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Statistics cheatsheet

.menu>li.statistics-on>a {border-color:#444;cursor: default;}

In the Python code import pandas as pd has been run

Basics

Operation STATA Pandas Base R
Create new dataset from values 
input a b
1 4
2 5
3 6
end
 
d = {'a' : [1,2,3], 'b' : [4,5,6]}

df = pd.DataFrame(d)
 
df <- data.frame(a=1:3, b=4:6)
Create new dataset from csv file 
import delim mydata.csv, delimiters(",")
 
df = pd.read_csv('mydata.csv', sep=',')
 
df <- read.csv('my_data.csv', sep=',')
Print observations 
list
 
df
 
df
Print observations of variable x 
list x
 
df['x']
 
df$x
Select only variable x 
keep x
 
df = df['x']
 
df <- df$x
Select only variables x and y 
keep x y
 
df = df[['x', 'y']]
 
df <- df[c(‘x’, ‘y’)]
Drop variable x 
drop x
 
df = df.drop('x', axis=1)
 
df$x <- NULL
Generate new variable 
gen z = x + y
 
df['z'] = df['x'] + df['y']
 
df$z <- df$x + df$y
Rename variable 
rename x y
 
df.rename(columns = {'x' : 'y'})
 
names(df)[names(df) == ‘x’] <- ‘y’
Sort by variable 
sort x
 
df.sort_values('x')
 
df[order(df$x), ]

Filtering data

Operation STATA Pandas Base R
Conditionally print observations 
list if x > 1
 
df[df['x'] > 1]
 
subset(df, x == 1)
Conditionally print observations with 'or' operator 
list if x > 1 | y < 0
 
df[(df['x'] > 1) | (df['y'] < 0)]
 
subset(df, x == 1 | y < 0)
Conditionally print observations with 'and' operator 
list if x < 1 & y > 5
 
df[(df['x'] > 1) & (df['y'] < 0)]
 
subset(df, x == 1 & y < 0)
Print subset of observations based on location 
list in 1/3
 
df[0:3]
 
df[1:3, ]
Print observations with missing values in x 
list if missing(x)
 
df[df['x'].isnull()]
 
subset(df, is.na(x))

Summarizing data

Description STATA Pandas Base R
Print summary statistics 
summarize
 
df.describe()
 
summary(df)
Print information about variables and data types 
describe
 
df.info()
 
str(df)
Print aggregation of variable 
mean x
 
df['x'].mean()
 
mean(df$x)
Group data by variable and summarize 
bysort x: summarize
 
df.groupby('x').describe()
 
aggregate(. ~ x, df, summary)
Print frequency table 
tab x
 
df['x'].value_counts()
 
table(df$x)
Print cross-tabulation 
tab x y
 
pd.crosstab(df['x'], df['y'])
 
table(df$x, df$y)
Create bins based on values in x in new column 'bins' 
egen bins = cut x, group(3)
 
df['bins'] = pd.cut(df['x'], 3)
 
df$bins <- cut(df$x, 3)

Reshaping data

Operation STATA Pandas Base R
Reshape data from wide to long panel 
reshape long x, i(i) j(j)
 
pd.wide_to_long(df, ['x'], i='i', j='j')
 
reshape(df, direction='long', varying=grep('j', names(df), value=TRUE), sep='')
Reshape data from long to wide panel 
reshape wide
 
df.unstack()

# returns hierarchical columns
 
reshape(df, timevar='x', idvar='i', direction='wide')

Merging data

Operation STATA Pandas Base R
Vertically concatenate datasets 
append using y
 
pd.concat([x, y])
 
rbind(x, y)

# note that columns must be the same for each dataset
Merge datasets on key 
merge 1:1 key using y
 
pd.merge(x, y, on='key', how='inner')
 
merge(x, y, by='key')

Plotting

Operation STATA Pandas Base R
Scatter plot 
plot x y
 
df.plot.scatter('x', 'y')
 
plot(df$x, df$y)
Line plot 
line x y
 
df.plot('x', 'y')
 
lines(df$x, df$y)
Histogram 
hist x
 
df.hist('x')
 
hist(df$x)
Boxplot 
graph box x
 
df.boxplot('x')
 
boxplot(df$x)