data science." } ] \ No newline at end of file diff --git a/lectures/lesson-2-pandas-basics.qmd b/lectures/lesson-2-pandas-basics.qmd index 2332f2d..672d573 100644 --- a/lectures/lesson-2-pandas-basics.qmd +++ b/lectures/lesson-2-pandas-basics.qmd @@ -38,7 +38,7 @@ print(s) ``` -### How to create a `pandas.Series` +### Creating a `pandas.Series` The basic method to create a `pandas.Series` is to call @@ -54,16 +54,13 @@ The `data` parameter can be: The `index` parameter is a list of index labels. -#### From a numpy array or list - -To use this method we need to pass a numpy array (or a list of objects that can be converted to NumPy types) as `data` and a list of indices of the same length as data. +For now, we will create a `pandas.Series` from a numpy array or list. To use this method we need to pass a numpy array (or a list of objects that can be converted to NumPy types) as `data` and a list of indices of the same length as data. ```{python} # a Series from a numpy array pd.Series(np.arange(3), index=['a','b','c']) ``` - The `index` parameter is optional. If we don't include it, the default is to make the index equal to `[0,...,len(data)-1]`. For example: ```{python} @@ -71,7 +68,7 @@ pd.Series(np.arange(3), index=['a','b','c']) pd.Series(['EDS 220', 'EDS 222', 'EDS 223', 'EDS 242']) ``` - + -### Arithmetic operations + +### Simple operations Arithmetic operations work on series and also most NumPy functions. For example: @@ -111,17 +110,45 @@ print(s) ``` +We can also produce new `pandas.Series` with `True`/`False` values indicating whether the elements in a series satisfy a condition or not: - +```{python} +s > 10 +``` +This kind of simple conditions on `pandas.Series` will be key when we are selecting data from data frames. -There's much more to say about `pandas.Series`, but this is enought to get us going. Mainly, we want to know about `pandas.Series` because they will be the columns of our `pandas.DataFrame`. + ### Attributes & Methods `pandas.Series` have *many* attributes and methods, you can see a [full list in the `pandas` documentation](https://pandas.pydata.org/docs/reference/api/pandas.Series.html). For now we will cover two examples that have to do with identifying missing values. -`pandas` represents a missing or NA value with `NaN`, which stands for not a number. Sometime +`pandas` represents a missing or NA value with `NaN`, which stands for not a number. Let's construct a small series with some NA values: + +```{python} +# series with NAs in it +s = pd.Series([1, 2, np.NaN, 4, np.NaN]) +``` + +A `pandas.Series` has an *attribute* called `hasnans` that returns `True` if there are any NaNs: + +```{python} +# check if series has NAs +s.hasnans +``` + +Then we might be intersted in knowing which elements in the series are NAs. We can do this using the [`isna` method](https://pandas.pydata.org/docs/reference/api/pandas.Series.isna.html): +```{python} +s.isna() +``` + +We can see the ouput is a `pd.Series` of boolean values indicating if an element in the row at the given index is NA (`True` = is NA) or not (`False` = not NA). + +:::{.callout-note} +## moving on +There's much more to say about `pandas.Series`, but this is enought to get us going. At this point, we mainly want to know about `pandas.Series` because `pandas.Series` are the columns of `pandas.DataFrame`s. +::: - - - - ## Data Frames The Data Frame is the most used `pandas` object. It represents tabular data and we can think of it as a spreadhseet. Each column of a `pandas.DataFrame` is a `pandas.Series`. ### Creating a `pandas.DataFrame` -There are [many ways of creating a `pandas.DataFrame`](https://pandas.pydata.org/docs/user_guide/dsintro.html#dataframe). Although we likely won't be creating data frames from scratch often, I'd like to go over creating a `pandas.DataFrame` from a `dict` of `pandas.Series` as this will help us understand the syntax for other Data Frame methods. +There are [many ways of creating a `pandas.DataFrame`](https://pandas.pydata.org/docs/user_guide/dsintro.html#dataframe). + + We already mentioned each column of a `pandas.DataFrame` is a `pandas.Series`. In fact, the `pandas.DataFrame` is a dictionary of `pandas.Series`, with each column name being the key and the column values being the key's value. Thus, we can create a `pandas.DataFrame` in this way: @@ -426,7 +451,7 @@ Notice since we are back to indexing by position the right endpoint of the slice Selecting rows and columns simultaneously can be done using `loc` (labels or conditions) or `iloc` (integer position). -# ...by labels or conditions +#### ...by labels or conditions When we want to select rows and columns simultaneously by labels or conditions we can use the method `loc` with the syntax ```python @@ -447,7 +472,7 @@ Let's break it down: - the `column-selection` parameter is `['year','europe']` which is a list with the names of the two columns we are intersted in. -# ... by position +#### ... by position When we want to select rows and columns simultaneously by position we use the `iloc` method with the syntax: ```python @@ -507,14 +532,14 @@ Unless you are *really* looking for information about *the 10th column*, do not ::: +## Resources -What is presented in this section is not an exhaustive list of methods to select data, there are *so many* ways to subset the same data and [it can get quite fancy.](https://pandas.pydata.org/docs/user_guide/indexing.html#selection-by-label) For a simple take on some of these concepts, the `pandas` [Getting started tutorials - How do I select a subset of a -DataFrame](https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html) is a good reference. - +What is presented in this section is a comprehensive, but not an exhaustive list of methods to select data in `pandas.DataFrames`. There are *so many* ways to subset data to get the same result. Some of the content from this lesson is adapted from the following resources and I encourage you to read them to learn more! -## Resources +📖 [Pandas getting started tutorials - How to I select a subset of a +DataFrame](https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html) -📖 [Pandas documentation - User Guide](https://pandas.pydata.org/docs/user_guide/index.html) +📖 [Pandas documentation - User Guide - Indexing and Selecting Data](https://pandas.pydata.org/docs/user_guide/indexing.html#indexing-slicing-with-labels) 📖 [Python for Data Analysis, 3E - Getting started with pandas](https://wesmckinney.com/book/pandas-basics) From 77fd94dc5068203b03ffccc78f9de8c61ec52e4f Mon Sep 17 00:00:00 2001 From: carmengg Date: Sat, 30 Sep 2023 01:08:40 +0000 Subject: [PATCH 05/31] some ideas for first lab --- discussion-sections/discussion-sect-week-1.qmd | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 discussion-sections/discussion-sect-week-1.qmd diff --git a/discussion-sections/discussion-sect-week-1.qmd b/discussion-sections/discussion-sect-week-1.qmd new file mode 100644 index 0000000..f0276ae --- /dev/null +++ b/discussion-sections/discussion-sect-week-1.qmd @@ -0,0 +1,11 @@ +Some ideas for discussion section: + +Fork and clone a repository for this lab. + +Add 1 or 2 exercises from class/numpy revision. + +Work on some subsetting + +Fix some broken code with subsetting + +Do the iloc vs loc difference \ No newline at end of file From ddb92c64c1fa463ed4c196e78b40b2ea8f24a6cc Mon Sep 17 00:00:00 2001 From: carmengg Date: Mon, 2 Oct 2023 22:54:07 +0000 Subject: [PATCH 06/31] fixed typos --- lectures/lesson-1-python-review.qmd | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/lectures/lesson-1-python-review.qmd b/lectures/lesson-1-python-review.qmd index e2d9e0b..b5746c1 100644 --- a/lectures/lesson-1-python-review.qmd +++ b/lectures/lesson-1-python-review.qmd @@ -50,7 +50,7 @@ In Python we use the equal sign `=` to assign values to variables in the same wa ::: {.callout-caution} ## Naming Variables -There are many ways of constructing multi-word variable names as we can see in the previous example. In this course we will name variables using **snake_case**, where words are all in small caps and separated by underscores (ex: `miles_run`). This is the naming convention suggested by the [Style Guide for Python Code](https://peps.python.org/pep-0008/). +There are many ways of constructing multi-word variable names. In this course we will name variables using **snake_case**, where words are all in small caps and separated by underscores (ex: `my_variable`). This is the naming convention suggested by the [Style Guide for Python Code](https://peps.python.org/pep-0008/). ::: ## Variables and Objects @@ -80,7 +80,7 @@ type(a[0,0]) ::: {.callout-tip} ## Check-in -How would you access the value 4 in the array `a`? **Remember indexing in Python starts from 0!** +How would you access the value 5 in the array `a`? **Remember indexing in Python starts from 0!** ::: Notice the type of the value 1 in the array is `numpy.int64` and not just the core Python integer type `int`. The NumPy type `numpy.int64` is telling us 1 is an integer stored as a 64-bit number. NumPy has its own data types to deal with numbers depending on memory storage and floating point precision, [click here to know see all the types](https://numpy.org/doc/stable/reference/arrays.scalars.html#sized-aliases). @@ -125,7 +125,7 @@ print('I am changing the default end argument of the print function', end=' ^_^' ## Attributes & Methods -An object in Python has attributes and methods. An **attribute** is a property of the object, some piece of information about it. A **method** is is a procedure associated with an object, so it is an action where the main ingredient is the object. +An object in Python has attributes and methods. An **attribute** is a property of the object, some piece of information about it. A **method** is a procedure associated with an object, so it is an action where the main ingredient is the object. For example, these could be some attributes and methods a class `cat`: @@ -136,6 +136,11 @@ More formally, **a method is a function** that acts on the object it is part of. We can access a variable's attributes and methods by adding a period `.` at the end of the variable's name. So we would write `variable.variable_method()` or `variable.variable_attribute`. +:::{.callout-tip} +## Check-in +Suppose we have a class `fish`, make a diagram similar to the `cat` class diagram showing 3 attributes for the class and 3 methods. +::: + **Example** NumPy arrays have many methods and attributes. Let's see some concrete examples. @@ -153,13 +158,13 @@ print(type(var.T)) ``` ```{python} -# shape, another attribute, tells us the array dimensions +# shape, another attribute, tells us the shape of the array (3x3) print(var.shape) print(type(var.shape)) ``` ```{python} -# ndim is an attribute hilding the number of array dimensions. +# ndim is an attribute holding the number of array dimensions print(var.ndim) print(type(var.ndim)) ``` @@ -213,15 +218,15 @@ var <- array(c(1,4,7,2,5,8,3,6,9), dim =c(3,3)) min(var) ``` -Using the pipe operator `%` in R's tidyverse is closer to the dot `.` in Python: +Using the pipe operator `%>%` in R's tidyverse is closer to the dot `.` in Python: ``` R # this is R code var <- array(c(1,4,7,2,5,8,3,6,9), dim =c(3,3)) -var % min() +var %>% min() ``` -What happens here is that the pipe `%` is passing `var` to the `min()` function as its first argument. This is essentially what happens in Python when a function is a method of a class: +What happens here is that the pipe `%>%` is passing `var` to the `min()` function as its first argument. This is essentially what happens in Python when a function is a method of a class: ``` python # this is Python code @@ -235,11 +240,6 @@ When working in Python, remember that *methods are functions that are part of an + + \ No newline at end of file From b3e61ae86cfcebbb18ed654400eb6b405866a5fd Mon Sep 17 00:00:00 2001 From: carmengg Date: Mon, 2 Oct 2023 19:02:15 -0700 Subject: [PATCH 09/31] added instructions for notebook setup --- .../discussion-sect-week-1.qmd | 39 ++++++++++++++----- 1 file changed, 29 insertions(+), 10 deletions(-) diff --git a/discussion-sections/discussion-sect-week-1.qmd b/discussion-sections/discussion-sect-week-1.qmd index db03351..0c65635 100644 --- a/discussion-sections/discussion-sect-week-1.qmd +++ b/discussion-sections/discussion-sect-week-1.qmd @@ -4,11 +4,11 @@ Date: October 6, 2023. Fork and clone a repository for this lab? -::: {.callout-tip} ## Exercise 1 Consider the following code: -```python + +```{python} import numpy as np ?np.ones @@ -33,19 +33,38 @@ Complete the following paragraph using the given words: `abc` is a ________ assigned to the NumPy ________ `np.ones([3,2])`. We construct `np.ones([3,2])` by calling a ________ from the NumPy ________. `[3,2]` and `np.int8` are ________ we pass to the `np.ones` ________. `np.int8` is a ________ parameter of `np.ones`. `abc.mean` is an ________ of `abc` and `x` is its ________. -::: - -:::{.callout-tip} ## Exercise 2 -Work on some subsetting +:::{.callout-tip} +## GitHub Setup +1. Create a new repository on GitHub. Use the following settings: + + a. Call the repository 'eds-220-section-1'. + + b. Add a brief description for your new repository. For example: EDS 220 section - practice session for data selection in pandas. + + c. Keep the repository public. + d. Initialize the repository with a `README` file and a Python .gitignore template. +2. Clone the repository to a new directory in the Taylor server under your eds-220 directory. + +3. In the terminal use `cd` to navigate into the 'eds-220-section-1' directory. Use `pwd` to verify this is your current working directory. + +3. Create a new Python Notebook in the 'eds-220-section-1' directory. + +4. Update the notebook's name to something useful like 'exercise-data-selection.qmd'. + +5. Use the terminal to stage, commit, and push this file to the remote repository (on GitHub). Remember: + - to stage: `git add FILE_NAME` + - to commit with message: `git commit -m "COMMIT_MESSAGE"` + - to push: `git push` + +6. If you are prompted for your credentials and need to set up a new Personal Access Token (PAT) follow [steps 13-18 in this guide](https://docs.google.com/document/d/1Pk6_rUDdFjdGg-YVo9Cl8ET3iDsYahyaP0VMRD4UHUk/edit?usp=sharing/) to set it up. ::: -Fix some broken code with subsetting :::{.callout-tip} -## Exercise 3 -Do the iloc vs loc difference -::: +## Data Access + +::: From 542b8932e47c0ae5ccc043063efeaf46a8f7abae Mon Sep 17 00:00:00 2001 From: carmengg Date: Mon, 2 Oct 2023 20:34:54 -0700 Subject: [PATCH 10/31] added some exercises --- .../discussion-sect-week-1.qmd | 74 ++++++++++++++++++- 1 file changed, 72 insertions(+), 2 deletions(-) diff --git a/discussion-sections/discussion-sect-week-1.qmd b/discussion-sections/discussion-sect-week-1.qmd index 0c65635..bb2c226 100644 --- a/discussion-sections/discussion-sect-week-1.qmd +++ b/discussion-sections/discussion-sect-week-1.qmd @@ -63,8 +63,78 @@ Complete the following paragraph using the given words: ::: -:::{.callout-tip} -## Data Access +:::{.callout-note} +## About the data + +For this exercise we are going to use data about [Western Indian Ocean Coral Diversity](https://knb.ecoinformatics.org/view/doi%3A10.5063%2FF1K35S3H). +This dataset is stored in the [Knowledge Network for Biocomplexity (KNB)](https://knb.ecoinformatics.org) data repository. This is an international repository intended to facilitate ecological and environmental research. It has thousands of open datasets and it's hosted by NCEAS! + Navigate to dataset's link and briefly explore the data package. ::: + +0. Be sure to comment your code throughout this exercise! + +1. Import the pandas package using standard abbreviation in a code cell. + +2. Use the `pandas.read_csv()` function to read in the csv file in the Coral Diversity data by following these steps: + + a. Create a new code cell under the “Read and explore data” header. + + b. Navigate to the [data package site](https://knb.ecoinformatics.org/view/doi%3A10.5063%2FF1K35S3H) and copy the the URL to access the `WesternIndianOceanCoralDiversity` csv file. To copy the URL: hover over the Download button –> right click –> “Copy Link". + + c. Read in the data from the URL using the `pd.read_csv` function and store it as `coral_div`: + + ```python + # read in data + coral_div = pd.read_csv('the URL goes here') + ``` + + d. Use attributes and methods of the `coral_div` data frame to obtain the following information about it: + - how many rows and columns does it have? + - see the first 5 rows + - what are its column names? + - what are the data types of the columns? + + In this last step, what is the difference between the `int64` and `float64` data types? + + e. In which countries was this data collected? HINT: the `Country` column indicates the country of reef location. + + f. How many observers collected this data? HINT: use the `len` Python function. Not sure what it is? Use `?`. + + g. + + + +```{python} +import pandas as pd + +coral_div = pd.read_csv('https://knb.ecoinformatics.org/knb/d1/mn/v2/object/urn%3Auuid%3Ae7d12a5a-f1a9-4a3e-aaa5-5222f67e799d') +``` + +```{python} +coral_div.shape +``` + +```{python} +coral_div.head() +``` + +```{python} +coral_div.columns +``` + +```{python} +coral_div.dtypes +``` + +```{python} +coral_div.Country.unique() +``` + +```{python} +len(coral_div.Observer.unique()) +``` + +## Reference +Tim McClanahan. (2023). Western Indian Ocean Coral Diversity. Knowledge Network for Biocomplexity. doi:10.5063/F1K35S3H. \ No newline at end of file From c4a9e56cb456132b6976358ded9e889a7e1289de Mon Sep 17 00:00:00 2001 From: carmengg Date: Mon, 2 Oct 2023 21:11:38 -0700 Subject: [PATCH 11/31] finished exercises --- .../discussion-sect-week-1.qmd | 41 +++++++++++++++++-- 1 file changed, 37 insertions(+), 4 deletions(-) diff --git a/discussion-sections/discussion-sect-week-1.qmd b/discussion-sections/discussion-sect-week-1.qmd index bb2c226..37ca87c 100644 --- a/discussion-sections/discussion-sect-week-1.qmd +++ b/discussion-sections/discussion-sect-week-1.qmd @@ -2,8 +2,6 @@ Date: October 6, 2023. -Fork and clone a repository for this lab? - ## Exercise 1 Consider the following code: @@ -100,9 +98,26 @@ This dataset is stored in the [Knowledge Network for Biocomplexity (KNB)](https: e. In which countries was this data collected? HINT: the `Country` column indicates the country of reef location. - f. How many observers collected this data? HINT: use the `len` Python function. Not sure what it is? Use `?`. + f. How many observers collected this data? HINT: use the `len` Python function. Not sure what `len` does? Use `?`. + + g. The `Coralcover` column indicates the percent of hard coral cover in each 6km reef cell surveyed. Select the observations which have a coral cover of at least 50%. + + h. Are there any sites with a 30% coral cover in Tanzania? + + i. We are interested in looking at the PH of sea water (`PH` column), mean salinity (PSS) (`Salinity_mean` column), and depth of survey location in meters (`Depth` column). Select this data. + + j. The `max_yr` column indicates the year of last sampling at a given site. Select the PH, salinity, and depth for sites where sampling ended in 2017 + + k. Run `coral_div.Current_vel_mean.isna()`. What is the ouput telling us? - g. + l. Run `coral_div.Current_vel_mean.isna().any()`. What is the ouput telling us? HINT: Check the [documentation of `pandas.Sries.any`](https://pandas.pydata.org/docs/reference/api/pandas.Series.any.html). + + m. Select the rows in `coral_div` that have NA in the `Current_vel_mean` column. + + + + + @@ -136,5 +151,23 @@ coral_div.Country.unique() len(coral_div.Observer.unique()) ``` +```{python} +coral_div[coral_div['Coralcover'] >= 50] +``` + +```{python} +coral_div[(coral_div['Coralcover']==30) & (coral_div.Country == 'Tanzania')] +``` + +```{python} +coral_div.Current_vel_mean.isna() +``` + +```{python} +coral_div.loc[coral_div['Current_vel_mean'].isna()] +``` + + + ## Reference Tim McClanahan. (2023). Western Indian Ocean Coral Diversity. Knowledge Network for Biocomplexity. doi:10.5063/F1K35S3H. \ No newline at end of file From 897fb8ef3a96ab56875c30e7199c9a74599c7304 Mon Sep 17 00:00:00 2001 From: carmengg Date: Tue, 3 Oct 2023 18:44:21 +0000 Subject: [PATCH 12/31] finished exercise --- .../discussion-sect-week-1.qmd | 126 +++++++++++------- 1 file changed, 77 insertions(+), 49 deletions(-) diff --git a/discussion-sections/discussion-sect-week-1.qmd b/discussion-sections/discussion-sect-week-1.qmd index 37ca87c..4b503fc 100644 --- a/discussion-sections/discussion-sect-week-1.qmd +++ b/discussion-sections/discussion-sect-week-1.qmd @@ -1,7 +1,7 @@ # Discussion Section 1 Date: October 6, 2023. - + -## Exercise 2 :::{.callout-tip} -## GitHub Setup +## Notebook Setup 1. Create a new repository on GitHub. Use the following settings: - a. Call the repository 'eds-220-section-1'. + a. Call the repository `eds-220-section-1`. b. Add a brief description for your new repository. For example: EDS 220 section - practice session for data selection in pandas. @@ -44,89 +44,103 @@ Complete the following paragraph using the given words: d. Initialize the repository with a `README` file and a Python .gitignore template. -2. Clone the repository to a new directory in the Taylor server under your eds-220 directory. +2. Clone the repository to a new directory in the Taylor server under your `eds-220` directory. -3. In the terminal use `cd` to navigate into the 'eds-220-section-1' directory. Use `pwd` to verify this is your current working directory. +3. In the terminal use `cd` to navigate into the `eds-220-section-1` directory. Use `pwd` to verify `eds-220-section-1` is your current working directory. -3. Create a new Python Notebook in the 'eds-220-section-1' directory. +3. Create a new Python Notebook in `eds-220-section-1`. 4. Update the notebook's name to something useful like 'exercise-data-selection.qmd'. -5. Use the terminal to stage, commit, and push this file to the remote repository (on GitHub). Remember: - - to stage: `git add FILE_NAME` - - to commit with message: `git commit -m "COMMIT_MESSAGE"` - - to push: `git push` +5. Use the terminal to stage, commit, and push this file to the remote repository. Remember: + - stage: `git add FILE_NAME` + - commit with message: `git commit -m "COMMIT_MESSAGE"` + - push: `git push` 6. If you are prompted for your credentials and need to set up a new Personal Access Token (PAT) follow [steps 13-18 in this guide](https://docs.google.com/document/d/1Pk6_rUDdFjdGg-YVo9Cl8ET3iDsYahyaP0VMRD4UHUk/edit?usp=sharing/) to set it up. ::: +:::{.callout-note} +## General directions +Add comments in each one of your code cells and include markdown cells to add titles to the different execises. +::: + :::{.callout-note} ## About the data -For this exercise we are going to use data about [Western Indian Ocean Coral Diversity](https://knb.ecoinformatics.org/view/doi%3A10.5063%2FF1K35S3H). +For this exercise we are going to use data about [Western Indian Ocean Coral Diversity (McClanahan, 2023)](https://knb.ecoinformatics.org/view/doi%3A10.5063%2FF1K35S3H). -This dataset is stored in the [Knowledge Network for Biocomplexity (KNB)](https://knb.ecoinformatics.org) data repository. This is an international repository intended to facilitate ecological and environmental research. It has thousands of open datasets and it's hosted by NCEAS! +This dataset is stored in the [Knowledge Network for Biocomplexity (KNB)](https://knb.ecoinformatics.org) data repository. This is an international repository intended to facilitate ecological and environmental research. It has thousands of open datasets and is hosted by NCEAS! Navigate to dataset's link and briefly explore the data package. ::: -0. Be sure to comment your code throughout this exercise! + +**1.** Import the pandas package using standard abbreviation in a code cell. Then follow these steps to read in the csv file in the Western Indian Ocean Coral Diversity data using the `pandas.read_csv()` function: -1. Import the pandas package using standard abbreviation in a code cell. +a. Navigate to the [data package site](https://knb.ecoinformatics.org/view/doi%3A10.5063%2FF1K35S3H) and copy the the URL to access the `WesternIndianOceanCoralDiversity` csv file. To copy the URL: -2. Use the `pandas.read_csv()` function to read in the csv file in the Coral Diversity data by following these steps: +- hover over the Download button –> right click –> “Copy Link". - a. Create a new code cell under the “Read and explore data” header. - - b. Navigate to the [data package site](https://knb.ecoinformatics.org/view/doi%3A10.5063%2FF1K35S3H) and copy the the URL to access the `WesternIndianOceanCoralDiversity` csv file. To copy the URL: hover over the Download button –> right click –> “Copy Link". - - c. Read in the data from the URL using the `pd.read_csv` function and store it as `coral_div`: +b. Read in the data from the URL using the `pd.read_csv` function and store it as `coral_div` like this: ```python # read in data coral_div = pd.read_csv('the URL goes here') ``` - d. Use attributes and methods of the `coral_div` data frame to obtain the following information about it: - - how many rows and columns does it have? - - see the first 5 rows - - what are its column names? - - what are the data types of the columns? - - In this last step, what is the difference between the `int64` and `float64` data types? + +**2.** Use attributes and methods of the `coral_div` data frame to obtain the following information about it: - e. In which countries was this data collected? HINT: the `Country` column indicates the country of reef location. +- how many rows and columns does it have? +- see the first 5 rows +- what are its column names? +- what are the data types of the columns? - f. How many observers collected this data? HINT: use the `len` Python function. Not sure what `len` does? Use `?`. +In this last step, what is the difference between the `int64` and `float64` data types? - g. The `Coralcover` column indicates the percent of hard coral cover in each 6km reef cell surveyed. Select the observations which have a coral cover of at least 50%. + +**3.** The `Country` column indicates the country of reef location. Explain in your words what is the ouput of `coral_div.Country` and `coral_div.Country.unique()`. - h. Are there any sites with a 30% coral cover in Tanzania? - - i. We are interested in looking at the PH of sea water (`PH` column), mean salinity (PSS) (`Salinity_mean` column), and depth of survey location in meters (`Depth` column). Select this data. + +**4.** Check the dataset's [metadata file README_md.doc](https://knb.ecoinformatics.org/view/doi:10.5063/F1K35S3H) to find which column has information about who made an observation. How many observers collected this data? HINT: use the `len` Python function. Not sure what `len` does? Use `?`. + + +**5.** The `Coralcover` column indicates the percent of hard coral cover in each 6km reef cell surveyed. What data is `coral_div[coral_div['Coralcover'] >= 50]` selecting? + + +**6.** Are there any sites with a 30% coral cover in Tanzania? + + +**7.** We are interested in looking at the PH of sea water (`PH` column), mean salinity (PSS) (`Salinity_mean` column), and depth of survey location in meters (`Depth` column). Select this data. - j. The `max_yr` column indicates the year of last sampling at a given site. Select the PH, salinity, and depth for sites where sampling ended in 2017 + +**8.** The `max_yr` column indicates the year of last sampling at a given site. Select the PH, salinity, and depth for sites where sampling ended in 2017. - k. Run `coral_div.Current_vel_mean.isna()`. What is the ouput telling us? + +**9.** Use `iloc` to select rows 95 throuhg 100 (including 100) in the 17th, 20th, and 28th columns and assign these values to a new variable called `subset`. HINT: the ouput columns should be `Salinity_mean`, `mean.npp` and `Current_vel_mean`. Are you indexing from 0? - l. Run `coral_div.Current_vel_mean.isna().any()`. What is the ouput telling us? HINT: Check the [documentation of `pandas.Sries.any`](https://pandas.pydata.org/docs/reference/api/pandas.Series.any.html). + +**10.** Run `subset.isna()`. Explain what this code does and what the output represents. + +**11.** Run `subset.isna().any()`. Explain what this code does and what the output represents. HINT: It could be helpful to check the [documentation of `pandas.Sries.any`](https://pandas.pydata.org/docs/reference/api/pandas.Series.any.html). - m. Select the rows in `coral_div` that have NA in the `Current_vel_mean` column. +**12.** Select the rows in `subset` that have NA in the `Current_vel_mean` column. HINT: you can use your answer for 9. - +## References +Tim McClanahan. (2023). Western Indian Ocean Coral Diversity. Knowledge Network for Biocomplexity. doi:10.5063/F1K35S3H. - - ```{python} import pandas as pd coral_div = pd.read_csv('https://knb.ecoinformatics.org/knb/d1/mn/v2/object/urn%3Auuid%3Ae7d12a5a-f1a9-4a3e-aaa5-5222f67e799d') ``` + ```{python} coral_div.shape ``` @@ -159,15 +173,29 @@ coral_div[coral_div['Coralcover'] >= 50] coral_div[(coral_div['Coralcover']==30) & (coral_div.Country == 'Tanzania')] ``` + +```{python} +coral_div[['PH','Salinity_mean','Depth']] +``` + + +```{python} +coral_div.loc[coral_div.max_yr == 2017 ,['PH','Salinity_mean','Depth']] +``` + ```{python} -coral_div.Current_vel_mean.isna() +subset = coral_div.iloc[95:101,[16,19,27]] ``` ```{python} -coral_div.loc[coral_div['Current_vel_mean'].isna()] +subset.isna() ``` +```{python} +subset.isna().any() +``` -## Reference -Tim McClanahan. (2023). Western Indian Ocean Coral Diversity. Knowledge Network for Biocomplexity. doi:10.5063/F1K35S3H. \ No newline at end of file +```{python} +subset[subset['Current_vel_mean'].isna()] +``` From 5b64ea35479ecba4c32f4e968ab082231988f4b7 Mon Sep 17 00:00:00 2001 From: carmengg Date: Tue, 3 Oct 2023 21:58:39 +0000 Subject: [PATCH 13/31] added solutions as drop-down --- .../discussion-sect-week-1.qmd | 150 ++++++++++++------ 1 file changed, 104 insertions(+), 46 deletions(-) diff --git a/discussion-sections/discussion-sect-week-1.qmd b/discussion-sections/discussion-sect-week-1.qmd index 4b503fc..f258cb9 100644 --- a/discussion-sections/discussion-sect-week-1.qmd +++ b/discussion-sections/discussion-sect-week-1.qmd @@ -90,112 +90,170 @@ b. Read in the data from the URL using the `pd.read_csv` function and store it a coral_div = pd.read_csv('the URL goes here') ``` + +:::{.callout-tip collapse="true"} +## Solution +```{python} +# import pandas library +import pandas as pd + +# load data directly from data archive +coral_div = pd.read_csv('https://knb.ecoinformatics.org/knb/d1/mn/v2/object/urn%3Auuid%3Ae7d12a5a-f1a9-4a3e-aaa5-5222f67e799d') +``` +::: + **2.** Use attributes and methods of the `coral_div` data frame to obtain the following information about it: - how many rows and columns does it have? - see the first 5 rows -- what are its column names? +- what are the column names? - what are the data types of the columns? In this last step, what is the difference between the `int64` and `float64` data types? - -**3.** The `Country` column indicates the country of reef location. Explain in your words what is the ouput of `coral_div.Country` and `coral_div.Country.unique()`. - - -**4.** Check the dataset's [metadata file README_md.doc](https://knb.ecoinformatics.org/view/doi:10.5063/F1K35S3H) to find which column has information about who made an observation. How many observers collected this data? HINT: use the `len` Python function. Not sure what `len` does? Use `?`. - - -**5.** The `Coralcover` column indicates the percent of hard coral cover in each 6km reef cell surveyed. What data is `coral_div[coral_div['Coralcover'] >= 50]` selecting? - - -**6.** Are there any sites with a 30% coral cover in Tanzania? - - -**7.** We are interested in looking at the PH of sea water (`PH` column), mean salinity (PSS) (`Salinity_mean` column), and depth of survey location in meters (`Depth` column). Select this data. - - -**8.** The `max_yr` column indicates the year of last sampling at a given site. Select the PH, salinity, and depth for sites where sampling ended in 2017. - - -**9.** Use `iloc` to select rows 95 throuhg 100 (including 100) in the 17th, 20th, and 28th columns and assign these values to a new variable called `subset`. HINT: the ouput columns should be `Salinity_mean`, `mean.npp` and `Current_vel_mean`. Are you indexing from 0? - - -**10.** Run `subset.isna()`. Explain what this code does and what the output represents. - -**11.** Run `subset.isna().any()`. Explain what this code does and what the output represents. HINT: It could be helpful to check the [documentation of `pandas.Sries.any`](https://pandas.pydata.org/docs/reference/api/pandas.Series.any.html). - -**12.** Select the rows in `subset` that have NA in the `Current_vel_mean` column. HINT: you can use your answer for 9. - - -## References -Tim McClanahan. (2023). Western Indian Ocean Coral Diversity. Knowledge Network for Biocomplexity. doi:10.5063/F1K35S3H. - - - -```{python} -import pandas as pd - -coral_div = pd.read_csv('https://knb.ecoinformatics.org/knb/d1/mn/v2/object/urn%3Auuid%3Ae7d12a5a-f1a9-4a3e-aaa5-5222f67e799d') -``` - - +:::{.callout-tip collapse="true"} +## Solution ```{python} +# check the shape of dataframe: (# rows, # columns) coral_div.shape ``` ```{python} +# see the first five rows of dataframe coral_div.head() ``` ```{python} +# obtain the column names as an array coral_div.columns ``` ```{python} +# check the data type of each column coral_div.dtypes ``` +::: + + +**3.** The `Country` column indicates the country of reef location. Explain in your words what is the ouput of `coral_div.Country` and `coral_div.Country.unique()`. + +:::{.callout-tip collapse="true"} +## Solution +`coral_div.Country` returns the `Country` column of the dataframe, this is a `pandas.Series`. + +`coral_div.Country.unique()` returns the uniqeu values of the `Country` column. These are the countries where the reefs are located: ```{python} +# countries where reefs are located coral_div.Country.unique() ``` +::: + +**4.** Check the dataset's [metadata file README_md.doc](https://knb.ecoinformatics.org/view/doi:10.5063/F1K35S3H) to find which column has information about who made an observation. How many observers collected this data? HINT: use the `len` Python function. Not sure what `len` does? Use `?`. + +:::{.callout-tip collapse="true"} +## Solution ```{python} +# nunber of observers in the dataset len(coral_div.Observer.unique()) ``` -```{python} -coral_div[coral_div['Coralcover'] >= 50] -``` +::: + +**5.** The `Coralcover` column indicates the percent of hard coral cover in each 6km reef cell surveyed. What data is `coral_div[coral_div['Coralcover'] >= 50]` selecting? + +:::{.callout-tip collapse="true"} +## Solution +We are selecting the rows where the coral cover was at least 50 percent. +::: + + +**6.** Are there any sites with a 30% coral cover in Tanzania? + +:::{.callout-tip collapse="true"} +## Solution +We can check this by using two conditions to select rows. The first condition is `coral_div['Coralcover']==30` and the second is `coral_div.Country == 'Tanzania'`. We use the `&` and operator to look for rows that satisfy both condtions: ```{python} +# remember to add parenthesis around each condition +# check if ther are any sites with a 30% coral cover in Tanzania coral_div[(coral_div['Coralcover']==30) & (coral_div.Country == 'Tanzania')] ``` +::: + + +**7.** We are interested in looking at the PH of sea water (`PH` column), mean salinity (PSS) (`Salinity_mean` column), and depth of survey location in meters (`Depth` column). Select this data. +:::{.callout-tip collapse="true"} +## Solution ```{python} +# select columns using a list with their names coral_div[['PH','Salinity_mean','Depth']] ``` +::: + +**8.** The `max_yr` column indicates the year of last sampling at a given site. Select the PH, salinity, and depth for sites where sampling ended in 2017. +:::{.callout-tip collapse="true"} +## Solution ```{python} +# use loc to select rows and columns simultaneously by label or conditions coral_div.loc[coral_div.max_yr == 2017 ,['PH','Salinity_mean','Depth']] ``` +::: + + +**9.** Use `iloc` to select rows 95 throuhg 100 (including 100) in the 17th, 20th, and 28th columns and assign these values to a new variable called `subset`. HINT: the ouput columns should be `Salinity_mean`, `mean.npp` and `Current_vel_mean`. Are you indexing from 0? +:::{.callout-tip collapse="true"} +## Solution ```{python} -subset = coral_div.iloc[95:101,[16,19,27]] +# use iloc to subset data by location +subset = coral_div.iloc[ 95:101 , [16,19,27] ] ``` +::: + + +**10.** Run `subset.isna()`. Explain what this code does and what the output represents. + +:::{.callout-tip collapse="true"} +## Solution +We are using the method `isna()` of the dataframe `susbset`. We can see the ouput is a dataframe of the same shape as `subset` indicating wheter an entry is an `NA` or not. ```{python} subset.isna() ``` +::: + +**11.** Run `subset.isna().any()`. Explain what this code does and what the output represents. HINT: It could be helpful to check the [documentation of `pandas.Sries.any`](https://pandas.pydata.org/docs/reference/api/pandas.Series.any.html). + +:::{.callout-tip collapse="true"} +## Solution +Adding `.any()` to `susbet.isna()` tells us which column have `NA` values in them. If the output is `False` then there are no `NA` values, if the output is `True`, then there are `NA`s in the column. ```{python} subset.isna().any() ``` +::: + +**12.** Select the rows in `subset` that have NA in the `Current_vel_mean` column. HINT: you can use your answer for 9. + +:::{.callout-tip collapse="true"} +## Solution ```{python} +# another example of selecting rows using a condition subset[subset['Current_vel_mean'].isna()] ``` +::: + + +## References +Tim McClanahan. (2023). Western Indian Ocean Coral Diversity. Knowledge Network for Biocomplexity. doi:10.5063/F1K35S3H. + + From b04b2afc624dba83b3df4563b45d1f4d3ee34e19 Mon Sep 17 00:00:00 2001 From: carmengg Date: Tue, 3 Oct 2023 22:04:41 +0000 Subject: [PATCH 14/31] no solutions --- .../discussion-sect-week-1-no-solutions.qmd | 207 ++++++++++++++++++ 1 file changed, 207 insertions(+) create mode 100644 discussion-sections/discussion-sect-week-1-no-solutions.qmd diff --git a/discussion-sections/discussion-sect-week-1-no-solutions.qmd b/discussion-sections/discussion-sect-week-1-no-solutions.qmd new file mode 100644 index 0000000..baa04e8 --- /dev/null +++ b/discussion-sections/discussion-sect-week-1-no-solutions.qmd @@ -0,0 +1,207 @@ +# Discussion Section 1 + + +Date: October 6, 2023. + +:::{.callout-tip} +## Notebook Setup +1. Create a new repository on GitHub. Use the following settings: + + a. Call the repository `eds-220-section-1`. + + b. Add a brief description for your new repository. For example: EDS 220 section - practice session for data selection in pandas. + + c. Keep the repository public. + + d. Initialize the repository with a `README` file and a Python .gitignore template. + +2. Clone the repository to a new directory in the Taylor server under your `eds-220` directory. + +3. In the terminal use `cd` to navigate into the `eds-220-section-1` directory. Use `pwd` to verify `eds-220-section-1` is your current working directory. + +3. Create a new Python Notebook in `eds-220-section-1`. + +4. Update the notebook's name to something useful like 'exercise-data-selection.qmd'. + +5. Use the terminal to stage, commit, and push this file to the remote repository. Remember: + - stage: `git add FILE_NAME` + - commit with message: `git commit -m "COMMIT_MESSAGE"` + - push: `git push` + +6. If you are prompted for your credentials and need to set up a new Personal Access Token (PAT) follow [steps 13-18 in this guide](https://docs.google.com/document/d/1Pk6_rUDdFjdGg-YVo9Cl8ET3iDsYahyaP0VMRD4UHUk/edit?usp=sharing/) to set it up. +::: + + +:::{.callout-note} +## General directions +Add comments in each one of your code cells and include markdown cells to add titles to the different execises. +::: + +:::{.callout-note} +## About the data + +For this exercise we are going to use data about [Western Indian Ocean Coral Diversity (McClanahan, 2023)](https://knb.ecoinformatics.org/view/doi%3A10.5063%2FF1K35S3H). + +This dataset is stored in the [Knowledge Network for Biocomplexity (KNB)](https://knb.ecoinformatics.org) data repository. This is an international repository intended to facilitate ecological and environmental research. It has thousands of open datasets and is hosted by NCEAS! + + Navigate to dataset's link and briefly explore the data package. +::: + + +**1.** Import the pandas package using standard abbreviation in a code cell. Then follow these steps to read in the csv file in the Western Indian Ocean Coral Diversity data using the `pandas.read_csv()` function: + +a. Navigate to the [data package site](https://knb.ecoinformatics.org/view/doi%3A10.5063%2FF1K35S3H) and copy the the URL to access the `WesternIndianOceanCoralDiversity` csv file. To copy the URL: + +- hover over the Download button –> right click –> “Copy Link". + +b. Read in the data from the URL using the `pd.read_csv` function and store it as `coral_div` like this: + + ```python + # read in data + coral_div = pd.read_csv('the URL goes here') + ``` + + +**2.** Use attributes and methods of the `coral_div` data frame to obtain the following information about it: + +- how many rows and columns does it have? +- see the first 5 rows +- what are the column names? +- what are the data types of the columns? + +In this last step, what is the difference between the `int64` and `float64` data types? + + + +**3.** The `Country` column indicates the country of reef location. Explain in your words what is the ouput of `coral_div.Country` and `coral_div.Country.unique()`. + + +**4.** Check the dataset's [metadata file README_md.doc](https://knb.ecoinformatics.org/view/doi:10.5063/F1K35S3H) to find which column has information about who made an observation. How many observers collected this data? HINT: use the `len` Python function. Not sure what `len` does? Use `?`. + + +**5.** The `Coralcover` column indicates the percent of hard coral cover in each 6km reef cell surveyed. What data is `coral_div[coral_div['Coralcover'] >= 50]` selecting? + + +**6.** Are there any sites with a 30% coral cover in Tanzania? + + +**7.** We are interested in looking at the PH of sea water (`PH` column), mean salinity (PSS) (`Salinity_mean` column), and depth of survey location in meters (`Depth` column). Select this data. + + +**8.** The `max_yr` column indicates the year of last sampling at a given site. Select the PH, salinity, and depth for sites where sampling ended in 2017. + + +**9.** Use `iloc` to select rows 95 throuhg 100 (including 100) in the 17th, 20th, and 28th columns and assign these values to a new variable called `subset`. HINT: the ouput columns should be `Salinity_mean`, `mean.npp` and `Current_vel_mean`. Are you indexing from 0? + + +**10.** Run `subset.isna()`. Explain what this code does and what the output represents. + + +**11.** Run `subset.isna().any()`. Explain what this code does and what the output represents. HINT: It could be helpful to check the [documentation of `pandas.Sries.any`](https://pandas.pydata.org/docs/reference/api/pandas.Series.any.html). + + +**12.** Select the rows in `subset` that have NA in the `Current_vel_mean` column. HINT: you can use your answer for 9. + + +## References +Tim McClanahan. (2023). Western Indian Ocean Coral Diversity. Knowledge Network for Biocomplexity. doi:10.5063/F1K35S3H. + + + + \ No newline at end of file From cb437a73a091dfc097562ad4f599b5afd28f77d9 Mon Sep 17 00:00:00 2001 From: carmengg Date: Tue, 3 Oct 2023 23:00:44 +0000 Subject: [PATCH 15/31] added freeze for rendering to yaml --- _quarto.yml | 5 +++-- ...son-3-pandas-computing.qmd => lesson-3-basic-ploting.qmd} | 0 2 files changed, 3 insertions(+), 2 deletions(-) rename lectures/{lesson-3-pandas-computing.qmd => lesson-3-basic-ploting.qmd} (100%) diff --git a/_quarto.yml b/_quarto.yml index da11e59..fbb2073 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -16,8 +16,9 @@ book: - lectures/lesson-1-python-review.qmd - lectures/lesson-2-pandas-basics.qmd - - +execute: + freeze: auto # re-render only when source changes + format: html: theme: cosmo \ No newline at end of file diff --git a/lectures/lesson-3-pandas-computing.qmd b/lectures/lesson-3-basic-ploting.qmd similarity index 100% rename from lectures/lesson-3-pandas-computing.qmd rename to lectures/lesson-3-basic-ploting.qmd From 16e855ff08a47a5627284968971c780c12b20bb4 Mon Sep 17 00:00:00 2001 From: carmengg Date: Wed, 4 Oct 2023 00:12:26 +0000 Subject: [PATCH 16/31] started plot lesson --- lectures/lesson-2-pandas-basics.qmd | 5 ++ lectures/lesson-3-basic-ploting.qmd | 101 +++++++++++++++++++++------- 2 files changed, 82 insertions(+), 24 deletions(-) diff --git a/lectures/lesson-2-pandas-basics.qmd b/lectures/lesson-2-pandas-basics.qmd index 21b9300..f6b72c4 100644 --- a/lectures/lesson-2-pandas-basics.qmd +++ b/lectures/lesson-2-pandas-basics.qmd @@ -1,3 +1,8 @@ +```{python} +import os +os.getcwd() +``` + # Pandas Fundamentals ## About diff --git a/lectures/lesson-3-basic-ploting.qmd b/lectures/lesson-3-basic-ploting.qmd index b7fbaf0..cc127c3 100644 --- a/lectures/lesson-3-basic-ploting.qmd +++ b/lectures/lesson-3-basic-ploting.qmd @@ -1,43 +1,96 @@ - -:::{.callout-tip} -## Check-in -Look at the x-axis. What do these values correspond to? The default for `plot` is to use the values of the index as the x-axis values. -::: +```{python} +import pandas as pd -As we can see, this doesn't make any sense! We need to subset the information to visualize it. +# read in file +df = pd.read_csv('data/lesson-1/glacial_loss.csv') ---> +# see the first five rows +df.head() +``` + +## `plot()` method + +A `pandas.DataFrame` has a [built-in method `plot()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html) for plotting . +When we call it without specifying any other parameters `plot()` creates one line plot for each of the columns with numeric data. + +```{python} +# one line plot per column with numeric data - a mess +df.plot() +``` + +As we can see, this doesn't make any sense! In particular, look at the x-axis. The default for `plot` is to use the values of the index as the x-axis values. Let's see some examples about how to improve this situation. + +### Example: one line plot - \ No newline at end of file + +Notice we used a few different arguments here: + +- `kind`: controls what kind of plot is drawn, [see `plot()` documentation for all options](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html) +- `color`: here we used a HEX code to specify the color +- `alpha`: controls the transparency From 33a8db2df93ec46b3b74357f1bd81d4dde33a663 Mon Sep 17 00:00:00 2001 From: carmengg Date: Thu, 5 Oct 2023 00:55:38 +0000 Subject: [PATCH 17/31] initial commit --- .../discussion-sect-week-1-methods.qmd | 72 +++++++++++++++++++ 1 file changed, 72 insertions(+) create mode 100644 discussion-sections/discussion-sect-week-1-methods.qmd diff --git a/discussion-sections/discussion-sect-week-1-methods.qmd b/discussion-sections/discussion-sect-week-1-methods.qmd new file mode 100644 index 0000000..0f48b2f --- /dev/null +++ b/discussion-sections/discussion-sect-week-1-methods.qmd @@ -0,0 +1,72 @@ + +:::{.callout-tip} +## Notebook Setup +1. Create a new repository on GitHub. Use the following settings: + + a. Call the repository `eds-220-section-1`. + + b. Add a brief description for your new repository. For example: EDS 220 section - practice session for data selection in pandas. + + c. Keep the repository public. + + d. Initialize the repository with a `README` file and a Python .gitignore template. + +2. Clone the repository to a new directory in the Taylor server under your `eds-220` directory. + +3. In the terminal use `cd` to navigate into the `eds-220-section-1` directory. Use `pwd` to verify `eds-220-section-1` is your current working directory. + +3. Create a new Python Notebook in `eds-220-section-1`. + +4. Update the notebook's name to something useful like 'exercise-data-selection.qmd'. + +5. Use the terminal to stage, commit, and push this file to the remote repository. Remember: + - stage: `git add FILE_NAME` + - commit with message: `git commit -m "COMMIT_MESSAGE"` + - push: `git push` + +6. If you are prompted for your credentials and need to set up a new Personal Access Token (PAT) follow [steps 13-18 in this guide](https://docs.google.com/document/d/1Pk6_rUDdFjdGg-YVo9Cl8ET3iDsYahyaP0VMRD4UHUk/edit?usp=sharing/) to set it up. +::: + + +:::{.callout-note} +## General directions +Add comments in each one of your code cells and include markdown cells to add titles to the different execises. +::: + +:::{.callout-note} +## About the data + +For this exercise we will use data about [prey items for endangered terrestrial vertebrate species within central California drylands (King et. al, 2023)](https://knb.ecoinformatics.org/view/doi%3A10.5063%2FF1VM49RH). + +This dataset is stored in the [Knowledge Network for Biocomplexity (KNB)](https://knb.ecoinformatics.org) data repository. This is an international repository intended to facilitate ecological and environmental research. It has thousands of open datasets and is hosted by NCEAS! + + Navigate to dataset's link and briefly explore the data package. +::: + + +For many datasets data exploration begins at the data repository. Take some time to look through the dataset's description in KNB. Discuss the following questions with your team: + +a. Does this dataset come with an associated metadata file? +b. Is this data collected in-situ by the authors or is it a compilation and synthesis of multiple datasets? +c. + + + +**1.** Import the `pandas` package using standard abbreviation in a code cell. Then follow these steps to read in the csv file in the Western Indian Ocean Coral Diversity data using the `pandas.read_csv()` function: + +a. Navigate to the [data package site](https://knb.ecoinformatics.org/view/doi%3A10.5063%2FF1K35S3H) and copy the the URL to access the `WesternIndianOceanCoralDiversity` csv file. To copy the URL: + +- hover over the Download button –> right click –> “Copy Link". + +b. Read in the data from the URL using the `pd.read_csv` function and store it as `coral_div` like this: + + ```python + # read in data + coral_div = pd.read_csv('the URL goes here') + ``` + +# References + +Rachel King, Jenna Braun, Michael Westphal, & CJ Lortie. (2023). Compiled occurrence records for prey items of listed species found in California drylands with associated environmental data. Knowledge Network for Biocomplexity. doi:10.5063/F1VM49RH. + +Lortie, C. J., Braun, J., King, R., & Westphal, M. (2023). The importance of open data describing prey item species lists for endangered species. Ecological Solutions and Evidence, 4(2), e12251. https://doi.org/10.1002/2688-8319.12251 From d765e950b3b5650a1fd1c5d20948e5728aa90206 Mon Sep 17 00:00:00 2001 From: carmengg Date: Thu, 5 Oct 2023 01:14:52 +0000 Subject: [PATCH 18/31] finished setup --- .../discussion-sect-week-1-methods.qmd | 37 ++++++++++++++----- 1 file changed, 27 insertions(+), 10 deletions(-) diff --git a/discussion-sections/discussion-sect-week-1-methods.qmd b/discussion-sections/discussion-sect-week-1-methods.qmd index 0f48b2f..73b5509 100644 --- a/discussion-sections/discussion-sect-week-1-methods.qmd +++ b/discussion-sections/discussion-sect-week-1-methods.qmd @@ -25,12 +25,18 @@ - push: `git push` 6. If you are prompted for your credentials and need to set up a new Personal Access Token (PAT) follow [steps 13-18 in this guide](https://docs.google.com/document/d/1Pk6_rUDdFjdGg-YVo9Cl8ET3iDsYahyaP0VMRD4UHUk/edit?usp=sharing/) to set it up. + + +

    +**CHECK IN WITH YOUR TEAM** +

    + ::: :::{.callout-note} ## General directions -Add comments in each one of your code cells and include markdown cells to add titles to the different execises. +Add comments in each one of your code cells and include markdown cells in between your code cells to add titles to the different execises. ::: :::{.callout-note} @@ -39,30 +45,41 @@ Add comments in each one of your code cells and include markdown cells to add ti For this exercise we will use data about [prey items for endangered terrestrial vertebrate species within central California drylands (King et. al, 2023)](https://knb.ecoinformatics.org/view/doi%3A10.5063%2FF1VM49RH). This dataset is stored in the [Knowledge Network for Biocomplexity (KNB)](https://knb.ecoinformatics.org) data repository. This is an international repository intended to facilitate ecological and environmental research. It has thousands of open datasets and is hosted by NCEAS! - - Navigate to dataset's link and briefly explore the data package. ::: -For many datasets data exploration begins at the data repository. Take some time to look through the dataset's description in KNB. Discuss the following questions with your team: +**1. Archive exploration** + +For many datasets, data exploration begins at the data repository. Take some time to look through the dataset's description in KNB. Discuss the following questions with your team: a. Does this dataset come with an associated metadata file? -b. Is this data collected in-situ by the authors or is it a compilation and synthesis of multiple datasets? -c. +b. Is this data collected in-situ by the authors or is it a synthesis of multiple datasets? +c. Does the dataset contain sensitive data? +d. During what time frame were the observations in the dataset collected? + +**2. Metadata exploration** + +You may have noticed two metadata files: `Compiled_occurrence_records_for_prey_items_of.xml` and `metadata_arth_occurrences.csv`. Download both of them. + +a. Notice the `.xml` document file type is `EML` which stands for [EML: Ecological Metadata Language](https://eml.ecoinformatics.org). +b. Open the `.xml` file: there's a lot going on. This is a machine-readable file that has metadata about *the whole dataset*. You can proably identify some items like title and creators. +c. Open the `.csv` file: much easier to understand! Take a minute to look at the column descriptions. -**1.** Import the `pandas` package using standard abbreviation in a code cell. Then follow these steps to read in the csv file in the Western Indian Ocean Coral Diversity data using the `pandas.read_csv()` function: +**3. Data loading** + +Back in your notebook, import the `pandas` package using standard abbreviation in a code cell. Then follow these steps to read in the data's csv file in the California drylands prey data using the `pandas.read_csv()` function: -a. Navigate to the [data package site](https://knb.ecoinformatics.org/view/doi%3A10.5063%2FF1K35S3H) and copy the the URL to access the `WesternIndianOceanCoralDiversity` csv file. To copy the URL: +a. Navigate to the [data package site](https://knb.ecoinformatics.org/view/doi%3A10.5063%2FF1VM49RH) and copy the URL to access the `arth_occurrences_with_env` csv file. To copy the URL: - hover over the Download button –> right click –> “Copy Link". -b. Read in the data from the URL using the `pd.read_csv` function and store it as `coral_div` like this: +b. Read in the data from the URL using the `pd.read_csv()` function and store it as `prey` like this: ```python # read in data - coral_div = pd.read_csv('the URL goes here') + prey = pd.read_csv('the URL goes here') ``` # References From 7605572a2b9c69973f845183146b5228c0c8630e Mon Sep 17 00:00:00 2001 From: carmengg Date: Thu, 5 Oct 2023 01:44:20 +0000 Subject: [PATCH 19/31] up to data loading --- .../discussion-sect-week-1-methods.qmd | 69 +++++++++++++++---- 1 file changed, 57 insertions(+), 12 deletions(-) diff --git a/discussion-sections/discussion-sect-week-1-methods.qmd b/discussion-sections/discussion-sect-week-1-methods.qmd index 73b5509..158b078 100644 --- a/discussion-sections/discussion-sect-week-1-methods.qmd +++ b/discussion-sections/discussion-sect-week-1-methods.qmd @@ -28,7 +28,10 @@

    -**CHECK IN WITH YOUR TEAM** +**CHECK IN WITH YOUR TEAM** +

    +

    +**MAKE SURE YOU'VE ALL SUCCESSFULLY SET UP YOUR NOTEBOOKS BEFORE CONTINUING**

    ::: @@ -36,7 +39,10 @@ :::{.callout-note} ## General directions -Add comments in each one of your code cells and include markdown cells in between your code cells to add titles to the different execises. +- Add comments in each one of your code cells +- Include markdown cells in between your code cells to add titles/information to each exercise +- Check the git status and commit/push after each exercise. +- You don't need to write anything in the notebook about exercises 1 and 2. ::: :::{.callout-note} @@ -48,8 +54,7 @@ This dataset is stored in the [Knowledge Network for Biocomplexity (KNB)](https: ::: -**1. Archive exploration** - +## 1. Archive exploration For many datasets, data exploration begins at the data repository. Take some time to look through the dataset's description in KNB. Discuss the following questions with your team: a. Does this dataset come with an associated metadata file? @@ -57,33 +62,73 @@ b. Is this data collected in-situ by the authors or is it a synthesis of multipl c. Does the dataset contain sensitive data? d. During what time frame were the observations in the dataset collected? -**2. Metadata exploration** +## 2. `.xml` metadata exploration You may have noticed two metadata files: `Compiled_occurrence_records_for_prey_items_of.xml` and `metadata_arth_occurrences.csv`. Download both of them. a. Notice the `.xml` document file type is `EML` which stands for [EML: Ecological Metadata Language](https://eml.ecoinformatics.org). b. Open the `.xml` file: there's a lot going on. This is a machine-readable file that has metadata about *the whole dataset*. You can proably identify some items like title and creators. -c. Open the `.csv` file: much easier to understand! Take a minute to look at the column descriptions. +## 3. `.csv` metadata exploration - -**3. Data loading** - -Back in your notebook, import the `pandas` package using standard abbreviation in a code cell. Then follow these steps to read in the data's csv file in the California drylands prey data using the `pandas.read_csv()` function: +Back in your notebook, import the `pandas` package using standard abbreviation in a code cell. Then follow these steps to read in the metadata csv using the `pandas.read_csv()` function: a. Navigate to the [data package site](https://knb.ecoinformatics.org/view/doi%3A10.5063%2FF1VM49RH) and copy the URL to access the `arth_occurrences_with_env` csv file. To copy the URL: - hover over the Download button –> right click –> “Copy Link". -b. Read in the data from the URL using the `pd.read_csv()` function and store it as `prey` like this: +b. Read in the data from the URL using the `pd.read_csv()` function like this: ```python # read in data - prey = pd.read_csv('the URL goes here') + pd.read_csv('the URL goes here') ``` +c. Take a minute to look at the descriptions for the columns. + +*Note:* Not all datasets have column descriptions in a `csv` file. Often they come with a `doc` or `txt` file with information. + + +## 4. Data loading +a. Follow steps (a) and (b) from the previous exercise to read in the drylands prey data file `arth_occurrences_with_env.csv` using `pd.read_csv()`. Store the dataframe to a variable called `prey` like this: + +```python +# read in data +prey = pd.read_csv('the URL goes here') +``` + +b. Use a Python function to see what is the type of the `prey` variable. + +

    +**CHECK IN WITH YOUR TEAM** +

    +

    +**MAKE SURE YOU'VE ALL SUCCESSFULLY ACCESSED THE DATA BEFORE CONTINUING** +

    + +## 5. Look at your data + +a. Run `prey` in a cell. What do you notice in the columns section? + +b. + + + # References Rachel King, Jenna Braun, Michael Westphal, & CJ Lortie. (2023). Compiled occurrence records for prey items of listed species found in California drylands with associated environmental data. Knowledge Network for Biocomplexity. doi:10.5063/F1VM49RH. Lortie, C. J., Braun, J., King, R., & Westphal, M. (2023). The importance of open data describing prey item species lists for endangered species. Ecological Solutions and Evidence, 4(2), e12251. https://doi.org/10.1002/2688-8319.12251 + + + +```{python} +import pandas as pd +pd.read_csv("https://knb.ecoinformatics.org/knb/d1/mn/v2/object/urn%3Auuid%3A3baf7289-bf90-4db3-ad11-58785c09b26e") +``` + + +```{python} +prey = pd.read_csv("https://knb.ecoinformatics.org/knb/d1/mn/v2/object/urn%3Auuid%3A23d42528-1048-45d4-85d1-7e13b666e744") +prey +``` From 417d65064e402d83113092ed7822ecb5dc08f60f Mon Sep 17 00:00:00 2001 From: carmengg Date: Wed, 4 Oct 2023 20:46:32 -0700 Subject: [PATCH 20/31] added methods and attrs for prelim exploration --- .../discussion-sect-week-1-methods.qmd | 37 ++++++++++++++++++- 1 file changed, 36 insertions(+), 1 deletion(-) diff --git a/discussion-sections/discussion-sect-week-1-methods.qmd b/discussion-sections/discussion-sect-week-1-methods.qmd index 158b078..48abb83 100644 --- a/discussion-sections/discussion-sect-week-1-methods.qmd +++ b/discussion-sections/discussion-sect-week-1-methods.qmd @@ -110,7 +110,42 @@ b. Use a Python function to see what is the type of the `prey` variable. a. Run `prey` in a cell. What do you notice in the columns section? -b. +b. To see all the column names in the same display we need to set a `pandas` option. Run the following command and then look at the `prey` data again: +```python +pd.set_option("display.max.columns",None) +``` + +## 6. `pd.DataFrame` preliminary exploration + +Run each of the following methods for `prey` in a different cell and write a brief description of what they do as a comment: + +a. `head()` +b. `tail()` +c. `info()` +d. `nunique()` + +For example: + +``` +# head() +# returns the first five rows of the data frame +prey.head() +``` + +If you're not sure about what the method does, try looking it up in the [`pandas.DataFrame` documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html). + +e. Check the [documentation for `head()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.head.html#pandas.DataFrame.head). If this function has any default parameters, change them to get a different output. + +Print each of the following attributes of `prey` in a different cell and write a brief explanation of what they do as a comment: + +f. `shape` +g. `columns` +h. `dtypes` + +If you're not sure about what info is the attribute showing, try looking it up in the [`pandas.DataFrame` documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html). + + + From fbbde93ab8096ea4df3f271b5d4f5599309fb1df Mon Sep 17 00:00:00 2001 From: carmengg Date: Wed, 4 Oct 2023 20:59:27 -0700 Subject: [PATCH 21/31] added disc sect --- _quarto.yml | 3 + .../discussion-sect-week-1-methods.qmd | 9 +- .../discussion-sect-week-1-methods.html | 1185 ++++++++ docs/index.html | 166 +- docs/lectures/lesson-1-python-review.html | 243 +- docs/lectures/lesson-2-pandas-basics.html | 2588 +++++++++-------- docs/search.json | 66 +- docs/site_libs/bootstrap/bootstrap-icons.css | 316 +- docs/site_libs/bootstrap/bootstrap-icons.woff | Bin 137124 -> 164168 bytes docs/site_libs/bootstrap/bootstrap.min.css | 4 +- docs/site_libs/clipboard/clipboard.min.js | 4 +- .../quarto-syntax-highlighting.css | 32 + docs/site_libs/quarto-html/quarto.js | 372 ++- docs/site_libs/quarto-nav/quarto-nav.js | 57 +- .../quarto-search/autocomplete.umd.js | 4 +- docs/site_libs/quarto-search/fuse.min.js | 6 +- docs/site_libs/quarto-search/quarto-search.js | 33 +- 17 files changed, 3615 insertions(+), 1473 deletions(-) create mode 100644 docs/discussion-sections/discussion-sect-week-1-methods.html diff --git a/_quarto.yml b/_quarto.yml index fbb2073..323463c 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -15,6 +15,9 @@ book: chapters: - lectures/lesson-1-python-review.qmd - lectures/lesson-2-pandas-basics.qmd + - part: "Discussion Sections" + chapters: + - discussion-sections/discussion-sect-week-1-methods.qmd execute: freeze: auto # re-render only when source changes diff --git a/discussion-sections/discussion-sect-week-1-methods.qmd b/discussion-sections/discussion-sect-week-1-methods.qmd index 48abb83..22e3956 100644 --- a/discussion-sections/discussion-sect-week-1-methods.qmd +++ b/discussion-sections/discussion-sect-week-1-methods.qmd @@ -144,10 +144,8 @@ h. `dtypes` If you're not sure about what info is the attribute showing, try looking it up in the [`pandas.DataFrame` documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html). - - - - +## 7. For fun +Change the column names of `institutionCode` and `datasetKey` to `institution_code` and `dataset_key`, respectively. HINT: yesterday's class. # References @@ -156,7 +154,7 @@ Rachel King, Jenna Braun, Michael Westphal, & CJ Lortie. (2023). Compiled occurr Lortie, C. J., Braun, J., King, R., & Westphal, M. (2023). The importance of open data describing prey item species lists for endangered species. Ecological Solutions and Evidence, 4(2), e12251. https://doi.org/10.1002/2688-8319.12251 - + \ No newline at end of file diff --git a/docs/discussion-sections/discussion-sect-week-1-methods.html b/docs/discussion-sections/discussion-sect-week-1-methods.html new file mode 100644 index 0000000..588e2ee --- /dev/null +++ b/docs/discussion-sections/discussion-sect-week-1-methods.html @@ -0,0 +1,1185 @@ + + + + + + + + + +EDS 220 - Working with Environmental Datasets - 3  Notebook Setup + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + +
    + + +
    + + + +
    + +
    +
    +

    3  Notebook Setup

    +
    + + + +
    + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +Tip +
    +
    +
    +
      +

    1. Create a new repository on GitHub. Use the following settings:

      +
        +

      1. Call the repository eds-220-section-1.

        2. +

      2. Add a brief description for your new repository. For example: EDS 220 section - practice session for data selection in pandas.

        3. +

      3. Keep the repository public.

        4. +

      4. Initialize the repository with a README file and a Python .gitignore template.

        5. +
      2. +

    2. Clone the repository to a new directory in the Taylor server under your eds-220 directory.

      3. +

    3. In the terminal use cd to navigate into the eds-220-section-1 directory. Use pwd to verify eds-220-section-1 is your current working directory.

      4. +

    4. Create a new Python Notebook in eds-220-section-1.

      5. +

    5. Update the notebook’s name to something useful like ‘exercise-data-selection.qmd’.

      6. +

    6. Use the terminal to stage, commit, and push this file to the remote repository. Remember:

      +
        +
      • stage: git add FILE_NAME
        • +
      • commit with message: git commit -m "COMMIT_MESSAGE"
        • +
      • push: git push
        • +
      7. +

    7. If you are prompted for your credentials and need to set up a new Personal Access Token (PAT) follow steps 13-18 in this guide to set it up.

      8. +
    +

    +CHECK IN WITH YOUR TEAM +

    +

    +MAKE SURE YOU’VE ALL SUCCESSFULLY SET UP YOUR NOTEBOOKS BEFORE CONTINUING +

    +
    +
    +
    +
    +
    + +
    +
    +General directions +
    +
    +
    +
      +
    • Add comments in each one of your code cells
      • +
    • Include markdown cells in between your code cells to add titles/information to each exercise
      • +
    • Check the git status and commit/push after each exercise.
      • +
    • You don’t need to write anything in the notebook about exercises 1 and 2.
      • +
    +
    +
    +
    +
    +
    + +
    +
    +About the data +
    +
    +
    +

    For this exercise we will use data about prey items for endangered terrestrial vertebrate species within central California drylands (King et. al, 2023).

    +

    This dataset is stored in the Knowledge Network for Biocomplexity (KNB) data repository. This is an international repository intended to facilitate ecological and environmental research. It has thousands of open datasets and is hosted by NCEAS!

    +
    +
    + +
    +

    3.1 1. Archive exploration

    +

    For many datasets, data exploration begins at the data repository. Take some time to look through the dataset’s description in KNB. Discuss the following questions with your team:

    +
      +
    1. Does this dataset come with an associated metadata file?
      2. +
    2. Is this data collected in-situ by the authors or is it a synthesis of multiple datasets?
      3. +
    3. Does the dataset contain sensitive data?
      4. +
    4. During what time frame were the observations in the dataset collected?
      5. +
    +
    +
    +

    3.2 2. .xml metadata exploration

    +

    You may have noticed two metadata files: Compiled_occurrence_records_for_prey_items_of.xml and metadata_arth_occurrences.csv. Download both of them.

    +
      +
    1. Notice the .xml document file type is EML which stands for EML: Ecological Metadata Language.
      2. +
    2. Open the .xml file: there’s a lot going on. This is a machine-readable file that has metadata about the whole dataset. You can proably identify some items like title and creators.
      3. +
    +
    +
    +

    3.3 3. .csv metadata exploration

    +

    Back in your notebook, import the pandas package using standard abbreviation in a code cell. Then follow these steps to read in the metadata csv using the pandas.read_csv() function:

    +
      +
    1. Navigate to the data package site and copy the URL to access the arth_occurrences_with_env csv file. To copy the URL:
      2. +
    +
      +
    • hover over the Download button –> right click –> “Copy Link”.
      • +
    +
      +

    1. Read in the data from the URL using the pd.read_csv() function like this:

      +
      # read in data
+pd.read_csv('the URL goes here')
      2. +

    2. Take a minute to look at the descriptions for the columns.

      3. +
    +

    Note: Not all datasets have column descriptions in a csv file. Often they come with a doc or txt file with information.

    + +
    +
    +

    3.4 4. Data loading

    +
      +
    1. Follow steps (a) and (b) from the previous exercise to read in the drylands prey data file arth_occurrences_with_env.csv using pd.read_csv(). Store the dataframe to a variable called prey like this:
      2. +
    +
    # read in data
+prey = pd.read_csv('the URL goes here')
    +
      +
    1. Use a Python function to see what is the type of the prey variable.
      2. +
    +

    +CHECK IN WITH YOUR TEAM +

    +

    +MAKE SURE YOU’VE ALL SUCCESSFULLY ACCESSED THE DATA BEFORE CONTINUING +

    +
    +
    +

    3.5 5. Look at your data

    +
      +

    1. Run prey in a cell. What do you notice in the columns section?

      2. +

    2. To see all the column names in the same display we need to set a pandas option. Run the following command and then look at the prey data again:

      3. +
    +
    pd.set_option("display.max.columns",None)
    +
    +
    +

    3.6 6. pd.DataFrame preliminary exploration

    +

    Run each of the following methods for prey in a different cell and write a brief description of what they do as a comment:

    +
      +
    1. head()
      2. +
    2. tail()
      3. +
    3. info()
      4. +
    4. nunique()
      5. +
    +

    For example:

    +
    # head()
+# returns the first five rows of the data frame
+prey.head()
    +

    If you’re not sure about what the method does, try looking it up in the pandas.DataFrame documentation.

    +
      +
    1. Check the documentation for head(). If this function has any default parameters, change them to get a different output.
      2. +
    +

    Print each of the following attributes of prey in a different cell and write a brief explanation of what they do as a comment:

    +
      +
    1. shape
      2. +
    2. columns
      3. +
    3. dtypes
      4. +
    +

    If you’re not sure about what info is the attribute showing, try looking it up in the pandas.DataFrame documentation.

    +
    +
    +

    3.7 7. For fun

    +

    Change the column names of institutionCode and datasetKey to institution_code and dataset_key, respectively. HINT: yesterday’s class.

    +
    +
    +

    4 References

    +

    Rachel King, Jenna Braun, Michael Westphal, & CJ Lortie. (2023). Compiled occurrence records for prey items of listed species found in California drylands with associated environmental data. Knowledge Network for Biocomplexity. doi:10.5063/F1VM49RH.

    +

    Lortie, C. J., Braun, J., King, R., & Westphal, M. (2023). The importance of open data describing prey item species lists for endangered species. Ecological Solutions and Evidence, 4(2), e12251. https://doi.org/10.1002/2688-8319.12251

    + + + +
    + +
    + + +
    + + + + \ No newline at end of file diff --git a/docs/index.html b/docs/index.html index a8daa8b..8377db9 100644 --- a/docs/index.html +++ b/docs/index.html @@ -2,7 +2,7 @@ - + @@ -18,7 +18,7 @@ ul.task-list{list-style: none;} ul.task-list li input[type="checkbox"] { width: 0.8em; - margin: 0 0.8em 0.2em -1.6em; + margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */ vertical-align: middle; } @@ -57,7 +57,8 @@ "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", "search-detached-cancel-button-title": "Cancel", - "search-submit-button-title": "Submit" + "search-submit-button-title": "Submit", + "search-label": "Search" } } @@ -68,11 +69,16 @@
    - @@ -80,40 +86,61 @@

    EDS 220 - Working with Environmental Data
    -

    diff --git a/docs/search.json b/docs/search.json index 6ba44f3..8100663 100644 --- a/docs/search.json +++ b/docs/search.json @@ -158,7 +158,7 @@ "href": "discussion-sections/discussion-sect-week-1-methods.html#archive-exploration", "title": "3  1. Preliminary Data Exploration", "section": "3.1 Archive exploration", - "text": "3.1 Archive exploration\nFor many datasets, data exploration begins at the data repository. Take some time to look through the dataset’s description in KNB. Discuss the following questions with your team:\n\nWhat is this data about?\nIs this data collected in-situ by the authors or is it a synthesis of multiple datasets?\nDuring what time frame were the observations in the dataset collected?\nDoes this dataset come with an associated metadata file?\nDoes the dataset contain sensitive data?\n\nIn your notebook: use a markdown cell to add a brief description of the dataset, including a citation, date of access, and a link to the archive.\n\n** check git status -> stage changes -> check git status -> commit with message -> push changes**" + "text": "3.1 Archive exploration\nFor many datasets, data exploration begins at the data repository. Take some time to look through the dataset’s description in KNB. Discuss the following questions with your team:\n\nWhat is this data about?\nIs this data collected in-situ by the authors or is it a synthesis of multiple datasets?\nDuring what time frame were the observations in the dataset collected?\nDoes this dataset come with an associated metadata file?\nDoes the dataset contain sensitive data?\n\nIn your notebook: use a markdown cell to add a brief description of the dataset, including a citation, date of access, and a link to the archive.\n\ncheck git status -> stage changes -> check git status -> commit with message -> push changes" }, { "objectID": "discussion-sections/discussion-sect-week-1-methods.html#xml-metadata-exploration", @@ -179,14 +179,14 @@ "href": "discussion-sections/discussion-sect-week-1-methods.html#data-loading", "title": "3  1. Preliminary Data Exploration", "section": "3.4 Data loading", - "text": "3.4 Data loading\n\nFollow steps (a) and (b) from the previous exercise to read in the drylands prey data file arth_occurrences_with_env.csv using pd.read_csv(). Store the dataframe to a variable called prey like this:\n\n# read in data\nprey = pd.read_csv('the URL goes here')\n\nUse a Python function to see what is the type of the prey variable.\n\n\nCHECK IN WITH YOUR TEAM\n\n\nMAKE SURE YOU’VE ALL SUCCESSFULLY ACCESSED THE DATA BEFORE CONTINUING\n\n\n** check git status -> stage changes -> check git status -> commit with message -> push changes**" + "text": "3.4 Data loading\n\nFollow steps (a) and (b) from the previous exercise to read in the drylands prey data file arth_occurrences_with_env.csv using pd.read_csv(). Store the dataframe to a variable called prey like this:\n\n# read in data\nprey = pd.read_csv('the URL goes here')\n\nUse a Python function to see what is the type of the prey variable.\n\n\ncheck git status -> stage changes -> check git status -> commit with message -> push changes\n\n\nCHECK IN WITH YOUR TEAM\n\n\nMAKE SURE YOU’VE ALL SUCCESSFULLY ACCESSED THE DATA BEFORE CONTINUING" }, { "objectID": "discussion-sections/discussion-sect-week-1-methods.html#look-at-your-data", "href": "discussion-sections/discussion-sect-week-1-methods.html#look-at-your-data", "title": "3  1. Preliminary Data Exploration", "section": "3.5 Look at your data", - "text": "3.5 Look at your data\n\nRun prey in a cell. What do you notice in the columns section?\nTo see all the column names in the same display we need to set a pandas option. Run the following command and then look at the prey data again:\n\npd.set_option(\"display.max.columns\", None)\n\n** check git status -> stage changes -> check git status -> commit with message -> push changes**" + "text": "3.5 Look at your data\n\nRun prey in a cell. What do you notice in the columns section?\nTo see all the column names in the same display we need to set a pandas option. Run the following command and then look at the prey data again:\n\npd.set_option(\"display.max.columns\", None)\n\ncheck git status -> stage changes -> check git status -> commit with message -> push changes" }, { "objectID": "discussion-sections/discussion-sect-week-1-methods.html#pd.dataframe-preliminary-exploration", From c8d901992c0c463f2d498b9e6edb5414a2c0a584 Mon Sep 17 00:00:00 2001 From: carmengg Date: Thu, 5 Oct 2023 09:18:39 -0700 Subject: [PATCH 26/31] updated commit bold --- discussion-sections/discussion-sect-week-1-methods.qmd | 6 +++--- .../discussion-sect-week-1-methods.html | 10 +++++----- docs/search.json | 10 +++++----- 3 files changed, 13 insertions(+), 13 deletions(-) diff --git a/discussion-sections/discussion-sect-week-1-methods.qmd b/discussion-sections/discussion-sect-week-1-methods.qmd index b98c38a..b945048 100644 --- a/discussion-sections/discussion-sect-week-1-methods.qmd +++ b/discussion-sections/discussion-sect-week-1-methods.qmd @@ -174,14 +174,14 @@ h. `dtypes` If you're not sure about what info is the attribute showing, try looking it up in the [`pandas.DataFrame` documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html).

    -** check git status -> stage changes -> check git status -> commit with message -> push changes** +**check git status -> stage changes -> check git status -> commit with message -> push changes**

    -## For fun +## Update some column names Change the column names of `institutionCode` and `datasetKey` to `institution_code` and `dataset_key`, respectively. Make sure you're actually updating the dataframe. HINT: yesterday's class.

    -** check git status -> stage changes -> check git status -> commit with message -> push changes** +**check git status -> stage changes -> check git status -> commit with message -> push changes**

    # References diff --git a/docs/discussion-sections/discussion-sect-week-1-methods.html b/docs/discussion-sections/discussion-sect-week-1-methods.html index 738c765..7baf9a7 100644 --- a/docs/discussion-sections/discussion-sect-week-1-methods.html +++ b/docs/discussion-sections/discussion-sect-week-1-methods.html @@ -194,7 +194,7 @@

    Table of contents

  • 3.4 Data loading
  • 3.5 Look at your data
  • 3.6 pd.DataFrame preliminary exploration
    • -
  • 3.7 For fun
    • +
  • 3.7 Update some column names
  • 4 References
    • @@ -397,14 +397,14 @@

    pandas.DataFrame documentation.

    -** check git status -> stage changes -> check git status -> commit with message -> push changes** +check git status -> stage changes -> check git status -> commit with message -> push changes

    -
    -

    3.7 For fun

    +
    +

    3.7 Update some column names

    Change the column names of institutionCode and datasetKey to institution_code and dataset_key, respectively. Make sure you’re actually updating the dataframe. HINT: yesterday’s class.

    -** check git status -> stage changes -> check git status -> commit with message -> push changes** +check git status -> stage changes -> check git status -> commit with message -> push changes

    diff --git a/docs/search.json b/docs/search.json index 8100663..48ad405 100644 --- a/docs/search.json +++ b/docs/search.json @@ -193,13 +193,13 @@ "href": "discussion-sections/discussion-sect-week-1-methods.html#pd.dataframe-preliminary-exploration", "title": "3  1. Preliminary Data Exploration", "section": "3.6 pd.DataFrame preliminary exploration", - "text": "3.6 pd.DataFrame preliminary exploration\nRun each of the following methods for prey in a different cell and write a brief description of what they do as a comment:\n\nhead()\ntail()\ninfo()\nnunique()\n\nFor example:\n# head()\n# returns the first five rows of the data frame\nprey.head()\nIf you’re not sure about what the method does, try looking it up in the pandas.DataFrame documentation.\n\nCheck the documentation for head(). If this function has any default parameters, change them to get a different output.\n\nPrint each of the following attributes of prey in a different cell and write a brief explanation of what they do as a comment:\n\nshape\ncolumns\ndtypes\n\nIf you’re not sure about what info is the attribute showing, try looking it up in the pandas.DataFrame documentation.\n\n** check git status -> stage changes -> check git status -> commit with message -> push changes**" + "text": "3.6 pd.DataFrame preliminary exploration\nRun each of the following methods for prey in a different cell and write a brief description of what they do as a comment:\n\nhead()\ntail()\ninfo()\nnunique()\n\nFor example:\n# head()\n# returns the first five rows of the data frame\nprey.head()\nIf you’re not sure about what the method does, try looking it up in the pandas.DataFrame documentation.\n\nCheck the documentation for head(). If this function has any default parameters, change them to get a different output.\n\nPrint each of the following attributes of prey in a different cell and write a brief explanation of what they do as a comment:\n\nshape\ncolumns\ndtypes\n\nIf you’re not sure about what info is the attribute showing, try looking it up in the pandas.DataFrame documentation.\n\ncheck git status -> stage changes -> check git status -> commit with message -> push changes" }, { - "objectID": "discussion-sections/discussion-sect-week-1-methods.html#for-fun", - "href": "discussion-sections/discussion-sect-week-1-methods.html#for-fun", + "objectID": "discussion-sections/discussion-sect-week-1-methods.html#update-some-column-names", + "href": "discussion-sections/discussion-sect-week-1-methods.html#update-some-column-names", "title": "3  1. Preliminary Data Exploration", - "section": "3.7 For fun", - "text": "3.7 For fun\nChange the column names of institutionCode and datasetKey to institution_code and dataset_key, respectively. Make sure you’re actually updating the dataframe. HINT: yesterday’s class.\n\n** check git status -> stage changes -> check git status -> commit with message -> push changes**" + "section": "3.7 Update some column names", + "text": "3.7 Update some column names\nChange the column names of institutionCode and datasetKey to institution_code and dataset_key, respectively. Make sure you’re actually updating the dataframe. HINT: yesterday’s class.\n\ncheck git status -> stage changes -> check git status -> commit with message -> push changes" } ] \ No newline at end of file From 0b78aa88c131922a793976f9ea5ea9a2e8dd7264 Mon Sep 17 00:00:00 2001 From: carmengg Date: Thu, 5 Oct 2023 10:29:25 -0700 Subject: [PATCH 27/31] fix typos after first section --- discussion-sections/discussion-sect-week-1-methods.qmd | 6 ++++-- .../discussion-sections/discussion-sect-week-1-methods.html | 6 ++++-- docs/search.json | 4 ++-- 3 files changed, 10 insertions(+), 6 deletions(-) diff --git a/discussion-sections/discussion-sect-week-1-methods.qmd b/discussion-sections/discussion-sect-week-1-methods.qmd index b945048..905d220 100644 --- a/discussion-sections/discussion-sect-week-1-methods.qmd +++ b/discussion-sections/discussion-sect-week-1-methods.qmd @@ -31,7 +31,7 @@ This discussion section will guide you through preliminary data exploration for 5. Create a new Python Notebook in `eds-220-section-1`. -6. Update the notebook's name to something useful like 'exercise-data-selection.qmd'. +6. Update the notebook's name to something useful like 'exercise-data-selection.ipynb'. 7. Use the terminal to stage, commit, and push this file to the remote repository. Remember: - stage: `git add FILE_NAME` @@ -56,6 +56,7 @@ This discussion section will guide you through preliminary data exploration for - Add comments in each one of your code cells - Include markdown cells in between your code cells to add titles/information to each exercise - Indications about when to commit and push changes are included, but you are welcome to commit and push more often. +- You won't need to upload any data ::: :::{.callout-note} @@ -89,12 +90,13 @@ You may have noticed there are two metadata files: `Compiled_occurrence_records_ a. In the archive's dataset description, notice the `.xml` document file type is `EML` which stands for [EML: Ecological Metadata Language](https://eml.ecoinformatics.org). b. Open the `.xml` file: there's a lot going on. This is a machine-readable file that has metadata about *the whole dataset*. You can proably identify some items like title and creators. c. Close the file and delete it - we won't use it today. +d. You don't need to write anything in your notebook about this section. ## `.csv` metadata exploration Back in your notebook, import the `pandas` package using standard abbreviation in a code cell. Then follow these steps to read in the metadata csv using the `pandas.read_csv()` function: -a. Navigate to the [data package site](https://knb.ecoinformatics.org/view/doi%3A10.5063%2FF1VM49RH) and copy the URL to access the `arth_occurrences_with_env` csv file. To copy the URL: +a. Navigate to the [data package site](https://knb.ecoinformatics.org/view/doi%3A10.5063%2FF1VM49RH) and copy the URL to access the `metadata_arth_occurrences` csv file. To copy the URL: - hover over the Download button –> right click –> “Copy Link". diff --git a/docs/discussion-sections/discussion-sect-week-1-methods.html b/docs/discussion-sections/discussion-sect-week-1-methods.html index 7baf9a7..f8b7135 100644 --- a/docs/discussion-sections/discussion-sect-week-1-methods.html +++ b/docs/discussion-sections/discussion-sect-week-1-methods.html @@ -250,7 +250,7 @@

    3 

    3.3 .csv metadata exploration

    Back in your notebook, import the pandas package using standard abbreviation in a code cell. Then follow these steps to read in the metadata csv using the pandas.read_csv() function:

      -
    1. Navigate to the data package site and copy the URL to access the arth_occurrences_with_env csv file. To copy the URL:
      2. +
    2. Navigate to the data package site and copy the URL to access the metadata_arth_occurrences csv file. To copy the URL:
    • hover over the Download button –> right click –> “Copy Link”.
      • diff --git a/docs/search.json b/docs/search.json index 48ad405..b6675b5 100644 --- a/docs/search.json +++ b/docs/search.json @@ -165,14 +165,14 @@ "href": "discussion-sections/discussion-sect-week-1-methods.html#xml-metadata-exploration", "title": "3  1. Preliminary Data Exploration", "section": "3.2 .xml metadata exploration", - "text": "3.2 .xml metadata exploration\nYou may have noticed there are two metadata files: Compiled_occurrence_records_for_prey_items_of.xml and metadata_arth_occurrences.csv.\n\nIn the archive’s dataset description, notice the .xml document file type is EML which stands for EML: Ecological Metadata Language.\nOpen the .xml file: there’s a lot going on. This is a machine-readable file that has metadata about the whole dataset. You can proably identify some items like title and creators.\nClose the file and delete it - we won’t use it today." + "text": "3.2 .xml metadata exploration\nYou may have noticed there are two metadata files: Compiled_occurrence_records_for_prey_items_of.xml and metadata_arth_occurrences.csv.\n\nIn the archive’s dataset description, notice the .xml document file type is EML which stands for EML: Ecological Metadata Language.\nOpen the .xml file: there’s a lot going on. This is a machine-readable file that has metadata about the whole dataset. You can proably identify some items like title and creators.\nClose the file and delete it - we won’t use it today.\nYou don’t need to write anything in your notebook about this section." }, { "objectID": "discussion-sections/discussion-sect-week-1-methods.html#csv-metadata-exploration", "href": "discussion-sections/discussion-sect-week-1-methods.html#csv-metadata-exploration", "title": "3  1. Preliminary Data Exploration", "section": "3.3 .csv metadata exploration", - "text": "3.3 .csv metadata exploration\nBack in your notebook, import the pandas package using standard abbreviation in a code cell. Then follow these steps to read in the metadata csv using the pandas.read_csv() function:\n\nNavigate to the data package site and copy the URL to access the arth_occurrences_with_env csv file. To copy the URL:\n\n\nhover over the Download button –> right click –> “Copy Link”.\n\n\nRead in the data from the URL using the pd.read_csv() function like this:\n# look at metadata\npd.read_csv('the URL goes here')\nTake a minute to look at the descriptions for the columns.\n\nNote: Not all datasets have column descriptions in a csv file. Often they come with a doc or txt file with information." + "text": "3.3 .csv metadata exploration\nBack in your notebook, import the pandas package using standard abbreviation in a code cell. Then follow these steps to read in the metadata csv using the pandas.read_csv() function:\n\nNavigate to the data package site and copy the URL to access the metadata_arth_occurrences csv file. To copy the URL:\n\n\nhover over the Download button –> right click –> “Copy Link”.\n\n\nRead in the data from the URL using the pd.read_csv() function like this:\n# look at metadata\npd.read_csv('the URL goes here')\nTake a minute to look at the descriptions for the columns.\n\nNote: Not all datasets have column descriptions in a csv file. Often they come with a doc or txt file with information." }, { "objectID": "discussion-sections/discussion-sect-week-1-methods.html#data-loading", From 717439284aca38899743f43d1b27bbb5e103a52a Mon Sep 17 00:00:00 2001 From: carmengg Date: Thu, 5 Oct 2023 17:03:23 -0700 Subject: [PATCH 28/31] small changes after sections --- discussion-sections/discussion-sect-week-1-methods.qmd | 6 ++++-- .../discussion-sect-week-1-methods.html | 7 +++++-- docs/search.json | 4 ++-- 3 files changed, 11 insertions(+), 6 deletions(-) diff --git a/discussion-sections/discussion-sect-week-1-methods.qmd b/discussion-sections/discussion-sect-week-1-methods.qmd index 905d220..02c9063 100644 --- a/discussion-sections/discussion-sect-week-1-methods.qmd +++ b/discussion-sections/discussion-sect-week-1-methods.qmd @@ -142,6 +142,8 @@ b. To see all the column names in the same display we need to set a `pandas` opt pd.set_option("display.max.columns", None) ``` +c. Add a comment explaining what `pd.set_option("display.max.columns", None)` does. +

      **check git status -> stage changes -> check git status -> commit with message -> push changes**

      @@ -165,9 +167,9 @@ prey.head() If you're not sure about what the method does, try looking it up in the [`pandas.DataFrame` documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html). -e. Check the [documentation for `head()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.head.html#pandas.DataFrame.head). If this function has any default parameters, change them to get a different output. +e. Check the [documentation for `head()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.head.html#pandas.DataFrame.head). If this function has any optional parameters, change the default value to get a different output. -Print each of the following attributes of `prey` in a different cell and write a brief explanation of what they do as a comment: +Print each of the following attributes of `prey` in a different cell and write a brief explanation of what they are as a comment: f. `shape` g. `columns` diff --git a/docs/discussion-sections/discussion-sect-week-1-methods.html b/docs/discussion-sections/discussion-sect-week-1-methods.html index f8b7135..31449fd 100644 --- a/docs/discussion-sections/discussion-sect-week-1-methods.html +++ b/docs/discussion-sections/discussion-sect-week-1-methods.html @@ -370,6 +370,9 @@

      To see all the column names in the same display we need to set a pandas option. Run the following command and then look at the prey data again:

      pd.set_option("display.max.columns", None)
      +
        +
      1. Add a comment explaining what pd.set_option("display.max.columns", None) does.
        2. +

      check git status -> stage changes -> check git status -> commit with message -> push changes

      @@ -389,9 +392,9 @@

      pandas.DataFrame documentation.

        -
      1. Check the documentation for head(). If this function has any default parameters, change them to get a different output.
        2. +
      2. Check the documentation for head(). If this function has any optional parameters, change the default value to get a different output.
      -

      Print each of the following attributes of prey in a different cell and write a brief explanation of what they do as a comment:

      +

      Print each of the following attributes of prey in a different cell and write a brief explanation of what they are as a comment:

      1. shape
      2. columns
        3. diff --git a/docs/search.json b/docs/search.json index b6675b5..8013ebf 100644 --- a/docs/search.json +++ b/docs/search.json @@ -186,14 +186,14 @@ "href": "discussion-sections/discussion-sect-week-1-methods.html#look-at-your-data", "title": "3  1. Preliminary Data Exploration", "section": "3.5 Look at your data", - "text": "3.5 Look at your data\n\nRun prey in a cell. What do you notice in the columns section?\nTo see all the column names in the same display we need to set a pandas option. Run the following command and then look at the prey data again:\n\npd.set_option(\"display.max.columns\", None)\n\ncheck git status -> stage changes -> check git status -> commit with message -> push changes" + "text": "3.5 Look at your data\n\nRun prey in a cell. What do you notice in the columns section?\nTo see all the column names in the same display we need to set a pandas option. Run the following command and then look at the prey data again:\n\npd.set_option(\"display.max.columns\", None)\n\nAdd a comment explaining what pd.set_option(\"display.max.columns\", None) does.\n\n\ncheck git status -> stage changes -> check git status -> commit with message -> push changes" }, { "objectID": "discussion-sections/discussion-sect-week-1-methods.html#pd.dataframe-preliminary-exploration", "href": "discussion-sections/discussion-sect-week-1-methods.html#pd.dataframe-preliminary-exploration", "title": "3  1. Preliminary Data Exploration", "section": "3.6 pd.DataFrame preliminary exploration", - "text": "3.6 pd.DataFrame preliminary exploration\nRun each of the following methods for prey in a different cell and write a brief description of what they do as a comment:\n\nhead()\ntail()\ninfo()\nnunique()\n\nFor example:\n# head()\n# returns the first five rows of the data frame\nprey.head()\nIf you’re not sure about what the method does, try looking it up in the pandas.DataFrame documentation.\n\nCheck the documentation for head(). If this function has any default parameters, change them to get a different output.\n\nPrint each of the following attributes of prey in a different cell and write a brief explanation of what they do as a comment:\n\nshape\ncolumns\ndtypes\n\nIf you’re not sure about what info is the attribute showing, try looking it up in the pandas.DataFrame documentation.\n\ncheck git status -> stage changes -> check git status -> commit with message -> push changes" + "text": "3.6 pd.DataFrame preliminary exploration\nRun each of the following methods for prey in a different cell and write a brief description of what they do as a comment:\n\nhead()\ntail()\ninfo()\nnunique()\n\nFor example:\n# head()\n# returns the first five rows of the data frame\nprey.head()\nIf you’re not sure about what the method does, try looking it up in the pandas.DataFrame documentation.\n\nCheck the documentation for head(). If this function has any optional parameters, change the default value to get a different output.\n\nPrint each of the following attributes of prey in a different cell and write a brief explanation of what they are as a comment:\n\nshape\ncolumns\ndtypes\n\nIf you’re not sure about what info is the attribute showing, try looking it up in the pandas.DataFrame documentation.\n\ncheck git status -> stage changes -> check git status -> commit with message -> push changes" }, { "objectID": "discussion-sections/discussion-sect-week-1-methods.html#update-some-column-names", From e2520bb234fd04d5cc5838ce8384817c292b26d1 Mon Sep 17 00:00:00 2001 From: carmengg Date: Fri, 6 Oct 2023 17:02:23 +0000 Subject: [PATCH 29/31] adding freeze + renamed section --- .../execute-results/html.json | 11 + .../execute-results/html.json | 15 + .../libs/bootstrap/bootstrap-icons.css | 1704 +++++++++++++ .../libs/bootstrap/bootstrap-icons.woff | Bin 0 -> 137124 bytes .../libs/bootstrap/bootstrap.min.css | 10 + .../libs/bootstrap/bootstrap.min.js | 7 + .../libs/clipboard/clipboard.min.js | 7 + .../libs/quarto-html/anchor.min.js | 9 + .../libs/quarto-html/popper.min.js | 6 + .../quarto-syntax-highlighting.css | 171 ++ .../libs/quarto-html/quarto.js | 770 ++++++ .../libs/quarto-html/tippy.css | 1 + .../libs/quarto-html/tippy.umd.min.js | 2 + _freeze/site_libs/clipboard/clipboard.min.js | 7 + _quarto.yml | 2 +- .../ds-coral-pd-selection-NS.qmd} | 0 .../ds-coral-pd-selection.qmd} | 0 ... => ds1-anthr-prelim-data-exploration.qmd} | 0 ...=> ds1-anthr-prelim-data-exploration.html} | 218 +- docs/index.html | 157 +- docs/lectures/lesson-1-python-review.html | 254 +- docs/lectures/lesson-2-pandas-basics.html | 2260 ++++++++--------- docs/search.json | 54 +- docs/site_libs/bootstrap/bootstrap-icons.css | 316 +-- docs/site_libs/bootstrap/bootstrap-icons.woff | Bin 164168 -> 137124 bytes docs/site_libs/bootstrap/bootstrap.min.css | 4 +- docs/site_libs/clipboard/clipboard.min.js | 4 +- .../quarto-syntax-highlighting.css | 32 - docs/site_libs/quarto-html/quarto.js | 372 +-- docs/site_libs/quarto-nav/quarto-nav.js | 57 +- .../quarto-search/autocomplete.umd.js | 4 +- docs/site_libs/quarto-search/fuse.min.js | 6 +- docs/site_libs/quarto-search/quarto-search.js | 33 +- 33 files changed, 4143 insertions(+), 2350 deletions(-) create mode 100644 _freeze/lectures/lesson-1-python-review/execute-results/html.json create mode 100644 _freeze/lectures/lesson-2-pandas-basics/execute-results/html.json create mode 100644 _freeze/lectures/lesson-2-pandas-basics/libs/bootstrap/bootstrap-icons.css create mode 100644 _freeze/lectures/lesson-2-pandas-basics/libs/bootstrap/bootstrap-icons.woff create mode 100644 _freeze/lectures/lesson-2-pandas-basics/libs/bootstrap/bootstrap.min.css create mode 100644 _freeze/lectures/lesson-2-pandas-basics/libs/bootstrap/bootstrap.min.js create mode 100644 _freeze/lectures/lesson-2-pandas-basics/libs/clipboard/clipboard.min.js create mode 100644 _freeze/lectures/lesson-2-pandas-basics/libs/quarto-html/anchor.min.js create mode 100644 _freeze/lectures/lesson-2-pandas-basics/libs/quarto-html/popper.min.js create mode 100644 _freeze/lectures/lesson-2-pandas-basics/libs/quarto-html/quarto-syntax-highlighting.css create mode 100644 _freeze/lectures/lesson-2-pandas-basics/libs/quarto-html/quarto.js create mode 100644 _freeze/lectures/lesson-2-pandas-basics/libs/quarto-html/tippy.css create mode 100644 _freeze/lectures/lesson-2-pandas-basics/libs/quarto-html/tippy.umd.min.js create mode 100644 _freeze/site_libs/clipboard/clipboard.min.js rename discussion-sections/{discussion-sect-week-1-no-solutions.qmd => archived-discussions/ds-coral-pd-selection-NS.qmd} (100%) rename discussion-sections/{discussion-sect-week-1.qmd => archived-discussions/ds-coral-pd-selection.qmd} (100%) rename discussion-sections/{discussion-sect-week-1-methods.qmd => ds1-anthr-prelim-data-exploration.qmd} (100%) rename docs/discussion-sections/{discussion-sect-week-1-methods.html => ds1-anthr-prelim-data-exploration.html} (75%) diff --git a/_freeze/lectures/lesson-1-python-review/execute-results/html.json b/_freeze/lectures/lesson-1-python-review/execute-results/html.json new file mode 100644 index 0000000..bd53936 --- /dev/null +++ b/_freeze/lectures/lesson-1-python-review/execute-results/html.json @@ -0,0 +1,11 @@ +{ + "hash": "b629e25d687f375e7a87308affe96248", + "result": { + "markdown": "# Python Review\n\n## About\nThis is a short reveiw about some core concepts in Python exemplified by objects in the `numpy` library. \n\n## `numpy`\n\nNumPy is one of the core packages for numerical computing in Python. Many of the packages we will use in this course use NumPy's arrays as their building blocks. Additionally, numpy objects have been optimized for processing, so computations on them are really fast and use less memory than doing the equivalent using base Python. \n\nIn this lesson we will use `numpy` to review some core concepts in Python you're already familiar with. \n\nFirst, let's start by importing the library:\n\n::: {.cell execution_count=1}\n``` {.python .cell-code}\nimport numpy as np\n```\n:::\n\n\n## Variables\nWe can think of a **variable** as a name we assign to a particular object in Python. For example:\n\n::: {.cell execution_count=2}\n``` {.python .cell-code}\n# assign a small array to variable a\na = np.array([[1,1,2],[3,5,8]])\n```\n:::\n\n\nWhen we run the cell, we store the variables and their value. We can view a variable's value in two ways:\n\n1. running a cell with the variable name\n\n2. using the `print` function to print the value\n\n::: {.cell execution_count=3}\n``` {.python .cell-code}\n# show the value\na\n```\n\n::: {.cell-output .cell-output-display execution_count=22}\n```\narray([[1, 1, 2],\n [3, 5, 8]])\n```\n:::\n:::\n\n\n::: {.cell execution_count=4}\n``` {.python .cell-code}\n# print the value \nprint(a)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[[1 1 2]\n [3 5 8]]\n```\n:::\n:::\n\n\n:::{.callout-note}\n## R and Python\n\nIn Python we use the equal sign `=` to assign values to variables in the same way the left-arrow `<-` is used in R.\n:::\n\n::: {.callout-caution}\n## Naming Variables\n\nThere are many ways of constructing multi-word variable names. In this course we will name variables using **snake_case**, where words are all in small caps and separated by underscores (ex: `my_variable`). This is the naming convention suggested by the [Style Guide for Python Code](https://peps.python.org/pep-0008/).\n:::\n\n## Variables and Objects\nYou will often encounter the word **object** in Python documentation and tutorials. Informally speaking, an object is a bundle of *properties* and *actions* about something specific. For example, an object could represent a data frame with properties such as number of rows, names of columns, and date created, and actions suchs as selecting a specific row or adding a new column. \n\nA variable is the name we give a specific object, and the same object can be referenced by different variables. An analogy for this is the following: the Sun (object) is called \"sol\" in Spanish and \"soleil\" in French, so two different names (variables) represent the same object. You can read more technical details about the [difference between objects and variables in Python here](https://realpython.com/python-variables/#object-references).\n\nIn practice, we can often use the word variable and object interchangeably. I want to bring up what objects are so you're not caught off-guard with vocabulary you'll often encounter in the documentation, StackExchange, etc. We'll often use the word object too (for example, in the next subsection!).\n\n## Types\n\n Every object in Python has a **type**, the type tells us what kind of object it is. We can also call the type of an object, the **class** of an object (so class and type both mean what kind of object we have). \n \n We can see the type/class of a variable/object by using the `type` function:\n\n::: {.cell execution_count=5}\n``` {.python .cell-code}\nprint(a)\ntype(a)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[[1 1 2]\n [3 5 8]]\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=24}\n```\nnumpy.ndarray\n```\n:::\n:::\n\n\nThe `numpy.ndarray` is the core object/data type in the NumPy pakcage. We can check the type of an entry in the array by indexing:\n\n::: {.cell execution_count=6}\n``` {.python .cell-code}\nprint(a[0,0])\ntype(a[0,0])\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n1\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=25}\n```\nnumpy.int64\n```\n:::\n:::\n\n\n::: {.callout-tip}\n## Check-in\nHow would you access the value 5 in the array `a`? **Remember indexing in Python starts from 0!**\n:::\n\nNotice the type of the value 1 in the array is `numpy.int64` and not just the core Python integer type `int`. The NumPy type `numpy.int64` is telling us 1 is an integer stored as a 64-bit number. NumPy has its own data types to deal with numbers depending on memory storage and floating point precision, [click here to know see all the types](https://numpy.org/doc/stable/reference/arrays.scalars.html#sized-aliases). \n\nSince \"everything in Python is an object\" and every object has a class, we will interact with SO MANY classes in this course. Often, knowing the type of an object is the first step to finding information to code what you want!\n\n## Functions\n\n`print` was our first example of a Python **function**. Functions take in a set of **arguments**, separated by commas, and use those arguments to create some **output**. There are several built-in funcions in Python, most of them are for interacting with the basic Python data types. You can see a [list of them here](https://realpython.com/python-data-types/#built-in-functions).\n\n::: {.callout-caution}\n## Argument or Parameter?\nWe can interchangeably say arguments or parameters. You will see argument more often in the documentation.\n:::\n\nWe can ask for information about a function by executing `?` followed by the function name:\n\n::: {.cell execution_count=7}\n``` {.python .cell-code}\n?print\n```\n:::\n\n\n![](/images/lesson-1/print_docstring.png)\n\nThe first line is always the function showing all of its arguments in parenthesis. \nThen there is a short description of what the function does.\nAnd finally a list of the arguments and a brief explanation about each of them.\n\nYou can see there are different types of arguments inside the parenthesis. Roughly speaking, a function has two types of arguments:\n\n- **non-optional arguments**: arguments *you* need to specify for the function to do something, and\n\n- **optional arguments**: arguments that are pre-filled with a default value by the function, but you can override them. Optional arguments appear inside the parenthesis () in the form `optional_argument = default_value`. \n\n**Example:**\n\n`end` is an argument in `print` with the default value a new line. We can change this argument so that finishes the line with ` ^_^` instead:\n\n::: {.cell execution_count=8}\n``` {.python .cell-code}\n# notice we had always used print withough specifying any value for the `end` argument\nprint('I am changing the default end argument of the print function', end=' ^_^')\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nI am changing the default end argument of the print function ^_^\n```\n:::\n:::\n\n\n## Attributes & Methods\n\nAn object in Python has attributes and methods. An **attribute** is a property of the object, some piece of information about it. A **method** is a procedure associated with an object, so it is an action where the main ingredient is the object. \n\nFor example, these could be some attributes and methods a class `cat`:\n\n![. ](/images/lesson-1/cat_class.png){width=45%}\n\n\nMore formally, **a method is a function** that acts on the object it is part of.\n\nWe can access a variable's attributes and methods by adding a period `.` at the end of the variable's name. So we would write `variable.variable_method()` or `variable.variable_attribute`. \n\n:::{.callout-tip}\n## Check-in\nSuppose we have a class `fish`, make a diagram similar to the `cat` class diagram showing 3 attributes for the class and 3 methods.\n:::\n\n**Example**\n\nNumPy arrays have many methods and attributes. Let's see some concrete examples.\n\n::: {.cell execution_count=9}\n``` {.python .cell-code}\n# define a 3x3 array\nvar = np.array([[1,2,3],[4,5,6],[7,8,9]])\nvar\n```\n\n::: {.cell-output .cell-output-display execution_count=28}\n```\narray([[1, 2, 3],\n [4, 5, 6],\n [7, 8, 9]])\n```\n:::\n:::\n\n\n::: {.cell execution_count=10}\n``` {.python .cell-code}\n# T is an example of attribute, it returns the transpose of var\nprint(var.T)\nprint(type(var.T))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[[1 4 7]\n [2 5 8]\n [3 6 9]]\n\n```\n:::\n:::\n\n\n::: {.cell execution_count=11}\n``` {.python .cell-code}\n# shape, another attribute, tells us the shape of the array (3x3)\nprint(var.shape)\nprint(type(var.shape))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n(3, 3)\n\n```\n:::\n:::\n\n\n::: {.cell execution_count=12}\n``` {.python .cell-code}\n# ndim is an attribute holding the number of array dimensions\nprint(var.ndim)\nprint(type(var.ndim))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n2\n\n```\n:::\n:::\n\n\nNotice these attributes can have many different data types. Here we saw tuples and int, two of the core Python classes, and also a numpy array as attributes of `var`. \n\nNow some examples of methods:\n\n::: {.cell execution_count=13}\n``` {.python .cell-code}\n# the tolist method returns the array as a nested list of scalars\nvar.tolist()\n```\n\n::: {.cell-output .cell-output-display execution_count=32}\n```\n[[1, 2, 3], [4, 5, 6], [7, 8, 9]]\n```\n:::\n:::\n\n\n::: {.cell execution_count=14}\n``` {.python .cell-code}\n# the min method returns the minimum value in the array along an axis\nvar.min(axis=0)\n```\n\n::: {.cell-output .cell-output-display execution_count=33}\n```\narray([1, 2, 3])\n```\n:::\n:::\n\n\n::: {.callout-tip}\n## Check-in\n\nWe can also call the `min` method without any parameters:\n\n::: {.cell execution_count=15}\n``` {.python .cell-code}\nvar.min()\n```\n\n::: {.cell-output .cell-output-display execution_count=34}\n```\n1\n```\n:::\n:::\n\n\nWhat kind of parameter is `axis` in our previous call of the `var` method?\n:::\n\nRemember, methods are functions associated to an object. We can check this!\n\n::: {.cell execution_count=16}\n``` {.python .cell-code}\ntype(var.tolist)\n```\n\n::: {.cell-output .cell-output-display execution_count=35}\n```\nbuiltin_function_or_method\n```\n:::\n:::\n\n\n::: {.cell execution_count=17}\n``` {.python .cell-code}\ntype(var.min)\n```\n\n::: {.cell-output .cell-output-display execution_count=36}\n```\nbuiltin_function_or_method\n```\n:::\n:::\n\n\nYou can see a complete list of [NumPy array's methods and attributes in the documentation](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html).\n\n:::{.callout-note}\n## R and Python\n\nIn R we don't use methods within an object. Rather, functions are extrinsic to (outside) the objects they are acting on. In R, for example, there would be two separate items: the variable `var` and a separate function `min` that gets `var` as a parameter:\n\n``` R\n# this is R code\nvar <- array(c(1,4,7,2,5,8,3,6,9), dim =c(3,3))\nmin(var)\n```\n\nUsing the pipe operator `%>%` in R's tidyverse is closer to the dot `.` in Python:\n\n``` R\n# this is R code\nvar <- array(c(1,4,7,2,5,8,3,6,9), dim =c(3,3))\nvar %>% min()\n```\n\nWhat happens here is that the pipe `%>%` is passing `var` to the `min()` function as its first argument. This is essentially what happens in Python when a function is a method of a class:\n\n``` python\n# this is Python code\nvar = np.array([[1,2,3],[4,5,6],[7,8,9]])\nvar.min()\n```\n\nWhen working in Python, remember that *methods are functions that are part of an object* and a method uses the object it is part of to produce some information.\n:::\n\n\n\n", + "supporting": [ + "lesson-1-python-review_files" + ], + "filters": [], + "includes": {} + } +} \ No newline at end of file diff --git a/_freeze/lectures/lesson-2-pandas-basics/execute-results/html.json b/_freeze/lectures/lesson-2-pandas-basics/execute-results/html.json new file mode 100644 index 0000000..154b82e --- /dev/null +++ b/_freeze/lectures/lesson-2-pandas-basics/execute-results/html.json @@ -0,0 +1,15 @@ +{ + "hash": "33c85abbf1b8e1c0fed1bf4c13462336", + "result": { + "markdown": "::: {.cell execution_count=1}\n``` {.python .cell-code}\nimport os\nos.getcwd()\n```\n\n::: {.cell-output .cell-output-display execution_count=109}\n```\n'/Users/c_galazgarcia/eds-220-book'\n```\n:::\n:::\n\n\n# Pandas Fundamentals\n\n## About\nIn this lesson we cover the two core objects in the `pandas` library, the `pandas.Series` and the `pandas.DataFrame`. We will also learn methods to select data from our datasets.\n\n## `pandas`\n\n`pandas` is a Python package to wrangle and analyze tabular data. It is built on top of NumPy and has become the core tool for doing data analysis in Python. \n\nThe convention to import it is:\n\n::: {.cell execution_count=2}\n``` {.python .cell-code}\nimport pandas as pd\n\n# we will also import numpy \nimport numpy as np\n```\n:::\n\n\nThere is so much to learn about `pandas`. While we won't be able to cover every single functionality of this package in the next three lecutres, the goal is to get you started with the basic tools for data wrangling and give you a solid basis on which you can explore further. \n\n## Series\n\nThe first core data structure of pandas is the **series**. \nA series is a *one-dimensional* array of *indexed* data. A `pandas.Series` having an **index** is the main difference between a `pandas.Series` and a numpy array. See the difference:\n\n::: {.cell execution_count=3}\n``` {.python .cell-code}\n# a numpy array\n# np.random.randn returns values from the std normal distribution\narr = np.random.randn(4) \nprint(type(arr))\nprint(arr, \"\\n\")\n\n# a pandas series made from the previous array\ns = pd.Series(arr)\nprint(type(s))\nprint(s)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n[ 1.29006588 0.60107937 -0.52572693 -0.33036333] \n\n\n0 1.290066\n1 0.601079\n2 -0.525727\n3 -0.330363\ndtype: float64\n```\n:::\n:::\n\n\n### Creating a `pandas.Series`\n\nThe basic method to create a `pandas.Series` is to call\n\n``` python\ns = pd.Series(data, index=index)\n```\n\nThe `data` parameter can be:\n\n- a [numpy array](https://numpy.org/doc/stable/reference/arrays.ndarray.html) or [a list](https://realpython.com/python-list/)\n- a [Python dictionary](https://realpython.com/lessons/dictionary-python/)\n- [a number](https://commons.wikimedia.org/wiki/File:Number-three.JPG)\n\nThe `index` parameter is a list of index labels.\n\nFor now, we will create a `pandas.Series` from a numpy array or list. To use this method we need to pass a numpy array (or a list of objects that can be converted to NumPy types) as `data` and a list of indices of the same length as data. \n\n::: {.cell execution_count=4}\n``` {.python .cell-code}\n# a Series from a numpy array \npd.Series(np.arange(3), index=['a','b','c'])\n```\n\n::: {.cell-output .cell-output-display execution_count=112}\n```\na 0\nb 1\nc 2\ndtype: int64\n```\n:::\n:::\n\n\n The `index` parameter is optional. If we don't include it, the default is to make the index equal to `[0,...,len(data)-1]`. For example:\n\n::: {.cell execution_count=5}\n``` {.python .cell-code}\n# a Series from a list of strings with default index\npd.Series(['EDS 220', 'EDS 222', 'EDS 223', 'EDS 242'])\n```\n\n::: {.cell-output .cell-output-display execution_count=113}\n```\n0 EDS 220\n1 EDS 222\n2 EDS 223\n3 EDS 242\ndtype: object\n```\n:::\n:::\n\n\n\n\n\n### Simple operations\n\nArithmetic operations work on series and also most NumPy functions. For example:\n\n::: {.cell execution_count=8}\n``` {.python .cell-code}\n# define a series\ns = pd.Series([98,73,65],index=['Andrea', 'Beth', 'Carolina'])\n\n# divide each element in series by 10\nprint(s /10, '\\n')\n\n# take the exponential of each element in series\nprint(np.exp(s), '\\n')\n\n# notice this doesn't change the values of our series\nprint(s)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nAndrea 9.8\nBeth 7.3\nCarolina 6.5\ndtype: float64 \n\nAndrea 3.637971e+42\nBeth 5.052394e+31\nCarolina 1.694889e+28\ndtype: float64 \n\nAndrea 98\nBeth 73\nCarolina 65\ndtype: int64\n```\n:::\n:::\n\n\nWe can also produce new `pandas.Series` with `True`/`False` values indicating whether the elements in a series satisfy a condition or not:\n\n::: {.cell execution_count=9}\n``` {.python .cell-code}\ns > 10\n```\n\n::: {.cell-output .cell-output-display execution_count=117}\n```\nAndrea True\nBeth True\nCarolina True\ndtype: bool\n```\n:::\n:::\n\n\nThis kind of simple conditions on `pandas.Series` will be key when we are selecting data from data frames.\n\n\n\n### Attributes & Methods\n`pandas.Series` have *many* attributes and methods, you can see a [full list in the `pandas` documentation](https://pandas.pydata.org/docs/reference/api/pandas.Series.html). For now we will cover two examples that have to do with identifying missing values.\n\n`pandas` represents a missing or NA value with `NaN`, which stands for not a number. Let's construct a small series with some NA values:\n\n::: {.cell execution_count=10}\n``` {.python .cell-code}\n# series with NAs in it\ns = pd.Series([1, 2, np.NaN, 4, np.NaN])\n```\n:::\n\n\nA `pandas.Series` has an *attribute* called `hasnans` that returns `True` if there are any NaNs:\n\n::: {.cell execution_count=11}\n``` {.python .cell-code}\n# check if series has NAs\ns.hasnans\n```\n\n::: {.cell-output .cell-output-display execution_count=119}\n```\nTrue\n```\n:::\n:::\n\n\nThen we might be intersted in knowing which elements in the series are NAs. We can do this using the [`isna` method](https://pandas.pydata.org/docs/reference/api/pandas.Series.isna.html):\n\n::: {.cell execution_count=12}\n``` {.python .cell-code}\ns.isna()\n```\n\n::: {.cell-output .cell-output-display execution_count=120}\n```\n0 False\n1 False\n2 True\n3 False\n4 True\ndtype: bool\n```\n:::\n:::\n\n\nWe can see the ouput is a `pd.Series` of boolean values indicating if an element in the row at the given index is NA (`True` = is NA) or not (`False` = not NA).\n\n:::{.callout-note}\n## moving on\nThere's much more to say about `pandas.Series`, but this is enought to get us going. At this point, we mainly want to know about `pandas.Series` because `pandas.Series` are the columns of `pandas.DataFrame`s.\n:::\n\n\n\n## Data Frames\n\nThe Data Frame is the most used `pandas` object. It represents tabular data and we can think of it as a spreadhseet. Each column of a `pandas.DataFrame` is a `pandas.Series`. \n\n### Creating a `pandas.DataFrame`\nThere are [many ways of creating a `pandas.DataFrame`](https://pandas.pydata.org/docs/user_guide/dsintro.html#dataframe). \n\n\n\nWe already mentioned each column of a `pandas.DataFrame` is a `pandas.Series`. In fact, the `pandas.DataFrame` is a dictionary of `pandas.Series`, with each column name being the key and the column values being the key's value. Thus, we can create a `pandas.DataFrame` in this way:\n\n::: {.cell execution_count=16}\n``` {.python .cell-code}\n# initialize dictionary with columns' data \nd = {'col_name_1' : pd.Series(np.arange(3)),\n 'col_name_2' : pd.Series([3.1, 3.2, 3.3]),\n }\n\n# create data frame\ndf = pd.DataFrame(d)\ndf\n```\n\n::: {.cell-output .cell-output-display execution_count=124}\n```{=html}\n
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        223.3
        \n
        \n```\n:::\n:::\n\n\nWe can change the index and column names by changing the `index` and `columns` attributes in the data frame. \n\n::: {.cell execution_count=17}\n``` {.python .cell-code}\n# print original index\nprint(df.index)\n\n# change the index\ndf.index = ['a','b','c']\ndf\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nRangeIndex(start=0, stop=3, step=1)\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=125}\n```{=html}\n
        \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
        col_name_1col_name_2
        a03.1
        b13.2
        c23.3
        \n
        \n```\n:::\n:::\n\n\n::: {.cell execution_count=18}\n``` {.python .cell-code}\n# print original column names\nprint(df.columns)\n\n# change column names \ndf.columns = ['C1','C2']\ndf\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nIndex(['col_name_1', 'col_name_2'], dtype='object')\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=126}\n```{=html}\n
        \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
        C1C2
        a03.1
        b13.2
        c23.3
        \n
        \n```\n:::\n:::\n\n\n## Subsetting a `pandas.DataFrame`\n\nLike it's often the case when working with `pandas`, there are *many* ways in which we can subset a data frame. We will review the core methods to do this. \n\n\nFor all examples we will use simplified data (glacial_loss.csv) from the National Snow and Ice Data Center ([Original dataset](​http://dx.doi.org/10.7265/N52N506F)). The column descriptions are:\n\n- **year**: ​calendar year\n- **europe - antarctica**: ​change in glacial volume (km3​ ​) in each region that year\n- **global_glacial_volume_change**: ​cumulative​ global glacial volume change (km3),\nstarting in 1961\n- **annual_sea_level_rise**: ​annual rise in sea level (mm)\n- **cumulative_sea_level_rise**:​ cumulative rise in sea level (mm) since 1961\n\nFirst, we read-in the file and get some baisc information about this data frame:\n\n::: {.cell execution_count=19}\n``` {.python .cell-code}\n# read in file\ndf = pd.read_csv('data/lesson-1/glacial_loss.csv')\n\n# see the first five rows\ndf.head()\n```\n\n::: {.cell-output .cell-output-display execution_count=127}\n```{=html}\n
        \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
        yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
        01961-5.128903-108.382987-18.721190-32.350759-14.359007-4.739367-35.116389-220.8235150.6100100.610010
        119625.576282-173.252450-24.324790-4.675440-2.161842-13.694367-78.222887-514.2698620.8106251.420635
        21963-10.123105-0.423751-2.047567-3.027298-27.5358813.4196333.765109-550.5756400.1002921.520927
        31964-4.50835820.0701480.477800-18.675385-2.24828620.73263314.853096-519.589859-0.0855961.435331
        4196510.62938543.695389-0.115332-18.414602-19.3987656.86210222.793484-473.112003-0.1283921.306939
        \n
        \n```\n:::\n:::\n\n\n::: {.cell execution_count=20}\n``` {.python .cell-code}\n# get column names\ndf.columns\n```\n\n::: {.cell-output .cell-output-display execution_count=128}\n```\nIndex(['year', 'europe', 'arctic', 'alaska', 'asia', 'north_america',\n 'south_america', 'antarctica', 'global_glacial_volume_change',\n 'annual_sea_level_rise', 'cumulative_sea_level_rise'],\n dtype='object')\n```\n:::\n:::\n\n\n::: {.cell execution_count=21}\n``` {.python .cell-code}\n# check the data types of each column\ndf.dtypes\n```\n\n::: {.cell-output .cell-output-display execution_count=129}\n```\nyear int64\neurope float64\narctic float64\nalaska float64\nasia float64\nnorth_america float64\nsouth_america float64\nantarctica float64\nglobal_glacial_volume_change float64\nannual_sea_level_rise float64\ncumulative_sea_level_rise float64\ndtype: object\n```\n:::\n:::\n\n\n::: {.cell execution_count=22}\n``` {.python .cell-code}\n# data frame's shape: output is a tuple (# rows, # columns)\ndf.shape\n```\n\n::: {.cell-output .cell-output-display execution_count=130}\n```\n(43, 11)\n```\n:::\n:::\n\n\n### Selecting a single column...\n\n#### ...by column name\nThis is the simplest case for selecting data. Suppose we are interested in the annual sea level rise. Then we can access that single column in this way:\n\n::: {.cell execution_count=23}\n``` {.python .cell-code}\n# seelect a single column by using square brackets []\nannual_rise = df['annual_sea_level_rise']\n\n# check the type of the ouput\nprint(type(annual_rise))\n\nannual_rise.head()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=131}\n```\n0 0.610010\n1 0.810625\n2 0.100292\n3 -0.085596\n4 -0.128392\nName: annual_sea_level_rise, dtype: float64\n```\n:::\n:::\n\n\nSince we only selected a single column the output is a `pandas.Series`. \n\n:::{.callout-note}\n## `pd.DataFrame` = dictionary of columns\nRemember we can think of a `pandas.DataFrame` as a dictionary of its columns? Then we can access a single column using the column name as the key, just like we would do in a dictionary. That is the we just used: `df['column_name']`.\n:::\n\nThis is an example of **selecting by label**, which means we want to select data from our data frame using the *names* of the columns, *not their position*.\n\n\n#### ... with attribute syntax\n\nWe can also access a single column by using attribute syntax:\n\n::: {.cell execution_count=24}\n``` {.python .cell-code}\nannual_rise_2 = df.annual_sea_level_rise\nannual_rise_2.head()\n```\n\n::: {.cell-output .cell-output-display execution_count=132}\n```\n0 0.610010\n1 0.810625\n2 0.100292\n3 -0.085596\n4 -0.128392\nName: annual_sea_level_rise, dtype: float64\n```\n:::\n:::\n\n\n### Selecting multiple columns...\n\n#### ... using a list of column names\nThis is another example of selecting by labels. We just need to pass a list with the column names to the square brackets `[]`. For example, say we want to look at the change in glacial volume in Europe and Asia, then we can select those columns like this:\n\n::: {.cell execution_count=25}\n``` {.python .cell-code}\n# select columns with names \"europe\" and \"asia\"\neurope_asia = df[['europe','asia']]\n```\n:::\n\n\nNotice there are double square brackets. This is because we are passing the list of names `['europe','asia']` to the selection brakcets `[]`. \n\n::: {.cell execution_count=26}\n``` {.python .cell-code}\n# check the type of the resulting selection\nprint(type(europe_asia))\n\n# check the shape of the selection\nprint((europe_asia.shape))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n(43, 2)\n```\n:::\n:::\n\n\n#### ... using a slice\nYet another example of selecting by label! In this case we will use the `loc` function. This is a powerful function! The general syntax is \n\n```python\ndf.loc[ row-selection , column-selection]\n```\nwhere `row-selection` and `column-selection` are the rows and columns we want to subset from the data frame. \n\nLet's start by a simple example, where we want to select a slice of columns, say the change in glacial volume per year in all regions. This corresponds to all columns between `arctic` and `antarctica`.\n\n::: {.cell execution_count=27}\n``` {.python .cell-code}\n# select all columns between 'arctic' and 'antarctica'\nall_regions = df.loc[:,'arctic':'antarctica']\nall_regions.head()\n```\n\n::: {.cell-output .cell-output-display execution_count=135}\n```{=html}\n
        \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
        arcticalaskaasianorth_americasouth_americaantarctica
        0-108.382987-18.721190-32.350759-14.359007-4.739367-35.116389
        1-173.252450-24.324790-4.675440-2.161842-13.694367-78.222887
        2-0.423751-2.047567-3.027298-27.5358813.4196333.765109
        320.0701480.477800-18.675385-2.24828620.73263314.853096
        443.695389-0.115332-18.414602-19.3987656.86210222.793484
        \n
        \n```\n:::\n:::\n\n\nNotice two things:\n\n- we used the colon `:` as the `row-selection` parameter, which means \"select all the rows\"\n- the slice of the data frame we got includes both endpoints of the slice `'arctic':'antarctica'`. In other words we get the `arctic` column *and* the `antarctica` column. This is different from how slicing works in base Python and NumPy, where the end point is not included.\n\n### Selecting rows...\nNow that we are familiar with some methods for selecting columns, let's move on to selecting rows. \n\n#### ... using a condition\nSelecting which rows satisfy a particular condition is, in my experience, the most usual kind of row subsetting. The general syntax for this type of selection is `df[condition_on_rows]`. For example, suppose we are intersted in all data after 1996. We can select those rows in this way:\n\n::: {.cell execution_count=28}\n``` {.python .cell-code}\n# select all rows with year > 1996\nafter_96 = df[df['year']>1996]\nafter_96\n```\n\n::: {.cell-output .cell-output-display execution_count=136}\n```{=html}\n
        \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
        yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
        361997-13.724106-24.832246-167.229145-34.406403-27.680661-38.213286-20.179090-4600.6860130.90962512.709077
        371998-13.083338-110.429302-107.879027-58.11570230.169987-3.797978-48.129928-4914.8319660.86780713.576884
        381999-8.039555-64.644068-87.714653-26.2117235.888512-8.038630-40.653001-5146.3682310.63960314.216487
        392000-17.008590-96.494055-44.445000-37.518173-29.191986-2.767698-58.873830-5435.3171750.79820215.014688
        402001-8.419109-145.415483-55.749505-35.977022-0.9261347.553503-86.774675-5764.0399310.90807415.922762
        412002-3.392361-48.718943-87.120000-36.127226-27.853498-13.484593-30.203960-6013.2255000.68835816.611120
        422003-3.392361-48.718943-67.253634-36.021991-75.066475-13.223430-30.203960-6289.6409760.76357917.374699
        \n
        \n```\n:::\n:::\n\n\nLet's break down what is happening here. In this case the condition for our rows is `df['year']>1996`, this checks which rows have a value greater than 1996 in the year column. Let's see this explicitely:\n\n::: {.cell execution_count=29}\n``` {.python .cell-code}\n# check the type of df['year']>1996\nprint(type(df['year']>1996))\n\ndf['year']>1996\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=137}\n```\n0 False\n1 False\n2 False\n3 False\n4 False\n5 False\n6 False\n7 False\n8 False\n9 False\n10 False\n11 False\n12 False\n13 False\n14 False\n15 False\n16 False\n17 False\n18 False\n19 False\n20 False\n21 False\n22 False\n23 False\n24 False\n25 False\n26 False\n27 False\n28 False\n29 False\n30 False\n31 False\n32 False\n33 False\n34 False\n35 False\n36 True\n37 True\n38 True\n39 True\n40 True\n41 True\n42 True\nName: year, dtype: bool\n```\n:::\n:::\n\n\nThe output is a `pandas.Series` with boolean values (`True` or `False`) indicating which rows satisfy the condition year>1996. When we pass such a series of boolean values to the selection brackets `[]` we keep only those rows with a `True` value. \n\nHere's another example of using a condition. Suppose we want to look at data from years 1970 to 1979. One way of doing this is to use the `in` operator in our condition:\n\n::: {.cell execution_count=30}\n``` {.python .cell-code}\nseventies = df[df['year'].isin(range(1970,1980))]\nseventies\n```\n\n::: {.cell-output .cell-output-display execution_count=138}\n```{=html}\n
        \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
        yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
        91970-6.452316-24.494667-0.125296-36.12019911.61979011.6369114.400377-999.0181770.1102252.759719
        1019710.414711-42.90418928.103328-8.702938-9.9645421.061299-6.735536-1038.1044590.1079732.867692
        111972-5.144729-27.004031-22.143350-40.88335732.363730-14.968034-6.223849-1122.8855060.2342023.101894
        1219734.0810909.83944422.985188-31.432594-20.8832322.10364910.539823-1125.6777430.0077133.109607
        1319741.545615-40.126998-29.517874-43.861622-23.991402-21.3388254.419343-1279.9642870.4262063.535813
        1419757.431192-32.410467-44.094084-43.357442-30.858810-2.368842-7.775315-1434.8180370.4277733.963586
        1519763.98675321.686639-28.234725-67.292125-12.534421-19.46535819.250607-1518.1851290.2302964.193882
        1619774.891410-33.123010-5.662139-62.165684-15.9053322.654950-23.727249-1652.4534000.3709074.564788
        1719788.404591-77.561015-12.503384-22.858040-31.0976097.127708-9.140167-1791.3550220.3837064.948495
        1819793.916703-88.351684-63.938851-49.242043-12.076624-17.718503-9.578557-2030.5378480.6607265.609221
        \n
        \n```\n:::\n:::\n\n\nLet's break it down: \n\n- `df['year']` is the column with the year values, a `pandas.Series`,\n\n- in `df['year'].isin()`, we have that [`isin` is a method for the `pandas.Series`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.isin.html) and we are calling it using the dot `.`. \n\n- `range(1970,1980)` constructs consecutive integers from 1970 to 1979 - remember the right endopoint (1980) is not included!\n\n- `df['year'].isin(range(1970,1980))` is then a `pandas.Series` of boolean values indicating which rows have year equal to 1970, ..., 1979. \n\n- when we put `df['year'].isin(range(1970,1980))` inside the selection brackets `[]` we obtain the rows of the data frame with year equal to 1970, ..., 1979.\n\n:::{.callout-note}\n## `loc` for row selection\nIt is equivalent to write\n\n```python\n# select rows with year<1965\ndf[df['year'] < 1965]\n```\nand\n```python\n# select rows with year<1965 using love\ndf.loc[ df['year'] <1965 , :]\n```\nIn the second one:\n\n- we are using the `df.loc[ row-selection , column-selection]` syntax\n\n- the `row-selection` parameter is the condition `df['year']<1965`\n\n- the `column-selection` parameter is a colon `:`, which indicates we want all columns for the rows we are selecting.\n\nWe prefer the first syntax when we are selecting rows and not columns since it is simpler.\n:::\n\n#### ... using multiple conditions\nWe can combine multipe conditions by surrounding each one in parenthesis `()` and using the or operator `|` and the and operator `and`.\n\n***or* example**:\n\n::: {.cell execution_count=31}\n``` {.python .cell-code}\n# select rows with \n# annual_sea_level_rise<0.5 mm OR annual_sea_level_rise>0.8 mm\n\ndf[ (df['annual_sea_level_rise']<0.5) | (df['annual_sea_level_rise']>0.8)]\ndf.head()\n```\n\n::: {.cell-output .cell-output-display execution_count=139}\n```{=html}\n
        \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
        yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
        01961-5.128903-108.382987-18.721190-32.350759-14.359007-4.739367-35.116389-220.8235150.6100100.610010
        119625.576282-173.252450-24.324790-4.675440-2.161842-13.694367-78.222887-514.2698620.8106251.420635
        21963-10.123105-0.423751-2.047567-3.027298-27.5358813.4196333.765109-550.5756400.1002921.520927
        31964-4.50835820.0701480.477800-18.675385-2.24828620.73263314.853096-519.589859-0.0855961.435331
        4196510.62938543.695389-0.115332-18.414602-19.3987656.86210222.793484-473.112003-0.1283921.306939
        \n
        \n```\n:::\n:::\n\n\n***and* example**\n\n::: {.cell execution_count=32}\n``` {.python .cell-code}\n# select rows with cumulative_sea_level_rise>10 AND global_glacial_volume_change<-300\ndf[ (df['cumulative_sea_level_rise']>10) & (df['global_glacial_volume_change']<-300)]\n```\n\n::: {.cell-output .cell-output-display execution_count=140}\n```{=html}\n
        \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
        yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
        32199316.685013-73.666274-43.702040-65.995130-33.151246-20.578403-20.311577-3672.5820820.67112610.145254
        3319940.741751-3.069084-59.962273-59.004710-89.506142-15.258449-8.168498-3908.9771910.65302510.798280
        341995-2.139665-58.167778-74.1417623.500155-0.699374-19.863392-25.951496-4088.0828730.49476711.293047
        351996-6.809834-4.550205-74.847017-67.4365914.867530-21.080115-11.781489-4271.4015940.50640511.799452
        361997-13.724106-24.832246-167.229145-34.406403-27.680661-38.213286-20.179090-4600.6860130.90962512.709077
        371998-13.083338-110.429302-107.879027-58.11570230.169987-3.797978-48.129928-4914.8319660.86780713.576884
        381999-8.039555-64.644068-87.714653-26.2117235.888512-8.038630-40.653001-5146.3682310.63960314.216487
        392000-17.008590-96.494055-44.445000-37.518173-29.191986-2.767698-58.873830-5435.3171750.79820215.014688
        402001-8.419109-145.415483-55.749505-35.977022-0.9261347.553503-86.774675-5764.0399310.90807415.922762
        412002-3.392361-48.718943-87.120000-36.127226-27.853498-13.484593-30.203960-6013.2255000.68835816.611120
        422003-3.392361-48.718943-67.253634-36.021991-75.066475-13.223430-30.203960-6289.6409760.76357917.374699
        \n
        \n```\n:::\n:::\n\n\n#### ... by position\n\nAll the selections we have done so far have been using labels or using a condition. Sometimes we might want to select certain rows depending on their *actual position* in the data frame. In this case we use the `iloc` method with the syntax `df.iloc[row-indices]`. `iloc` stands for integer-location based indexing. Let's see some examples:\n\n::: {.cell execution_count=33}\n``` {.python .cell-code}\n# select the fifht row = index 4\ndf.iloc[4]\n```\n\n::: {.cell-output .cell-output-display execution_count=141}\n```\nyear 1965.000000\neurope 10.629385\narctic 43.695389\nalaska -0.115332\nasia -18.414602\nnorth_america -19.398765\nsouth_america 6.862102\nantarctica 22.793484\nglobal_glacial_volume_change -473.112003\nannual_sea_level_rise -0.128392\ncumulative_sea_level_rise 1.306939\nName: 4, dtype: float64\n```\n:::\n:::\n\n\n::: {.cell execution_count=34}\n``` {.python .cell-code}\n# select rows 23 through 30, inclduing 30\ndf.iloc[23:31]\n```\n\n::: {.cell-output .cell-output-display execution_count=142}\n```{=html}\n
        \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
        yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
        2319848.581427-5.755672-33.466092-20.528535-20.734676-8.267686-3.261011-2569.3398020.2326097.097624
        241985-5.970980-49.65108912.065473-31.571622-33.83398510.072906-13.587886-2682.8579260.3135867.411210
        251986-5.68064222.9008477.557447-18.920773-33.014743-4.65203030.482473-2684.1976320.0037017.414911
        2619878.19147712.387780-24.007862-41.121970-48.5609961.6707333.130190-2773.3255680.2462107.661120
        271988-11.117228-31.06648949.897712-21.300712-46.54543513.460422-37.986834-2858.7676210.2360287.897148
        28198914.863220-23.462392-36.112726-46.528372-57.756422-21.687470-10.044757-3041.1691310.5038728.401020
        291990-1.226009-27.484542-92.713339-35.553433-56.563056-31.077022-29.893352-3318.2203970.7653359.166355
        301991-14.391425-34.898689-8.822063-15.338299-31.458010-7.162909-35.968429-3467.6302840.4127349.579089
        \n
        \n```\n:::\n:::\n\n\nNotice since we are back to indexing by position the right endpoint of the slice (6) is not included in the ouput. \n\n### Selecting rows and columns simultaneously...\n\nSelecting rows and columns simultaneously can be done using `loc` (labels or conditions) or `iloc` (integer position).\n\n#### ...by labels or conditions\nWhen we want to select rows and columns simultaneously by labels or conditions we can use the method `loc` with the syntax \n\n```python\ndf.loc[ row-selection , column-selection]\n```\n\nspecifying both paratmers: `row-selection` and `column-selection`. These parameters can be a condition (which generates a boolean array) or a subset of labels from the index or the column names. Let's see an examples:\n\n::: {.cell execution_count=35}\n``` {.python .cell-code}\n# select change in glacial volume in Europe per year after 2000\ndf.loc[df['year']>2000,['year','europe']]\n```\n\n::: {.cell-output .cell-output-display execution_count=143}\n```{=html}\n
        \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
        yeareurope
        402001-8.419109
        412002-3.392361
        422003-3.392361
        \n
        \n```\n:::\n:::\n\n\nLet's break it down:\n\n- we are using the `df.loc[ row-selection , column-selection]` syntax\n\n- the `row-selection` parameter is the condition `df['year']>1990`, which is a boolean array saying which years are greater than 1990\n\n- the `column-selection` parameter is `['year','europe']` which is a list with the names of the two columns we are intersted in. \n\n#### ... by position\n\nWhen we want to select rows and columns simultaneously by position we use the `iloc` method with the syntax:\n```python\ndf.iloc[ row-indices , column-indices]\n```\n\nFor example, \n\n::: {.cell execution_count=36}\n``` {.python .cell-code}\n# select rows 3-7 (including 7) and columns 3 and 4\ndf.iloc[ 3:8, [3,4] ]\n```\n\n::: {.cell-output .cell-output-display execution_count=144}\n```{=html}\n
        \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
        alaskaasia
        30.477800-18.675385
        4-0.115332-18.414602
        50.224762-14.630284
        6-7.174030-39.013695
        7-0.6605567.879589
        \n
        \n```\n:::\n:::\n\n\nLet's break it down:\n\n- we are using the `df.iloc[ row-indices , column-indices]` syntax\n\n- the `row-indices` parameter is the slice *of integer indices* 3:8. Remember the right endpoint (8) won't be included.\n\n- the `column-indices` parameter is the list of integer indices 3 and 4. This means we are selecting the fourth and fifth column.\n\n\n### Notes about `loc` and `iloc`\n\n::: {.callout-caution}\n## `iloc` vs. `loc`\nAt the beginning, the difference between `iloc` and `loc` can be confusing. Remember the `i` in `iloc` stands for *integer-location*, so this function only uses integer indexing to retrieve information from the data frames in the same way as indexing for Python lists.\n\nIf you want to dive deeper, this is a great discussion about the difference between `iloc` and `loc`: [Stackoverflow - How are iloc and loc different?](https://stackoverflow.com/questions/31593201/how-are-iloc-and-loc-different/31593712#31593712)\n\nAnd, as always, the documentation will provide you with more information:\n[`pandas.DataFrame.loc`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.loc.html) and [`pandas.DataFrame.iloc`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.iloc.html).\n:::\n\n\n:::{.callout-warning}\n## `iloc` for column selection? Avoid it!\nWe can also access columns by position using `iloc` - but it is best not to if possible.\n\nSuppose we want to access the 10th column in the data frame - then we want to select a column *by position*. In this case the 10th column is the annual sea level rise data and the 10th position corresponds to the index 9. We can select this column by position using the `iloc` method*:\n\n::: {.cell execution_count=37}\n``` {.python .cell-code}\n# select column by position using iloc\n# the syntax is iloc[rows,columns]\n# [:,9] means \"select all rows from the 10th column\"\nannual_rise_3 = df.iloc[:,9]\nannual_rise_3.head()\n```\n\n::: {.cell-output .cell-output-display execution_count=145}\n```\n0 0.610010\n1 0.810625\n2 0.100292\n3 -0.085596\n4 -0.128392\nName: annual_sea_level_rise, dtype: float64\n```\n:::\n:::\n\n\nUnless you are *really* looking for information about *the 10th column*, do not access a column by position. This is bound to break in many ways:\n\n- it relies on a person correctly counting the position of a column. Even with a small dataset this can be prone to error.\n\n- it is not explicit: if we want information about sea level rise `df.annual_sea_level_rise` or `df['annual_sea_level_rise']` are explicitely telling us we are accessing that information. `df.iloc[:,9]` is obscure and uninformative.\n\n- datastets can get updated. Maybe a new column was added before `annual_sea_level_rise`, this would change the position of the column, which would make any code depending on `df.iloc[:,9]` invalid. Accessing by label helps reproducibility!\n\n:::\n\n## Resources\n\nWhat is presented in this section is a comprehensive, but not an exhaustive list of methods to select data in `pandas.DataFrames`. There are *so many* ways to subset data to get the same result. Some of the content from this lesson is adapted from the following resources and I encourage you to read them to learn more! \n\n📖 [Pandas getting started tutorials - How to I select a subset of a\nDataFrame](https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html) \n\n📖 [Pandas documentation - User Guide - Indexing and Selecting Data](https://pandas.pydata.org/docs/user_guide/indexing.html#indexing-slicing-with-labels)\n\n📖 [Python for Data Analysis, 3E - Getting started with pandas](https://wesmckinney.com/book/pandas-basics)\n\n## Acknowledgements\n\nThe simplified glacial_loss.csv dataset was created by [Dr. Allison Horst](https://allisonhorst.github.io) as part of her course materials on environmental data science. \n\n", + "supporting": [ + "lesson-2-pandas-basics_files/figure-html" + ], + "filters": [], + "includes": { + "include-in-header": [ + "\n\n\n" + ] + } + } +} \ No newline at end of file diff --git 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Be={placement:"bottom",modifiers:[],strategy:"absolute"};function Re(){for(var t=arguments.length,e=new Array(t),i=0;ij.on(t,"mouseover",d))),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Je),this._element.classList.add(Je),j.trigger(this._element,"shown.bs.dropdown",t)}hide(){if(c(this._element)||!this._isShown(this._menu))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){j.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(t){if(void 0===Fe)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let e=this._element;"parent"===this._config.reference?e=t:o(this._config.reference)?e=r(this._config.reference):"object"==typeof this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;ie+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'',trigger:"hover 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t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof 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file mode 100644 index 0000000..36cb328 --- /dev/null +++ b/_freeze/lectures/lesson-2-pandas-basics/libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,171 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; +} + +code span.at { + color: #657422; +} + +code span.ss { + color: #20794D; +} + +code span.an { + color: #5E5E5E; +} + +code span.fu { + color: #4758AB; +} + +code span.st { + color: #20794D; +} + +code span.cf { + color: #003B4F; +} + +code span.op { + color: #5E5E5E; +} + +code span.er { + color: #AD0000; +} + +code span.bn { + color: #AD0000; +} + +code span.al { + color: #AD0000; +} + +code span.va { + color: #111111; +} + +code span.pp { + color: #AD0000; +} + +code span.in { + color: #5E5E5E; +} + +code span.vs { + color: #20794D; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; +} + +code span.ch { + color: #20794D; +} + +code span.dt { + color: #AD0000; +} + +code span.fl { + color: #AD0000; +} + +code span.co { + color: #5E5E5E; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; +} + +code span.sc { + color: #5E5E5E; +} + +code span.dv { + color: #AD0000; +} + +code span.kw { + color: #003B4F; +} + +.prevent-inlining { + content: " { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id=${anchor}]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + sectionIndex = 0; + } else { + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + } + + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append(titleEl.innerText, toggleIcon); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + const elRect = el.getBoundingClientRect(); + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + positionToggle(); + }, 50) + ); + positionToggle(); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > * " + ); + + nexttick(() => { + let lastBottom = 0; + for (const marginChild of marginChildren) { + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const margin = lastBottom - top; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + }); + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const top = + el.getBoundingClientRect().top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightSideConflictEls)); + sidebarScrollVisiblity(toRegions(leftSideConflictEls)); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility(toRegions(leftSideConflictEls)); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (depth === 1 || hasActiveChild || prevSiblingIsActiveLink(el)) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + + if (tocEl) { + walk(tocEl, 0); + updateActiveLink(); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } +}); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git 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b/_freeze/site_libs/clipboard/clipboard.min.js new file mode 100644 index 0000000..41c6a0f --- /dev/null +++ b/_freeze/site_libs/clipboard/clipboard.min.js @@ -0,0 +1,7 @@ +/*! + * clipboard.js v2.0.10 + * https://clipboardjs.com/ + * + * Licensed MIT © Zeno Rocha + */ +!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return o}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),c=n.n(e);function a(t){try{return document.execCommand(t)}catch(t){return}}var f=function(t){t=c()(t);return a("cut"),t};var l=function(t){var e,n,o,r=1 - + @@ -17,10 +17,9 @@ ul.task-list{list-style: none;} ul.task-list li input[type="checkbox"] { width: 0.8em; - margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */ + margin: 0 0.8em 0.2em -1.6em; vertical-align: middle; } -/* CSS for syntax highlighting */ pre > code.sourceCode { white-space: pre; position: relative; } pre > code.sourceCode > span { display: inline-block; line-height: 1.25; } pre > code.sourceCode > span:empty { height: 1.2em; } @@ -47,13 +46,43 @@ -khtml-user-select: none; -moz-user-select: none; -ms-user-select: none; user-select: none; padding: 0 4px; width: 4em; + color: #aaaaaa; } -pre.numberSource { margin-left: 3em; padding-left: 4px; } +pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa; padding-left: 4px; } div.sourceCode { } @media screen { pre > code.sourceCode > span > a:first-child::before { text-decoration: underline; } } +code span.al { color: #ff0000; font-weight: bold; } /* Alert */ +code span.an { color: #60a0b0; font-weight: bold; font-style: italic; } /* Annotation */ +code span.at { color: #7d9029; } /* Attribute */ +code span.bn { color: #40a070; } /* BaseN */ +code span.bu { color: #008000; } 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        - @@ -119,61 +142,55 @@
        -

    -
    +
    -
    +
    loc for row selection
    @@ -1421,101 +1430,100 @@

    df[ (df['annual_sea_level_rise']<0.5) | (df['annual_sea_level_rise']>0.8)] df.head()

    -
    -
    +
    +
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +
    yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
    01961-5.128903-108.382987-18.721190-32.350759-14.359007-4.739367-35.116389-220.8235150.6100100.610010
    119625.576282-173.252450-24.324790-4.675440-2.161842-13.694367-78.222887-514.2698620.8106251.420635
    21963-10.123105-0.423751-2.047567-3.027298-27.5358813.4196333.765109-550.5756400.1002921.520927
    31964-4.50835820.0701480.477800-18.675385-2.24828620.73263314.853096-519.589859-0.0855961.435331
    4196510.62938543.695389-0.115332-18.414602-19.3987656.86210222.793484-473.112003-0.1283921.306939
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
    01961-5.128903-108.382987-18.721190-32.350759-14.359007-4.739367-35.116389-220.8235150.6100100.610010
    119625.576282-173.252450-24.324790-4.675440-2.161842-13.694367-78.222887-514.2698620.8106251.420635
    21963-10.123105-0.423751-2.047567-3.027298-27.5358813.4196333.765109-550.5756400.1002921.520927
    31964-4.50835820.0701480.477800-18.675385-2.24828620.73263314.853096-519.589859-0.0855961.435331
    4196510.62938543.695389-0.115332-18.414602-19.3987656.86210222.793484-473.112003-0.1283921.306939
    -
    @@ -1523,185 +1531,184 @@ 

    # select rows with cumulative_sea_level_rise>10 AND  global_glacial_volume_change<-300
 df[ (df['cumulative_sea_level_rise']>10) & (df['global_glacial_volume_change']<-300)]
    -
    -
    +
    +
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +
    yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
    32199316.685013-73.666274-43.702040-65.995130-33.151246-20.578403-20.311577-3672.5820820.67112610.145254
    3319940.741751-3.069084-59.962273-59.004710-89.506142-15.258449-8.168498-3908.9771910.65302510.798280
    341995-2.139665-58.167778-74.1417623.500155-0.699374-19.863392-25.951496-4088.0828730.49476711.293047
    351996-6.809834-4.550205-74.847017-67.4365914.867530-21.080115-11.781489-4271.4015940.50640511.799452
    361997-13.724106-24.832246-167.229145-34.406403-27.680661-38.213286-20.179090-4600.6860130.90962512.709077
    371998-13.083338-110.429302-107.879027-58.11570230.169987-3.797978-48.129928-4914.8319660.86780713.576884
    381999-8.039555-64.644068-87.714653-26.2117235.888512-8.038630-40.653001-5146.3682310.63960314.216487
    392000-17.008590-96.494055-44.445000-37.518173-29.191986-2.767698-58.873830-5435.3171750.79820215.014688
    402001-8.419109-145.415483-55.749505-35.977022-0.9261347.553503-86.774675-5764.0399310.90807415.922762
    412002-3.392361-48.718943-87.120000-36.127226-27.853498-13.484593-30.203960-6013.2255000.68835816.611120
    422003-3.392361-48.718943-67.253634-36.021991-75.066475-13.223430-30.203960-6289.6409760.76357917.374699
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
    32199316.685013-73.666274-43.702040-65.995130-33.151246-20.578403-20.311577-3672.5820820.67112610.145254
    3319940.741751-3.069084-59.962273-59.004710-89.506142-15.258449-8.168498-3908.9771910.65302510.798280
    341995-2.139665-58.167778-74.1417623.500155-0.699374-19.863392-25.951496-4088.0828730.49476711.293047
    351996-6.809834-4.550205-74.847017-67.4365914.867530-21.080115-11.781489-4271.4015940.50640511.799452
    361997-13.724106-24.832246-167.229145-34.406403-27.680661-38.213286-20.179090-4600.6860130.90962512.709077
    371998-13.083338-110.429302-107.879027-58.11570230.169987-3.797978-48.129928-4914.8319660.86780713.576884
    381999-8.039555-64.644068-87.714653-26.2117235.888512-8.038630-40.653001-5146.3682310.63960314.216487
    392000-17.008590-96.494055-44.445000-37.518173-29.191986-2.767698-58.873830-5435.3171750.79820215.014688
    402001-8.419109-145.415483-55.749505-35.977022-0.9261347.553503-86.774675-5764.0399310.90807415.922762
    412002-3.392361-48.718943-87.120000-36.127226-27.853498-13.484593-30.203960-6013.2255000.68835816.611120
    422003-3.392361-48.718943-67.253634-36.021991-75.066475-13.223430-30.203960-6289.6409760.76357917.374699
    -
    @@ -1712,7 +1719,7 @@ 

    # select the fifht row = index 4
 df.iloc[4]
    -
    +
    year                            1965.000000
 europe                            10.629385
 arctic                            43.695389
@@ -1730,143 +1737,142 @@ 
    
 
    # select rows 23 through 30, inclduing 30
 df.iloc[23:31]
    
-
    
-
    
+
    
 
+
    
 
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    yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
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    301991-14.391425-34.898689-8.822063-15.338299-31.458010-7.162909-35.968429-3467.6302840.4127349.579089
    
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    yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
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    # select change in glacial volume in Europe per year after 2000
 df.loc[df['year']>2000,['year','europe']]
    
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    402001-8.419109
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    # select rows 3-7 (including 7) and columns 3 and 4
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    30.477800-18.675385
    4-0.115332-18.414602
    50.224762-14.630284
    6-7.174030-39.013695
    7-0.6605567.879589
    
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    2.5.5 Notes about loc and iloc
    
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    # [:,9] means "select all rows from the 10th column"
 annual_rise_3 = df.iloc[:,9]
 annual_rise_3.head()
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@@ -2081,23 +2085,9 @@ 
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@@ -2294,7 +2198,7 @@
    - + 3  1. Preliminary Data Exploration
    diff --git a/docs/search.json b/docs/search.json index 8013ebf..1971d2c 100644 --- a/docs/search.json +++ b/docs/search.json @@ -67,7 +67,7 @@ "href": "lectures/lesson-1-python-review.html#variables", "title": "1  Python Review", "section": "1.3 Variables", - "text": "1.3 Variables\nWe can think of a variable as a name we assign to a particular object in Python. For example:\n\n# assign a small array to variable a\na = np.array([[1,1,2],[3,5,8]])\n\nWhen we run the cell, we store the variables and their value. We can view a variable’s value in two ways:\n\nrunning a cell with the variable name\nusing the print function to print the value\n\n\n# show the value\na\n\narray([[1, 1, 2],\n [3, 5, 8]])\n\n\n\n# print the value \nprint(a)\n\n[[1 1 2]\n [3 5 8]]\n\n\n\n\n\n\n\n\nR and Python\n\n\n\nIn Python we use the equal sign = to assign values to variables in the same way the left-arrow <- is used in R.\n\n\n\n\n\n\n\n\nNaming Variables\n\n\n\nThere are many ways of constructing multi-word variable names. In this course we will name variables using snake_case, where words are all in small caps and separated by underscores (ex: my_variable). This is the naming convention suggested by the Style Guide for Python Code." + "text": "1.3 Variables\nWe can think of a variable as a name we assign to a particular object in Python. For example:\n\n# assign a small array to variable a\na = np.array([[1,1,2],[3,5,8]])\n\nWhen we run the cell, we store the variables and their value. We can view a variable’s value in two ways:\n\nrunning a cell with the variable name\nusing the print function to print the value\n\n\n# show the value\na\n\narray([[1, 1, 2],\n [3, 5, 8]])\n\n\n\n# print the value \nprint(a)\n\n[[1 1 2]\n [3 5 8]]\n\n\n\n\n\n\n\n\nR and Python\n\n\n\nIn Python we use the equal sign = to assign values to variables in the same way the left-arrow <- is used in R.\n\n\n\n\n\n\n\n\nNaming Variables\n\n\n\nThere are many ways of constructing multi-word variable names. In this course we will name variables using snake_case, where words are all in small caps and separated by underscores (ex: my_variable). This is the naming convention suggested by the Style Guide for Python Code." }, { "objectID": "lectures/lesson-1-python-review.html#variables-and-objects", @@ -95,7 +95,7 @@ "href": "lectures/lesson-1-python-review.html#attributes-methods", "title": "1  Python Review", "section": "1.7 Attributes & Methods", - "text": "1.7 Attributes & Methods\nAn object in Python has attributes and methods. An attribute is a property of the object, some piece of information about it. A method is a procedure associated with an object, so it is an action where the main ingredient is the object.\nFor example, these could be some attributes and methods a class cat:\n\n\n\n.\n\n\nMore formally, a method is a function that acts on the object it is part of.\nWe can access a variable’s attributes and methods by adding a period . at the end of the variable’s name. So we would write variable.variable_method() or variable.variable_attribute.\n\n\n\n\n\n\nCheck-in\n\n\n\nSuppose we have a class fish, make a diagram similar to the cat class diagram showing 3 attributes for the class and 3 methods.\n\n\nExample\nNumPy arrays have many methods and attributes. Let’s see some concrete examples.\n\n# define a 3x3 array\nvar = np.array([[1,2,3],[4,5,6],[7,8,9]])\nvar\n\narray([[1, 2, 3],\n [4, 5, 6],\n [7, 8, 9]])\n\n\n\n# T is an example of attribute, it returns the transpose of var\nprint(var.T)\nprint(type(var.T))\n\n[[1 4 7]\n [2 5 8]\n [3 6 9]]\n<class 'numpy.ndarray'>\n\n\n\n# shape, another attribute, tells us the shape of the array (3x3)\nprint(var.shape)\nprint(type(var.shape))\n\n(3, 3)\n<class 'tuple'>\n\n\n\n# ndim is an attribute holding the number of array dimensions\nprint(var.ndim)\nprint(type(var.ndim))\n\n2\n<class 'int'>\n\n\nNotice these attributes can have many different data types. Here we saw tuples and int, two of the core Python classes, and also a numpy array as attributes of var.\nNow some examples of methods:\n\n# the tolist method returns the array as a nested list of scalars\nvar.tolist()\n\n[[1, 2, 3], [4, 5, 6], [7, 8, 9]]\n\n\n\n# the min method returns the minimum value in the array along an axis\nvar.min(axis=0)\n\narray([1, 2, 3])\n\n\n\n\n\n\n\n\nCheck-in\n\n\n\nWe can also call the min method without any parameters:\n\nvar.min()\n\n1\n\n\nWhat kind of parameter is axis in our previous call of the var method?\n\n\nRemember, methods are functions associated to an object. We can check this!\n\ntype(var.tolist)\n\nbuiltin_function_or_method\n\n\n\ntype(var.min)\n\nbuiltin_function_or_method\n\n\nYou can see a complete list of NumPy array’s methods and attributes in the documentation.\n\n\n\n\n\n\nR and Python\n\n\n\nIn R we don’t use methods within an object. Rather, functions are extrinsic to (outside) the objects they are acting on. In R, for example, there would be two separate items: the variable var and a separate function min that gets var as a parameter:\n# this is R code\nvar <- array(c(1,4,7,2,5,8,3,6,9), dim =c(3,3))\nmin(var)\nUsing the pipe operator %>% in R’s tidyverse is closer to the dot . in Python:\n# this is R code\nvar <- array(c(1,4,7,2,5,8,3,6,9), dim =c(3,3))\nvar %>% min()\nWhat happens here is that the pipe %>% is passing var to the min() function as its first argument. This is essentially what happens in Python when a function is a method of a class:\n# this is Python code\nvar = np.array([[1,2,3],[4,5,6],[7,8,9]])\nvar.min()\nWhen working in Python, remember that methods are functions that are part of an object and a method uses the object it is part of to produce some information." + "text": "1.7 Attributes & Methods\nAn object in Python has attributes and methods. An attribute is a property of the object, some piece of information about it. A method is a procedure associated with an object, so it is an action where the main ingredient is the object.\nFor example, these could be some attributes and methods a class cat:\n\n\n\n.\n\n\nMore formally, a method is a function that acts on the object it is part of.\nWe can access a variable’s attributes and methods by adding a period . at the end of the variable’s name. So we would write variable.variable_method() or variable.variable_attribute.\n\n\n\n\n\n\nCheck-in\n\n\n\nSuppose we have a class fish, make a diagram similar to the cat class diagram showing 3 attributes for the class and 3 methods.\n\n\nExample\nNumPy arrays have many methods and attributes. Let’s see some concrete examples.\n\n# define a 3x3 array\nvar = np.array([[1,2,3],[4,5,6],[7,8,9]])\nvar\n\narray([[1, 2, 3],\n [4, 5, 6],\n [7, 8, 9]])\n\n\n\n# T is an example of attribute, it returns the transpose of var\nprint(var.T)\nprint(type(var.T))\n\n[[1 4 7]\n [2 5 8]\n [3 6 9]]\n\n\n\n\n# shape, another attribute, tells us the shape of the array (3x3)\nprint(var.shape)\nprint(type(var.shape))\n\n(3, 3)\n\n\n\n\n# ndim is an attribute holding the number of array dimensions\nprint(var.ndim)\nprint(type(var.ndim))\n\n2\n\n\n\nNotice these attributes can have many different data types. Here we saw tuples and int, two of the core Python classes, and also a numpy array as attributes of var.\nNow some examples of methods:\n\n# the tolist method returns the array as a nested list of scalars\nvar.tolist()\n\n[[1, 2, 3], [4, 5, 6], [7, 8, 9]]\n\n\n\n# the min method returns the minimum value in the array along an axis\nvar.min(axis=0)\n\narray([1, 2, 3])\n\n\n\n\n\n\n\n\nCheck-in\n\n\n\nWe can also call the min method without any parameters:\n\nvar.min()\n\n1\n\n\nWhat kind of parameter is axis in our previous call of the var method?\n\n\nRemember, methods are functions associated to an object. We can check this!\n\ntype(var.tolist)\n\nbuiltin_function_or_method\n\n\n\ntype(var.min)\n\nbuiltin_function_or_method\n\n\nYou can see a complete list of NumPy array’s methods and attributes in the documentation.\n\n\n\n\n\n\nR and Python\n\n\n\nIn R we don’t use methods within an object. Rather, functions are extrinsic to (outside) the objects they are acting on. In R, for example, there would be two separate items: the variable var and a separate function min that gets var as a parameter:\n# this is R code\nvar <- array(c(1,4,7,2,5,8,3,6,9), dim =c(3,3))\nmin(var)\nUsing the pipe operator %>% in R’s tidyverse is closer to the dot . in Python:\n# this is R code\nvar <- array(c(1,4,7,2,5,8,3,6,9), dim =c(3,3))\nvar %>% min()\nWhat happens here is that the pipe %>% is passing var to the min() function as its first argument. This is essentially what happens in Python when a function is a method of a class:\n# this is Python code\nvar = np.array([[1,2,3],[4,5,6],[7,8,9]])\nvar.min()\nWhen working in Python, remember that methods are functions that are part of an object and a method uses the object it is part of to produce some information." }, { "objectID": "lectures/lesson-2-pandas-basics.html#about", @@ -116,21 +116,21 @@ "href": "lectures/lesson-2-pandas-basics.html#series", "title": "2  Pandas Fundamentals", "section": "2.3 Series", - "text": "2.3 Series\nThe first core data structure of pandas is the series. A series is a one-dimensional array of indexed data. A pandas.Series having an index is the main difference between a pandas.Series and a numpy array. See the difference:\n\n# a numpy array\n# np.random.randn returns values from the std normal distribution\narr = np.random.randn(4) \nprint(type(arr))\nprint(arr, \"\\n\")\n\n# a pandas series made from the previous array\ns = pd.Series(arr)\nprint(type(s))\nprint(s)\n\n<class 'numpy.ndarray'>\n[ 0.55130267 -0.38950712 -1.50835146 1.01872105] \n\n<class 'pandas.core.series.Series'>\n0 0.551303\n1 -0.389507\n2 -1.508351\n3 1.018721\ndtype: float64\n\n\n\n2.3.1 Creating a pandas.Series\nThe basic method to create a pandas.Series is to call\ns = pd.Series(data, index=index)\nThe data parameter can be:\n\na numpy array or a list\na Python dictionary\na number\n\nThe index parameter is a list of index labels.\nFor now, we will create a pandas.Series from a numpy array or list. To use this method we need to pass a numpy array (or a list of objects that can be converted to NumPy types) as data and a list of indices of the same length as data.\n\n# a Series from a numpy array \npd.Series(np.arange(3), index=['a','b','c'])\n\na 0\nb 1\nc 2\ndtype: int64\n\n\nThe index parameter is optional. If we don’t include it, the default is to make the index equal to [0,...,len(data)-1]. For example:\n\n# a Series from a list of strings with default index\npd.Series(['EDS 220', 'EDS 222', 'EDS 223', 'EDS 242'])\n\n0 EDS 220\n1 EDS 222\n2 EDS 223\n3 EDS 242\ndtype: object\n\n\n\n\n\n2.3.2 Simple operations\nArithmetic operations work on series and also most NumPy functions. For example:\n\n# define a series\ns = pd.Series([98,73,65],index=['Andrea', 'Beth', 'Carolina'])\n\n# divide each element in series by 10\nprint(s /10, '\\n')\n\n# take the exponential of each element in series\nprint(np.exp(s), '\\n')\n\n# notice this doesn't change the values of our series\nprint(s)\n\nAndrea 9.8\nBeth 7.3\nCarolina 6.5\ndtype: float64 \n\nAndrea 3.637971e+42\nBeth 5.052394e+31\nCarolina 1.694889e+28\ndtype: float64 \n\nAndrea 98\nBeth 73\nCarolina 65\ndtype: int64\n\n\nWe can also produce new pandas.Series with True/False values indicating whether the elements in a series satisfy a condition or not:\n\ns > 10\n\nAndrea True\nBeth True\nCarolina True\ndtype: bool\n\n\nThis kind of simple conditions on pandas.Series will be key when we are selecting data from data frames.\n\n\n\n2.3.3 Attributes & Methods\npandas.Series have many attributes and methods, you can see a full list in the pandas documentation. For now we will cover two examples that have to do with identifying missing values.\npandas represents a missing or NA value with NaN, which stands for not a number. Let’s construct a small series with some NA values:\n\n# series with NAs in it\ns = pd.Series([1, 2, np.NaN, 4, np.NaN])\n\nA pandas.Series has an attribute called hasnans that returns True if there are any NaNs:\n\n# check if series has NAs\ns.hasnans\n\nTrue\n\n\nThen we might be intersted in knowing which elements in the series are NAs. We can do this using the isna method:\n\ns.isna()\n\n0 False\n1 False\n2 True\n3 False\n4 True\ndtype: bool\n\n\nWe can see the ouput is a pd.Series of boolean values indicating if an element in the row at the given index is NA (True = is NA) or not (False = not NA).\n\n\n\n\n\n\nmoving on\n\n\n\nThere’s much more to say about pandas.Series, but this is enought to get us going. At this point, we mainly want to know about pandas.Series because pandas.Series are the columns of pandas.DataFrames." + "text": "2.3 Series\nThe first core data structure of pandas is the series. A series is a one-dimensional array of indexed data. A pandas.Series having an index is the main difference between a pandas.Series and a numpy array. See the difference:\n\n# a numpy array\n# np.random.randn returns values from the std normal distribution\narr = np.random.randn(4) \nprint(type(arr))\nprint(arr, \"\\n\")\n\n# a pandas series made from the previous array\ns = pd.Series(arr)\nprint(type(s))\nprint(s)\n\n\n[ 1.29006588 0.60107937 -0.52572693 -0.33036333] \n\n\n0 1.290066\n1 0.601079\n2 -0.525727\n3 -0.330363\ndtype: float64\n\n\n\n2.3.1 Creating a pandas.Series\nThe basic method to create a pandas.Series is to call\ns = pd.Series(data, index=index)\nThe data parameter can be:\n\na numpy array or a list\na Python dictionary\na number\n\nThe index parameter is a list of index labels.\nFor now, we will create a pandas.Series from a numpy array or list. To use this method we need to pass a numpy array (or a list of objects that can be converted to NumPy types) as data and a list of indices of the same length as data.\n\n# a Series from a numpy array \npd.Series(np.arange(3), index=['a','b','c'])\n\na 0\nb 1\nc 2\ndtype: int64\n\n\nThe index parameter is optional. If we don’t include it, the default is to make the index equal to [0,...,len(data)-1]. For example:\n\n# a Series from a list of strings with default index\npd.Series(['EDS 220', 'EDS 222', 'EDS 223', 'EDS 242'])\n\n0 EDS 220\n1 EDS 222\n2 EDS 223\n3 EDS 242\ndtype: object\n\n\n\n\n\n2.3.2 Simple operations\nArithmetic operations work on series and also most NumPy functions. For example:\n\n# define a series\ns = pd.Series([98,73,65],index=['Andrea', 'Beth', 'Carolina'])\n\n# divide each element in series by 10\nprint(s /10, '\\n')\n\n# take the exponential of each element in series\nprint(np.exp(s), '\\n')\n\n# notice this doesn't change the values of our series\nprint(s)\n\nAndrea 9.8\nBeth 7.3\nCarolina 6.5\ndtype: float64 \n\nAndrea 3.637971e+42\nBeth 5.052394e+31\nCarolina 1.694889e+28\ndtype: float64 \n\nAndrea 98\nBeth 73\nCarolina 65\ndtype: int64\n\n\nWe can also produce new pandas.Series with True/False values indicating whether the elements in a series satisfy a condition or not:\n\ns > 10\n\nAndrea True\nBeth True\nCarolina True\ndtype: bool\n\n\nThis kind of simple conditions on pandas.Series will be key when we are selecting data from data frames.\n\n\n\n2.3.3 Attributes & Methods\npandas.Series have many attributes and methods, you can see a full list in the pandas documentation. For now we will cover two examples that have to do with identifying missing values.\npandas represents a missing or NA value with NaN, which stands for not a number. Let’s construct a small series with some NA values:\n\n# series with NAs in it\ns = pd.Series([1, 2, np.NaN, 4, np.NaN])\n\nA pandas.Series has an attribute called hasnans that returns True if there are any NaNs:\n\n# check if series has NAs\ns.hasnans\n\nTrue\n\n\nThen we might be intersted in knowing which elements in the series are NAs. We can do this using the isna method:\n\ns.isna()\n\n0 False\n1 False\n2 True\n3 False\n4 True\ndtype: bool\n\n\nWe can see the ouput is a pd.Series of boolean values indicating if an element in the row at the given index is NA (True = is NA) or not (False = not NA).\n\n\n\n\n\n\nmoving on\n\n\n\nThere’s much more to say about pandas.Series, but this is enought to get us going. At this point, we mainly want to know about pandas.Series because pandas.Series are the columns of pandas.DataFrames." }, { "objectID": "lectures/lesson-2-pandas-basics.html#data-frames", "href": "lectures/lesson-2-pandas-basics.html#data-frames", "title": "2  Pandas Fundamentals", "section": "2.4 Data Frames", - "text": "2.4 Data Frames\nThe Data Frame is the most used pandas object. It represents tabular data and we can think of it as a spreadhseet. Each column of a pandas.DataFrame is a pandas.Series.\n\n2.4.1 Creating a pandas.DataFrame\nThere are many ways of creating a pandas.DataFrame.\n\nWe already mentioned each column of a pandas.DataFrame is a pandas.Series. In fact, the pandas.DataFrame is a dictionary of pandas.Series, with each column name being the key and the column values being the key’s value. Thus, we can create a pandas.DataFrame in this way:\n\n# initialize dictionary with columns' data \nd = {'col_name_1' : pd.Series(np.arange(3)),\n 'col_name_2' : pd.Series([3.1, 3.2, 3.3]),\n }\n\n# create data frame\ndf = pd.DataFrame(d)\ndf\n\n\n\n\n\n\n\n\ncol_name_1\ncol_name_2\n\n\n\n\n0\n0\n3.1\n\n\n1\n1\n3.2\n\n\n2\n2\n3.3\n\n\n\n\n\n\n\nWe can change the index and column names by changing the index and columns attributes in the data frame.\n\n# print original index\nprint(df.index)\n\n# change the index\ndf.index = ['a','b','c']\ndf\n\nRangeIndex(start=0, stop=3, step=1)\n\n\n\n\n\n\n\n\n\ncol_name_1\ncol_name_2\n\n\n\n\na\n0\n3.1\n\n\nb\n1\n3.2\n\n\nc\n2\n3.3\n\n\n\n\n\n\n\n\n# print original column names\nprint(df.columns)\n\n# change column names \ndf.columns = ['C1','C2']\ndf\n\nIndex(['col_name_1', 'col_name_2'], dtype='object')\n\n\n\n\n\n\n\n\n\nC1\nC2\n\n\n\n\na\n0\n3.1\n\n\nb\n1\n3.2\n\n\nc\n2\n3.3" + "text": "2.4 Data Frames\nThe Data Frame is the most used pandas object. It represents tabular data and we can think of it as a spreadhseet. Each column of a pandas.DataFrame is a pandas.Series.\n\n2.4.1 Creating a pandas.DataFrame\nThere are many ways of creating a pandas.DataFrame.\n\nWe already mentioned each column of a pandas.DataFrame is a pandas.Series. In fact, the pandas.DataFrame is a dictionary of pandas.Series, with each column name being the key and the column values being the key’s value. Thus, we can create a pandas.DataFrame in this way:\n\n# initialize dictionary with columns' data \nd = {'col_name_1' : pd.Series(np.arange(3)),\n 'col_name_2' : pd.Series([3.1, 3.2, 3.3]),\n }\n\n# create data frame\ndf = pd.DataFrame(d)\ndf\n\n\n\n\n\n \n \n \n col_name_1\n col_name_2\n \n \n \n \n 0\n 0\n 3.1\n \n \n 1\n 1\n 3.2\n \n \n 2\n 2\n 3.3\n \n \n\n\n\n\nWe can change the index and column names by changing the index and columns attributes in the data frame.\n\n# print original index\nprint(df.index)\n\n# change the index\ndf.index = ['a','b','c']\ndf\n\nRangeIndex(start=0, stop=3, step=1)\n\n\n\n\n\n\n \n \n \n col_name_1\n col_name_2\n \n \n \n \n a\n 0\n 3.1\n \n \n b\n 1\n 3.2\n \n \n c\n 2\n 3.3\n \n \n\n\n\n\n\n# print original column names\nprint(df.columns)\n\n# change column names \ndf.columns = ['C1','C2']\ndf\n\nIndex(['col_name_1', 'col_name_2'], dtype='object')\n\n\n\n\n\n\n \n \n \n C1\n C2\n \n \n \n \n a\n 0\n 3.1\n \n \n b\n 1\n 3.2\n \n \n c\n 2\n 3.3" }, { "objectID": "lectures/lesson-2-pandas-basics.html#subsetting-a-pandas.dataframe", "href": "lectures/lesson-2-pandas-basics.html#subsetting-a-pandas.dataframe", "title": "2  Pandas Fundamentals", "section": "2.5 Subsetting a pandas.DataFrame", - "text": "2.5 Subsetting a pandas.DataFrame\nLike it’s often the case when working with pandas, there are many ways in which we can subset a data frame. We will review the core methods to do this. \nFor all examples we will use simplified data (glacial_loss.csv) from the National Snow and Ice Data Center (Original dataset). The column descriptions are:\n\nyear: ​calendar year\neurope - antarctica: ​change in glacial volume (km3​ ​) in each region that year\nglobal_glacial_volume_change: ​cumulative​ global glacial volume change (km3), starting in 1961\nannual_sea_level_rise: ​annual rise in sea level (mm)\ncumulative_sea_level_rise:​ cumulative rise in sea level (mm) since 1961\n\nFirst, we read-in the file and get some baisc information about this data frame:\n\n# read in file\ndf = pd.read_csv('data/lesson-1/glacial_loss.csv')\n\n# see the first five rows\ndf.head()\n\n\n\n\n\n\n\n\nyear\neurope\narctic\nalaska\nasia\nnorth_america\nsouth_america\nantarctica\nglobal_glacial_volume_change\nannual_sea_level_rise\ncumulative_sea_level_rise\n\n\n\n\n0\n1961\n-5.128903\n-108.382987\n-18.721190\n-32.350759\n-14.359007\n-4.739367\n-35.116389\n-220.823515\n0.610010\n0.610010\n\n\n1\n1962\n5.576282\n-173.252450\n-24.324790\n-4.675440\n-2.161842\n-13.694367\n-78.222887\n-514.269862\n0.810625\n1.420635\n\n\n2\n1963\n-10.123105\n-0.423751\n-2.047567\n-3.027298\n-27.535881\n3.419633\n3.765109\n-550.575640\n0.100292\n1.520927\n\n\n3\n1964\n-4.508358\n20.070148\n0.477800\n-18.675385\n-2.248286\n20.732633\n14.853096\n-519.589859\n-0.085596\n1.435331\n\n\n4\n1965\n10.629385\n43.695389\n-0.115332\n-18.414602\n-19.398765\n6.862102\n22.793484\n-473.112003\n-0.128392\n1.306939\n\n\n\n\n\n\n\n\n# get column names\ndf.columns\n\nIndex(['year', 'europe', 'arctic', 'alaska', 'asia', 'north_america',\n 'south_america', 'antarctica', 'global_glacial_volume_change',\n 'annual_sea_level_rise', 'cumulative_sea_level_rise'],\n dtype='object')\n\n\n\n# check the data types of each column\ndf.dtypes\n\nyear int64\neurope float64\narctic float64\nalaska float64\nasia float64\nnorth_america float64\nsouth_america float64\nantarctica float64\nglobal_glacial_volume_change float64\nannual_sea_level_rise float64\ncumulative_sea_level_rise float64\ndtype: object\n\n\n\n# data frame's shape: output is a tuple (# rows, # columns)\ndf.shape\n\n(43, 11)\n\n\n\n2.5.1 Selecting a single column…\n\n2.5.1.1 …by column name\nThis is the simplest case for selecting data. Suppose we are interested in the annual sea level rise. Then we can access that single column in this way:\n\n# seelect a single column by using square brackets []\nannual_rise = df['annual_sea_level_rise']\n\n# check the type of the ouput\nprint(type(annual_rise))\n\nannual_rise.head()\n\n<class 'pandas.core.series.Series'>\n\n\n0 0.610010\n1 0.810625\n2 0.100292\n3 -0.085596\n4 -0.128392\nName: annual_sea_level_rise, dtype: float64\n\n\nSince we only selected a single column the output is a pandas.Series.\n\n\n\n\n\n\npd.DataFrame = dictionary of columns\n\n\n\nRemember we can think of a pandas.DataFrame as a dictionary of its columns? Then we can access a single column using the column name as the key, just like we would do in a dictionary. That is the we just used: df['column_name'].\n\n\nThis is an example of selecting by label, which means we want to select data from our data frame using the names of the columns, not their position.\n\n\n2.5.1.2 … with attribute syntax\nWe can also access a single column by using attribute syntax:\n\nannual_rise_2 = df.annual_sea_level_rise\nannual_rise_2.head()\n\n0 0.610010\n1 0.810625\n2 0.100292\n3 -0.085596\n4 -0.128392\nName: annual_sea_level_rise, dtype: float64\n\n\n\n\n\n2.5.2 Selecting multiple columns…\n\n2.5.2.1 … using a list of column names\nThis is another example of selecting by labels. We just need to pass a list with the column names to the square brackets []. For example, say we want to look at the change in glacial volume in Europe and Asia, then we can select those columns like this:\n\n# select columns with names \"europe\" and \"asia\"\neurope_asia = df[['europe','asia']]\n\nNotice there are double square brackets. This is because we are passing the list of names ['europe','asia'] to the selection brakcets [].\n\n# check the type of the resulting selection\nprint(type(europe_asia))\n\n# check the shape of the selection\nprint((europe_asia.shape))\n\n<class 'pandas.core.frame.DataFrame'>\n(43, 2)\n\n\n\n\n2.5.2.2 … using a slice\nYet another example of selecting by label! In this case we will use the loc function. This is a powerful function! The general syntax is\ndf.loc[ row-selection , column-selection]\nwhere row-selection and column-selection are the rows and columns we want to subset from the data frame.\nLet’s start by a simple example, where we want to select a slice of columns, say the change in glacial volume per year in all regions. This corresponds to all columns between arctic and antarctica.\n\n# select all columns between 'arctic' and 'antarctica'\nall_regions = df.loc[:,'arctic':'antarctica']\nall_regions.head()\n\n\n\n\n\n\n\n\narctic\nalaska\nasia\nnorth_america\nsouth_america\nantarctica\n\n\n\n\n0\n-108.382987\n-18.721190\n-32.350759\n-14.359007\n-4.739367\n-35.116389\n\n\n1\n-173.252450\n-24.324790\n-4.675440\n-2.161842\n-13.694367\n-78.222887\n\n\n2\n-0.423751\n-2.047567\n-3.027298\n-27.535881\n3.419633\n3.765109\n\n\n3\n20.070148\n0.477800\n-18.675385\n-2.248286\n20.732633\n14.853096\n\n\n4\n43.695389\n-0.115332\n-18.414602\n-19.398765\n6.862102\n22.793484\n\n\n\n\n\n\n\nNotice two things:\n\nwe used the colon : as the row-selection parameter, which means “select all the rows”\nthe slice of the data frame we got includes both endpoints of the slice 'arctic':'antarctica'. In other words we get the arctic column and the antarctica column. This is different from how slicing works in base Python and NumPy, where the end point is not included.\n\n\n\n\n2.5.3 Selecting rows…\nNow that we are familiar with some methods for selecting columns, let’s move on to selecting rows.\n\n2.5.3.1 … using a condition\nSelecting which rows satisfy a particular condition is, in my experience, the most usual kind of row subsetting. The general syntax for this type of selection is df[condition_on_rows]. For example, suppose we are intersted in all data after 1996. We can select those rows in this way:\n\n# select all rows with year > 1996\nafter_96 = df[df['year']>1996]\nafter_96\n\n\n\n\n\n\n\n\nyear\neurope\narctic\nalaska\nasia\nnorth_america\nsouth_america\nantarctica\nglobal_glacial_volume_change\nannual_sea_level_rise\ncumulative_sea_level_rise\n\n\n\n\n36\n1997\n-13.724106\n-24.832246\n-167.229145\n-34.406403\n-27.680661\n-38.213286\n-20.179090\n-4600.686013\n0.909625\n12.709077\n\n\n37\n1998\n-13.083338\n-110.429302\n-107.879027\n-58.115702\n30.169987\n-3.797978\n-48.129928\n-4914.831966\n0.867807\n13.576884\n\n\n38\n1999\n-8.039555\n-64.644068\n-87.714653\n-26.211723\n5.888512\n-8.038630\n-40.653001\n-5146.368231\n0.639603\n14.216487\n\n\n39\n2000\n-17.008590\n-96.494055\n-44.445000\n-37.518173\n-29.191986\n-2.767698\n-58.873830\n-5435.317175\n0.798202\n15.014688\n\n\n40\n2001\n-8.419109\n-145.415483\n-55.749505\n-35.977022\n-0.926134\n7.553503\n-86.774675\n-5764.039931\n0.908074\n15.922762\n\n\n41\n2002\n-3.392361\n-48.718943\n-87.120000\n-36.127226\n-27.853498\n-13.484593\n-30.203960\n-6013.225500\n0.688358\n16.611120\n\n\n42\n2003\n-3.392361\n-48.718943\n-67.253634\n-36.021991\n-75.066475\n-13.223430\n-30.203960\n-6289.640976\n0.763579\n17.374699\n\n\n\n\n\n\n\nLet’s break down what is happening here. In this case the condition for our rows is df['year']>1996, this checks which rows have a value greater than 1996 in the year column. Let’s see this explicitely:\n\n# check the type of df['year']>1996\nprint(type(df['year']>1996))\n\ndf['year']>1996\n\n<class 'pandas.core.series.Series'>\n\n\n0 False\n1 False\n2 False\n3 False\n4 False\n5 False\n6 False\n7 False\n8 False\n9 False\n10 False\n11 False\n12 False\n13 False\n14 False\n15 False\n16 False\n17 False\n18 False\n19 False\n20 False\n21 False\n22 False\n23 False\n24 False\n25 False\n26 False\n27 False\n28 False\n29 False\n30 False\n31 False\n32 False\n33 False\n34 False\n35 False\n36 True\n37 True\n38 True\n39 True\n40 True\n41 True\n42 True\nName: year, dtype: bool\n\n\nThe output is a pandas.Series with boolean values (True or False) indicating which rows satisfy the condition year>1996. When we pass such a series of boolean values to the selection brackets [] we keep only those rows with a True value.\nHere’s another example of using a condition. Suppose we want to look at data from years 1970 to 1979. One way of doing this is to use the in operator in our condition:\n\nseventies = df[df['year'].isin(range(1970,1980))]\nseventies\n\n\n\n\n\n\n\n\nyear\neurope\narctic\nalaska\nasia\nnorth_america\nsouth_america\nantarctica\nglobal_glacial_volume_change\nannual_sea_level_rise\ncumulative_sea_level_rise\n\n\n\n\n9\n1970\n-6.452316\n-24.494667\n-0.125296\n-36.120199\n11.619790\n11.636911\n4.400377\n-999.018177\n0.110225\n2.759719\n\n\n10\n1971\n0.414711\n-42.904189\n28.103328\n-8.702938\n-9.964542\n1.061299\n-6.735536\n-1038.104459\n0.107973\n2.867692\n\n\n11\n1972\n-5.144729\n-27.004031\n-22.143350\n-40.883357\n32.363730\n-14.968034\n-6.223849\n-1122.885506\n0.234202\n3.101894\n\n\n12\n1973\n4.081090\n9.839444\n22.985188\n-31.432594\n-20.883232\n2.103649\n10.539823\n-1125.677743\n0.007713\n3.109607\n\n\n13\n1974\n1.545615\n-40.126998\n-29.517874\n-43.861622\n-23.991402\n-21.338825\n4.419343\n-1279.964287\n0.426206\n3.535813\n\n\n14\n1975\n7.431192\n-32.410467\n-44.094084\n-43.357442\n-30.858810\n-2.368842\n-7.775315\n-1434.818037\n0.427773\n3.963586\n\n\n15\n1976\n3.986753\n21.686639\n-28.234725\n-67.292125\n-12.534421\n-19.465358\n19.250607\n-1518.185129\n0.230296\n4.193882\n\n\n16\n1977\n4.891410\n-33.123010\n-5.662139\n-62.165684\n-15.905332\n2.654950\n-23.727249\n-1652.453400\n0.370907\n4.564788\n\n\n17\n1978\n8.404591\n-77.561015\n-12.503384\n-22.858040\n-31.097609\n7.127708\n-9.140167\n-1791.355022\n0.383706\n4.948495\n\n\n18\n1979\n3.916703\n-88.351684\n-63.938851\n-49.242043\n-12.076624\n-17.718503\n-9.578557\n-2030.537848\n0.660726\n5.609221\n\n\n\n\n\n\n\nLet’s break it down:\n\ndf['year'] is the column with the year values, a pandas.Series,\nin df['year'].isin(), we have that isin is a method for the pandas.Series and we are calling it using the dot ..\nrange(1970,1980) constructs consecutive integers from 1970 to 1979 - remember the right endopoint (1980) is not included!\ndf['year'].isin(range(1970,1980)) is then a pandas.Series of boolean values indicating which rows have year equal to 1970, …, 1979.\nwhen we put df['year'].isin(range(1970,1980)) inside the selection brackets [] we obtain the rows of the data frame with year equal to 1970, …, 1979.\n\n\n\n\n\n\n\nloc for row selection\n\n\n\nIt is equivalent to write\n# select rows with year<1965\ndf[df['year'] < 1965]\nand\n# select rows with year<1965 using love\ndf.loc[ df['year'] <1965 , :]\nIn the second one:\n\nwe are using the df.loc[ row-selection , column-selection] syntax\nthe row-selection parameter is the condition df['year']<1965\nthe column-selection parameter is a colon :, which indicates we want all columns for the rows we are selecting.\n\nWe prefer the first syntax when we are selecting rows and not columns since it is simpler.\n\n\n\n\n2.5.3.2 … using multiple conditions\nWe can combine multipe conditions by surrounding each one in parenthesis () and using the or operator | and the and operator and.\nor example:\n\n# select rows with \n# annual_sea_level_rise<0.5 mm OR annual_sea_level_rise>0.8 mm\n\ndf[ (df['annual_sea_level_rise']<0.5) | (df['annual_sea_level_rise']>0.8)]\ndf.head()\n\n\n\n\n\n\n\n\nyear\neurope\narctic\nalaska\nasia\nnorth_america\nsouth_america\nantarctica\nglobal_glacial_volume_change\nannual_sea_level_rise\ncumulative_sea_level_rise\n\n\n\n\n0\n1961\n-5.128903\n-108.382987\n-18.721190\n-32.350759\n-14.359007\n-4.739367\n-35.116389\n-220.823515\n0.610010\n0.610010\n\n\n1\n1962\n5.576282\n-173.252450\n-24.324790\n-4.675440\n-2.161842\n-13.694367\n-78.222887\n-514.269862\n0.810625\n1.420635\n\n\n2\n1963\n-10.123105\n-0.423751\n-2.047567\n-3.027298\n-27.535881\n3.419633\n3.765109\n-550.575640\n0.100292\n1.520927\n\n\n3\n1964\n-4.508358\n20.070148\n0.477800\n-18.675385\n-2.248286\n20.732633\n14.853096\n-519.589859\n-0.085596\n1.435331\n\n\n4\n1965\n10.629385\n43.695389\n-0.115332\n-18.414602\n-19.398765\n6.862102\n22.793484\n-473.112003\n-0.128392\n1.306939\n\n\n\n\n\n\n\nand example\n\n# select rows with cumulative_sea_level_rise>10 AND global_glacial_volume_change<-300\ndf[ (df['cumulative_sea_level_rise']>10) & (df['global_glacial_volume_change']<-300)]\n\n\n\n\n\n\n\n\nyear\neurope\narctic\nalaska\nasia\nnorth_america\nsouth_america\nantarctica\nglobal_glacial_volume_change\nannual_sea_level_rise\ncumulative_sea_level_rise\n\n\n\n\n32\n1993\n16.685013\n-73.666274\n-43.702040\n-65.995130\n-33.151246\n-20.578403\n-20.311577\n-3672.582082\n0.671126\n10.145254\n\n\n33\n1994\n0.741751\n-3.069084\n-59.962273\n-59.004710\n-89.506142\n-15.258449\n-8.168498\n-3908.977191\n0.653025\n10.798280\n\n\n34\n1995\n-2.139665\n-58.167778\n-74.141762\n3.500155\n-0.699374\n-19.863392\n-25.951496\n-4088.082873\n0.494767\n11.293047\n\n\n35\n1996\n-6.809834\n-4.550205\n-74.847017\n-67.436591\n4.867530\n-21.080115\n-11.781489\n-4271.401594\n0.506405\n11.799452\n\n\n36\n1997\n-13.724106\n-24.832246\n-167.229145\n-34.406403\n-27.680661\n-38.213286\n-20.179090\n-4600.686013\n0.909625\n12.709077\n\n\n37\n1998\n-13.083338\n-110.429302\n-107.879027\n-58.115702\n30.169987\n-3.797978\n-48.129928\n-4914.831966\n0.867807\n13.576884\n\n\n38\n1999\n-8.039555\n-64.644068\n-87.714653\n-26.211723\n5.888512\n-8.038630\n-40.653001\n-5146.368231\n0.639603\n14.216487\n\n\n39\n2000\n-17.008590\n-96.494055\n-44.445000\n-37.518173\n-29.191986\n-2.767698\n-58.873830\n-5435.317175\n0.798202\n15.014688\n\n\n40\n2001\n-8.419109\n-145.415483\n-55.749505\n-35.977022\n-0.926134\n7.553503\n-86.774675\n-5764.039931\n0.908074\n15.922762\n\n\n41\n2002\n-3.392361\n-48.718943\n-87.120000\n-36.127226\n-27.853498\n-13.484593\n-30.203960\n-6013.225500\n0.688358\n16.611120\n\n\n42\n2003\n-3.392361\n-48.718943\n-67.253634\n-36.021991\n-75.066475\n-13.223430\n-30.203960\n-6289.640976\n0.763579\n17.374699\n\n\n\n\n\n\n\n\n\n2.5.3.3 … by position\nAll the selections we have done so far have been using labels or using a condition. Sometimes we might want to select certain rows depending on their actual position in the data frame. In this case we use the iloc method with the syntax df.iloc[row-indices]. iloc stands for integer-location based indexing. Let’s see some examples:\n\n# select the fifht row = index 4\ndf.iloc[4]\n\nyear 1965.000000\neurope 10.629385\narctic 43.695389\nalaska -0.115332\nasia -18.414602\nnorth_america -19.398765\nsouth_america 6.862102\nantarctica 22.793484\nglobal_glacial_volume_change -473.112003\nannual_sea_level_rise -0.128392\ncumulative_sea_level_rise 1.306939\nName: 4, dtype: float64\n\n\n\n# select rows 23 through 30, inclduing 30\ndf.iloc[23:31]\n\n\n\n\n\n\n\n\nyear\neurope\narctic\nalaska\nasia\nnorth_america\nsouth_america\nantarctica\nglobal_glacial_volume_change\nannual_sea_level_rise\ncumulative_sea_level_rise\n\n\n\n\n23\n1984\n8.581427\n-5.755672\n-33.466092\n-20.528535\n-20.734676\n-8.267686\n-3.261011\n-2569.339802\n0.232609\n7.097624\n\n\n24\n1985\n-5.970980\n-49.651089\n12.065473\n-31.571622\n-33.833985\n10.072906\n-13.587886\n-2682.857926\n0.313586\n7.411210\n\n\n25\n1986\n-5.680642\n22.900847\n7.557447\n-18.920773\n-33.014743\n-4.652030\n30.482473\n-2684.197632\n0.003701\n7.414911\n\n\n26\n1987\n8.191477\n12.387780\n-24.007862\n-41.121970\n-48.560996\n1.670733\n3.130190\n-2773.325568\n0.246210\n7.661120\n\n\n27\n1988\n-11.117228\n-31.066489\n49.897712\n-21.300712\n-46.545435\n13.460422\n-37.986834\n-2858.767621\n0.236028\n7.897148\n\n\n28\n1989\n14.863220\n-23.462392\n-36.112726\n-46.528372\n-57.756422\n-21.687470\n-10.044757\n-3041.169131\n0.503872\n8.401020\n\n\n29\n1990\n-1.226009\n-27.484542\n-92.713339\n-35.553433\n-56.563056\n-31.077022\n-29.893352\n-3318.220397\n0.765335\n9.166355\n\n\n30\n1991\n-14.391425\n-34.898689\n-8.822063\n-15.338299\n-31.458010\n-7.162909\n-35.968429\n-3467.630284\n0.412734\n9.579089\n\n\n\n\n\n\n\nNotice since we are back to indexing by position the right endpoint of the slice (6) is not included in the ouput.\n\n\n\n2.5.4 Selecting rows and columns simultaneously…\nSelecting rows and columns simultaneously can be done using loc (labels or conditions) or iloc (integer position).\n\n2.5.4.1 …by labels or conditions\nWhen we want to select rows and columns simultaneously by labels or conditions we can use the method loc with the syntax\ndf.loc[ row-selection , column-selection]\nspecifying both paratmers: row-selection and column-selection. These parameters can be a condition (which generates a boolean array) or a subset of labels from the index or the column names. Let’s see an examples:\n\n# select change in glacial volume in Europe per year after 2000\ndf.loc[df['year']>2000,['year','europe']]\n\n\n\n\n\n\n\n\nyear\neurope\n\n\n\n\n40\n2001\n-8.419109\n\n\n41\n2002\n-3.392361\n\n\n42\n2003\n-3.392361\n\n\n\n\n\n\n\nLet’s break it down:\n\nwe are using the df.loc[ row-selection , column-selection] syntax\nthe row-selection parameter is the condition df['year']>1990, which is a boolean array saying which years are greater than 1990\nthe column-selection parameter is ['year','europe'] which is a list with the names of the two columns we are intersted in.\n\n\n\n2.5.4.2 … by position\nWhen we want to select rows and columns simultaneously by position we use the iloc method with the syntax:\ndf.iloc[ row-indices , column-indices]\nFor example,\n\n# select rows 3-7 (including 7) and columns 3 and 4\ndf.iloc[ 3:8, [3,4] ]\n\n\n\n\n\n\n\n\nalaska\nasia\n\n\n\n\n3\n0.477800\n-18.675385\n\n\n4\n-0.115332\n-18.414602\n\n\n5\n0.224762\n-14.630284\n\n\n6\n-7.174030\n-39.013695\n\n\n7\n-0.660556\n7.879589\n\n\n\n\n\n\n\nLet’s break it down:\n\nwe are using the df.iloc[ row-indices , column-indices] syntax\nthe row-indices parameter is the slice of integer indices 3:8. Remember the right endpoint (8) won’t be included.\nthe column-indices parameter is the list of integer indices 3 and 4. This means we are selecting the fourth and fifth column.\n\n\n\n\n2.5.5 Notes about loc and iloc\n\n\n\n\n\n\niloc vs. loc\n\n\n\nAt the beginning, the difference between iloc and loc can be confusing. Remember the i in iloc stands for integer-location, so this function only uses integer indexing to retrieve information from the data frames in the same way as indexing for Python lists.\nIf you want to dive deeper, this is a great discussion about the difference between iloc and loc: Stackoverflow - How are iloc and loc different?\nAnd, as always, the documentation will provide you with more information: pandas.DataFrame.loc and pandas.DataFrame.iloc.\n\n\n\n\n\n\n\n\niloc for column selection? Avoid it!\n\n\n\nWe can also access columns by position using iloc - but it is best not to if possible.\nSuppose we want to access the 10th column in the data frame - then we want to select a column by position. In this case the 10th column is the annual sea level rise data and the 10th position corresponds to the index 9. We can select this column by position using the iloc method*:\n\n# select column by position using iloc\n# the syntax is iloc[rows,columns]\n# [:,9] means \"select all rows from the 10th column\"\nannual_rise_3 = df.iloc[:,9]\nannual_rise_3.head()\n\n0 0.610010\n1 0.810625\n2 0.100292\n3 -0.085596\n4 -0.128392\nName: annual_sea_level_rise, dtype: float64\n\n\nUnless you are really looking for information about the 10th column, do not access a column by position. This is bound to break in many ways:\n\nit relies on a person correctly counting the position of a column. Even with a small dataset this can be prone to error.\nit is not explicit: if we want information about sea level rise df.annual_sea_level_rise or df['annual_sea_level_rise'] are explicitely telling us we are accessing that information. df.iloc[:,9] is obscure and uninformative.\ndatastets can get updated. Maybe a new column was added before annual_sea_level_rise, this would change the position of the column, which would make any code depending on df.iloc[:,9] invalid. Accessing by label helps reproducibility!" + "text": "2.5 Subsetting a pandas.DataFrame\nLike it’s often the case when working with pandas, there are many ways in which we can subset a data frame. We will review the core methods to do this. \nFor all examples we will use simplified data (glacial_loss.csv) from the National Snow and Ice Data Center (Original dataset). The column descriptions are:\n\nyear: ​calendar year\neurope - antarctica: ​change in glacial volume (km3​ ​) in each region that year\nglobal_glacial_volume_change: ​cumulative​ global glacial volume change (km3), starting in 1961\nannual_sea_level_rise: ​annual rise in sea level (mm)\ncumulative_sea_level_rise:​ cumulative rise in sea level (mm) since 1961\n\nFirst, we read-in the file and get some baisc information about this data frame:\n\n# read in file\ndf = pd.read_csv('data/lesson-1/glacial_loss.csv')\n\n# see the first five rows\ndf.head()\n\n\n\n\n\n \n \n \n year\n europe\n arctic\n alaska\n asia\n north_america\n south_america\n antarctica\n global_glacial_volume_change\n annual_sea_level_rise\n cumulative_sea_level_rise\n \n \n \n \n 0\n 1961\n -5.128903\n -108.382987\n -18.721190\n -32.350759\n -14.359007\n -4.739367\n -35.116389\n -220.823515\n 0.610010\n 0.610010\n \n \n 1\n 1962\n 5.576282\n -173.252450\n -24.324790\n -4.675440\n -2.161842\n -13.694367\n -78.222887\n -514.269862\n 0.810625\n 1.420635\n \n \n 2\n 1963\n -10.123105\n -0.423751\n -2.047567\n -3.027298\n -27.535881\n 3.419633\n 3.765109\n -550.575640\n 0.100292\n 1.520927\n \n \n 3\n 1964\n -4.508358\n 20.070148\n 0.477800\n -18.675385\n -2.248286\n 20.732633\n 14.853096\n -519.589859\n -0.085596\n 1.435331\n \n \n 4\n 1965\n 10.629385\n 43.695389\n -0.115332\n -18.414602\n -19.398765\n 6.862102\n 22.793484\n -473.112003\n -0.128392\n 1.306939\n \n \n\n\n\n\n\n# get column names\ndf.columns\n\nIndex(['year', 'europe', 'arctic', 'alaska', 'asia', 'north_america',\n 'south_america', 'antarctica', 'global_glacial_volume_change',\n 'annual_sea_level_rise', 'cumulative_sea_level_rise'],\n dtype='object')\n\n\n\n# check the data types of each column\ndf.dtypes\n\nyear int64\neurope float64\narctic float64\nalaska float64\nasia float64\nnorth_america float64\nsouth_america float64\nantarctica float64\nglobal_glacial_volume_change float64\nannual_sea_level_rise float64\ncumulative_sea_level_rise float64\ndtype: object\n\n\n\n# data frame's shape: output is a tuple (# rows, # columns)\ndf.shape\n\n(43, 11)\n\n\n\n2.5.1 Selecting a single column…\n\n2.5.1.1 …by column name\nThis is the simplest case for selecting data. Suppose we are interested in the annual sea level rise. Then we can access that single column in this way:\n\n# seelect a single column by using square brackets []\nannual_rise = df['annual_sea_level_rise']\n\n# check the type of the ouput\nprint(type(annual_rise))\n\nannual_rise.head()\n\n\n\n\n0 0.610010\n1 0.810625\n2 0.100292\n3 -0.085596\n4 -0.128392\nName: annual_sea_level_rise, dtype: float64\n\n\nSince we only selected a single column the output is a pandas.Series.\n\n\n\n\n\n\npd.DataFrame = dictionary of columns\n\n\n\nRemember we can think of a pandas.DataFrame as a dictionary of its columns? Then we can access a single column using the column name as the key, just like we would do in a dictionary. That is the we just used: df['column_name'].\n\n\nThis is an example of selecting by label, which means we want to select data from our data frame using the names of the columns, not their position.\n\n\n2.5.1.2 … with attribute syntax\nWe can also access a single column by using attribute syntax:\n\nannual_rise_2 = df.annual_sea_level_rise\nannual_rise_2.head()\n\n0 0.610010\n1 0.810625\n2 0.100292\n3 -0.085596\n4 -0.128392\nName: annual_sea_level_rise, dtype: float64\n\n\n\n\n\n2.5.2 Selecting multiple columns…\n\n2.5.2.1 … using a list of column names\nThis is another example of selecting by labels. We just need to pass a list with the column names to the square brackets []. For example, say we want to look at the change in glacial volume in Europe and Asia, then we can select those columns like this:\n\n# select columns with names \"europe\" and \"asia\"\neurope_asia = df[['europe','asia']]\n\nNotice there are double square brackets. This is because we are passing the list of names ['europe','asia'] to the selection brakcets [].\n\n# check the type of the resulting selection\nprint(type(europe_asia))\n\n# check the shape of the selection\nprint((europe_asia.shape))\n\n\n(43, 2)\n\n\n\n\n2.5.2.2 … using a slice\nYet another example of selecting by label! In this case we will use the loc function. This is a powerful function! The general syntax is\ndf.loc[ row-selection , column-selection]\nwhere row-selection and column-selection are the rows and columns we want to subset from the data frame.\nLet’s start by a simple example, where we want to select a slice of columns, say the change in glacial volume per year in all regions. This corresponds to all columns between arctic and antarctica.\n\n# select all columns between 'arctic' and 'antarctica'\nall_regions = df.loc[:,'arctic':'antarctica']\nall_regions.head()\n\n\n\n\n\n \n \n \n arctic\n alaska\n asia\n north_america\n south_america\n antarctica\n \n \n \n \n 0\n -108.382987\n -18.721190\n -32.350759\n -14.359007\n -4.739367\n -35.116389\n \n \n 1\n -173.252450\n -24.324790\n -4.675440\n -2.161842\n -13.694367\n -78.222887\n \n \n 2\n -0.423751\n -2.047567\n -3.027298\n -27.535881\n 3.419633\n 3.765109\n \n \n 3\n 20.070148\n 0.477800\n -18.675385\n -2.248286\n 20.732633\n 14.853096\n \n \n 4\n 43.695389\n -0.115332\n -18.414602\n -19.398765\n 6.862102\n 22.793484\n \n \n\n\n\n\nNotice two things:\n\nwe used the colon : as the row-selection parameter, which means “select all the rows”\nthe slice of the data frame we got includes both endpoints of the slice 'arctic':'antarctica'. In other words we get the arctic column and the antarctica column. This is different from how slicing works in base Python and NumPy, where the end point is not included.\n\n\n\n\n2.5.3 Selecting rows…\nNow that we are familiar with some methods for selecting columns, let’s move on to selecting rows.\n\n2.5.3.1 … using a condition\nSelecting which rows satisfy a particular condition is, in my experience, the most usual kind of row subsetting. The general syntax for this type of selection is df[condition_on_rows]. For example, suppose we are intersted in all data after 1996. We can select those rows in this way:\n\n# select all rows with year > 1996\nafter_96 = df[df['year']>1996]\nafter_96\n\n\n\n\n\n \n \n \n year\n europe\n arctic\n alaska\n asia\n north_america\n south_america\n antarctica\n global_glacial_volume_change\n annual_sea_level_rise\n cumulative_sea_level_rise\n \n \n \n \n 36\n 1997\n -13.724106\n -24.832246\n -167.229145\n -34.406403\n -27.680661\n -38.213286\n -20.179090\n -4600.686013\n 0.909625\n 12.709077\n \n \n 37\n 1998\n -13.083338\n -110.429302\n -107.879027\n -58.115702\n 30.169987\n -3.797978\n -48.129928\n -4914.831966\n 0.867807\n 13.576884\n \n \n 38\n 1999\n -8.039555\n -64.644068\n -87.714653\n -26.211723\n 5.888512\n -8.038630\n -40.653001\n -5146.368231\n 0.639603\n 14.216487\n \n \n 39\n 2000\n -17.008590\n -96.494055\n -44.445000\n -37.518173\n -29.191986\n -2.767698\n -58.873830\n -5435.317175\n 0.798202\n 15.014688\n \n \n 40\n 2001\n -8.419109\n -145.415483\n -55.749505\n -35.977022\n -0.926134\n 7.553503\n -86.774675\n -5764.039931\n 0.908074\n 15.922762\n \n \n 41\n 2002\n -3.392361\n -48.718943\n -87.120000\n -36.127226\n -27.853498\n -13.484593\n -30.203960\n -6013.225500\n 0.688358\n 16.611120\n \n \n 42\n 2003\n -3.392361\n -48.718943\n -67.253634\n -36.021991\n -75.066475\n -13.223430\n -30.203960\n -6289.640976\n 0.763579\n 17.374699\n \n \n\n\n\n\nLet’s break down what is happening here. In this case the condition for our rows is df['year']>1996, this checks which rows have a value greater than 1996 in the year column. Let’s see this explicitely:\n\n# check the type of df['year']>1996\nprint(type(df['year']>1996))\n\ndf['year']>1996\n\n\n\n\n0 False\n1 False\n2 False\n3 False\n4 False\n5 False\n6 False\n7 False\n8 False\n9 False\n10 False\n11 False\n12 False\n13 False\n14 False\n15 False\n16 False\n17 False\n18 False\n19 False\n20 False\n21 False\n22 False\n23 False\n24 False\n25 False\n26 False\n27 False\n28 False\n29 False\n30 False\n31 False\n32 False\n33 False\n34 False\n35 False\n36 True\n37 True\n38 True\n39 True\n40 True\n41 True\n42 True\nName: year, dtype: bool\n\n\nThe output is a pandas.Series with boolean values (True or False) indicating which rows satisfy the condition year>1996. When we pass such a series of boolean values to the selection brackets [] we keep only those rows with a True value.\nHere’s another example of using a condition. Suppose we want to look at data from years 1970 to 1979. One way of doing this is to use the in operator in our condition:\n\nseventies = df[df['year'].isin(range(1970,1980))]\nseventies\n\n\n\n\n\n \n \n \n year\n europe\n arctic\n alaska\n asia\n north_america\n south_america\n antarctica\n global_glacial_volume_change\n annual_sea_level_rise\n cumulative_sea_level_rise\n \n \n \n \n 9\n 1970\n -6.452316\n -24.494667\n -0.125296\n -36.120199\n 11.619790\n 11.636911\n 4.400377\n -999.018177\n 0.110225\n 2.759719\n \n \n 10\n 1971\n 0.414711\n -42.904189\n 28.103328\n -8.702938\n -9.964542\n 1.061299\n -6.735536\n -1038.104459\n 0.107973\n 2.867692\n \n \n 11\n 1972\n -5.144729\n -27.004031\n -22.143350\n -40.883357\n 32.363730\n -14.968034\n -6.223849\n -1122.885506\n 0.234202\n 3.101894\n \n \n 12\n 1973\n 4.081090\n 9.839444\n 22.985188\n -31.432594\n -20.883232\n 2.103649\n 10.539823\n -1125.677743\n 0.007713\n 3.109607\n \n \n 13\n 1974\n 1.545615\n -40.126998\n -29.517874\n -43.861622\n -23.991402\n -21.338825\n 4.419343\n -1279.964287\n 0.426206\n 3.535813\n \n \n 14\n 1975\n 7.431192\n -32.410467\n -44.094084\n -43.357442\n -30.858810\n -2.368842\n -7.775315\n -1434.818037\n 0.427773\n 3.963586\n \n \n 15\n 1976\n 3.986753\n 21.686639\n -28.234725\n -67.292125\n -12.534421\n -19.465358\n 19.250607\n -1518.185129\n 0.230296\n 4.193882\n \n \n 16\n 1977\n 4.891410\n -33.123010\n -5.662139\n -62.165684\n -15.905332\n 2.654950\n -23.727249\n -1652.453400\n 0.370907\n 4.564788\n \n \n 17\n 1978\n 8.404591\n -77.561015\n -12.503384\n -22.858040\n -31.097609\n 7.127708\n -9.140167\n -1791.355022\n 0.383706\n 4.948495\n \n \n 18\n 1979\n 3.916703\n -88.351684\n -63.938851\n -49.242043\n -12.076624\n -17.718503\n -9.578557\n -2030.537848\n 0.660726\n 5.609221\n \n \n\n\n\n\nLet’s break it down:\n\ndf['year'] is the column with the year values, a pandas.Series,\nin df['year'].isin(), we have that isin is a method for the pandas.Series and we are calling it using the dot ..\nrange(1970,1980) constructs consecutive integers from 1970 to 1979 - remember the right endopoint (1980) is not included!\ndf['year'].isin(range(1970,1980)) is then a pandas.Series of boolean values indicating which rows have year equal to 1970, …, 1979.\nwhen we put df['year'].isin(range(1970,1980)) inside the selection brackets [] we obtain the rows of the data frame with year equal to 1970, …, 1979.\n\n\n\n\n\n\n\nloc for row selection\n\n\n\nIt is equivalent to write\n# select rows with year<1965\ndf[df['year'] < 1965]\nand\n# select rows with year<1965 using love\ndf.loc[ df['year'] <1965 , :]\nIn the second one:\n\nwe are using the df.loc[ row-selection , column-selection] syntax\nthe row-selection parameter is the condition df['year']<1965\nthe column-selection parameter is a colon :, which indicates we want all columns for the rows we are selecting.\n\nWe prefer the first syntax when we are selecting rows and not columns since it is simpler.\n\n\n\n\n2.5.3.2 … using multiple conditions\nWe can combine multipe conditions by surrounding each one in parenthesis () and using the or operator | and the and operator and.\nor example:\n\n# select rows with \n# annual_sea_level_rise<0.5 mm OR annual_sea_level_rise>0.8 mm\n\ndf[ (df['annual_sea_level_rise']<0.5) | (df['annual_sea_level_rise']>0.8)]\ndf.head()\n\n\n\n\n\n \n \n \n year\n europe\n arctic\n alaska\n asia\n north_america\n south_america\n antarctica\n global_glacial_volume_change\n annual_sea_level_rise\n cumulative_sea_level_rise\n \n \n \n \n 0\n 1961\n -5.128903\n -108.382987\n -18.721190\n -32.350759\n -14.359007\n -4.739367\n -35.116389\n -220.823515\n 0.610010\n 0.610010\n \n \n 1\n 1962\n 5.576282\n -173.252450\n -24.324790\n -4.675440\n -2.161842\n -13.694367\n -78.222887\n -514.269862\n 0.810625\n 1.420635\n \n \n 2\n 1963\n -10.123105\n -0.423751\n -2.047567\n -3.027298\n -27.535881\n 3.419633\n 3.765109\n -550.575640\n 0.100292\n 1.520927\n \n \n 3\n 1964\n -4.508358\n 20.070148\n 0.477800\n -18.675385\n -2.248286\n 20.732633\n 14.853096\n -519.589859\n -0.085596\n 1.435331\n \n \n 4\n 1965\n 10.629385\n 43.695389\n -0.115332\n -18.414602\n -19.398765\n 6.862102\n 22.793484\n -473.112003\n -0.128392\n 1.306939\n \n \n\n\n\n\nand example\n\n# select rows with cumulative_sea_level_rise>10 AND global_glacial_volume_change<-300\ndf[ (df['cumulative_sea_level_rise']>10) & (df['global_glacial_volume_change']<-300)]\n\n\n\n\n\n \n \n \n year\n europe\n arctic\n alaska\n asia\n north_america\n south_america\n antarctica\n global_glacial_volume_change\n annual_sea_level_rise\n cumulative_sea_level_rise\n \n \n \n \n 32\n 1993\n 16.685013\n -73.666274\n -43.702040\n -65.995130\n -33.151246\n -20.578403\n -20.311577\n -3672.582082\n 0.671126\n 10.145254\n \n \n 33\n 1994\n 0.741751\n -3.069084\n -59.962273\n -59.004710\n -89.506142\n -15.258449\n -8.168498\n -3908.977191\n 0.653025\n 10.798280\n \n \n 34\n 1995\n -2.139665\n -58.167778\n -74.141762\n 3.500155\n -0.699374\n -19.863392\n -25.951496\n -4088.082873\n 0.494767\n 11.293047\n \n \n 35\n 1996\n -6.809834\n -4.550205\n -74.847017\n -67.436591\n 4.867530\n -21.080115\n -11.781489\n -4271.401594\n 0.506405\n 11.799452\n \n \n 36\n 1997\n -13.724106\n -24.832246\n -167.229145\n -34.406403\n -27.680661\n -38.213286\n -20.179090\n -4600.686013\n 0.909625\n 12.709077\n \n \n 37\n 1998\n -13.083338\n -110.429302\n -107.879027\n -58.115702\n 30.169987\n -3.797978\n -48.129928\n -4914.831966\n 0.867807\n 13.576884\n \n \n 38\n 1999\n -8.039555\n -64.644068\n -87.714653\n -26.211723\n 5.888512\n -8.038630\n -40.653001\n -5146.368231\n 0.639603\n 14.216487\n \n \n 39\n 2000\n -17.008590\n -96.494055\n -44.445000\n -37.518173\n -29.191986\n -2.767698\n -58.873830\n -5435.317175\n 0.798202\n 15.014688\n \n \n 40\n 2001\n -8.419109\n -145.415483\n -55.749505\n -35.977022\n -0.926134\n 7.553503\n -86.774675\n -5764.039931\n 0.908074\n 15.922762\n \n \n 41\n 2002\n -3.392361\n -48.718943\n -87.120000\n -36.127226\n -27.853498\n -13.484593\n -30.203960\n -6013.225500\n 0.688358\n 16.611120\n \n \n 42\n 2003\n -3.392361\n -48.718943\n -67.253634\n -36.021991\n -75.066475\n -13.223430\n -30.203960\n -6289.640976\n 0.763579\n 17.374699\n \n \n\n\n\n\n\n\n2.5.3.3 … by position\nAll the selections we have done so far have been using labels or using a condition. Sometimes we might want to select certain rows depending on their actual position in the data frame. In this case we use the iloc method with the syntax df.iloc[row-indices]. iloc stands for integer-location based indexing. Let’s see some examples:\n\n# select the fifht row = index 4\ndf.iloc[4]\n\nyear 1965.000000\neurope 10.629385\narctic 43.695389\nalaska -0.115332\nasia -18.414602\nnorth_america -19.398765\nsouth_america 6.862102\nantarctica 22.793484\nglobal_glacial_volume_change -473.112003\nannual_sea_level_rise -0.128392\ncumulative_sea_level_rise 1.306939\nName: 4, dtype: float64\n\n\n\n# select rows 23 through 30, inclduing 30\ndf.iloc[23:31]\n\n\n\n\n\n \n \n \n year\n europe\n arctic\n alaska\n asia\n north_america\n south_america\n antarctica\n global_glacial_volume_change\n annual_sea_level_rise\n cumulative_sea_level_rise\n \n \n \n \n 23\n 1984\n 8.581427\n -5.755672\n -33.466092\n -20.528535\n -20.734676\n -8.267686\n -3.261011\n -2569.339802\n 0.232609\n 7.097624\n \n \n 24\n 1985\n -5.970980\n -49.651089\n 12.065473\n -31.571622\n -33.833985\n 10.072906\n -13.587886\n -2682.857926\n 0.313586\n 7.411210\n \n \n 25\n 1986\n -5.680642\n 22.900847\n 7.557447\n -18.920773\n -33.014743\n -4.652030\n 30.482473\n -2684.197632\n 0.003701\n 7.414911\n \n \n 26\n 1987\n 8.191477\n 12.387780\n -24.007862\n -41.121970\n -48.560996\n 1.670733\n 3.130190\n -2773.325568\n 0.246210\n 7.661120\n \n \n 27\n 1988\n -11.117228\n -31.066489\n 49.897712\n -21.300712\n -46.545435\n 13.460422\n -37.986834\n -2858.767621\n 0.236028\n 7.897148\n \n \n 28\n 1989\n 14.863220\n -23.462392\n -36.112726\n -46.528372\n -57.756422\n -21.687470\n -10.044757\n -3041.169131\n 0.503872\n 8.401020\n \n \n 29\n 1990\n -1.226009\n -27.484542\n -92.713339\n -35.553433\n -56.563056\n -31.077022\n -29.893352\n -3318.220397\n 0.765335\n 9.166355\n \n \n 30\n 1991\n -14.391425\n -34.898689\n -8.822063\n -15.338299\n -31.458010\n -7.162909\n -35.968429\n -3467.630284\n 0.412734\n 9.579089\n \n \n\n\n\n\nNotice since we are back to indexing by position the right endpoint of the slice (6) is not included in the ouput.\n\n\n\n2.5.4 Selecting rows and columns simultaneously…\nSelecting rows and columns simultaneously can be done using loc (labels or conditions) or iloc (integer position).\n\n2.5.4.1 …by labels or conditions\nWhen we want to select rows and columns simultaneously by labels or conditions we can use the method loc with the syntax\ndf.loc[ row-selection , column-selection]\nspecifying both paratmers: row-selection and column-selection. These parameters can be a condition (which generates a boolean array) or a subset of labels from the index or the column names. Let’s see an examples:\n\n# select change in glacial volume in Europe per year after 2000\ndf.loc[df['year']>2000,['year','europe']]\n\n\n\n\n\n \n \n \n year\n europe\n \n \n \n \n 40\n 2001\n -8.419109\n \n \n 41\n 2002\n -3.392361\n \n \n 42\n 2003\n -3.392361\n \n \n\n\n\n\nLet’s break it down:\n\nwe are using the df.loc[ row-selection , column-selection] syntax\nthe row-selection parameter is the condition df['year']>1990, which is a boolean array saying which years are greater than 1990\nthe column-selection parameter is ['year','europe'] which is a list with the names of the two columns we are intersted in.\n\n\n\n2.5.4.2 … by position\nWhen we want to select rows and columns simultaneously by position we use the iloc method with the syntax:\ndf.iloc[ row-indices , column-indices]\nFor example,\n\n# select rows 3-7 (including 7) and columns 3 and 4\ndf.iloc[ 3:8, [3,4] ]\n\n\n\n\n\n \n \n \n alaska\n asia\n \n \n \n \n 3\n 0.477800\n -18.675385\n \n \n 4\n -0.115332\n -18.414602\n \n \n 5\n 0.224762\n -14.630284\n \n \n 6\n -7.174030\n -39.013695\n \n \n 7\n -0.660556\n 7.879589\n \n \n\n\n\n\nLet’s break it down:\n\nwe are using the df.iloc[ row-indices , column-indices] syntax\nthe row-indices parameter is the slice of integer indices 3:8. Remember the right endpoint (8) won’t be included.\nthe column-indices parameter is the list of integer indices 3 and 4. This means we are selecting the fourth and fifth column.\n\n\n\n\n2.5.5 Notes about loc and iloc\n\n\n\n\n\n\niloc vs. loc\n\n\n\nAt the beginning, the difference between iloc and loc can be confusing. Remember the i in iloc stands for integer-location, so this function only uses integer indexing to retrieve information from the data frames in the same way as indexing for Python lists.\nIf you want to dive deeper, this is a great discussion about the difference between iloc and loc: Stackoverflow - How are iloc and loc different?\nAnd, as always, the documentation will provide you with more information: pandas.DataFrame.loc and pandas.DataFrame.iloc.\n\n\n\n\n\n\n\n\niloc for column selection? Avoid it!\n\n\n\nWe can also access columns by position using iloc - but it is best not to if possible.\nSuppose we want to access the 10th column in the data frame - then we want to select a column by position. In this case the 10th column is the annual sea level rise data and the 10th position corresponds to the index 9. We can select this column by position using the iloc method*:\n\n# select column by position using iloc\n# the syntax is iloc[rows,columns]\n# [:,9] means \"select all rows from the 10th column\"\nannual_rise_3 = df.iloc[:,9]\nannual_rise_3.head()\n\n0 0.610010\n1 0.810625\n2 0.100292\n3 -0.085596\n4 -0.128392\nName: annual_sea_level_rise, dtype: float64\n\n\nUnless you are really looking for information about the 10th column, do not access a column by position. This is bound to break in many ways:\n\nit relies on a person correctly counting the position of a column. Even with a small dataset this can be prone to error.\nit is not explicit: if we want information about sea level rise df.annual_sea_level_rise or df['annual_sea_level_rise'] are explicitely telling us we are accessing that information. df.iloc[:,9] is obscure and uninformative.\ndatastets can get updated. Maybe a new column was added before annual_sea_level_rise, this would change the position of the column, which would make any code depending on df.iloc[:,9] invalid. Accessing by label helps reproducibility!" }, { "objectID": "lectures/lesson-2-pandas-basics.html#resources", @@ -147,59 +147,59 @@ "text": "2.7 Acknowledgements\nThe simplified glacial_loss.csv dataset was created by Dr. Allison Horst as part of her course materials on environmental data science." }, { - "objectID": "discussion-sections/discussion-sect-week-1-methods.html", - "href": "discussion-sections/discussion-sect-week-1-methods.html", + "objectID": "discussion-sections/ds1-anthr-prelim-data-exploration.html", + "href": "discussion-sections/ds1-anthr-prelim-data-exploration.html", "title": "3  1. Preliminary Data Exploration", "section": "", "text": "4 References\nRachel King, Jenna Braun, Michael Westphal, & CJ Lortie. (2023). Compiled occurrence records for prey items of listed species found in California drylands with associated environmental data. Knowledge Network for Biocomplexity. doi:10.5063/F1VM49RH.\nLortie, C. J., Braun, J., King, R., & Westphal, M. (2023). The importance of open data describing prey item species lists for endangered species. Ecological Solutions and Evidence, 4(2), e12251. https://doi.org/10.1002/2688-8319.12251" }, { - "objectID": "discussion-sections/discussion-sect-week-1-methods.html#archive-exploration", - "href": "discussion-sections/discussion-sect-week-1-methods.html#archive-exploration", + "objectID": "discussion-sections/ds1-anthr-prelim-data-exploration.html#archive-exploration", + "href": "discussion-sections/ds1-anthr-prelim-data-exploration.html#archive-exploration", "title": "3  1. Preliminary Data Exploration", "section": "3.1 Archive exploration", - "text": "3.1 Archive exploration\nFor many datasets, data exploration begins at the data repository. Take some time to look through the dataset’s description in KNB. Discuss the following questions with your team:\n\nWhat is this data about?\nIs this data collected in-situ by the authors or is it a synthesis of multiple datasets?\nDuring what time frame were the observations in the dataset collected?\nDoes this dataset come with an associated metadata file?\nDoes the dataset contain sensitive data?\n\nIn your notebook: use a markdown cell to add a brief description of the dataset, including a citation, date of access, and a link to the archive.\n\ncheck git status -> stage changes -> check git status -> commit with message -> push changes" + "text": "3.1 Archive exploration\nFor many datasets, data exploration begins at the data repository. Take some time to look through the dataset’s description in KNB. Discuss the following questions with your team:\n\nWhat is this data about?\nIs this data collected in-situ by the authors or is it a synthesis of multiple datasets?\nDuring what time frame were the observations in the dataset collected?\nDoes this dataset come with an associated metadata file?\nDoes the dataset contain sensitive data?\n\nIn your notebook: use a markdown cell to add a brief description of the dataset, including a citation, date of access, and a link to the archive.\n\ncheck git status -> stage changes -> check git status -> commit with message -> push changes" }, { - "objectID": "discussion-sections/discussion-sect-week-1-methods.html#xml-metadata-exploration", - "href": "discussion-sections/discussion-sect-week-1-methods.html#xml-metadata-exploration", + "objectID": "discussion-sections/ds1-anthr-prelim-data-exploration.html#xml-metadata-exploration", + "href": "discussion-sections/ds1-anthr-prelim-data-exploration.html#xml-metadata-exploration", "title": "3  1. Preliminary Data Exploration", "section": "3.2 .xml metadata exploration", "text": "3.2 .xml metadata exploration\nYou may have noticed there are two metadata files: Compiled_occurrence_records_for_prey_items_of.xml and metadata_arth_occurrences.csv.\n\nIn the archive’s dataset description, notice the .xml document file type is EML which stands for EML: Ecological Metadata Language.\nOpen the .xml file: there’s a lot going on. This is a machine-readable file that has metadata about the whole dataset. You can proably identify some items like title and creators.\nClose the file and delete it - we won’t use it today.\nYou don’t need to write anything in your notebook about this section." }, { - "objectID": "discussion-sections/discussion-sect-week-1-methods.html#csv-metadata-exploration", - "href": "discussion-sections/discussion-sect-week-1-methods.html#csv-metadata-exploration", + "objectID": "discussion-sections/ds1-anthr-prelim-data-exploration.html#csv-metadata-exploration", + "href": "discussion-sections/ds1-anthr-prelim-data-exploration.html#csv-metadata-exploration", "title": "3  1. Preliminary Data Exploration", "section": "3.3 .csv metadata exploration", - "text": "3.3 .csv metadata exploration\nBack in your notebook, import the pandas package using standard abbreviation in a code cell. Then follow these steps to read in the metadata csv using the pandas.read_csv() function:\n\nNavigate to the data package site and copy the URL to access the metadata_arth_occurrences csv file. To copy the URL:\n\n\nhover over the Download button –> right click –> “Copy Link”.\n\n\nRead in the data from the URL using the pd.read_csv() function like this:\n# look at metadata\npd.read_csv('the URL goes here')\nTake a minute to look at the descriptions for the columns.\n\nNote: Not all datasets have column descriptions in a csv file. Often they come with a doc or txt file with information." + "text": "3.3 .csv metadata exploration\nBack in your notebook, import the pandas package using standard abbreviation in a code cell. Then follow these steps to read in the metadata csv using the pandas.read_csv() function:\n\nNavigate to the data package site and copy the URL to access the metadata_arth_occurrences csv file. To copy the URL:\n\n\nhover over the Download button –> right click –> “Copy Link”.\n\n\nRead in the data from the URL using the pd.read_csv() function like this:\n# look at metadata\npd.read_csv('the URL goes here')\nTake a minute to look at the descriptions for the columns.\n\nNote: Not all datasets have column descriptions in a csv file. Often they come with a doc or txt file with information." }, { - "objectID": "discussion-sections/discussion-sect-week-1-methods.html#data-loading", - "href": "discussion-sections/discussion-sect-week-1-methods.html#data-loading", + "objectID": "discussion-sections/ds1-anthr-prelim-data-exploration.html#data-loading", + "href": "discussion-sections/ds1-anthr-prelim-data-exploration.html#data-loading", "title": "3  1. Preliminary Data Exploration", "section": "3.4 Data loading", - "text": "3.4 Data loading\n\nFollow steps (a) and (b) from the previous exercise to read in the drylands prey data file arth_occurrences_with_env.csv using pd.read_csv(). Store the dataframe to a variable called prey like this:\n\n# read in data\nprey = pd.read_csv('the URL goes here')\n\nUse a Python function to see what is the type of the prey variable.\n\n\ncheck git status -> stage changes -> check git status -> commit with message -> push changes\n\n\nCHECK IN WITH YOUR TEAM\n\n\nMAKE SURE YOU’VE ALL SUCCESSFULLY ACCESSED THE DATA BEFORE CONTINUING" + "text": "3.4 Data loading\n\nFollow steps (a) and (b) from the previous exercise to read in the drylands prey data file arth_occurrences_with_env.csv using pd.read_csv(). Store the dataframe to a variable called prey like this:\n\n# read in data\nprey = pd.read_csv('the URL goes here')\n\nUse a Python function to see what is the type of the prey variable.\n\n\ncheck git status -> stage changes -> check git status -> commit with message -> push changes\n\n\nCHECK IN WITH YOUR TEAM\n\n\nMAKE SURE YOU’VE ALL SUCCESSFULLY ACCESSED THE DATA BEFORE CONTINUING" }, { - "objectID": "discussion-sections/discussion-sect-week-1-methods.html#look-at-your-data", - "href": "discussion-sections/discussion-sect-week-1-methods.html#look-at-your-data", + "objectID": "discussion-sections/ds1-anthr-prelim-data-exploration.html#look-at-your-data", + "href": "discussion-sections/ds1-anthr-prelim-data-exploration.html#look-at-your-data", "title": "3  1. Preliminary Data Exploration", "section": "3.5 Look at your data", - "text": "3.5 Look at your data\n\nRun prey in a cell. What do you notice in the columns section?\nTo see all the column names in the same display we need to set a pandas option. Run the following command and then look at the prey data again:\n\npd.set_option(\"display.max.columns\", None)\n\nAdd a comment explaining what pd.set_option(\"display.max.columns\", None) does.\n\n\ncheck git status -> stage changes -> check git status -> commit with message -> push changes" + "text": "3.5 Look at your data\n\nRun prey in a cell. What do you notice in the columns section?\nTo see all the column names in the same display we need to set a pandas option. Run the following command and then look at the prey data again:\n\npd.set_option(\"display.max.columns\", None)\n\nAdd a comment explaining what pd.set_option(\"display.max.columns\", None) does.\n\n\ncheck git status -> stage changes -> check git status -> commit with message -> push changes" }, { - "objectID": "discussion-sections/discussion-sect-week-1-methods.html#pd.dataframe-preliminary-exploration", - "href": "discussion-sections/discussion-sect-week-1-methods.html#pd.dataframe-preliminary-exploration", + "objectID": "discussion-sections/ds1-anthr-prelim-data-exploration.html#pd.dataframe-preliminary-exploration", + "href": "discussion-sections/ds1-anthr-prelim-data-exploration.html#pd.dataframe-preliminary-exploration", "title": "3  1. Preliminary Data Exploration", "section": "3.6 pd.DataFrame preliminary exploration", - "text": "3.6 pd.DataFrame preliminary exploration\nRun each of the following methods for prey in a different cell and write a brief description of what they do as a comment:\n\nhead()\ntail()\ninfo()\nnunique()\n\nFor example:\n# head()\n# returns the first five rows of the data frame\nprey.head()\nIf you’re not sure about what the method does, try looking it up in the pandas.DataFrame documentation.\n\nCheck the documentation for head(). If this function has any optional parameters, change the default value to get a different output.\n\nPrint each of the following attributes of prey in a different cell and write a brief explanation of what they are as a comment:\n\nshape\ncolumns\ndtypes\n\nIf you’re not sure about what info is the attribute showing, try looking it up in the pandas.DataFrame documentation.\n\ncheck git status -> stage changes -> check git status -> commit with message -> push changes" + "text": "3.6 pd.DataFrame preliminary exploration\nRun each of the following methods for prey in a different cell and write a brief description of what they do as a comment:\n\nhead()\ntail()\ninfo()\nnunique()\n\nFor example:\n# head()\n# returns the first five rows of the data frame\nprey.head()\nIf you’re not sure about what the method does, try looking it up in the pandas.DataFrame documentation.\n\nCheck the documentation for head(). If this function has any optional parameters, change the default value to get a different output.\n\nPrint each of the following attributes of prey in a different cell and write a brief explanation of what they are as a comment:\n\nshape\ncolumns\ndtypes\n\nIf you’re not sure about what info is the attribute showing, try looking it up in the pandas.DataFrame documentation.\n\ncheck git status -> stage changes -> check git status -> commit with message -> push changes" }, { - "objectID": "discussion-sections/discussion-sect-week-1-methods.html#update-some-column-names", - "href": "discussion-sections/discussion-sect-week-1-methods.html#update-some-column-names", + "objectID": "discussion-sections/ds1-anthr-prelim-data-exploration.html#update-some-column-names", + "href": "discussion-sections/ds1-anthr-prelim-data-exploration.html#update-some-column-names", "title": "3  1. Preliminary Data Exploration", "section": "3.7 Update some column names", - "text": "3.7 Update some column names\nChange the column names of institutionCode and datasetKey to institution_code and dataset_key, respectively. Make sure you’re actually updating the dataframe. HINT: yesterday’s class.\n\ncheck git status -> stage changes -> check git status -> commit with message -> push changes" + "text": "3.7 Update some column names\nChange the column names of institutionCode and datasetKey to institution_code and dataset_key, respectively. Make sure you’re actually updating the dataframe. 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page-start] minmax(50px, 100px) [page-start-inset] 50px [body-start-outset] 50px [body-start] 1.5em [body-content-start] minmax(500px, calc( 1000px - 3em )) [body-content-end] 1.5em [body-end] 50px [body-end-outset] minmax(0px, 100px) [page-end-inset] 50px [page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.floating.slimcontent .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset] 5fr [page-start] 50px [page-start-inset] minmax(50px, 150px) [body-start-outset] 50px [body-start] 1.5em [body-content-start] minmax(450px, calc(750px - 3em)) [body-content-end] 1.5em [body-end] 50px [body-end-outset] minmax(50px, 150px) [page-end-inset] 50px [page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.floating.listing .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset] 5fr [page-start] minmax(25px, 50px) [page-start-inset] minmax(50px, 150px) [body-start-outset] minmax(25px, 50px) [body-start] 1.5em [body-content-start] minmax(500px, calc(800px - 3em)) [body-content-end] 1.5em [body-end] minmax(25px, 50px) [body-end-outset] minmax(50px, 150px) [page-end-inset] minmax(25px, 50px) [page-end] 5fr [screen-end-inset] 1.5em [screen-end]}}@media(max-width: 991.98px){body .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start page-start-inset body-start-outset] 5fr [body-start] 1.5em [body-content-start] minmax(500px, calc(750px - 3em)) [body-content-end] 1.5em [body-end] 35px [body-end-outset] minmax(75px, 145px) [page-end-inset] 35px [page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.fullcontent:not(.floating):not(.docked) .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start page-start-inset body-start-outset] 5fr [body-start] 1.5em [body-content-start] minmax(500px, calc(750px - 3em)) [body-content-end] 1.5em [body-end body-end-outset page-end-inset page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.slimcontent:not(.floating):not(.docked) .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start page-start-inset body-start-outset] 5fr [body-start] 1.5em [body-content-start] minmax(500px, calc(750px - 3em)) [body-content-end] 1.5em [body-end] 35px [body-end-outset] minmax(75px, 145px) [page-end-inset] 35px [page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.listing:not(.floating):not(.docked) .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start page-start-inset body-start-outset] 5fr [body-start] 1.5em [body-content-start] minmax(500px, calc(1200px - 3em)) [body-content-end body-end body-end-outset page-end-inset page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body:not(.floating):not(.docked) .page-columns.toc-left{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset] 5fr [page-start] 35px [page-start-inset] minmax(0px, 145px) [body-start-outset] 35px [body-start] 1.5em [body-content-start] minmax(450px, calc(750px - 3em)) [body-content-end] 1.5em [body-end body-end-outset page-end-inset page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body:not(.floating):not(.docked) .page-columns.toc-left .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset] 5fr [page-start] 35px [page-start-inset] minmax(0px, 145px) [body-start-outset] 35px [body-start] 1.5em [body-content-start] minmax(450px, calc(750px - 3em)) [body-content-end] 1.5em [body-end body-end-outset page-end-inset page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.floating .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset] 5fr [page-start page-start-inset body-start-outset body-start] 1em [body-content-start] minmax(500px, calc(750px - 3em)) [body-content-end] 1.5em [body-end] 50px [body-end-outset] minmax(75px, 150px) [page-end-inset] 25px [page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.docked .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start page-start-inset body-start-outset body-start body-content-start] minmax(500px, calc(750px - 3em)) [body-content-end] 1.5em [body-end] 50px [body-end-outset] minmax(25px, 50px) [page-end-inset] 50px [page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.docked.fullcontent .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start page-start-inset body-start-outset body-start body-content-start] minmax(500px, calc( 1000px - 3em )) [body-content-end] 1.5em [body-end body-end-outset page-end-inset page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.floating.fullcontent .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset] 5fr [page-start page-start-inset body-start-outset body-start] 1em [body-content-start] minmax(500px, calc(800px - 3em)) [body-content-end] 1.5em [body-end body-end-outset page-end-inset page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.docked.slimcontent .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start page-start-inset body-start-outset body-start body-content-start] minmax(500px, calc(750px - 3em)) [body-content-end] 1.5em [body-end] 35px [body-end-outset] minmax(75px, 145px) [page-end-inset] 35px [page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.docked.listing .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start page-start-inset body-start-outset body-start body-content-start] minmax(500px, calc(750px - 3em)) [body-content-end] 1.5em [body-end] 50px [body-end-outset] minmax(25px, 50px) [page-end-inset] 50px [page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.floating.slimcontent .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset] 5fr [page-start page-start-inset body-start-outset body-start] 1em [body-content-start] minmax(500px, calc(750px - 3em)) [body-content-end] 1.5em [body-end] 35px [body-end-outset] minmax(75px, 145px) [page-end-inset] 35px [page-end] 4fr [screen-end-inset] 1.5em [screen-end]}body.floating.listing .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset] 5fr [page-start page-start-inset body-start-outset body-start] 1em [body-content-start] minmax(500px, calc(750px - 3em)) [body-content-end] 1.5em [body-end] 50px [body-end-outset] minmax(75px, 150px) [page-end-inset] 25px [page-end] 5fr [screen-end-inset] 1.5em [screen-end]}}@media(max-width: 767.98px){body .page-columns,body.fullcontent:not(.floating):not(.docked) .page-columns,body.slimcontent:not(.floating):not(.docked) .page-columns,body.docked .page-columns,body.docked.slimcontent 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body-content-start] minmax(0px, 1fr) [body-content-end body-end body-end-outset page-end-inset page-end screen-end-inset] 1.5em [screen-end]}nav[role=doc-toc]{display:none}}body,.page-row-navigation{grid-template-rows:[page-top] max-content [contents-top] max-content [contents-bottom] max-content [page-bottom]}.page-rows-contents{grid-template-rows:[content-top] minmax(max-content, 1fr) [content-bottom] minmax(60px, max-content) [page-bottom]}.page-full{grid-column:screen-start/screen-end !important}.page-columns>*{grid-column:body-content-start/body-content-end}.page-columns.column-page>*{grid-column:page-start/page-end}.page-columns.column-page-left>*{grid-column:page-start/body-content-end}.page-columns.column-page-right>*{grid-column:body-content-start/page-end}.page-rows{grid-auto-rows:auto}.header{grid-column:screen-start/screen-end;grid-row:page-top/contents-top}#quarto-content{padding:0;grid-column:screen-start/screen-end;grid-row:contents-top/contents-bottom}body.floating 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- console.log({ - childtop: marginChild.getBoundingClientRect().top, - scroll: window.scrollY, - top, - lastBottom, - }); - if (top < lastBottom) { - const margin = lastBottom - top; - marginChild.style.marginTop = `${margin}px`; - } - const styles = window.getComputedStyle(marginChild); - const marginTop = parseFloat(styles["marginTop"]); - - console.log({ - top, - height: marginChild.getBoundingClientRect().height, - marginTop, - total: top + marginChild.getBoundingClientRect().height + marginTop, - }); - lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; - } - } -}; - window.document.addEventListener("DOMContentLoaded", function (_event) { - // Recompute the position of margin elements anytime the body size changes - if (window.ResizeObserver) { - const resizeObserver = new window.ResizeObserver( - throttle(layoutMarginEls, 50) - ); - resizeObserver.observe(window.document.body); - } - const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); const sidebarEl = window.document.getElementById("quarto-sidebar"); const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); @@ -304,7 +259,6 @@ window.document.addEventListener("DOMContentLoaded", function (_event) { const manageSidebarVisiblity = (el, placeholderDescriptor) => { let isVisible = true; - let elRect; return (hiddenRegions) => { if (el === null) { @@ -315,6 +269,11 @@ window.document.addEventListener("DOMContentLoaded", function (_event) { const lastChildEl = el.lastElementChild; if (lastChildEl) { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + // Converts the sidebar to a menu const convertToMenu = () => { for (const child of el.children) { @@ -322,112 +281,100 @@ window.document.addEventListener("DOMContentLoaded", function (_event) { child.style.overflow = 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+ toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append(titleEl.innerText, toggleIcon); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; } - toggleTitle.classList.add("zindex-over-content"); - toggleTitle.classList.add("quarto-sidebar-toggle-title"); - toggleContainer.append(toggleTitle); - - const toggleContents = window.document.createElement("div"); - toggleContents.classList = el.classList; - toggleContents.classList.add("zindex-over-content"); - toggleContents.classList.add("quarto-sidebar-toggle-contents"); - for (const child of el.children) { - if (child.id === "toc-title") { - continue; - } - - const clone = child.cloneNode(true); - clone.style.opacity = 1; - clone.style.display = null; - toggleContents.append(clone); + }; + + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + const elRect = el.getBoundingClientRect(); + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + positionToggle(); + }, 50) + ); + positionToggle(); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); } - toggleContents.style.height = "0px"; - const positionToggle = () => { - // position the element (top left of parent, same width as parent) - if (!elRect) { - elRect = el.getBoundingClientRect(); - } - toggleContainer.style.left = `${elRect.left}px`; - toggleContainer.style.top = `${elRect.top}px`; - toggleContainer.style.width = `${elRect.width}px`; - }; - positionToggle(); - - toggleContainer.append(toggleContents); - el.parentElement.prepend(toggleContainer); - - // Process clicks - let tocShowing = false; - // Allow the caller to control whether this is dismissed - // when it is clicked (e.g. sidebar navigation supports - // opening and closing the nav tree, so don't dismiss on click) - const clickEl = placeholderDescriptor.dismissOnClick - ? toggleContainer - : toggleTitle; - - const closeToggle = () => { - if (tocShowing) { - toggleContainer.classList.remove("expanded"); - toggleContents.style.height = "0px"; - tocShowing = false; - } - }; - - // Get rid of any expanded toggle if the user scrolls - window.document.addEventListener( - "scroll", - throttle(() => { - closeToggle(); - }, 50) - ); - - // Handle positioning of the toggle - window.addEventListener( - "resize", - throttle(() => { - elRect = undefined; - positionToggle(); - }, 50) - ); - - window.addEventListener("quarto-hrChanged", () => { - elRect = undefined; - }); - - // Process the click - clickEl.onclick = () => { - if (!tocShowing) { - toggleContainer.classList.add("expanded"); - toggleContents.style.height = null; - tocShowing = true; - } else { - closeToggle(); - } - }; - }); + }; }; // Converts a sidebar from a menu back to a sidebar @@ -451,11 +398,6 @@ window.document.addEventListener("DOMContentLoaded", function (_event) { convertToMenu(); isVisible = false; } else { - // Find the top and bottom o the element that is being managed - const elTop = el.offsetTop; - const elBottom = - elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; - if (!isVisible) { // If the element is current not visible reveal if there are // no conflicts with overlay regions @@ -476,51 +418,26 @@ window.document.addEventListener("DOMContentLoaded", function (_event) { }; }; - const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); - for (const tabEl of tabEls) { - const id = tabEl.getAttribute("data-bs-target"); - if (id) { - const columnEl = document.querySelector( - `${id} .column-margin, .tabset-margin-content` - ); - if (columnEl) - tabEl.addEventListener("shown.bs.tab", function (event) { - const el = event.srcElement; - if (el) { - const visibleCls = `${el.id}-margin-content`; - // walk up until we find a parent tabset - let panelTabsetEl = el.parentElement; - while (panelTabsetEl) { - if (panelTabsetEl.classList.contains("panel-tabset")) { - break; - } - panelTabsetEl = panelTabsetEl.parentElement; - } + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > * " + ); - if (panelTabsetEl) { - const prevSib = panelTabsetEl.previousElementSibling; - if ( - prevSib && - prevSib.classList.contains("tabset-margin-container") - ) { - const childNodes = prevSib.querySelectorAll( - ".tabset-margin-content" - ); - for (const childEl of childNodes) { - if (childEl.classList.contains(visibleCls)) { - childEl.classList.remove("collapse"); - } else { - childEl.classList.add("collapse"); - } - } - } - } - } + nexttick(() => { + let lastBottom = 0; + for (const marginChild of marginChildren) { + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const margin = lastBottom - top; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); - layoutMarginEls(); - }); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; } - } + }); // Manage the visibility of the toc and the sidebar const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { @@ -589,9 +506,8 @@ window.document.addEventListener("DOMContentLoaded", function (_event) { const kOverlapPaddingSize = 10; function toRegions(els) { return els.map((el) => { - const boundRect = el.getBoundingClientRect(); const top = - boundRect.top + + el.getBoundingClientRect().top + document.documentElement.scrollTop - kOverlapPaddingSize; return { @@ -601,51 +517,11 @@ window.document.addEventListener("DOMContentLoaded", function (_event) { }); } - let hasObserved = false; - const visibleItemObserver = (els) => { - let visibleElements = [...els]; - const intersectionObserver = new IntersectionObserver( - (entries, _observer) => { - entries.forEach((entry) => { - if (entry.isIntersecting) { - if (visibleElements.indexOf(entry.target) === -1) { - visibleElements.push(entry.target); - } - } else { - visibleElements = visibleElements.filter((visibleEntry) => { - return visibleEntry !== entry; - }); - } - }); - - if (!hasObserved) { - hideOverlappedSidebars(); - } - hasObserved = true; - }, - {} - ); - els.forEach((el) => { - intersectionObserver.observe(el); - }); - - return { - getVisibleEntries: () => { - return visibleElements; - }, - }; - }; - - const rightElementObserver = visibleItemObserver(rightSideConflictEls); - const leftElementObserver = visibleItemObserver(leftSideConflictEls); - const hideOverlappedSidebars = () => { - marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); - sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + marginScrollVisibility(toRegions(rightSideConflictEls)); + sidebarScrollVisiblity(toRegions(leftSideConflictEls)); if (tocLeftScrollVisibility) { - tocLeftScrollVisibility( - toRegions(leftElementObserver.getVisibleEntries()) - ); + tocLeftScrollVisibility(toRegions(leftSideConflictEls)); } }; @@ -667,6 +543,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) { manageTransition("TOC", slow); manageTransition("quarto-sidebar", slow); }; + const readerMode = !isReaderMode(); setReaderModeValue(readerMode); @@ -713,9 +590,6 @@ window.document.addEventListener("DOMContentLoaded", function (_event) { }; let localReaderMode = null; - const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); - const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; - // Walk the TOC and collapse/expand nodes // Nodes are expanded if: // - they are top level @@ -741,13 +615,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) { // Process the collapse state if this is an UL if (el.tagName === "UL") { - if (tocOpenDepth === -1 && depth > 1) { - el.classList.add("collapse"); - } else if ( - depth <= tocOpenDepth || - hasActiveChild || - prevSiblingIsActiveLink(el) - ) { + if (depth === 1 || hasActiveChild || prevSiblingIsActiveLink(el)) { el.classList.remove("collapse"); } else { el.classList.add("collapse"); diff --git a/docs/site_libs/quarto-nav/quarto-nav.js b/docs/site_libs/quarto-nav/quarto-nav.js index 3b21201..b41b31e 100644 --- a/docs/site_libs/quarto-nav/quarto-nav.js +++ b/docs/site_libs/quarto-nav/quarto-nav.js @@ -8,47 +8,6 @@ const headroomChanged = new CustomEvent("quarto-hrChanged", { window.document.addEventListener("DOMContentLoaded", function () { let init = false; - // Manage the back to top button, if one is present. - let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop; - const scrollDownBuffer = 5; - const scrollUpBuffer = 35; - const btn = document.getElementById("quarto-back-to-top"); - const hideBackToTop = () => { - btn.style.display = "none"; - }; - const showBackToTop = () => { - btn.style.display = "inline-block"; - }; - if (btn) { - window.document.addEventListener( - "scroll", - function () { - const currentScrollTop = - window.pageYOffset || document.documentElement.scrollTop; - - // Shows and hides the button 'intelligently' as the user scrolls - if (currentScrollTop - scrollDownBuffer > lastScrollTop) { - hideBackToTop(); - lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; - } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) { - showBackToTop(); - lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; - } - - // Show the button at the bottom, hides it at the top - if (currentScrollTop <= 0) { - hideBackToTop(); - } else if ( - window.innerHeight + currentScrollTop >= - document.body.offsetHeight - ) { - showBackToTop(); - } - }, - false - ); - } - function throttle(func, wait) { var timeout; return function () { @@ -190,18 +149,6 @@ window.document.addEventListener("DOMContentLoaded", function () { }; } - window.addEventListener( - "hashchange", - function (e) { - if ( - getComputedStyle(document.documentElement).scrollBehavior !== "smooth" - ) { - window.scrollTo(0, window.pageYOffset - headerOffset()); - } - }, - false - ); - // Observe size changed for the header const headerEl = window.document.querySelector("header.fixed-top"); if (headerEl && window.ResizeObserver) { @@ -225,9 +172,7 @@ window.document.addEventListener("DOMContentLoaded", function () { if (window.location.protocol !== "file:") { const links = window.document.querySelectorAll("a"); for (let i = 0; i < links.length; i++) { - if (links[i].href) { - links[i].href = links[i].href.replace(/\/index\.html/, "/"); - } + links[i].href = links[i].href.replace(/\/index\.html/, "/"); } // Fixup any sharing links that require urls diff --git a/docs/site_libs/quarto-search/autocomplete.umd.js b/docs/site_libs/quarto-search/autocomplete.umd.js index 619c57c..3f2dcf0 100644 --- a/docs/site_libs/quarto-search/autocomplete.umd.js +++ b/docs/site_libs/quarto-search/autocomplete.umd.js @@ -1,3 +1,3 @@ -/*! @algolia/autocomplete-js 1.7.3 | MIT License | © Algolia, Inc. and contributors | https://github.com/algolia/autocomplete */ -!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports):"function"==typeof define&&define.amd?define(["exports"],t):t((e="undefined"!=typeof globalThis?globalThis:e||self)["@algolia/autocomplete-js"]={})}(this,(function(e){"use strict";function t(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return 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a/docs/site_libs/quarto-search/quarto-search.js b/docs/site_libs/quarto-search/quarto-search.js index f5d852d..6fd4b5b 100644 --- a/docs/site_libs/quarto-search/quarto-search.js +++ b/docs/site_libs/quarto-search/quarto-search.js @@ -45,7 +45,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) { // highlight matches on the page if (query !== null && mainEl) { // perform any highlighting - highlight(escapeRegExp(query), mainEl); + highlight(query, mainEl); // fix up the URL to remove the q query param const replacementUrl = new URL(window.location); @@ -80,20 +80,23 @@ window.document.addEventListener("DOMContentLoaded", function (_event) { // the media query since we generate different HTML for sidebar overlays than we do // for sidebar input UI) const detachedMediaQuery = - quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; + quartoSearchOptions.type === "overlay" + ? "all" + : quartoSearchOptions.location === "navbar" + ? "(max-width: 991px)" + : "none"; // If configured, include the analytics client to send insights const plugins = configurePlugins(quartoSearchOptions); let lastState = null; - const { setIsOpen, setQuery, setCollections } = autocomplete({ + const { setIsOpen } = autocomplete({ container: searchEl, detachedMediaQuery: detachedMediaQuery, defaultActiveItemId: 0, panelContainer: "#quarto-search-results", panelPlacement: quartoSearchOptions["panel-placement"], debug: false, - openOnFocus: true, plugins, classNames: { form: "d-flex", @@ -277,10 +280,6 @@ window.document.addEventListener("DOMContentLoaded", function (_event) { } }, getItems({ query }) { - if (query === null || query === "") { - return []; - } - const limit = quartoSearchOptions.limit; if (quartoSearchOptions.algolia) { return algoliaSearch(query, limit, quartoSearchOptions.algolia); @@ -300,15 +299,9 @@ window.document.addEventListener("DOMContentLoaded", function (_event) { }, templates: { noResults({ createElement }) { - const hasQuery = lastState.query; - return createElement( "div", - { - class: `quarto-search-no-results${ - hasQuery ? "" : " no-query" - }`, - }, + { class: "quarto-search-no-results" }, language["search-no-results-text"] ); }, @@ -368,12 +361,6 @@ window.document.addEventListener("DOMContentLoaded", function (_event) { }, }); - window.quartoOpenSearch = () => { - setIsOpen(false); - setIsOpen(true); - focusSearchInput(); - }; - // Remove the labeleledby attribute since it is pointing // to a non-existent label if (quartoSearchOptions.type === "overlay") { @@ -989,10 +976,6 @@ function clearHighlight(searchterm, el) { } } -function escapeRegExp(string) { - return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string -} - // highlight matches function highlight(term, el) { const termRegex = new RegExp(term, "ig"); From 86ef59e55740fddb5db1771e0ce86b420bbce834 Mon Sep 17 00:00:00 2001 From: carmengg Date: Mon, 9 Oct 2023 18:53:39 +0000 Subject: [PATCH 30/31] fixed typos --- .../execute-results/html.json | 4 +- .../ds-coral-pd-selection.qmd | 10 +- docs/images/selection-flow-chart.jpg | Bin 0 -> 1769828 bytes docs/lectures/lesson-2-pandas-basics.html | 429 +++++++++--------- docs/search.json | 19 +- lectures/lesson-2-pandas-basics.qmd | 25 +- 6 files changed, 248 insertions(+), 239 deletions(-) create mode 100644 docs/images/selection-flow-chart.jpg diff --git a/_freeze/lectures/lesson-2-pandas-basics/execute-results/html.json b/_freeze/lectures/lesson-2-pandas-basics/execute-results/html.json index 154b82e..925e831 100644 --- a/_freeze/lectures/lesson-2-pandas-basics/execute-results/html.json +++ b/_freeze/lectures/lesson-2-pandas-basics/execute-results/html.json @@ -1,7 +1,7 @@ { - "hash": "33c85abbf1b8e1c0fed1bf4c13462336", + "hash": "2555c250f06fe03822ad47e33b2e8cbc", "result": { - "markdown": "::: {.cell execution_count=1}\n``` {.python .cell-code}\nimport os\nos.getcwd()\n```\n\n::: {.cell-output .cell-output-display execution_count=109}\n```\n'/Users/c_galazgarcia/eds-220-book'\n```\n:::\n:::\n\n\n# Pandas Fundamentals\n\n## About\nIn this lesson we cover the two core objects in the `pandas` library, the `pandas.Series` and the `pandas.DataFrame`. We will also learn methods to select data from our datasets.\n\n## `pandas`\n\n`pandas` is a Python package to wrangle and analyze tabular data. It is built on top of NumPy and has become the core tool for doing data analysis in Python. \n\nThe convention to import it is:\n\n::: {.cell execution_count=2}\n``` {.python .cell-code}\nimport pandas as pd\n\n# we will also import numpy \nimport numpy as np\n```\n:::\n\n\nThere is so much to learn about `pandas`. While we won't be able to cover every single functionality of this package in the next three lecutres, the goal is to get you started with the basic tools for data wrangling and give you a solid basis on which you can explore further. \n\n## Series\n\nThe first core data structure of pandas is the **series**. \nA series is a *one-dimensional* array of *indexed* data. A `pandas.Series` having an **index** is the main difference between a `pandas.Series` and a numpy array. See the difference:\n\n::: {.cell execution_count=3}\n``` {.python .cell-code}\n# a numpy array\n# np.random.randn returns values from the std normal distribution\narr = np.random.randn(4) \nprint(type(arr))\nprint(arr, \"\\n\")\n\n# a pandas series made from the previous array\ns = pd.Series(arr)\nprint(type(s))\nprint(s)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n[ 1.29006588 0.60107937 -0.52572693 -0.33036333] \n\n\n0 1.290066\n1 0.601079\n2 -0.525727\n3 -0.330363\ndtype: float64\n```\n:::\n:::\n\n\n### Creating a `pandas.Series`\n\nThe basic method to create a `pandas.Series` is to call\n\n``` python\ns = pd.Series(data, index=index)\n```\n\nThe `data` parameter can be:\n\n- a [numpy array](https://numpy.org/doc/stable/reference/arrays.ndarray.html) or [a list](https://realpython.com/python-list/)\n- a [Python dictionary](https://realpython.com/lessons/dictionary-python/)\n- [a number](https://commons.wikimedia.org/wiki/File:Number-three.JPG)\n\nThe `index` parameter is a list of index labels.\n\nFor now, we will create a `pandas.Series` from a numpy array or list. To use this method we need to pass a numpy array (or a list of objects that can be converted to NumPy types) as `data` and a list of indices of the same length as data. \n\n::: {.cell execution_count=4}\n``` {.python .cell-code}\n# a Series from a numpy array \npd.Series(np.arange(3), index=['a','b','c'])\n```\n\n::: {.cell-output .cell-output-display execution_count=112}\n```\na 0\nb 1\nc 2\ndtype: int64\n```\n:::\n:::\n\n\n The `index` parameter is optional. If we don't include it, the default is to make the index equal to `[0,...,len(data)-1]`. For example:\n\n::: {.cell execution_count=5}\n``` {.python .cell-code}\n# a Series from a list of strings with default index\npd.Series(['EDS 220', 'EDS 222', 'EDS 223', 'EDS 242'])\n```\n\n::: {.cell-output .cell-output-display execution_count=113}\n```\n0 EDS 220\n1 EDS 222\n2 EDS 223\n3 EDS 242\ndtype: object\n```\n:::\n:::\n\n\n\n\n\n### Simple operations\n\nArithmetic operations work on series and also most NumPy functions. For example:\n\n::: {.cell execution_count=8}\n``` {.python .cell-code}\n# define a series\ns = pd.Series([98,73,65],index=['Andrea', 'Beth', 'Carolina'])\n\n# divide each element in series by 10\nprint(s /10, '\\n')\n\n# take the exponential of each element in series\nprint(np.exp(s), '\\n')\n\n# notice this doesn't change the values of our series\nprint(s)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nAndrea 9.8\nBeth 7.3\nCarolina 6.5\ndtype: float64 \n\nAndrea 3.637971e+42\nBeth 5.052394e+31\nCarolina 1.694889e+28\ndtype: float64 \n\nAndrea 98\nBeth 73\nCarolina 65\ndtype: int64\n```\n:::\n:::\n\n\nWe can also produce new `pandas.Series` with `True`/`False` values indicating whether the elements in a series satisfy a condition or not:\n\n::: {.cell execution_count=9}\n``` {.python .cell-code}\ns > 10\n```\n\n::: {.cell-output .cell-output-display execution_count=117}\n```\nAndrea True\nBeth True\nCarolina True\ndtype: bool\n```\n:::\n:::\n\n\nThis kind of simple conditions on `pandas.Series` will be key when we are selecting data from data frames.\n\n\n\n### Attributes & Methods\n`pandas.Series` have *many* attributes and methods, you can see a [full list in the `pandas` documentation](https://pandas.pydata.org/docs/reference/api/pandas.Series.html). For now we will cover two examples that have to do with identifying missing values.\n\n`pandas` represents a missing or NA value with `NaN`, which stands for not a number. Let's construct a small series with some NA values:\n\n::: {.cell execution_count=10}\n``` {.python .cell-code}\n# series with NAs in it\ns = pd.Series([1, 2, np.NaN, 4, np.NaN])\n```\n:::\n\n\nA `pandas.Series` has an *attribute* called `hasnans` that returns `True` if there are any NaNs:\n\n::: {.cell execution_count=11}\n``` {.python .cell-code}\n# check if series has NAs\ns.hasnans\n```\n\n::: {.cell-output .cell-output-display execution_count=119}\n```\nTrue\n```\n:::\n:::\n\n\nThen we might be intersted in knowing which elements in the series are NAs. We can do this using the [`isna` method](https://pandas.pydata.org/docs/reference/api/pandas.Series.isna.html):\n\n::: {.cell execution_count=12}\n``` {.python .cell-code}\ns.isna()\n```\n\n::: {.cell-output .cell-output-display execution_count=120}\n```\n0 False\n1 False\n2 True\n3 False\n4 True\ndtype: bool\n```\n:::\n:::\n\n\nWe can see the ouput is a `pd.Series` of boolean values indicating if an element in the row at the given index is NA (`True` = is NA) or not (`False` = not NA).\n\n:::{.callout-note}\n## moving on\nThere's much more to say about `pandas.Series`, but this is enought to get us going. At this point, we mainly want to know about `pandas.Series` because `pandas.Series` are the columns of `pandas.DataFrame`s.\n:::\n\n\n\n## Data Frames\n\nThe Data Frame is the most used `pandas` object. It represents tabular data and we can think of it as a spreadhseet. Each column of a `pandas.DataFrame` is a `pandas.Series`. \n\n### Creating a `pandas.DataFrame`\nThere are [many ways of creating a `pandas.DataFrame`](https://pandas.pydata.org/docs/user_guide/dsintro.html#dataframe). \n\n\n\nWe already mentioned each column of a `pandas.DataFrame` is a `pandas.Series`. In fact, the `pandas.DataFrame` is a dictionary of `pandas.Series`, with each column name being the key and the column values being the key's value. Thus, we can create a `pandas.DataFrame` in this way:\n\n::: {.cell execution_count=16}\n``` {.python .cell-code}\n# initialize dictionary with columns' data \nd = {'col_name_1' : pd.Series(np.arange(3)),\n 'col_name_2' : pd.Series([3.1, 3.2, 3.3]),\n }\n\n# create data frame\ndf = pd.DataFrame(d)\ndf\n```\n\n::: {.cell-output .cell-output-display execution_count=124}\n```{=html}\n

      \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
      col_name_1col_name_2
      003.1
      113.2
      223.3
      \n
      \n```\n:::\n:::\n\n\nWe can change the index and column names by changing the `index` and `columns` attributes in the data frame. \n\n::: {.cell execution_count=17}\n``` {.python .cell-code}\n# print original index\nprint(df.index)\n\n# change the index\ndf.index = ['a','b','c']\ndf\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nRangeIndex(start=0, stop=3, step=1)\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=125}\n```{=html}\n
      \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
      col_name_1col_name_2
      a03.1
      b13.2
      c23.3
      \n
      \n```\n:::\n:::\n\n\n::: {.cell execution_count=18}\n``` {.python .cell-code}\n# print original column names\nprint(df.columns)\n\n# change column names \ndf.columns = ['C1','C2']\ndf\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nIndex(['col_name_1', 'col_name_2'], dtype='object')\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=126}\n```{=html}\n
      \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
      C1C2
      a03.1
      b13.2
      c23.3
      \n
      \n```\n:::\n:::\n\n\n## Subsetting a `pandas.DataFrame`\n\nLike it's often the case when working with `pandas`, there are *many* ways in which we can subset a data frame. We will review the core methods to do this. \n\n\nFor all examples we will use simplified data (glacial_loss.csv) from the National Snow and Ice Data Center ([Original dataset](​http://dx.doi.org/10.7265/N52N506F)). The column descriptions are:\n\n- **year**: ​calendar year\n- **europe - antarctica**: ​change in glacial volume (km3​ ​) in each region that year\n- **global_glacial_volume_change**: ​cumulative​ global glacial volume change (km3),\nstarting in 1961\n- **annual_sea_level_rise**: ​annual rise in sea level (mm)\n- **cumulative_sea_level_rise**:​ cumulative rise in sea level (mm) since 1961\n\nFirst, we read-in the file and get some baisc information about this data frame:\n\n::: {.cell execution_count=19}\n``` {.python .cell-code}\n# read in file\ndf = pd.read_csv('data/lesson-1/glacial_loss.csv')\n\n# see the first five rows\ndf.head()\n```\n\n::: {.cell-output .cell-output-display execution_count=127}\n```{=html}\n
      \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
      yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
      01961-5.128903-108.382987-18.721190-32.350759-14.359007-4.739367-35.116389-220.8235150.6100100.610010
      119625.576282-173.252450-24.324790-4.675440-2.161842-13.694367-78.222887-514.2698620.8106251.420635
      21963-10.123105-0.423751-2.047567-3.027298-27.5358813.4196333.765109-550.5756400.1002921.520927
      31964-4.50835820.0701480.477800-18.675385-2.24828620.73263314.853096-519.589859-0.0855961.435331
      4196510.62938543.695389-0.115332-18.414602-19.3987656.86210222.793484-473.112003-0.1283921.306939
      \n
      \n```\n:::\n:::\n\n\n::: {.cell execution_count=20}\n``` {.python .cell-code}\n# get column names\ndf.columns\n```\n\n::: {.cell-output .cell-output-display execution_count=128}\n```\nIndex(['year', 'europe', 'arctic', 'alaska', 'asia', 'north_america',\n 'south_america', 'antarctica', 'global_glacial_volume_change',\n 'annual_sea_level_rise', 'cumulative_sea_level_rise'],\n dtype='object')\n```\n:::\n:::\n\n\n::: {.cell execution_count=21}\n``` {.python .cell-code}\n# check the data types of each column\ndf.dtypes\n```\n\n::: {.cell-output .cell-output-display execution_count=129}\n```\nyear int64\neurope float64\narctic float64\nalaska float64\nasia float64\nnorth_america float64\nsouth_america float64\nantarctica float64\nglobal_glacial_volume_change float64\nannual_sea_level_rise float64\ncumulative_sea_level_rise float64\ndtype: object\n```\n:::\n:::\n\n\n::: {.cell execution_count=22}\n``` {.python .cell-code}\n# data frame's shape: output is a tuple (# rows, # columns)\ndf.shape\n```\n\n::: {.cell-output .cell-output-display execution_count=130}\n```\n(43, 11)\n```\n:::\n:::\n\n\n### Selecting a single column...\n\n#### ...by column name\nThis is the simplest case for selecting data. Suppose we are interested in the annual sea level rise. Then we can access that single column in this way:\n\n::: {.cell execution_count=23}\n``` {.python .cell-code}\n# seelect a single column by using square brackets []\nannual_rise = df['annual_sea_level_rise']\n\n# check the type of the ouput\nprint(type(annual_rise))\n\nannual_rise.head()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=131}\n```\n0 0.610010\n1 0.810625\n2 0.100292\n3 -0.085596\n4 -0.128392\nName: annual_sea_level_rise, dtype: float64\n```\n:::\n:::\n\n\nSince we only selected a single column the output is a `pandas.Series`. \n\n:::{.callout-note}\n## `pd.DataFrame` = dictionary of columns\nRemember we can think of a `pandas.DataFrame` as a dictionary of its columns? Then we can access a single column using the column name as the key, just like we would do in a dictionary. That is the we just used: `df['column_name']`.\n:::\n\nThis is an example of **selecting by label**, which means we want to select data from our data frame using the *names* of the columns, *not their position*.\n\n\n#### ... with attribute syntax\n\nWe can also access a single column by using attribute syntax:\n\n::: {.cell execution_count=24}\n``` {.python .cell-code}\nannual_rise_2 = df.annual_sea_level_rise\nannual_rise_2.head()\n```\n\n::: {.cell-output .cell-output-display execution_count=132}\n```\n0 0.610010\n1 0.810625\n2 0.100292\n3 -0.085596\n4 -0.128392\nName: annual_sea_level_rise, dtype: float64\n```\n:::\n:::\n\n\n### Selecting multiple columns...\n\n#### ... using a list of column names\nThis is another example of selecting by labels. We just need to pass a list with the column names to the square brackets `[]`. For example, say we want to look at the change in glacial volume in Europe and Asia, then we can select those columns like this:\n\n::: {.cell execution_count=25}\n``` {.python .cell-code}\n# select columns with names \"europe\" and \"asia\"\neurope_asia = df[['europe','asia']]\n```\n:::\n\n\nNotice there are double square brackets. This is because we are passing the list of names `['europe','asia']` to the selection brakcets `[]`. \n\n::: {.cell execution_count=26}\n``` {.python .cell-code}\n# check the type of the resulting selection\nprint(type(europe_asia))\n\n# check the shape of the selection\nprint((europe_asia.shape))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n(43, 2)\n```\n:::\n:::\n\n\n#### ... using a slice\nYet another example of selecting by label! In this case we will use the `loc` function. This is a powerful function! The general syntax is \n\n```python\ndf.loc[ row-selection , column-selection]\n```\nwhere `row-selection` and `column-selection` are the rows and columns we want to subset from the data frame. \n\nLet's start by a simple example, where we want to select a slice of columns, say the change in glacial volume per year in all regions. This corresponds to all columns between `arctic` and `antarctica`.\n\n::: {.cell execution_count=27}\n``` {.python .cell-code}\n# select all columns between 'arctic' and 'antarctica'\nall_regions = df.loc[:,'arctic':'antarctica']\nall_regions.head()\n```\n\n::: {.cell-output .cell-output-display execution_count=135}\n```{=html}\n
      \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
      arcticalaskaasianorth_americasouth_americaantarctica
      0-108.382987-18.721190-32.350759-14.359007-4.739367-35.116389
      1-173.252450-24.324790-4.675440-2.161842-13.694367-78.222887
      2-0.423751-2.047567-3.027298-27.5358813.4196333.765109
      320.0701480.477800-18.675385-2.24828620.73263314.853096
      443.695389-0.115332-18.414602-19.3987656.86210222.793484
      \n
      \n```\n:::\n:::\n\n\nNotice two things:\n\n- we used the colon `:` as the `row-selection` parameter, which means \"select all the rows\"\n- the slice of the data frame we got includes both endpoints of the slice `'arctic':'antarctica'`. In other words we get the `arctic` column *and* the `antarctica` column. This is different from how slicing works in base Python and NumPy, where the end point is not included.\n\n### Selecting rows...\nNow that we are familiar with some methods for selecting columns, let's move on to selecting rows. \n\n#### ... using a condition\nSelecting which rows satisfy a particular condition is, in my experience, the most usual kind of row subsetting. The general syntax for this type of selection is `df[condition_on_rows]`. For example, suppose we are intersted in all data after 1996. We can select those rows in this way:\n\n::: {.cell execution_count=28}\n``` {.python .cell-code}\n# select all rows with year > 1996\nafter_96 = df[df['year']>1996]\nafter_96\n```\n\n::: {.cell-output .cell-output-display execution_count=136}\n```{=html}\n
      \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
      yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
      361997-13.724106-24.832246-167.229145-34.406403-27.680661-38.213286-20.179090-4600.6860130.90962512.709077
      371998-13.083338-110.429302-107.879027-58.11570230.169987-3.797978-48.129928-4914.8319660.86780713.576884
      381999-8.039555-64.644068-87.714653-26.2117235.888512-8.038630-40.653001-5146.3682310.63960314.216487
      392000-17.008590-96.494055-44.445000-37.518173-29.191986-2.767698-58.873830-5435.3171750.79820215.014688
      402001-8.419109-145.415483-55.749505-35.977022-0.9261347.553503-86.774675-5764.0399310.90807415.922762
      412002-3.392361-48.718943-87.120000-36.127226-27.853498-13.484593-30.203960-6013.2255000.68835816.611120
      422003-3.392361-48.718943-67.253634-36.021991-75.066475-13.223430-30.203960-6289.6409760.76357917.374699
      \n
      \n```\n:::\n:::\n\n\nLet's break down what is happening here. In this case the condition for our rows is `df['year']>1996`, this checks which rows have a value greater than 1996 in the year column. Let's see this explicitely:\n\n::: {.cell execution_count=29}\n``` {.python .cell-code}\n# check the type of df['year']>1996\nprint(type(df['year']>1996))\n\ndf['year']>1996\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=137}\n```\n0 False\n1 False\n2 False\n3 False\n4 False\n5 False\n6 False\n7 False\n8 False\n9 False\n10 False\n11 False\n12 False\n13 False\n14 False\n15 False\n16 False\n17 False\n18 False\n19 False\n20 False\n21 False\n22 False\n23 False\n24 False\n25 False\n26 False\n27 False\n28 False\n29 False\n30 False\n31 False\n32 False\n33 False\n34 False\n35 False\n36 True\n37 True\n38 True\n39 True\n40 True\n41 True\n42 True\nName: year, dtype: bool\n```\n:::\n:::\n\n\nThe output is a `pandas.Series` with boolean values (`True` or `False`) indicating which rows satisfy the condition year>1996. When we pass such a series of boolean values to the selection brackets `[]` we keep only those rows with a `True` value. \n\nHere's another example of using a condition. Suppose we want to look at data from years 1970 to 1979. One way of doing this is to use the `in` operator in our condition:\n\n::: {.cell execution_count=30}\n``` {.python .cell-code}\nseventies = df[df['year'].isin(range(1970,1980))]\nseventies\n```\n\n::: {.cell-output .cell-output-display execution_count=138}\n```{=html}\n
      \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
      yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
      91970-6.452316-24.494667-0.125296-36.12019911.61979011.6369114.400377-999.0181770.1102252.759719
      1019710.414711-42.90418928.103328-8.702938-9.9645421.061299-6.735536-1038.1044590.1079732.867692
      111972-5.144729-27.004031-22.143350-40.88335732.363730-14.968034-6.223849-1122.8855060.2342023.101894
      1219734.0810909.83944422.985188-31.432594-20.8832322.10364910.539823-1125.6777430.0077133.109607
      1319741.545615-40.126998-29.517874-43.861622-23.991402-21.3388254.419343-1279.9642870.4262063.535813
      1419757.431192-32.410467-44.094084-43.357442-30.858810-2.368842-7.775315-1434.8180370.4277733.963586
      1519763.98675321.686639-28.234725-67.292125-12.534421-19.46535819.250607-1518.1851290.2302964.193882
      1619774.891410-33.123010-5.662139-62.165684-15.9053322.654950-23.727249-1652.4534000.3709074.564788
      1719788.404591-77.561015-12.503384-22.858040-31.0976097.127708-9.140167-1791.3550220.3837064.948495
      1819793.916703-88.351684-63.938851-49.242043-12.076624-17.718503-9.578557-2030.5378480.6607265.609221
      \n
      \n```\n:::\n:::\n\n\nLet's break it down: \n\n- `df['year']` is the column with the year values, a `pandas.Series`,\n\n- in `df['year'].isin()`, we have that [`isin` is a method for the `pandas.Series`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.isin.html) and we are calling it using the dot `.`. \n\n- `range(1970,1980)` constructs consecutive integers from 1970 to 1979 - remember the right endopoint (1980) is not included!\n\n- `df['year'].isin(range(1970,1980))` is then a `pandas.Series` of boolean values indicating which rows have year equal to 1970, ..., 1979. \n\n- when we put `df['year'].isin(range(1970,1980))` inside the selection brackets `[]` we obtain the rows of the data frame with year equal to 1970, ..., 1979.\n\n:::{.callout-note}\n## `loc` for row selection\nIt is equivalent to write\n\n```python\n# select rows with year<1965\ndf[df['year'] < 1965]\n```\nand\n```python\n# select rows with year<1965 using love\ndf.loc[ df['year'] <1965 , :]\n```\nIn the second one:\n\n- we are using the `df.loc[ row-selection , column-selection]` syntax\n\n- the `row-selection` parameter is the condition `df['year']<1965`\n\n- the `column-selection` parameter is a colon `:`, which indicates we want all columns for the rows we are selecting.\n\nWe prefer the first syntax when we are selecting rows and not columns since it is simpler.\n:::\n\n#### ... using multiple conditions\nWe can combine multipe conditions by surrounding each one in parenthesis `()` and using the or operator `|` and the and operator `and`.\n\n***or* example**:\n\n::: {.cell execution_count=31}\n``` {.python .cell-code}\n# select rows with \n# annual_sea_level_rise<0.5 mm OR annual_sea_level_rise>0.8 mm\n\ndf[ (df['annual_sea_level_rise']<0.5) | (df['annual_sea_level_rise']>0.8)]\ndf.head()\n```\n\n::: {.cell-output .cell-output-display execution_count=139}\n```{=html}\n
      \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
      yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
      01961-5.128903-108.382987-18.721190-32.350759-14.359007-4.739367-35.116389-220.8235150.6100100.610010
      119625.576282-173.252450-24.324790-4.675440-2.161842-13.694367-78.222887-514.2698620.8106251.420635
      21963-10.123105-0.423751-2.047567-3.027298-27.5358813.4196333.765109-550.5756400.1002921.520927
      31964-4.50835820.0701480.477800-18.675385-2.24828620.73263314.853096-519.589859-0.0855961.435331
      4196510.62938543.695389-0.115332-18.414602-19.3987656.86210222.793484-473.112003-0.1283921.306939
      \n
      \n```\n:::\n:::\n\n\n***and* example**\n\n::: {.cell execution_count=32}\n``` {.python .cell-code}\n# select rows with cumulative_sea_level_rise>10 AND global_glacial_volume_change<-300\ndf[ (df['cumulative_sea_level_rise']>10) & (df['global_glacial_volume_change']<-300)]\n```\n\n::: {.cell-output .cell-output-display execution_count=140}\n```{=html}\n
      \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
      yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
      32199316.685013-73.666274-43.702040-65.995130-33.151246-20.578403-20.311577-3672.5820820.67112610.145254
      3319940.741751-3.069084-59.962273-59.004710-89.506142-15.258449-8.168498-3908.9771910.65302510.798280
      341995-2.139665-58.167778-74.1417623.500155-0.699374-19.863392-25.951496-4088.0828730.49476711.293047
      351996-6.809834-4.550205-74.847017-67.4365914.867530-21.080115-11.781489-4271.4015940.50640511.799452
      361997-13.724106-24.832246-167.229145-34.406403-27.680661-38.213286-20.179090-4600.6860130.90962512.709077
      371998-13.083338-110.429302-107.879027-58.11570230.169987-3.797978-48.129928-4914.8319660.86780713.576884
      381999-8.039555-64.644068-87.714653-26.2117235.888512-8.038630-40.653001-5146.3682310.63960314.216487
      392000-17.008590-96.494055-44.445000-37.518173-29.191986-2.767698-58.873830-5435.3171750.79820215.014688
      402001-8.419109-145.415483-55.749505-35.977022-0.9261347.553503-86.774675-5764.0399310.90807415.922762
      412002-3.392361-48.718943-87.120000-36.127226-27.853498-13.484593-30.203960-6013.2255000.68835816.611120
      422003-3.392361-48.718943-67.253634-36.021991-75.066475-13.223430-30.203960-6289.6409760.76357917.374699
      \n
      \n```\n:::\n:::\n\n\n#### ... by position\n\nAll the selections we have done so far have been using labels or using a condition. Sometimes we might want to select certain rows depending on their *actual position* in the data frame. In this case we use the `iloc` method with the syntax `df.iloc[row-indices]`. `iloc` stands for integer-location based indexing. Let's see some examples:\n\n::: {.cell execution_count=33}\n``` {.python .cell-code}\n# select the fifht row = index 4\ndf.iloc[4]\n```\n\n::: {.cell-output .cell-output-display execution_count=141}\n```\nyear 1965.000000\neurope 10.629385\narctic 43.695389\nalaska -0.115332\nasia -18.414602\nnorth_america -19.398765\nsouth_america 6.862102\nantarctica 22.793484\nglobal_glacial_volume_change -473.112003\nannual_sea_level_rise -0.128392\ncumulative_sea_level_rise 1.306939\nName: 4, dtype: float64\n```\n:::\n:::\n\n\n::: {.cell execution_count=34}\n``` {.python .cell-code}\n# select rows 23 through 30, inclduing 30\ndf.iloc[23:31]\n```\n\n::: {.cell-output .cell-output-display execution_count=142}\n```{=html}\n
      \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
      yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
      2319848.581427-5.755672-33.466092-20.528535-20.734676-8.267686-3.261011-2569.3398020.2326097.097624
      241985-5.970980-49.65108912.065473-31.571622-33.83398510.072906-13.587886-2682.8579260.3135867.411210
      251986-5.68064222.9008477.557447-18.920773-33.014743-4.65203030.482473-2684.1976320.0037017.414911
      2619878.19147712.387780-24.007862-41.121970-48.5609961.6707333.130190-2773.3255680.2462107.661120
      271988-11.117228-31.06648949.897712-21.300712-46.54543513.460422-37.986834-2858.7676210.2360287.897148
      28198914.863220-23.462392-36.112726-46.528372-57.756422-21.687470-10.044757-3041.1691310.5038728.401020
      291990-1.226009-27.484542-92.713339-35.553433-56.563056-31.077022-29.893352-3318.2203970.7653359.166355
      301991-14.391425-34.898689-8.822063-15.338299-31.458010-7.162909-35.968429-3467.6302840.4127349.579089
      \n
      \n```\n:::\n:::\n\n\nNotice since we are back to indexing by position the right endpoint of the slice (6) is not included in the ouput. \n\n### Selecting rows and columns simultaneously...\n\nSelecting rows and columns simultaneously can be done using `loc` (labels or conditions) or `iloc` (integer position).\n\n#### ...by labels or conditions\nWhen we want to select rows and columns simultaneously by labels or conditions we can use the method `loc` with the syntax \n\n```python\ndf.loc[ row-selection , column-selection]\n```\n\nspecifying both paratmers: `row-selection` and `column-selection`. These parameters can be a condition (which generates a boolean array) or a subset of labels from the index or the column names. Let's see an examples:\n\n::: {.cell execution_count=35}\n``` {.python .cell-code}\n# select change in glacial volume in Europe per year after 2000\ndf.loc[df['year']>2000,['year','europe']]\n```\n\n::: {.cell-output .cell-output-display execution_count=143}\n```{=html}\n
      \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
      yeareurope
      402001-8.419109
      412002-3.392361
      422003-3.392361
      \n
      \n```\n:::\n:::\n\n\nLet's break it down:\n\n- we are using the `df.loc[ row-selection , column-selection]` syntax\n\n- the `row-selection` parameter is the condition `df['year']>1990`, which is a boolean array saying which years are greater than 1990\n\n- the `column-selection` parameter is `['year','europe']` which is a list with the names of the two columns we are intersted in. \n\n#### ... by position\n\nWhen we want to select rows and columns simultaneously by position we use the `iloc` method with the syntax:\n```python\ndf.iloc[ row-indices , column-indices]\n```\n\nFor example, \n\n::: {.cell execution_count=36}\n``` {.python .cell-code}\n# select rows 3-7 (including 7) and columns 3 and 4\ndf.iloc[ 3:8, [3,4] ]\n```\n\n::: {.cell-output .cell-output-display execution_count=144}\n```{=html}\n
      \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
      alaskaasia
      30.477800-18.675385
      4-0.115332-18.414602
      50.224762-14.630284
      6-7.174030-39.013695
      7-0.6605567.879589
      \n
      \n```\n:::\n:::\n\n\nLet's break it down:\n\n- we are using the `df.iloc[ row-indices , column-indices]` syntax\n\n- the `row-indices` parameter is the slice *of integer indices* 3:8. Remember the right endpoint (8) won't be included.\n\n- the `column-indices` parameter is the list of integer indices 3 and 4. This means we are selecting the fourth and fifth column.\n\n\n### Notes about `loc` and `iloc`\n\n::: {.callout-caution}\n## `iloc` vs. `loc`\nAt the beginning, the difference between `iloc` and `loc` can be confusing. Remember the `i` in `iloc` stands for *integer-location*, so this function only uses integer indexing to retrieve information from the data frames in the same way as indexing for Python lists.\n\nIf you want to dive deeper, this is a great discussion about the difference between `iloc` and `loc`: [Stackoverflow - How are iloc and loc different?](https://stackoverflow.com/questions/31593201/how-are-iloc-and-loc-different/31593712#31593712)\n\nAnd, as always, the documentation will provide you with more information:\n[`pandas.DataFrame.loc`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.loc.html) and [`pandas.DataFrame.iloc`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.iloc.html).\n:::\n\n\n:::{.callout-warning}\n## `iloc` for column selection? Avoid it!\nWe can also access columns by position using `iloc` - but it is best not to if possible.\n\nSuppose we want to access the 10th column in the data frame - then we want to select a column *by position*. In this case the 10th column is the annual sea level rise data and the 10th position corresponds to the index 9. We can select this column by position using the `iloc` method*:\n\n::: {.cell execution_count=37}\n``` {.python .cell-code}\n# select column by position using iloc\n# the syntax is iloc[rows,columns]\n# [:,9] means \"select all rows from the 10th column\"\nannual_rise_3 = df.iloc[:,9]\nannual_rise_3.head()\n```\n\n::: {.cell-output .cell-output-display execution_count=145}\n```\n0 0.610010\n1 0.810625\n2 0.100292\n3 -0.085596\n4 -0.128392\nName: annual_sea_level_rise, dtype: float64\n```\n:::\n:::\n\n\nUnless you are *really* looking for information about *the 10th column*, do not access a column by position. This is bound to break in many ways:\n\n- it relies on a person correctly counting the position of a column. Even with a small dataset this can be prone to error.\n\n- it is not explicit: if we want information about sea level rise `df.annual_sea_level_rise` or `df['annual_sea_level_rise']` are explicitely telling us we are accessing that information. `df.iloc[:,9]` is obscure and uninformative.\n\n- datastets can get updated. Maybe a new column was added before `annual_sea_level_rise`, this would change the position of the column, which would make any code depending on `df.iloc[:,9]` invalid. Accessing by label helps reproducibility!\n\n:::\n\n## Resources\n\nWhat is presented in this section is a comprehensive, but not an exhaustive list of methods to select data in `pandas.DataFrames`. There are *so many* ways to subset data to get the same result. Some of the content from this lesson is adapted from the following resources and I encourage you to read them to learn more! \n\n📖 [Pandas getting started tutorials - How to I select a subset of a\nDataFrame](https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html) \n\n📖 [Pandas documentation - User Guide - Indexing and Selecting Data](https://pandas.pydata.org/docs/user_guide/indexing.html#indexing-slicing-with-labels)\n\n📖 [Python for Data Analysis, 3E - Getting started with pandas](https://wesmckinney.com/book/pandas-basics)\n\n## Acknowledgements\n\nThe simplified glacial_loss.csv dataset was created by [Dr. Allison Horst](https://allisonhorst.github.io) as part of her course materials on environmental data science. \n\n", + "markdown": "# Pandas Fundamentals\n\n## About\nIn this lesson we cover the two core objects in the `pandas` library, the `pandas.Series` and the `pandas.DataFrame`. We will also learn methods to select data from our datasets.\n\n## `pandas`\n\n`pandas` is a Python package to wrangle and analyze tabular data. It is built on top of NumPy and has become the core tool for doing data analysis in Python. \n\nThe convention to import it is:\n\n::: {.cell execution_count=1}\n``` {.python .cell-code}\nimport pandas as pd\n\n# we will also import numpy \nimport numpy as np\n```\n:::\n\n\nThere is so much to learn about `pandas`. While we won't be able to cover every single functionality of this package in the next three lecutres, the goal is to get you started with the basic tools for data wrangling and give you a solid basis on which you can explore further. \n\n## Series\n\nThe first core data structure of pandas is the **series**. \nA series is a *one-dimensional* array of *indexed* data. A `pandas.Series` having an **index** is the main difference between a `pandas.Series` and a numpy array. See the difference:\n\n::: {.cell execution_count=2}\n``` {.python .cell-code}\n# a numpy array\n# np.random.randn returns values from the std normal distribution\narr = np.random.randn(4) \nprint(type(arr))\nprint(arr, \"\\n\")\n\n# a pandas series made from the previous array\ns = pd.Series(arr)\nprint(type(s))\nprint(s)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n[ 0.26088972 0.92497305 1.34752178 -1.23504893] \n\n\n0 0.260890\n1 0.924973\n2 1.347522\n3 -1.235049\ndtype: float64\n```\n:::\n:::\n\n\n### Creating a `pandas.Series`\n\nThe basic method to create a `pandas.Series` is to call\n\n``` python\ns = pd.Series(data, index=index)\n```\n\nThe `data` parameter can be:\n\n- a [numpy array](https://numpy.org/doc/stable/reference/arrays.ndarray.html) or [a list](https://realpython.com/python-list/)\n- a [Python dictionary](https://realpython.com/lessons/dictionary-python/)\n- [a number](https://commons.wikimedia.org/wiki/File:Number-three.JPG)\n\nThe `index` parameter is a list of index labels.\n\nFor now, we will create a `pandas.Series` from a numpy array or list. To use this method we need to pass a numpy array (or a list of objects that can be converted to NumPy types) as `data` and a list of indices of the same length as data. \n\n::: {.cell execution_count=3}\n``` {.python .cell-code}\n# a Series from a numpy array \npd.Series(np.arange(3), index=['a','b','c'])\n```\n\n::: {.cell-output .cell-output-display execution_count=3}\n```\na 0\nb 1\nc 2\ndtype: int64\n```\n:::\n:::\n\n\n The `index` parameter is optional. If we don't include it, the default is to make the index equal to `[0,...,len(data)-1]`. For example:\n\n::: {.cell execution_count=4}\n``` {.python .cell-code}\n# a Series from a list of strings with default index\npd.Series(['EDS 220', 'EDS 222', 'EDS 223', 'EDS 242'])\n```\n\n::: {.cell-output .cell-output-display execution_count=4}\n```\n0 EDS 220\n1 EDS 222\n2 EDS 223\n3 EDS 242\ndtype: object\n```\n:::\n:::\n\n\n\n\n\n### Simple operations\n\nArithmetic operations work on series and also most NumPy functions. For example:\n\n::: {.cell execution_count=7}\n``` {.python .cell-code}\n# define a series\ns = pd.Series([98,73,65],index=['Andrea', 'Beth', 'Carolina'])\n\n# divide each element in series by 10\nprint(s /10, '\\n')\n\n# take the exponential of each element in series\nprint(np.exp(s), '\\n')\n\n# notice this doesn't change the values of our series\nprint(s)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nAndrea 9.8\nBeth 7.3\nCarolina 6.5\ndtype: float64 \n\nAndrea 3.637971e+42\nBeth 5.052394e+31\nCarolina 1.694889e+28\ndtype: float64 \n\nAndrea 98\nBeth 73\nCarolina 65\ndtype: int64\n```\n:::\n:::\n\n\nWe can also produce new `pandas.Series` with `True`/`False` values indicating whether the elements in a series satisfy a condition or not:\n\n::: {.cell execution_count=8}\n``` {.python .cell-code}\ns > 10\n```\n\n::: {.cell-output .cell-output-display execution_count=8}\n```\nAndrea True\nBeth True\nCarolina True\ndtype: bool\n```\n:::\n:::\n\n\nThis kind of simple conditions on `pandas.Series` will be key when we are selecting data from data frames.\n\n\n\n### Attributes & Methods\n`pandas.Series` have *many* attributes and methods, you can see a [full list in the `pandas` documentation](https://pandas.pydata.org/docs/reference/api/pandas.Series.html). For now we will cover two examples that have to do with identifying missing values.\n\n`pandas` represents a missing or NA value with `NaN`, which stands for not a number. Let's construct a small series with some NA values:\n\n::: {.cell execution_count=9}\n``` {.python .cell-code}\n# series with NAs in it\ns = pd.Series([1, 2, np.NaN, 4, np.NaN])\n```\n:::\n\n\nA `pandas.Series` has an *attribute* called `hasnans` that returns `True` if there are any NaNs:\n\n::: {.cell execution_count=10}\n``` {.python .cell-code}\n# check if series has NAs\ns.hasnans\n```\n\n::: {.cell-output .cell-output-display execution_count=10}\n```\nTrue\n```\n:::\n:::\n\n\nThen we might be intersted in knowing which elements in the series are NAs. We can do this using the [`isna` method](https://pandas.pydata.org/docs/reference/api/pandas.Series.isna.html):\n\n::: {.cell execution_count=11}\n``` {.python .cell-code}\ns.isna()\n```\n\n::: {.cell-output .cell-output-display execution_count=11}\n```\n0 False\n1 False\n2 True\n3 False\n4 True\ndtype: bool\n```\n:::\n:::\n\n\nWe can see the ouput is a `pd.Series` of boolean values indicating if an element in the row at the given index is NA (`True` = is NA) or not (`False` = not NA).\n\n:::{.callout-note}\n## moving on\nThere's much more to say about `pandas.Series`, but this is enought to get us going. At this point, we mainly want to know about `pandas.Series` because `pandas.Series` are the columns of `pandas.DataFrame`s.\n:::\n\n\n\n## Data Frames\n\nThe Data Frame is the most used `pandas` object. It represents tabular data and we can think of it as a spreadhseet. Each column of a `pandas.DataFrame` is a `pandas.Series`. \n\n### Creating a `pandas.DataFrame`\nThere are [many ways of creating a `pandas.DataFrame`](https://pandas.pydata.org/docs/user_guide/dsintro.html#dataframe). \n\n\n\nWe already mentioned each column of a `pandas.DataFrame` is a `pandas.Series`. In fact, the `pandas.DataFrame` is a dictionary of `pandas.Series`, with each column name being the key and the column values being the key's value. Thus, we can create a `pandas.DataFrame` in this way:\n\n::: {.cell execution_count=15}\n``` {.python .cell-code}\n# initialize dictionary with columns' data \nd = {'col_name_1' : pd.Series(np.arange(3)),\n 'col_name_2' : pd.Series([3.1, 3.2, 3.3]),\n }\n\n# create data frame\ndf = pd.DataFrame(d)\ndf\n```\n\n::: {.cell-output .cell-output-display execution_count=15}\n```{=html}\n
      \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
      col_name_1col_name_2
      003.1
      113.2
      223.3
      \n
      \n```\n:::\n:::\n\n\nWe can change the index and column names by changing the `index` and `columns` attributes in the data frame. \n\n::: {.cell execution_count=16}\n``` {.python .cell-code}\n# print original index\nprint(df.index)\n\n# change the index\ndf.index = ['a','b','c']\ndf\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nRangeIndex(start=0, stop=3, step=1)\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=16}\n```{=html}\n
      \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
      col_name_1col_name_2
      a03.1
      b13.2
      c23.3
      \n
      \n```\n:::\n:::\n\n\n::: {.cell execution_count=17}\n``` {.python .cell-code}\n# print original column names\nprint(df.columns)\n\n# change column names \ndf.columns = ['C1','C2']\ndf\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nIndex(['col_name_1', 'col_name_2'], dtype='object')\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=17}\n```{=html}\n
      \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
      C1C2
      a03.1
      b13.2
      c23.3
      \n
      \n```\n:::\n:::\n\n\n## Subsetting a `pandas.DataFrame`\n\nLike it's often the case when working with `pandas`, there are *many* ways in which we can subset a data frame. We will review the core methods to do this. \n\n\nFor all examples we will use simplified data (glacial_loss.csv) from the National Snow and Ice Data Center ([Original dataset](​http://dx.doi.org/10.7265/N52N506F)). The column descriptions are:\n\n- **year**: ​calendar year\n- **europe - antarctica**: ​change in glacial volume (km3​ ​) in each region that year\n- **global_glacial_volume_change**: ​cumulative​ global glacial volume change (km3),\nstarting in 1961\n- **annual_sea_level_rise**: ​annual rise in sea level (mm)\n- **cumulative_sea_level_rise**:​ cumulative rise in sea level (mm) since 1961\n\nFirst, we read-in the file and get some baisc information about this data frame:\n\n::: {.cell execution_count=18}\n``` {.python .cell-code}\n# read in file\ndf = pd.read_csv('data/lesson-1/glacial_loss.csv')\n\n# see the first five rows\ndf.head()\n```\n\n::: {.cell-output .cell-output-display execution_count=18}\n```{=html}\n
      \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
      yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
      01961-5.128903-108.382987-18.721190-32.350759-14.359007-4.739367-35.116389-220.8235150.6100100.610010
      119625.576282-173.252450-24.324790-4.675440-2.161842-13.694367-78.222887-514.2698620.8106251.420635
      21963-10.123105-0.423751-2.047567-3.027298-27.5358813.4196333.765109-550.5756400.1002921.520927
      31964-4.50835820.0701480.477800-18.675385-2.24828620.73263314.853096-519.589859-0.0855961.435331
      4196510.62938543.695389-0.115332-18.414602-19.3987656.86210222.793484-473.112003-0.1283921.306939
      \n
      \n```\n:::\n:::\n\n\n::: {.cell execution_count=19}\n``` {.python .cell-code}\n# get column names\ndf.columns\n```\n\n::: {.cell-output .cell-output-display execution_count=19}\n```\nIndex(['year', 'europe', 'arctic', 'alaska', 'asia', 'north_america',\n 'south_america', 'antarctica', 'global_glacial_volume_change',\n 'annual_sea_level_rise', 'cumulative_sea_level_rise'],\n dtype='object')\n```\n:::\n:::\n\n\n::: {.cell execution_count=20}\n``` {.python .cell-code}\n# check the data types of each column\ndf.dtypes\n```\n\n::: {.cell-output .cell-output-display execution_count=20}\n```\nyear int64\neurope float64\narctic float64\nalaska float64\nasia float64\nnorth_america float64\nsouth_america float64\nantarctica float64\nglobal_glacial_volume_change float64\nannual_sea_level_rise float64\ncumulative_sea_level_rise float64\ndtype: object\n```\n:::\n:::\n\n\n::: {.cell execution_count=21}\n``` {.python .cell-code}\n# data frame's shape: output is a tuple (# rows, # columns)\ndf.shape\n```\n\n::: {.cell-output .cell-output-display execution_count=21}\n```\n(43, 11)\n```\n:::\n:::\n\n\n### Selecting a single column...\n\n#### ...by column name\nThis is the simplest case for selecting data. Suppose we are interested in the annual sea level rise. Then we can access that single column in this way:\n\n::: {.cell execution_count=22}\n``` {.python .cell-code}\n# seelect a single column by using square brackets []\nannual_rise = df['annual_sea_level_rise']\n\n# check the type of the ouput\nprint(type(annual_rise))\n\nannual_rise.head()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=22}\n```\n0 0.610010\n1 0.810625\n2 0.100292\n3 -0.085596\n4 -0.128392\nName: annual_sea_level_rise, dtype: float64\n```\n:::\n:::\n\n\nSince we only selected a single column the output is a `pandas.Series`. \n\n:::{.callout-note}\n## `pd.DataFrame` = dictionary of columns\nRemember we can think of a `pandas.DataFrame` as a dictionary of its columns? Then we can access a single column using the column name as the key, just like we would do in a dictionary. That is the we just used: `df['column_name']`.\n:::\n\nThis is an example of **selecting by label**, which means we want to select data from our data frame using the *names* of the columns, *not their position*.\n\n\n#### ... with attribute syntax\n\nWe can also access a single column by using attribute syntax:\n\n::: {.cell execution_count=23}\n``` {.python .cell-code}\nannual_rise_2 = df.annual_sea_level_rise\nannual_rise_2.head()\n```\n\n::: {.cell-output .cell-output-display execution_count=23}\n```\n0 0.610010\n1 0.810625\n2 0.100292\n3 -0.085596\n4 -0.128392\nName: annual_sea_level_rise, dtype: float64\n```\n:::\n:::\n\n\n### Selecting multiple columns...\n\n#### ... using a list of column names\nThis is another example of selecting by labels. We just need to pass a list with the column names to the square brackets `[]`. For example, say we want to look at the change in glacial volume in Europe and Asia, then we can select those columns like this:\n\n::: {.cell execution_count=24}\n``` {.python .cell-code}\n# select columns with names \"europe\" and \"asia\"\neurope_asia = df[['europe','asia']]\n```\n:::\n\n\nNotice there are double square brackets. This is because we are passing the list of names `['europe','asia']` to the selection brakcets `[]`. \n\n::: {.cell execution_count=25}\n``` {.python .cell-code}\n# check the type of the resulting selection\nprint(type(europe_asia))\n\n# check the shape of the selection\nprint((europe_asia.shape))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n(43, 2)\n```\n:::\n:::\n\n\n#### ... using a slice\nYet another example of selecting by label! In this case we will use the `loc` selection. The general syntax is \n\n```python\ndf.loc[ row-selection , column-selection]\n```\nwhere `row-selection` and `column-selection` are the rows and columns we want to subset from the data frame. \n\nLet's start by a simple example, where we want to select a slice of columns, say the change in glacial volume per year in all regions. This corresponds to all columns between `arctic` and `antarctica`.\n\n::: {.cell execution_count=26}\n``` {.python .cell-code}\n# select all columns between 'arctic' and 'antarctica'\nall_regions = df.loc[:,'arctic':'antarctica']\nall_regions.head()\n```\n\n::: {.cell-output .cell-output-display execution_count=26}\n```{=html}\n
      \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
      arcticalaskaasianorth_americasouth_americaantarctica
      0-108.382987-18.721190-32.350759-14.359007-4.739367-35.116389
      1-173.252450-24.324790-4.675440-2.161842-13.694367-78.222887
      2-0.423751-2.047567-3.027298-27.5358813.4196333.765109
      320.0701480.477800-18.675385-2.24828620.73263314.853096
      443.695389-0.115332-18.414602-19.3987656.86210222.793484
      \n
      \n```\n:::\n:::\n\n\nNotice two things:\n\n- we used the colon `:` as the `row-selection` parameter, which means \"select all the rows\"\n- the slice of the data frame we got includes both endpoints of the slice `'arctic':'antarctica'`. In other words we get the `arctic` column *and* the `antarctica` column. This is different from how slicing works in base Python and NumPy, where the end point is not included.\n\n### Selecting rows...\nNow that we are familiar with some methods for selecting columns, let's move on to selecting rows. \n\n#### ... using a condition\nSelecting which rows satisfy a particular condition is, in my experience, the most usual kind of row subsetting. The general syntax for this type of selection is `df[condition_on_rows]`. For example, suppose we are intersted in all data after 1996. We can select those rows in this way:\n\n::: {.cell execution_count=27}\n``` {.python .cell-code}\n# select all rows with year > 1996\nafter_96 = df[df['year']>1996]\nafter_96\n```\n\n::: {.cell-output .cell-output-display execution_count=27}\n```{=html}\n
      \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
      yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
      361997-13.724106-24.832246-167.229145-34.406403-27.680661-38.213286-20.179090-4600.6860130.90962512.709077
      371998-13.083338-110.429302-107.879027-58.11570230.169987-3.797978-48.129928-4914.8319660.86780713.576884
      381999-8.039555-64.644068-87.714653-26.2117235.888512-8.038630-40.653001-5146.3682310.63960314.216487
      392000-17.008590-96.494055-44.445000-37.518173-29.191986-2.767698-58.873830-5435.3171750.79820215.014688
      402001-8.419109-145.415483-55.749505-35.977022-0.9261347.553503-86.774675-5764.0399310.90807415.922762
      412002-3.392361-48.718943-87.120000-36.127226-27.853498-13.484593-30.203960-6013.2255000.68835816.611120
      422003-3.392361-48.718943-67.253634-36.021991-75.066475-13.223430-30.203960-6289.6409760.76357917.374699
      \n
      \n```\n:::\n:::\n\n\nLet's break down what is happening here. In this case the condition for our rows is `df['year']>1996`, this checks which rows have a value greater than 1996 in the year column. Let's see this explicitely:\n\n::: {.cell execution_count=28}\n``` {.python .cell-code}\n# check the type of df['year']>1996\nprint(type(df['year']>1996))\n\ndf['year']>1996\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=28}\n```\n0 False\n1 False\n2 False\n3 False\n4 False\n5 False\n6 False\n7 False\n8 False\n9 False\n10 False\n11 False\n12 False\n13 False\n14 False\n15 False\n16 False\n17 False\n18 False\n19 False\n20 False\n21 False\n22 False\n23 False\n24 False\n25 False\n26 False\n27 False\n28 False\n29 False\n30 False\n31 False\n32 False\n33 False\n34 False\n35 False\n36 True\n37 True\n38 True\n39 True\n40 True\n41 True\n42 True\nName: year, dtype: bool\n```\n:::\n:::\n\n\nThe output is a `pandas.Series` with boolean values (`True` or `False`) indicating which rows satisfy the condition year>1996. When we pass such a series of boolean values to the selection brackets `[]` we keep only those rows with a `True` value. \n\nHere's another example of using a condition. Suppose we want to look at data from years 1970 to 1979. One way of doing this is to use the `in` operator in our condition:\n\n::: {.cell execution_count=29}\n``` {.python .cell-code}\nseventies = df[df['year'].isin(range(1970,1980))]\nseventies\n```\n\n::: {.cell-output .cell-output-display execution_count=29}\n```{=html}\n
      \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
      yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
      91970-6.452316-24.494667-0.125296-36.12019911.61979011.6369114.400377-999.0181770.1102252.759719
      1019710.414711-42.90418928.103328-8.702938-9.9645421.061299-6.735536-1038.1044590.1079732.867692
      111972-5.144729-27.004031-22.143350-40.88335732.363730-14.968034-6.223849-1122.8855060.2342023.101894
      1219734.0810909.83944422.985188-31.432594-20.8832322.10364910.539823-1125.6777430.0077133.109607
      1319741.545615-40.126998-29.517874-43.861622-23.991402-21.3388254.419343-1279.9642870.4262063.535813
      1419757.431192-32.410467-44.094084-43.357442-30.858810-2.368842-7.775315-1434.8180370.4277733.963586
      1519763.98675321.686639-28.234725-67.292125-12.534421-19.46535819.250607-1518.1851290.2302964.193882
      1619774.891410-33.123010-5.662139-62.165684-15.9053322.654950-23.727249-1652.4534000.3709074.564788
      1719788.404591-77.561015-12.503384-22.858040-31.0976097.127708-9.140167-1791.3550220.3837064.948495
      1819793.916703-88.351684-63.938851-49.242043-12.076624-17.718503-9.578557-2030.5378480.6607265.609221
      \n
      \n```\n:::\n:::\n\n\nLet's break it down: \n\n- `df['year']` is the column with the year values, a `pandas.Series`,\n\n- in `df['year'].isin()`, we have that [`isin` is a method for the `pandas.Series`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.isin.html) and we are calling it using the dot `.`. \n\n- `range(1970,1980)` constructs consecutive integers from 1970 to 1979 - remember the right endopoint (1980) is not included!\n\n- `df['year'].isin(range(1970,1980))` is then a `pandas.Series` of boolean values indicating which rows have year equal to 1970, ..., 1979. \n\n- when we put `df['year'].isin(range(1970,1980))` inside the selection brackets `[]` we obtain the rows of the data frame with year equal to 1970, ..., 1979.\n\n:::{.callout-note}\n## `loc` for row selection\nIt is equivalent to write\n\n```python\n# select rows with year<1965\ndf[df['year'] < 1965]\n```\nand\n```python\n# select rows with year<1965 using love\ndf.loc[ df['year'] <1965 , :]\n```\nIn the second one:\n\n- we are using the `df.loc[ row-selection , column-selection]` syntax\n\n- the `row-selection` parameter is the condition `df['year']<1965`\n\n- the `column-selection` parameter is a colon `:`, which indicates we want all columns for the rows we are selecting.\n\nWe prefer the first syntax when we are selecting rows and not columns since it is simpler.\n:::\n\n#### ... using multiple conditions\nWe can combine multipe conditions by surrounding each one in parenthesis `()` and using the or operator `|` and the and operator `&`.\n\n***or* example**:\n\n::: {.cell execution_count=30}\n``` {.python .cell-code}\n# select rows with \n# annual_sea_level_rise<0.5 mm OR annual_sea_level_rise>0.8 mm\n\ndf[ (df['annual_sea_level_rise']<0.5) | (df['annual_sea_level_rise']>0.8)]\ndf.head()\n```\n\n::: {.cell-output .cell-output-display execution_count=30}\n```{=html}\n
      \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
      yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
      01961-5.128903-108.382987-18.721190-32.350759-14.359007-4.739367-35.116389-220.8235150.6100100.610010
      119625.576282-173.252450-24.324790-4.675440-2.161842-13.694367-78.222887-514.2698620.8106251.420635
      21963-10.123105-0.423751-2.047567-3.027298-27.5358813.4196333.765109-550.5756400.1002921.520927
      31964-4.50835820.0701480.477800-18.675385-2.24828620.73263314.853096-519.589859-0.0855961.435331
      4196510.62938543.695389-0.115332-18.414602-19.3987656.86210222.793484-473.112003-0.1283921.306939
      \n
      \n```\n:::\n:::\n\n\n***and* example**\n\n::: {.cell execution_count=31}\n``` {.python .cell-code}\n# select rows with cumulative_sea_level_rise>10 AND global_glacial_volume_change<-300\ndf[ (df['cumulative_sea_level_rise']>10) & (df['global_glacial_volume_change']<-300)]\n```\n\n::: {.cell-output .cell-output-display execution_count=31}\n```{=html}\n
      \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
      yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
      32199316.685013-73.666274-43.702040-65.995130-33.151246-20.578403-20.311577-3672.5820820.67112610.145254
      3319940.741751-3.069084-59.962273-59.004710-89.506142-15.258449-8.168498-3908.9771910.65302510.798280
      341995-2.139665-58.167778-74.1417623.500155-0.699374-19.863392-25.951496-4088.0828730.49476711.293047
      351996-6.809834-4.550205-74.847017-67.4365914.867530-21.080115-11.781489-4271.4015940.50640511.799452
      361997-13.724106-24.832246-167.229145-34.406403-27.680661-38.213286-20.179090-4600.6860130.90962512.709077
      371998-13.083338-110.429302-107.879027-58.11570230.169987-3.797978-48.129928-4914.8319660.86780713.576884
      381999-8.039555-64.644068-87.714653-26.2117235.888512-8.038630-40.653001-5146.3682310.63960314.216487
      392000-17.008590-96.494055-44.445000-37.518173-29.191986-2.767698-58.873830-5435.3171750.79820215.014688
      402001-8.419109-145.415483-55.749505-35.977022-0.9261347.553503-86.774675-5764.0399310.90807415.922762
      412002-3.392361-48.718943-87.120000-36.127226-27.853498-13.484593-30.203960-6013.2255000.68835816.611120
      422003-3.392361-48.718943-67.253634-36.021991-75.066475-13.223430-30.203960-6289.6409760.76357917.374699
      \n
      \n```\n:::\n:::\n\n\n#### ... by position\n\nAll the selections we have done so far have been using labels or using a condition. Sometimes we might want to select certain rows depending on their *actual position* in the data frame. In this case we use `iloc` selection with the syntax `df.iloc[row-indices]`. `iloc` stands for integer-location based indexing. Let's see some examples:\n\n::: {.cell execution_count=32}\n``` {.python .cell-code}\n# select the fifht row = index 4\ndf.iloc[4]\n```\n\n::: {.cell-output .cell-output-display execution_count=32}\n```\nyear 1965.000000\neurope 10.629385\narctic 43.695389\nalaska -0.115332\nasia -18.414602\nnorth_america -19.398765\nsouth_america 6.862102\nantarctica 22.793484\nglobal_glacial_volume_change -473.112003\nannual_sea_level_rise -0.128392\ncumulative_sea_level_rise 1.306939\nName: 4, dtype: float64\n```\n:::\n:::\n\n\n::: {.cell execution_count=33}\n``` {.python .cell-code}\n# select rows 23 through 30, inclduing 30\ndf.iloc[23:31]\n```\n\n::: {.cell-output .cell-output-display execution_count=33}\n```{=html}\n
      \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
      yeareuropearcticalaskaasianorth_americasouth_americaantarcticaglobal_glacial_volume_changeannual_sea_level_risecumulative_sea_level_rise
      2319848.581427-5.755672-33.466092-20.528535-20.734676-8.267686-3.261011-2569.3398020.2326097.097624
      241985-5.970980-49.65108912.065473-31.571622-33.83398510.072906-13.587886-2682.8579260.3135867.411210
      251986-5.68064222.9008477.557447-18.920773-33.014743-4.65203030.482473-2684.1976320.0037017.414911
      2619878.19147712.387780-24.007862-41.121970-48.5609961.6707333.130190-2773.3255680.2462107.661120
      271988-11.117228-31.06648949.897712-21.300712-46.54543513.460422-37.986834-2858.7676210.2360287.897148
      28198914.863220-23.462392-36.112726-46.528372-57.756422-21.687470-10.044757-3041.1691310.5038728.401020
      291990-1.226009-27.484542-92.713339-35.553433-56.563056-31.077022-29.893352-3318.2203970.7653359.166355
      301991-14.391425-34.898689-8.822063-15.338299-31.458010-7.162909-35.968429-3467.6302840.4127349.579089
      \n
      \n```\n:::\n:::\n\n\nNotice since we are back to indexing by position the right endpoint of the slice (6) is not included in the ouput. \n\n### Selecting rows and columns simultaneously...\n\nSelecting rows and columns simultaneously can be done using `loc` (labels or conditions) or `iloc` (integer position).\n\n#### ...by labels or conditions\nWhen we want to select rows and columns simultaneously by labels or conditions we can use `loc` selection with the syntax \n\n```python\ndf.loc[ row-selection , column-selection]\n```\n\nspecifying both paratmers: `row-selection` and `column-selection`. These parameters can be a condition (which generates a boolean array) or a subset of labels from the index or the column names. Let's see an examples:\n\n::: {.cell execution_count=34}\n``` {.python .cell-code}\n# select change in glacial volume in Europe per year after 2000\ndf.loc[df['year']>2000,['year','europe']]\n```\n\n::: {.cell-output .cell-output-display execution_count=34}\n```{=html}\n
      \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
      yeareurope
      402001-8.419109
      412002-3.392361
      422003-3.392361
      \n
      \n```\n:::\n:::\n\n\nLet's break it down:\n\n- we are using the `df.loc[ row-selection , column-selection]` syntax\n\n- the `row-selection` parameter is the condition `df['year']>1990`, which is a boolean array saying which years are greater than 1990\n\n- the `column-selection` parameter is `['year','europe']` which is a list with the names of the two columns we are intersted in. \n\n#### ... by position\n\nWhen we want to select rows and columns simultaneously by position we use `iloc` selection with the syntax:\n```python\ndf.iloc[ row-indices , column-indices]\n```\n\nFor example, \n\n::: {.cell execution_count=35}\n``` {.python .cell-code}\n# select rows 3-7 (including 7) and columns 3 and 4\ndf.iloc[ 3:8, [3,4] ]\n```\n\n::: {.cell-output .cell-output-display execution_count=35}\n```{=html}\n
      \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
      alaskaasia
      30.477800-18.675385
      4-0.115332-18.414602
      50.224762-14.630284
      6-7.174030-39.013695
      7-0.6605567.879589
      \n
      \n```\n:::\n:::\n\n\nLet's break it down:\n\n- we are using the `df.iloc[ row-indices , column-indices]` syntax\n\n- the `row-indices` parameter is the slice *of integer indices* 3:8. Remember the right endpoint (8) won't be included.\n\n- the `column-indices` parameter is the list of integer indices 3 and 4. This means we are selecting the fourth and fifth column.\n\n\n### Notes about `loc` and `iloc`\n\n::: {.callout-caution}\n## `iloc` vs. `loc`\nAt the beginning, the difference between `iloc` and `loc` can be confusing. Remember the `i` in `iloc` stands for *integer-location*, this reminds us `iloc` only uses integer indexing to retrieve information from the data frames in the same way as indexing for Python lists.\n\nIf you want to dive deeper, this is a great discussion about the difference between `iloc` and `loc`: [Stackoverflow - How are iloc and loc different?](https://stackoverflow.com/questions/31593201/how-are-iloc-and-loc-different/31593712#31593712)\n\nAnd, as always, the documentation will provide you with more information:\n[`pandas.DataFrame.loc`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.loc.html) and [`pandas.DataFrame.iloc`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.iloc.html).\n:::\n\n\n:::{.callout-warning}\n## `iloc` for column selection? Avoid it!\nWe can also access columns by position using `iloc` - but it is best not to if possible.\n\nSuppose we want to access the 10th column in the data frame - then we want to select a column *by position*. In this case the 10th column is the annual sea level rise data and the 10th position corresponds to the index 9. We can select this column by position using the `iloc` selection:\n\n::: {.cell execution_count=36}\n``` {.python .cell-code}\n# select column by position using iloc\n# the syntax is iloc[row-indices, column-indices]\n# [:,9] means \"select all rows from the 10th column\"\nannual_rise_3 = df.iloc[:,9]\nannual_rise_3.head()\n```\n\n::: {.cell-output .cell-output-display execution_count=36}\n```\n0 0.610010\n1 0.810625\n2 0.100292\n3 -0.085596\n4 -0.128392\nName: annual_sea_level_rise, dtype: float64\n```\n:::\n:::\n\n\nUnless you are *really* looking for information about *the 10th column*, do not access a column by position. This is bound to break in many ways:\n\n- it relies on a person correctly counting the position of a column. Even with a small dataset this can be prone to error.\n\n- it is not explicit: if we want information about sea level rise `df.annual_sea_level_rise` or `df['annual_sea_level_rise']` are explicitely telling us we are accessing that information. `df.iloc[:,9]` is obscure and uninformative.\n\n- datastets can get updated. Maybe a new column was added before `annual_sea_level_rise`, this would change the position of the column, which would make any code depending on `df.iloc[:,9]` invalid. Accessing by label helps reproducibility!\n\n:::\n\n## Summary\n\n![`pandas.DataFrame` selection flow chart](/images/selection-flow-chart.jpg)\n\n## Resources\n\nWhat is presented in this section is a comprehensive, but not an exhaustive list of methods to select data in `pandas.DataFrames`. There are *so many* ways to subset data to get the same result. Some of the content from this lesson is adapted from the following resources and I encourage you to read them to learn more! \n\n📖 [Pandas getting started tutorials - How to I select a subset of a\nDataFrame](https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html) \n\n📖 [Pandas documentation - User Guide - Indexing and Selecting Data](https://pandas.pydata.org/docs/user_guide/indexing.html#indexing-slicing-with-labels)\n\n📖 [Python for Data Analysis, 3E - Getting started with pandas](https://wesmckinney.com/book/pandas-basics)\n\n## Acknowledgements\n\nThe simplified glacial_loss.csv dataset was created by [Dr. Allison Horst](https://allisonhorst.github.io) as part of her course materials on environmental data science. \n\n", "supporting": [ "lesson-2-pandas-basics_files/figure-html" ], diff --git a/discussion-sections/archived-discussions/ds-coral-pd-selection.qmd b/discussion-sections/archived-discussions/ds-coral-pd-selection.qmd index f258cb9..3b30538 100644 --- a/discussion-sections/archived-discussions/ds-coral-pd-selection.qmd +++ b/discussion-sections/archived-discussions/ds-coral-pd-selection.qmd @@ -105,10 +105,10 @@ coral_div = pd.read_csv('https://knb.ecoinformatics.org/knb/d1/mn/v2/object/urn% **2.** Use attributes and methods of the `coral_div` data frame to obtain the following information about it: -- how many rows and columns does it have? -- see the first 5 rows -- what are the column names? -- what are the data types of the columns? +a. how many rows and columns does it have? +b. see the first 5 rows +c. what are the column names? +d. what are the data types of the columns? In this last step, what is the difference between the `int64` and `float64` data types? @@ -142,7 +142,7 @@ coral_div.dtypes ## Solution `coral_div.Country` returns the `Country` column of the dataframe, this is a `pandas.Series`. -`coral_div.Country.unique()` returns the uniqeu values of the `Country` column. These are the countries where the reefs are located: +`coral_div.Country.unique()` returns the unique values of the `Country` column. These are the countries where the reefs are located: ```{python} # countries where reefs are located diff --git a/docs/images/selection-flow-chart.jpg b/docs/images/selection-flow-chart.jpg new file mode 100644 index 0000000000000000000000000000000000000000..0e3722100feb99d981bb6e767fc30c7aaa53530c GIT binary patch literal 1769828 zcmeFYcTiMK_b%GQ07K3hBn?VNaz=uZ1(6(;oO1>N5fl)~Q4s+p=L{lQaz>)$oCV1u zDx8h)tKWA|{chE%``5iyx0{}R_S36ZuhqM|_nw-?<>$*Kj+e6DHkJUWr~ozq00zK; z;R6&1p+hANM(`VBLO2UX1mI8&gED|o0kq#3fD#z>Ut9xY`Bw(g0OR@H_e!n_#`_zS zK)4+y{#WN`6ypEr47CBM|LR;0yZ#4bC;))TDDCWQZvkx7teqS!=!GFE03iO#l7DI* zK`tQxc)5A`M0vPHx%udM_(Zt{MEQCCyALub<&UK(5kYVM;)F29|5zQy{4b^iAO-*g zGy*E5mNy3e#{-nq7}USGEasmXg5*%zV+eoqSGYfh=r10Nh5e%kh5`q1Fo5-s;b#m8}of9-+hF%I)zOaNi5zkPqkk^JVN*@L6T`CW~LdJJKlxY*w| zpo)m_gSfc=;EX?h@o;lz!2Z#Bc?m!U%AZ-sZ_0rG!B__G$p0M|x~rN0_yW6{`Ye=x z`6~?nFO2ptrh$0$|KfRn+YH%>0kZgh+XJmK4Eiko-jop82m#7hwO}cL;F) zk-Ax5NrN8*3hqfqelBU>)i?1<9hr;4l=Z$1#+@L!(SVEq!1V>_M8AAqEDutwR=UL-Kpj 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       -
    1. 2.6 Resources
      2. -
    2. 2.7 Acknowledgements
      3. +
    3. 2.6 Summary
      4. +
    4. 2.7 Resources
      5. +
    5. 2.8 Acknowledgements
    @@ -253,13 +254,6 @@

    2  -
    -
    import os
-os.getcwd()
    -
    -
    '/Users/c_galazgarcia/eds-220-book'
    -
    -

    2.1 About

    In this lesson we cover the two core objects in the pandas library, the pandas.Series and the pandas.DataFrame. We will also learn methods to select data from our datasets.

    @@ -268,44 +262,44 @@

    2.2 pandas

    pandas is a Python package to wrangle and analyze tabular data. It is built on top of NumPy and has become the core tool for doing data analysis in Python.

    The convention to import it is:

    -
    -
    import pandas as pd
-
-# we will also import numpy 
-import numpy as np
    +
    +
    import pandas as pd
+
+# we will also import numpy 
+import numpy as np

    There is so much to learn about pandas. While we won’t be able to cover every single functionality of this package in the next three lecutres, the goal is to get you started with the basic tools for data wrangling and give you a solid basis on which you can explore further.

    2.3 Series

    The first core data structure of pandas is the series. A series is a one-dimensional array of indexed data. A pandas.Series having an index is the main difference between a pandas.Series and a numpy array. See the difference:

    -
    -
    # a numpy array
-# np.random.randn returns values from the std normal distribution
-arr = np.random.randn(4) 
-print(type(arr))
-print(arr, "\n")
-
-# a pandas series made from the previous array
-s = pd.Series(arr)
-print(type(s))
-print(s)
    +
    +
    # a numpy array
+# np.random.randn returns values from the std normal distribution
+arr = np.random.randn(4) 
+print(type(arr))
+print(arr, "\n")
+
+# a pandas series made from the previous array
+s = pd.Series(arr)
+print(type(s))
+print(s)
    <class 'numpy.ndarray'>
-[ 1.29006588  0.60107937 -0.52572693 -0.33036333] 
+[ 0.26088972  0.92497305  1.34752178 -1.23504893] 
 
 <class 'pandas.core.series.Series'>
-0    1.290066
-1    0.601079
-2   -0.525727
-3   -0.330363
+0    0.260890
+1    0.924973
+2    1.347522
+3   -1.235049
 dtype: float64

    2.3.1 Creating a pandas.Series

    The basic method to create a pandas.Series is to call

    -
    s = pd.Series(data, index=index)
    +
    s = pd.Series(data, index=index)

    The data parameter can be: