Assignment_Data_150_Climate-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=color:green; align=text-align>Assignment 1 in Data 150. About the dataset Climate-Worldwide</h1>\n",
    "<h2 align=text-align>By Marco Antonio Corrales Cardenas</h2>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries.\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy import NaN, NAN, nan\n",
    "pd.options.display.float_format = '{:.2f}'.format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading all the sheets that the excel file contains in three different files.\n",
    "data = pd.read_excel('./data/climate/climate-change-excel-4-6-mb-.xls', sheet_name='Data')\n",
    "country = pd.read_excel('./data/climate/climate-change-excel-4-6-mb-.xls', sheet_name='Country')\n",
    "series = pd.read_excel('./data/climate/climate-change-excel-4-6-mb-.xls', sheet_name='Series')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here, I am converting all the sheets that the exce file \"climate-change-excel-4-6-mb-.xls\" contains into a three different\n",
    "# csv files. For instances, these files are: data.csv, country.csv and series.csv\n",
    "\n",
    "\n",
    "data.to_csv('./data/climate/data.csv', index = None, header=True)\n",
    "data= pd.DataFrame(pd.read_csv('./data/climate/data.csv'))\n",
    "country.to_csv('./data/climate/country.csv', index = None, header=True)\n",
    "country= pd.DataFrame(pd.read_csv('./data/climate/country.csv'))\n",
    "series.to_csv('./data/climate/series.csv', index = None, header=True)\n",
    "series= pd.DataFrame(pd.read_csv('./data/climate/series.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country code</th>\n",
       "      <th>Country name</th>\n",
       "      <th>Series code</th>\n",
       "      <th>Series name</th>\n",
       "      <th>SCALE</th>\n",
       "      <th>Decimals</th>\n",
       "      <th>1990</th>\n",
       "      <th>1991</th>\n",
       "      <th>1992</th>\n",
       "      <th>1993</th>\n",
       "      <th>...</th>\n",
       "      <th>2002</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>0</td>\n",
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       "      <td>29.57481</td>\n",
       "      <td>..</td>\n",
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       "      <td>..</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ADO</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>..</td>\n",
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       "      <td>..</td>\n",
       "      <td>...</td>\n",
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       "      <td>..</td>\n",
       "      <td>..</td>\n",
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       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>...</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2082346</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>...</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.967875</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>...</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country code Country name     Series code  \\\n",
       "0          ABW        Aruba  AG.LND.EL5M.ZS   \n",
       "1          ADO      Andorra  AG.LND.EL5M.ZS   \n",
       "2          AFG  Afghanistan  AG.LND.EL5M.ZS   \n",
       "3          AGO       Angola  AG.LND.EL5M.ZS   \n",
       "4          ALB      Albania  AG.LND.EL5M.ZS   \n",
       "\n",
       "                           Series name SCALE Decimals       1990 1991 1992  \\\n",
       "0  Land area below 5m (% of land area)     0        1   29.57481   ..   ..   \n",
       "1  Land area below 5m (% of land area)     0        1          0   ..   ..   \n",
       "2  Land area below 5m (% of land area)     0        1          0   ..   ..   \n",
       "3  Land area below 5m (% of land area)     0        1  0.2082346   ..   ..   \n",
       "4  Land area below 5m (% of land area)     0        1   4.967875   ..   ..   \n",
       "\n",
       "  1993  ... 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011  \n",
       "0   ..  ...   ..   ..   ..   ..   ..   ..   ..   ..   ..   ..  \n",
       "1   ..  ...   ..   ..   ..   ..   ..   ..   ..   ..   ..   ..  \n",
       "2   ..  ...   ..   ..   ..   ..   ..   ..   ..   ..   ..   ..  \n",
       "3   ..  ...   ..   ..   ..   ..   ..   ..   ..   ..   ..   ..  \n",
       "4   ..  ...   ..   ..   ..   ..   ..   ..   ..   ..   ..   ..  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the data I will deal with. Seeing the first 5 rows of the dataframe.\n",
    "data.head()\n",
    "#type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13512, 28)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Knowing the total rows and columns of Data dataframe.\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Country code</th>\n",
       "      <th>Country name</th>\n",
       "      <th>Capital city</th>\n",
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       "      <th>Income group</th>\n",
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       "      <th>0</th>\n",
       "      <td>EAP</td>\n",
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       "      <td>..</td>\n",
       "      <td>Aggregates</td>\n",
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       "      <th>1</th>\n",
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       "      <td>Europe &amp; Central Asia</td>\n",
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       "      <td>Aggregates</td>\n",
       "      <td>Aggregates</td>\n",
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       "      <td>EMU</td>\n",
       "      <td>Euro area</td>\n",
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       "      <td>Aggregates</td>\n",
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       "      <td>Aggregates</td>\n",
       "      <td>Aggregates</td>\n",
       "      <td>Aggregates</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LAC</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>..</td>\n",
       "      <td>Aggregates</td>\n",
       "      <td>Aggregates</td>\n",
       "      <td>Aggregates</td>\n",
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      "text/plain": [
       "  Country code               Country name Capital city      Region  \\\n",
       "0          EAP        East Asia & Pacific           ..  Aggregates   \n",
       "1          ECA      Europe & Central Asia           ..  Aggregates   \n",
       "2          EMU                  Euro area           ..  Aggregates   \n",
       "3          HIC                High income           ..  Aggregates   \n",
       "4          LAC  Latin America & Caribbean           ..  Aggregates   \n",
       "\n",
       "  Income group Lending category  \n",
       "0   Aggregates       Aggregates  \n",
       "1   Aggregates       Aggregates  \n",
       "2   Aggregates       Aggregates  \n",
       "3   Aggregates       Aggregates  \n",
       "4   Aggregates       Aggregates  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the data I will deal with. Seeing the first 5 rows of the dataframe.\n",
    "country.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(233, 6)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Knowing the total rows and columns of Country dataframe.\n",
    "country.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Series name</th>\n",
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       "      <th>Order</th>\n",
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       "      <th>0</th>\n",
       "      <td>SP.POP.TOTL</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Size of the economy</td>\n",
       "      <td>GDP is gross domestic product and measures the...</td>\n",
       "      <td>World Bank national accounts data, and OECD Na...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NY.GNP.PCAP.CD</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Size of the economy</td>\n",
       "      <td>GNI per capita is the gross national income, c...</td>\n",
       "      <td>World Bank national accounts data, and OECD Na...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EN.CLC.MMDT.C</td>\n",
       "      <td>Average daily min/max temperature (1961-1990, ...</td>\n",
       "      <td>Text</td>\n",
       "      <td>Text</td>\n",
       "      <td>5</td>\n",
       "      <td>Climate</td>\n",
       "      <td>Average daily min/max temperature are the mini...</td>\n",
       "      <td>Mitchell, T.D., Carter, T.R., Jones, P.D., Hul...</td>\n",
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      ],
      "text/plain": [
       "      Series code                                        Series name Scale  \\\n",
       "0     SP.POP.TOTL                                         Population     0   \n",
       "1     SP.POP.GROW                       Population growth (annual %)     0   \n",
       "2  NY.GDP.MKTP.CD                                            GDP ($)     0   \n",
       "3  NY.GNP.PCAP.CD                           GNI per capita (Atlas $)     0   \n",
       "4   EN.CLC.MMDT.C  Average daily min/max temperature (1961-1990, ...  Text   \n",
       "\n",
       "  Decimals  Order                Topic  \\\n",
       "0        0      1  Size of the economy   \n",
       "1        1      2  Size of the economy   \n",
       "2        0      3  Size of the economy   \n",
       "3        0      4  Size of the economy   \n",
       "4     Text      5              Climate   \n",
       "\n",
       "                                          Definition  \\\n",
       "0  Population includes all residents who are pres...   \n",
       "1  Annual population growth rate for year t is th...   \n",
       "2  GDP is gross domestic product and measures the...   \n",
       "3  GNI per capita is the gross national income, c...   \n",
       "4  Average daily min/max temperature are the mini...   \n",
       "\n",
       "                                              Source  \n",
       "0  (1) United Nations Population Division. 2011. ...  \n",
       "1  Derived from total population. Population sour...  \n",
       "2  World Bank national accounts data, and OECD Na...  \n",
       "3  World Bank national accounts data, and OECD Na...  \n",
       "4  Mitchell, T.D., Carter, T.R., Jones, P.D., Hul...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the data I will deal with. Seeing the first 5 rows of the dataframe.\n",
    "series.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(58, 8)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Knowing the total rows and columns of Series dataframe.\n",
    "series.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=13512, step=1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For getting the index that Pandas assign to the dataset by deafult based on its raws: The range goes from\n",
    "# 0 to 13 512 rows.\n",
    "data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13512 entries, 0 to 13511\n",
      "Data columns (total 28 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   Country code  13512 non-null  object\n",
      " 1   Country name  13512 non-null  object\n",
      " 2   Series code   13512 non-null  object\n",
      " 3   Series name   13512 non-null  object\n",
      " 4   SCALE         13512 non-null  object\n",
      " 5   Decimals      13512 non-null  object\n",
      " 6   1990          10017 non-null  object\n",
      " 7   1991          10017 non-null  object\n",
      " 8   1992          10017 non-null  object\n",
      " 9   1993          10017 non-null  object\n",
      " 10  1994          10017 non-null  object\n",
      " 11  1995          10017 non-null  object\n",
      " 12  1996          10017 non-null  object\n",
      " 13  1997          10017 non-null  object\n",
      " 14  1998          10017 non-null  object\n",
      " 15  1999          10017 non-null  object\n",
      " 16  2000          10017 non-null  object\n",
      " 17  2001          10017 non-null  object\n",
      " 18  2002          10017 non-null  object\n",
      " 19  2003          10017 non-null  object\n",
      " 20  2004          10017 non-null  object\n",
      " 21  2005          10017 non-null  object\n",
      " 22  2006          10017 non-null  object\n",
      " 23  2007          10017 non-null  object\n",
      " 24  2008          10017 non-null  object\n",
      " 25  2009          10017 non-null  object\n",
      " 26  2010          10017 non-null  object\n",
      " 27  2011          12382 non-null  object\n",
      "dtypes: object(28)\n",
      "memory usage: 2.9+ MB\n"
     ]
    }
   ],
   "source": [
    "# We can see that I have some missing data on this dataset in columns 1990 .. 2011\n",
    "data.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country code', 'Country name', 'Series code', 'Series name', 'SCALE',\n",
       "       'Decimals', '1990', '1991', '1992', '1993', '1994', '1995', '1996',\n",
       "       '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005',\n",
       "       '2006', '2007', '2008', '2009', '2010', '2011'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['ABW', 'Aruba', 'AG.LND.EL5M.ZS', ..., '..', '..', '..'],\n",
       "       ['ADO', 'Andorra', 'AG.LND.EL5M.ZS', ..., '..', '..', '..'],\n",
       "       ['AFG', 'Afghanistan', 'AG.LND.EL5M.ZS', ..., '..', '..', '..'],\n",
       "       ...,\n",
       "       ['ZAR', 'Congo, Dem. Rep.', 'SP.URB.TOTL', ..., '22201848.3232',\n",
       "        '23219959.84', '..'],\n",
       "       ['ZMB', 'Zambia', 'SP.URB.TOTL', ..., '4524564.0776',\n",
       "        '4614728.013', '..'],\n",
       "       ['ZWE', 'Zimbabwe', 'SP.URB.TOTL', ..., '4717663.7744',\n",
       "        '4814866.882', '..']], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andorra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13507</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13508</th>\n",
       "      <td>South Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13509</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13510</th>\n",
       "      <td>Zambia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13511</th>\n",
       "      <td>Zimbabwe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13512 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country name\n",
       "0                 Aruba\n",
       "1               Andorra\n",
       "2           Afghanistan\n",
       "3                Angola\n",
       "4               Albania\n",
       "...                 ...\n",
       "13507       Yemen, Rep.\n",
       "13508      South Africa\n",
       "13509  Congo, Dem. Rep.\n",
       "13510            Zambia\n",
       "13511          Zimbabwe\n",
       "\n",
       "[13512 rows x 1 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verifying some values that the dataframe contains. In this example, it would be printed a new dataframe \"countryN\"\n",
    "# that will contain all the contries on that column.\n",
    "countryN = data[['Country name']]\n",
    "countryN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country code    object\n",
       "Country name    object\n",
       "Series code     object\n",
       "Series name     object\n",
       "SCALE           object\n",
       "Decimals        object\n",
       "1990            object\n",
       "1991            object\n",
       "1992            object\n",
       "1993            object\n",
       "1994            object\n",
       "1995            object\n",
       "1996            object\n",
       "1997            object\n",
       "1998            object\n",
       "1999            object\n",
       "2000            object\n",
       "2001            object\n",
       "2002            object\n",
       "2003            object\n",
       "2004            object\n",
       "2005            object\n",
       "2006            object\n",
       "2007            object\n",
       "2008            object\n",
       "2009            object\n",
       "2010            object\n",
       "2011            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4> Dealing with missing valus in relation to all columns on the datasets</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country code       0\n",
       "Country name       0\n",
       "Series code        0\n",
       "Series name        0\n",
       "SCALE              0\n",
       "Decimals           0\n",
       "1990            3495\n",
       "1991            3495\n",
       "1992            3495\n",
       "1993            3495\n",
       "1994            3495\n",
       "1995            3495\n",
       "1996            3495\n",
       "1997            3495\n",
       "1998            3495\n",
       "1999            3495\n",
       "2000            3495\n",
       "2001            3495\n",
       "2002            3495\n",
       "2003            3495\n",
       "2004            3495\n",
       "2005            3495\n",
       "2006            3495\n",
       "2007            3495\n",
       "2008            3495\n",
       "2009            3495\n",
       "2010            3495\n",
       "2011            1130\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As We can see below, the columns that have years as a labels on the dataset, are those that have missing values\n",
    "# So, We need to handle these missing values.\n",
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country code       0\n",
       "Country name       0\n",
       "Series code        0\n",
       "Series name        0\n",
       "SCALE              0\n",
       "Decimals           0\n",
       "1990            3495\n",
       "1991            3495\n",
       "1992            3495\n",
       "1993            3495\n",
       "1994            3495\n",
       "1995            3495\n",
       "1996            3495\n",
       "1997            3495\n",
       "1998            3495\n",
       "1999            3495\n",
       "2000            3495\n",
       "2001            3495\n",
       "2002            3495\n",
       "2003            3495\n",
       "2004            3495\n",
       "2005            3495\n",
       "2006            3495\n",
       "2007            3495\n",
       "2008            3495\n",
       "2009            3495\n",
       "2010            3495\n",
       "2011            1130\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Another way to count the missing values: data.shape[0] - df_data.count()\n",
    "data.isnull().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Country code ['ABW' 'ADO' 'AFG' 'AGO' 'ALB' 'ARE' 'ARG' 'ARM' 'ASM' 'ATG' 'AUS' 'AUT'\n",
      " 'AZE' 'BDI' 'BEL' 'BEN' 'BFA' 'BGD' 'BGR' 'BHR' 'BHS' 'BIH' 'BLR' 'BLZ'\n",
      " 'BMU' 'BOL' 'BRA' 'BRB' 'BRN' 'BTN' 'BWA' 'CAF' 'CAN' 'CHE' 'CHI' 'CHL'\n",
      " 'CHN' 'CIV' 'CMR' 'COG' 'COK' 'COL' 'COM' 'CPV' 'CRI' 'CUB' 'CUW' 'CYM'\n",
      " 'CYP' 'CZE' 'DEU' 'DJI' 'DMA' 'DNK' 'DOM' 'DZA' 'EAP' 'ECA' 'ECU' 'EGY'\n",
      " 'EMU' 'ERI' 'ESP' 'EST' 'ETH' 'FIN' 'FJI' 'FRA' 'FRO' 'FSM' 'GAB' 'GBR'\n",
      " 'GEO' 'GHA' 'GIB' 'GIN' 'GMB' 'GNB' 'GNQ' 'GRC' 'GRD' 'GRL' 'GTM' 'GUM'\n",
      " 'GUY' 'HIC' 'HKG' 'HND' 'HRV' 'HTI' 'HUN' 'IDN' 'IMY' 'IND' 'IRL' 'IRN'\n",
      " 'IRQ' 'ISL' 'ISR' 'ITA' 'JAM' 'JOR' 'JPN' 'KAZ' 'KEN' 'KGZ' 'KHM' 'KIR'\n",
      " 'KNA' 'KOR' 'KSV' 'KWT' 'LAC' 'LAO' 'LBN' 'LBR' 'LBY' 'LCA' 'LIC' 'LIE'\n",
      " 'LKA' 'LMC' 'LMY' 'LSO' 'LTU' 'LUX' 'LVA' 'MAC' 'MAF' 'MAR' 'MCO' 'MDA'\n",
      " 'MDG' 'MDV' 'MEX' 'MHL' 'MIC' 'MKD' 'MLI' 'MLT' 'MMR' 'MNA' 'MNE' 'MNG'\n",
      " 'MNP' 'MOZ' 'MRT' 'MUS' 'MWI' 'MYS' 'MYT' 'NAM' 'NCL' 'NER' 'NGA' 'NIC'\n",
      " 'NIU' 'NLD' 'NOR' 'NPL' 'NRU' 'NZL' 'OMN' 'PAK' 'PAN' 'PER' 'PHL' 'PLW'\n",
      " 'PNG' 'POL' 'PRI' 'PRK' 'PRT' 'PRY' 'PYF' 'QAT' 'ROM' 'RUS' 'RWA' 'SAS'\n",
      " 'SAU' 'SDN' 'SEN' 'SGP' 'SID' 'SLB' 'SLE' 'SLV' 'SMR' 'SOM' 'SRB' 'SSA'\n",
      " 'STP' 'SUR' 'SVK' 'SVN' 'SWE' 'SWZ' 'SXM' 'SYC' 'SYR' 'TCA' 'TCD' 'TGO'\n",
      " 'THA' 'TJK' 'TKM' 'TMP' 'TON' 'TTO' 'TUN' 'TUR' 'TUV' 'TZA' 'UGA' 'UKR'\n",
      " 'UMC' 'URY' 'USA' 'UZB' 'VCT' 'VEN' 'VIR' 'VNM' 'VUT' 'WBG' 'WLD' 'WSM'\n",
      " 'YEM' 'ZAF' 'ZAR' 'ZMB' 'ZWE']\n",
      "Country name ['Aruba' 'Andorra' 'Afghanistan' 'Angola' 'Albania' 'United Arab Emirates'\n",
      " 'Argentina' 'Armenia' 'American Samoa' 'Antigua and Barbuda' 'Australia'\n",
      " 'Austria' 'Azerbaijan' 'Burundi' 'Belgium' 'Benin' 'Burkina Faso'\n",
      " 'Bangladesh' 'Bulgaria' 'Bahrain' 'Bahamas, The' 'Bosnia and Herzegovina'\n",
      " 'Belarus' 'Belize' 'Bermuda' 'Bolivia' 'Brazil' 'Barbados'\n",
      " 'Brunei Darussalam' 'Bhutan' 'Botswana' 'Central African Republic'\n",
      " 'Canada' 'Switzerland' 'Channel Islands' 'Chile' 'China' \"Cote d'Ivoire\"\n",
      " 'Cameroon' 'Congo, Rep.' 'Cook Islands' 'Colombia' 'Comoros' 'Cape Verde'\n",
      " 'Costa Rica' 'Cuba' 'Curacao' 'Cayman Islands' 'Cyprus' 'Czech Republic'\n",
      " 'Germany' 'Djibouti' 'Dominica' 'Denmark' 'Dominican Republic' 'Algeria'\n",
      " 'East Asia & Pacific' 'Europe & Central Asia' 'Ecuador'\n",
      " 'Egypt, Arab Rep.' 'Euro area' 'Eritrea' 'Spain' 'Estonia' 'Ethiopia'\n",
      " 'Finland' 'Fiji' 'France' 'Faeroe Islands' 'Micronesia, Fed. Sts.'\n",
      " 'Gabon' 'United Kingdom' 'Georgia' 'Ghana' 'Gibraltar' 'Guinea'\n",
      " 'Gambia, The' 'Guinea-Bissau' 'Equatorial Guinea' 'Greece' 'Grenada'\n",
      " 'Greenland' 'Guatemala' 'Guam' 'Guyana' 'High income'\n",
      " 'Hong Kong SAR, China' 'Honduras' 'Croatia' 'Haiti' 'Hungary' 'Indonesia'\n",
      " 'Isle of Man' 'India' 'Ireland' 'Iran, Islamic Rep.' 'Iraq' 'Iceland'\n",
      " 'Israel' 'Italy' 'Jamaica' 'Jordan' 'Japan' 'Kazakhstan' 'Kenya'\n",
      " 'Kyrgyz Republic' 'Cambodia' 'Kiribati' 'St. Kitts and Nevis'\n",
      " 'Korea, Rep.' 'Kosovo' 'Kuwait' 'Latin America & Caribbean' 'Lao PDR'\n",
      " 'Lebanon' 'Liberia' 'Libya' 'St. Lucia' 'Low income' 'Liechtenstein'\n",
      " 'Sri Lanka' 'Lower middle income' 'Low & middle income' 'Lesotho'\n",
      " 'Lithuania' 'Luxembourg' 'Latvia' 'Macao SAR, China'\n",
      " 'St. Martin (French part)' 'Morocco' 'Monaco' 'Moldova' 'Madagascar'\n",
      " 'Maldives' 'Mexico' 'Marshall Islands' 'Middle income' 'Macedonia, FYR'\n",
      " 'Mali' 'Malta' 'Myanmar' 'Middle East & North Africa' 'Montenegro'\n",
      " 'Mongolia' 'Northern Mariana Islands' 'Mozambique' 'Mauritania'\n",
      " 'Mauritius' 'Malawi' 'Malaysia' 'Mayotte' 'Namibia' 'New Caledonia'\n",
      " 'Niger' 'Nigeria' 'Nicaragua' 'Niue' 'Netherlands' 'Norway' 'Nepal'\n",
      " 'Nauru' 'New Zealand' 'Oman' 'Pakistan' 'Panama' 'Peru' 'Philippines'\n",
      " 'Palau' 'Papua New Guinea' 'Poland' 'Puerto Rico' 'Korea, Dem. Rep.'\n",
      " 'Portugal' 'Paraguay' 'French Polynesia' 'Qatar' 'Romania'\n",
      " 'Russian Federation' 'Rwanda' 'South Asia' 'Saudi Arabia' 'Sudan'\n",
      " 'Senegal' 'Singapore' 'Small island developing states' 'Solomon Islands'\n",
      " 'Sierra Leone' 'El Salvador' 'San Marino' 'Somalia' 'Serbia'\n",
      " 'Sub-Saharan Africa' 'Sao Tome and Principe' 'Suriname' 'Slovak Republic'\n",
      " 'Slovenia' 'Sweden' 'Swaziland' 'Sint Maarten (Dutch part)' 'Seychelles'\n",
      " 'Syrian Arab Republic' 'Turks and Caicos Islands' 'Chad' 'Togo'\n",
      " 'Thailand' 'Tajikistan' 'Turkmenistan' 'Timor-Leste' 'Tonga'\n",
      " 'Trinidad and Tobago' 'Tunisia' 'Turkey' 'Tuvalu' 'Tanzania' 'Uganda'\n",
      " 'Ukraine' 'Upper middle income' 'Uruguay' 'United States' 'Uzbekistan'\n",
      " 'St. Vincent and the Grenadines' 'Venezuela, RB' 'Virgin Islands (U.S.)'\n",
      " 'Vietnam' 'Vanuatu' 'West Bank and Gaza' 'World' 'Samoa' 'Yemen, Rep.'\n",
      " 'South Africa' 'Congo, Dem. Rep.' 'Zambia' 'Zimbabwe']\n",
      "Series code ['AG.LND.EL5M.ZS' 'AG.LND.IRIG.AG.ZS' 'AG.YLD.CREL.KG'\n",
      " 'BX.KLT.DINV.WD.GD.ZS' 'EG.ELC.ACCS.ZS' 'EG.USE.COMM.GD.PP.KD'\n",
      " 'EG.USE.PCAP.KG.OE' 'EN.ATM.CO2E.KT' 'EN.ATM.CO2E.PC'\n",
      " 'EN.ATM.CO2E.PP.GD.KD' 'EN.ATM.GHGO.KT.CE' 'EN.ATM.METH.KT.CE'\n",
      " 'EN.ATM.NOXE.KT.CE' 'EN.CLC.AERT' 'EN.CLC.DRSK.XQ' 'EN.CLC.GHGR.MT.CE'\n",
      " 'EN.CLC.HCDM' 'EN.CLC.HJIP' 'EN.CLC.HPPT.MM' 'EN.CLC.ICER' 'EN.CLC.IERU'\n",
      " 'EN.CLC.MDAT.ZS' 'EN.CLC.MMDT.C' 'EN.CLC.NAMA' 'EN.CLC.NAPA'\n",
      " 'EN.CLC.NCOM' 'EN.CLC.PCAT.C' 'EN.CLC.PCCC' 'EN.CLC.PCHW'\n",
      " 'EN.CLC.PCPT.MM' 'EN.CLC.RNET' 'EN.POP.EL5M.ZS' 'EN.URB.MCTY.TL.ZS'\n",
      " 'ER.H2O.FWTL.ZS' 'ER.LND.PTLD.ZS' 'IC.BUS.EASE.XQ' 'IE.PPI.ENGY.CD'\n",
      " 'IE.PPI.TELE.CD' 'IE.PPI.TRAN.CD' 'IE.PPI.WATR.CD' 'IQ.CPA.PUBS.XQ'\n",
      " 'IS.ROD.PAVE.ZS' 'NY.GDP.MKTP.CD' 'NY.GNP.PCAP.CD' 'SE.ENR.PRSC.FM.ZS'\n",
      " 'SE.PRM.CMPT.ZS' 'SH.DYN.MORT' 'SH.H2O.SAFE.ZS' 'SH.MED.NUMW.P3'\n",
      " 'SH.MED.PHYS.ZS' 'SH.MLR.INCD' 'SH.STA.ACSN' 'SH.STA.MALN.ZS'\n",
      " 'SI.POV.DDAY' 'SP.POP.GROW' 'SP.POP.TOTL' 'SP.URB.GROW' 'SP.URB.TOTL']\n",
      "Series name ['Land area below 5m (% of land area)'\n",
      " 'Agricultural land under irrigation (% of total ag. land)'\n",
      " 'Cereal yield (kg per hectare)'\n",
      " 'Foreign direct investment, net inflows (% of GDP)'\n",
      " 'Access to electricity (% of total population)'\n",
      " 'Energy use per units of GDP (kg oil eq./$1,000 of 2005 PPP $)'\n",
      " 'Energy use per capita (kilograms of oil equivalent)'\n",
      " 'CO2 emissions, total (KtCO2)' 'CO2 emissions per capita (metric tons)'\n",
      " 'CO2 emissions per units of GDP (kg/$1,000 of 2005 PPP $)'\n",
      " 'Other GHG emissions, total (KtCO2e)'\n",
      " 'Methane (CH4) emissions, total (KtCO2e)'\n",
      " 'Nitrous oxide (N2O) emissions, total (KtCO2e)'\n",
      " 'Annex-I emissions reduction target'\n",
      " 'Disaster risk reduction progress score (1-5 scale; 5=best)'\n",
      " 'GHG net emissions/removals by LUCF (MtCO2e)'\n",
      " 'Hosted Clean Development Mechanism (CDM) projects'\n",
      " 'Hosted Joint Implementation (JI) projects'\n",
      " 'Average annual precipitation (1961-1990, mm)'\n",
      " 'Issued Certified Emission Reductions (CERs) from CDM (thousands)'\n",
      " 'Issued Emission Reduction Units (ERUs) from JI (thousands)'\n",
      " 'Droughts, floods, extreme temps (% pop. avg. 1990-2009)'\n",
      " 'Average daily min/max temperature (1961-1990, Celsius)'\n",
      " 'NAMA submission' 'NAPA submission'\n",
      " 'Latest UNFCCC national communication'\n",
      " 'Projected annual temperature change (2045-2065, Celsius)'\n",
      " 'Projected change in annual cool days/cold nights'\n",
      " 'Projected change in annual hot days/warm nights'\n",
      " 'Projected annual precipitation change (2045-2065, mm)'\n",
      " 'Renewable energy target' 'Population below 5m (% of total)'\n",
      " 'Population in urban agglomerations >1million (%)'\n",
      " 'Annual freshwater withdrawals (% of internal resources)'\n",
      " 'Nationally terrestrial protected areas (% of total land area)'\n",
      " 'Ease of doing business (ranking 1-183; 1=best)'\n",
      " 'Invest. in energy w/ private participation ($)'\n",
      " 'Invest. in telecoms w/ private participation ($)'\n",
      " 'Invest. in transport w/ private participation ($)'\n",
      " 'Invest. in water/sanit. w/ private participation ($)'\n",
      " 'Public sector mgmt & institutions avg. (1-6 scale; 6=best)'\n",
      " 'Paved roads (% of total roads)' 'GDP ($)' 'GNI per capita (Atlas $)'\n",
      " 'Ratio of girls to boys in primary & secondary school (%)'\n",
      " 'Primary completion rate, total (% of relevant age group)'\n",
      " 'Under-five mortality rate (per 1,000)'\n",
      " 'Access to improved water source (% of total pop.)'\n",
      " 'Nurses and midwives (per 1,000 people)' 'Physicians (per 1,000 people)'\n",
      " 'Malaria incidence rate (per 100,000 people)'\n",
      " 'Access to improved sanitation (% of total pop.)'\n",
      " 'Child malnutrition, underweight (% of under age 5)'\n",
      " 'Population living below $1.25 a day (% of total)'\n",
      " 'Population growth (annual %)' 'Population'\n",
      " 'Urban population growth (annual %)' 'Urban population']\n",
      "SCALE ['0' 'Text']\n",
      "Decimals ['1' '0' 'Text']\n",
      "1990 ['29.57481' '0' '0.2082346' ... '10120930.828' '3096860.882' '3036068.58']\n",
      "1991 ['..' '1160' '417.4' ... '10569454.3904' '3141668.2968' '3175022.711']\n",
      "1992 ['..' '1097.7' '397' ... '11060046.8476' '3183257.2304' '3310511.552']\n",
      "1993 ['..' '1135.5' '267.8' ... '11568627.5584' '3223514.6742' '3443081.8224']\n",
      "1994 ['..' '1140.3' '298.4' ... '12061489.8516' '3264940.1844' '3574012.1152']\n",
      "1995 ['..' '1219.6' '402.2' ... '12515132.796' '3309118.176' '3704047.681']\n",
      "1996 ['..' '1203.7' '653.3' ... '12990617.1852' '3356931.5184' '3819482.348']\n",
      "1997 ['..' '1349.3' '567.3' ... '13428559.416' '3407475.816' '3932953.2826']\n",
      "1998 ['..' '1389.1' '701.6' ... '13852037.716' '3458431.1176' '4041470.1632']\n",
      "1999 ['..' '1286' '620.3' ... '14296499.9856' '3506647.549' '4140867.7768']\n",
      "2000 ['29.57481' '0' '0.2082346' ... '14788607.6' '3550143.576' '4228203.226']\n",
      "2001 ['..' '5.6684237014277' '0.549923195084485' ... '15429399.7024'\n",
      " '3640719.03' '4303170.133']\n",
      "2002 ['..' '4.62214923317352' '1.05130133428436' ... '16125336.2688'\n",
      " '3729882.6848' '4367338.8024']\n",
      "2003 ['..' '7.25929833816935' '0.541069397042093' ... '16867832.791'\n",
      " '3819640.7412' '4422102.3736']\n",
      "2004 ['..' '5.50236079238216' '0.545772647747154' ... '17640845.614'\n",
      " '3912870.7312' '4469726.7596']\n",
      "2005 ['..' '5.84014771828014' '0.540268539298078' ... '18431987.562'\n",
      " '4011827.75' '4512876.274']\n",
      "2006 ['..' '6.08546557636508' '8.7129840546697' ... '19333729.388'\n",
      " '4128986.897' '4558288.489']\n",
      "2007 ['..' '5.94038512265893' '17.426273458445' ... '20261443.145'\n",
      " '4253139.4752' '4600586.907']\n",
      "2008 ['..' '5.77947771036666' '9.99153259949196' ... '21216476.3796'\n",
      " '4384858.5704' '4649406.1562']\n",
      "2009 ['..' '4.84304932735426' '16.7774086378738' ... '22201848.3232'\n",
      " '4524564.0776' '4717663.7744']\n",
      "2010 ['..' '11.7804627333677' '9.41412431135909' ... '23219959.84'\n",
      " '4614728.013' '4814866.882']\n",
      "2011 ['..' nan '5% / 25%; BY: 2000' ... '35' '178' '84']\n"
     ]
    }
   ],
   "source": [
    "# I want to know how many values or missing values these columns have. For doing that, I need to know all the unique\n",
    "# values that these columns have as we will see in the following line after executing unique() function I created \n",
    "# for this purpose.\n",
    "\n",
    "def unique_values(data):\n",
    "    for col in data.columns:\n",
    "        print(col, data[col].unique())\n",
    "        \n",
    "unique_values(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a visual graphic of the missing values. We can see all of them are mainly located on the columns\n",
    "# that have years as a label. For instance, 1990, 1991 and so...until 2011.\n",
    "# data.dropna(thresh=5, inplace=True)\n",
    "sb.heatmap(data.isnull(), cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style=color:violet > Remember to uncomment the line with : #data.drop(labels=col_drop, axis=1, inplace=True) in the below line\n",
    "    if you are going to run all the cells againg, otherwise it would be an error</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In the dataframe \"data\" I saw that there are two columns that are not relevant for my goals. So, I decided to \n",
    "# drop them. Columns = SCALE and Decimals.\n",
    "col_drop = ['SCALE','Decimals']\n",
    "# Remember:For execcuting the line below must be uncommented with running all the code again.\n",
    "data.drop(labels=col_drop, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13512 entries, 0 to 13511\n",
      "Data columns (total 26 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   Country code  13512 non-null  object\n",
      " 1   Country name  13512 non-null  object\n",
      " 2   Series code   13512 non-null  object\n",
      " 3   Series name   13512 non-null  object\n",
      " 4   1990          10017 non-null  object\n",
      " 5   1991          10017 non-null  object\n",
      " 6   1992          10017 non-null  object\n",
      " 7   1993          10017 non-null  object\n",
      " 8   1994          10017 non-null  object\n",
      " 9   1995          10017 non-null  object\n",
      " 10  1996          10017 non-null  object\n",
      " 11  1997          10017 non-null  object\n",
      " 12  1998          10017 non-null  object\n",
      " 13  1999          10017 non-null  object\n",
      " 14  2000          10017 non-null  object\n",
      " 15  2001          10017 non-null  object\n",
      " 16  2002          10017 non-null  object\n",
      " 17  2003          10017 non-null  object\n",
      " 18  2004          10017 non-null  object\n",
      " 19  2005          10017 non-null  object\n",
      " 20  2006          10017 non-null  object\n",
      " 21  2007          10017 non-null  object\n",
      " 22  2008          10017 non-null  object\n",
      " 23  2009          10017 non-null  object\n",
      " 24  2010          10017 non-null  object\n",
      " 25  2011          12382 non-null  object\n",
      "dtypes: object(26)\n",
      "memory usage: 2.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Defining the categorical and continuous values on the dataset.</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defyning the categorical and continuos variables.\n",
    "Categorical_col = ['Country code', 'Country name', 'Series code', 'Series name']\n",
    "Continuous_col = ['1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000',\n",
    "                '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transforming all the continuous values -as we defined on the list above 'Continuous_col'- to numerical datatype.\n",
    "for col in Continuous_col:\n",
    "    data[col]=pd.to_numeric(data[col], errors='coerce')\n",
    "    data[col]=np.nan_to_num(data[col])\n",
    "\n",
    "# Transforming all the object values -as we defined on the list above 'Categorical_col'- to category datatype.\n",
    "for col in Categorical_col:\n",
    "    data[col] = data[col].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13512 entries, 0 to 13511\n",
      "Data columns (total 26 columns):\n",
      " #   Column        Non-Null Count  Dtype   \n",
      "---  ------        --------------  -----   \n",
      " 0   Country code  13512 non-null  category\n",
      " 1   Country name  13512 non-null  category\n",
      " 2   Series code   13512 non-null  category\n",
      " 3   Series name   13512 non-null  category\n",
      " 4   1990          13512 non-null  float64 \n",
      " 5   1991          13512 non-null  float64 \n",
      " 6   1992          13512 non-null  float64 \n",
      " 7   1993          13512 non-null  float64 \n",
      " 8   1994          13512 non-null  float64 \n",
      " 9   1995          13512 non-null  float64 \n",
      " 10  1996          13512 non-null  float64 \n",
      " 11  1997          13512 non-null  float64 \n",
      " 12  1998          13512 non-null  float64 \n",
      " 13  1999          13512 non-null  float64 \n",
      " 14  2000          13512 non-null  float64 \n",
      " 15  2001          13512 non-null  float64 \n",
      " 16  2002          13512 non-null  float64 \n",
      " 17  2003          13512 non-null  float64 \n",
      " 18  2004          13512 non-null  float64 \n",
      " 19  2005          13512 non-null  float64 \n",
      " 20  2006          13512 non-null  float64 \n",
      " 21  2007          13512 non-null  float64 \n",
      " 22  2008          13512 non-null  float64 \n",
      " 23  2009          13512 non-null  float64 \n",
      " 24  2010          13512 non-null  float64 \n",
      " 25  2011          13512 non-null  float64 \n",
      "dtypes: category(4), float64(22)\n",
      "memory usage: 2.4 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()\n",
    "# We can see that the transformation has been completed on all columns with their data type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country code</th>\n",
       "      <th>Country name</th>\n",
       "      <th>Series code</th>\n",
       "      <th>Series name</th>\n",
       "      <th>1990</th>\n",
       "      <th>1991</th>\n",
       "      <th>1992</th>\n",
       "      <th>1993</th>\n",
       "      <th>1994</th>\n",
       "      <th>1995</th>\n",
       "      <th>...</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>29.57</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ADO</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>4.97</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ARE</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>4.64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ARG</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ARM</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ASM</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>17.89</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ATG</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>32.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country code          Country name     Series code  \\\n",
       "0          ABW                 Aruba  AG.LND.EL5M.ZS   \n",
       "1          ADO               Andorra  AG.LND.EL5M.ZS   \n",
       "2          AFG           Afghanistan  AG.LND.EL5M.ZS   \n",
       "3          AGO                Angola  AG.LND.EL5M.ZS   \n",
       "4          ALB               Albania  AG.LND.EL5M.ZS   \n",
       "5          ARE  United Arab Emirates  AG.LND.EL5M.ZS   \n",
       "6          ARG             Argentina  AG.LND.EL5M.ZS   \n",
       "7          ARM               Armenia  AG.LND.EL5M.ZS   \n",
       "8          ASM        American Samoa  AG.LND.EL5M.ZS   \n",
       "9          ATG   Antigua and Barbuda  AG.LND.EL5M.ZS   \n",
       "\n",
       "                           Series name  1990  1991  1992  1993  1994  1995  \\\n",
       "0  Land area below 5m (% of land area) 29.57  0.00  0.00  0.00  0.00  0.00   \n",
       "1  Land area below 5m (% of land area)  0.00  0.00  0.00  0.00  0.00  0.00   \n",
       "2  Land area below 5m (% of land area)  0.00  0.00  0.00  0.00  0.00  0.00   \n",
       "3  Land area below 5m (% of land area)  0.21  0.00  0.00  0.00  0.00  0.00   \n",
       "4  Land area below 5m (% of land area)  4.97  0.00  0.00  0.00  0.00  0.00   \n",
       "5  Land area below 5m (% of land area)  4.64  0.00  0.00  0.00  0.00  0.00   \n",
       "6  Land area below 5m (% of land area)  1.23  0.00  0.00  0.00  0.00  0.00   \n",
       "7  Land area below 5m (% of land area)  0.00  0.00  0.00  0.00  0.00  0.00   \n",
       "8  Land area below 5m (% of land area) 17.89  0.00  0.00  0.00  0.00  0.00   \n",
       "9  Land area below 5m (% of land area) 32.42  0.00  0.00  0.00  0.00  0.00   \n",
       "\n",
       "   ...  2002  2003  2004  2005  2006  2007  2008  2009  2010  2011  \n",
       "0  ...  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  \n",
       "1  ...  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  \n",
       "2  ...  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  \n",
       "3  ...  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  \n",
       "4  ...  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  \n",
       "5  ...  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  \n",
       "6  ...  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  \n",
       "7  ...  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  \n",
       "8  ...  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  \n",
       "9  ...  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  \n",
       "\n",
       "[10 rows x 26 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now, I can see tha missing values are not presented anymore. So, I can start working on the data.\n",
    "sb.heatmap(data.isnull(), cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "# We can see that there are not duplicated values on the dataframe, otherwise it would have been important to drop all\n",
    "# of them as well.\n",
    "print((sum(data.duplicated())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country code</th>\n",
       "      <th>Country name</th>\n",
       "      <th>Series code</th>\n",
       "      <th>Series name</th>\n",
       "      <th>1990</th>\n",
       "      <th>1991</th>\n",
       "      <th>1992</th>\n",
       "      <th>1993</th>\n",
       "      <th>1994</th>\n",
       "      <th>1995</th>\n",
       "      <th>...</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>29.57</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ADO</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country code Country name     Series code  \\\n",
       "0          ABW        Aruba  AG.LND.EL5M.ZS   \n",
       "1          ADO      Andorra  AG.LND.EL5M.ZS   \n",
       "2          AFG  Afghanistan  AG.LND.EL5M.ZS   \n",
       "\n",
       "                           Series name  1990  1991  1992  1993  1994  1995  \\\n",
       "0  Land area below 5m (% of land area) 29.57  0.00  0.00  0.00  0.00  0.00   \n",
       "1  Land area below 5m (% of land area)  0.00  0.00  0.00  0.00  0.00  0.00   \n",
       "2  Land area below 5m (% of land area)  0.00  0.00  0.00  0.00  0.00  0.00   \n",
       "\n",
       "   ...  2002  2003  2004  2005  2006  2007  2008  2009  2010  2011  \n",
       "0  ...  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  \n",
       "1  ...  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  \n",
       "2  ...  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  \n",
       "\n",
       "[3 rows x 26 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loc, it will locate the index label on that row that matches to the values given in the list.\n",
    "data.loc[[0, 1, 2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country code</th>\n",
       "      <th>Country name</th>\n",
       "      <th>Series code</th>\n",
       "      <th>Series name</th>\n",
       "      <th>1990</th>\n",
       "      <th>1991</th>\n",
       "      <th>1992</th>\n",
       "      <th>1993</th>\n",
       "      <th>1994</th>\n",
       "      <th>1995</th>\n",
       "      <th>...</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>29.57</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ADO</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13511</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>SP.URB.TOTL</td>\n",
       "      <td>Urban population</td>\n",
       "      <td>3036068.58</td>\n",
       "      <td>3175022.71</td>\n",
       "      <td>3310511.55</td>\n",
       "      <td>3443081.82</td>\n",
       "      <td>3574012.12</td>\n",
       "      <td>3704047.68</td>\n",
       "      <td>...</td>\n",
       "      <td>4367338.80</td>\n",
       "      <td>4422102.37</td>\n",
       "      <td>4469726.76</td>\n",
       "      <td>4512876.27</td>\n",
       "      <td>4558288.49</td>\n",
       "      <td>4600586.91</td>\n",
       "      <td>4649406.16</td>\n",
       "      <td>4717663.77</td>\n",
       "      <td>4814866.88</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Country code Country name     Series code  \\\n",
       "0              ABW        Aruba  AG.LND.EL5M.ZS   \n",
       "1              ADO      Andorra  AG.LND.EL5M.ZS   \n",
       "13511          ZWE     Zimbabwe     SP.URB.TOTL   \n",
       "\n",
       "                               Series name       1990       1991       1992  \\\n",
       "0      Land area below 5m (% of land area)      29.57       0.00       0.00   \n",
       "1      Land area below 5m (% of land area)       0.00       0.00       0.00   \n",
       "13511                     Urban population 3036068.58 3175022.71 3310511.55   \n",
       "\n",
       "            1993       1994       1995  ...       2002       2003       2004  \\\n",
       "0           0.00       0.00       0.00  ...       0.00       0.00       0.00   \n",
       "1           0.00       0.00       0.00  ...       0.00       0.00       0.00   \n",
       "13511 3443081.82 3574012.12 3704047.68  ... 4367338.80 4422102.37 4469726.76   \n",
       "\n",
       "            2005       2006       2007       2008       2009       2010  2011  \n",
       "0           0.00       0.00       0.00       0.00       0.00       0.00  0.00  \n",
       "1           0.00       0.00       0.00       0.00       0.00       0.00  0.00  \n",
       "13511 4512876.27 4558288.49 4600586.91 4649406.16 4717663.77 4814866.88  0.00  \n",
       "\n",
       "[3 rows x 26 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# iloc, it will locate the index on that row that matches to the values given in the list.\n",
    "data.iloc[[0, 1, -1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Working with the dataframe on rows and columns and the TIDY concep</h3>\n",
    "<h4> Column headers are values, not variable names.</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I want to show three columns: Country name, 1990, and the last column 2011:\n",
    "subset = data.loc[:, ['Country name', '1990', '2011']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country name</th>\n",
       "      <th>1990</th>\n",
       "      <th>2011</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>29.57</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>4.97</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country name  1990  2011\n",
       "0        Aruba 29.57  0.00\n",
       "1      Andorra  0.00  0.00\n",
       "2  Afghanistan  0.00  0.00\n",
       "3       Angola  0.21  0.00\n",
       "4      Albania  4.97  0.00"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.heatmap(subset.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now, I will apply some Tidy criteria for this dataset. One of them is that all the columns with labels as a years\n",
    "# such as 1990, 1991, and so on, are not well defined. Columns don't need to have namelabels as a variables.\n",
    "# It would be better If I had assigned them another label such as \"year\"\n",
    "# I will use melt function of Pandas.\n",
    "\n",
    "data_melt= pd.melt(data, id_vars=['Country code', 'Country name', 'Series code', 'Series name'],\n",
    "                  var_name='Year', value_name='Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country code</th>\n",
       "      <th>Country name</th>\n",
       "      <th>Series code</th>\n",
       "      <th>Series name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>1990</td>\n",
       "      <td>29.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ADO</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>1990</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>1990</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>1990</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>1990</td>\n",
       "      <td>4.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country code Country name     Series code  \\\n",
       "0          ABW        Aruba  AG.LND.EL5M.ZS   \n",
       "1          ADO      Andorra  AG.LND.EL5M.ZS   \n",
       "2          AFG  Afghanistan  AG.LND.EL5M.ZS   \n",
       "3          AGO       Angola  AG.LND.EL5M.ZS   \n",
       "4          ALB      Albania  AG.LND.EL5M.ZS   \n",
       "\n",
       "                           Series name  Year  Rate  \n",
       "0  Land area below 5m (% of land area)  1990 29.57  \n",
       "1  Land area below 5m (% of land area)  1990  0.00  \n",
       "2  Land area below 5m (% of land area)  1990  0.00  \n",
       "3  Land area below 5m (% of land area)  1990  0.21  \n",
       "4  Land area below 5m (% of land area)  1990  4.97  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_melt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country code</th>\n",
       "      <th>Country name</th>\n",
       "      <th>Series code</th>\n",
       "      <th>Series name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>NOR</td>\n",
       "      <td>Norway</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>1990</td>\n",
       "      <td>4.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>NOR</td>\n",
       "      <td>Norway</td>\n",
       "      <td>AG.LND.IRIG.AG.ZS</td>\n",
       "      <td>Agricultural land under irrigation (% of total...</td>\n",
       "      <td>1990</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>NOR</td>\n",
       "      <td>Norway</td>\n",
       "      <td>AG.YLD.CREL.KG</td>\n",
       "      <td>Cereal yield (kg per hectare)</td>\n",
       "      <td>1990</td>\n",
       "      <td>4399.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>857</th>\n",
       "      <td>NOR</td>\n",
       "      <td>Norway</td>\n",
       "      <td>BX.KLT.DINV.WD.GD.ZS</td>\n",
       "      <td>Foreign direct investment, net inflows (% of GDP)</td>\n",
       "      <td>1990</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1090</th>\n",
       "      <td>NOR</td>\n",
       "      <td>Norway</td>\n",
       "      <td>EG.ELC.ACCS.ZS</td>\n",
       "      <td>Access to electricity (% of total population)</td>\n",
       "      <td>1990</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296259</th>\n",
       "      <td>NOR</td>\n",
       "      <td>Norway</td>\n",
       "      <td>SI.POV.DDAY</td>\n",
       "      <td>Population living below $1.25 a day (% of total)</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296492</th>\n",
       "      <td>NOR</td>\n",
       "      <td>Norway</td>\n",
       "      <td>SP.POP.GROW</td>\n",
       "      <td>Population growth (annual %)</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296724</th>\n",
       "      <td>NOR</td>\n",
       "      <td>Norway</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>Population</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296957</th>\n",
       "      <td>NOR</td>\n",
       "      <td>Norway</td>\n",
       "      <td>SP.URB.GROW</td>\n",
       "      <td>Urban population growth (annual %)</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297189</th>\n",
       "      <td>NOR</td>\n",
       "      <td>Norway</td>\n",
       "      <td>SP.URB.TOTL</td>\n",
       "      <td>Urban population</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1276 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country code Country name           Series code  \\\n",
       "158             NOR       Norway        AG.LND.EL5M.ZS   \n",
       "391             NOR       Norway     AG.LND.IRIG.AG.ZS   \n",
       "624             NOR       Norway        AG.YLD.CREL.KG   \n",
       "857             NOR       Norway  BX.KLT.DINV.WD.GD.ZS   \n",
       "1090            NOR       Norway        EG.ELC.ACCS.ZS   \n",
       "...             ...          ...                   ...   \n",
       "296259          NOR       Norway           SI.POV.DDAY   \n",
       "296492          NOR       Norway           SP.POP.GROW   \n",
       "296724          NOR       Norway           SP.POP.TOTL   \n",
       "296957          NOR       Norway           SP.URB.GROW   \n",
       "297189          NOR       Norway           SP.URB.TOTL   \n",
       "\n",
       "                                              Series name  Year    Rate  \n",
       "158                   Land area below 5m (% of land area)  1990    4.85  \n",
       "391     Agricultural land under irrigation (% of total...  1990    0.00  \n",
       "624                         Cereal yield (kg per hectare)  1990 4399.20  \n",
       "857     Foreign direct investment, net inflows (% of GDP)  1990    0.85  \n",
       "1090        Access to electricity (% of total population)  1990    0.00  \n",
       "...                                                   ...   ...     ...  \n",
       "296259   Population living below $1.25 a day (% of total)  2011    0.00  \n",
       "296492                       Population growth (annual %)  2011    0.00  \n",
       "296724                                         Population  2011    0.00  \n",
       "296957                 Urban population growth (annual %)  2011    0.00  \n",
       "297189                                   Urban population  2011    0.00  \n",
       "\n",
       "[1276 rows x 6 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#type(data_melt)\n",
    "#data_melt.shape\n",
    "\n",
    "#data_melt.loc[(data_melt['Country name'] == 'Norway') & (data_melt['Year'] == 1999)] \n",
    "data_melt.loc[data_melt['Country name'] == 'Norway']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country code</th>\n",
       "      <th>Country name</th>\n",
       "      <th>Series code</th>\n",
       "      <th>Series name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>1990</td>\n",
       "      <td>29.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ADO</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>1990</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>1990</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>1990</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>1990</td>\n",
       "      <td>4.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country code Country name     Series code  \\\n",
       "0          ABW        Aruba  AG.LND.EL5M.ZS   \n",
       "1          ADO      Andorra  AG.LND.EL5M.ZS   \n",
       "2          AFG  Afghanistan  AG.LND.EL5M.ZS   \n",
       "3          AGO       Angola  AG.LND.EL5M.ZS   \n",
       "4          ALB      Albania  AG.LND.EL5M.ZS   \n",
       "\n",
       "                           Series name  Year  Rate  \n",
       "0  Land area below 5m (% of land area)  1990 29.57  \n",
       "1  Land area below 5m (% of land area)  1990  0.00  \n",
       "2  Land area below 5m (% of land area)  1990  0.00  \n",
       "3  Land area below 5m (% of land area)  1990  0.21  \n",
       "4  Land area below 5m (% of land area)  1990  4.97  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_melt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I will create a new dataframe with thsi values and save it:\n",
    "#data_melt.to_csv(./data/climate/data_melt_countries.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_melt.plot(x = 'Country name', y = 'Rate', kind='bar')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data for testing. It doesn't do anything..\n",
    "#data_countries= data_melt[['Country code', 'Series code', 'Series name', 'Year', 'Rating']]\n",
    "#data_countries.head()\n",
    "#data_countries.shape\n",
    "#data_countries.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Series code</th>\n",
       "      <th>Series name</th>\n",
       "      <th>Scale</th>\n",
       "      <th>Decimals</th>\n",
       "      <th>Order</th>\n",
       "      <th>Topic</th>\n",
       "      <th>Definition</th>\n",
       "      <th>Source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>Population</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Size of the economy</td>\n",
       "      <td>Population includes all residents who are pres...</td>\n",
       "      <td>(1) United Nations Population Division. 2011. ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SP.POP.GROW</td>\n",
       "      <td>Population growth (annual %)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Size of the economy</td>\n",
       "      <td>Annual population growth rate for year t is th...</td>\n",
       "      <td>Derived from total population. Population sour...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>GDP ($)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Size of the economy</td>\n",
       "      <td>GDP is gross domestic product and measures the...</td>\n",
       "      <td>World Bank national accounts data, and OECD Na...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NY.GNP.PCAP.CD</td>\n",
       "      <td>GNI per capita (Atlas $)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Size of the economy</td>\n",
       "      <td>GNI per capita is the gross national income, c...</td>\n",
       "      <td>World Bank national accounts data, and OECD Na...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EN.CLC.MMDT.C</td>\n",
       "      <td>Average daily min/max temperature (1961-1990, ...</td>\n",
       "      <td>Text</td>\n",
       "      <td>Text</td>\n",
       "      <td>5</td>\n",
       "      <td>Climate</td>\n",
       "      <td>Average daily min/max temperature are the mini...</td>\n",
       "      <td>Mitchell, T.D., Carter, T.R., Jones, P.D., Hul...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Series code                                        Series name Scale  \\\n",
       "0     SP.POP.TOTL                                         Population     0   \n",
       "1     SP.POP.GROW                       Population growth (annual %)     0   \n",
       "2  NY.GDP.MKTP.CD                                            GDP ($)     0   \n",
       "3  NY.GNP.PCAP.CD                           GNI per capita (Atlas $)     0   \n",
       "4   EN.CLC.MMDT.C  Average daily min/max temperature (1961-1990, ...  Text   \n",
       "\n",
       "  Decimals  Order                Topic  \\\n",
       "0        0      1  Size of the economy   \n",
       "1        1      2  Size of the economy   \n",
       "2        0      3  Size of the economy   \n",
       "3        0      4  Size of the economy   \n",
       "4     Text      5              Climate   \n",
       "\n",
       "                                          Definition  \\\n",
       "0  Population includes all residents who are pres...   \n",
       "1  Annual population growth rate for year t is th...   \n",
       "2  GDP is gross domestic product and measures the...   \n",
       "3  GNI per capita is the gross national income, c...   \n",
       "4  Average daily min/max temperature are the mini...   \n",
       "\n",
       "                                              Source  \n",
       "0  (1) United Nations Population Division. 2011. ...  \n",
       "1  Derived from total population. Population sour...  \n",
       "2  World Bank national accounts data, and OECD Na...  \n",
       "3  World Bank national accounts data, and OECD Na...  \n",
       "4  Mitchell, T.D., Carter, T.R., Jones, P.D., Hul...  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, I will merge this dataset with another dataset \"series\" for joining the column: Topic in that dataset. So,\n",
    "# I will get the topic of the Series name. For example, if the series name is : \"LAnd area below 5m(%of land are)..\"\n",
    "# the topic in which this category fits is on the topic: \"Exposure to impacts\"\n",
    "# Let's explore first the dataset \"series\"\n",
    "\n",
    "series.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now, I will merge the two datasets : data_melt and series.\n",
    "data_series_topic = pd.merge(data_melt,series, on='Series code')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country code</th>\n",
       "      <th>Country name</th>\n",
       "      <th>Series code</th>\n",
       "      <th>Series name_x</th>\n",
       "      <th>Year</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Series name_y</th>\n",
       "      <th>Scale</th>\n",
       "      <th>Decimals</th>\n",
       "      <th>Order</th>\n",
       "      <th>Topic</th>\n",
       "      <th>Definition</th>\n",
       "      <th>Source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>1990</td>\n",
       "      <td>29.57</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>Center for International Earth Science Informa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ADO</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>1990</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>Center for International Earth Science Informa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>1990</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>Center for International Earth Science Informa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>1990</td>\n",
       "      <td>0.21</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>Center for International Earth Science Informa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>AG.LND.EL5M.ZS</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>1990</td>\n",
       "      <td>4.97</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>Center for International Earth Science Informa...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country code Country name     Series code  \\\n",
       "0          ABW        Aruba  AG.LND.EL5M.ZS   \n",
       "1          ADO      Andorra  AG.LND.EL5M.ZS   \n",
       "2          AFG  Afghanistan  AG.LND.EL5M.ZS   \n",
       "3          AGO       Angola  AG.LND.EL5M.ZS   \n",
       "4          ALB      Albania  AG.LND.EL5M.ZS   \n",
       "\n",
       "                         Series name_x  Year  Rate  \\\n",
       "0  Land area below 5m (% of land area)  1990 29.57   \n",
       "1  Land area below 5m (% of land area)  1990  0.00   \n",
       "2  Land area below 5m (% of land area)  1990  0.00   \n",
       "3  Land area below 5m (% of land area)  1990  0.21   \n",
       "4  Land area below 5m (% of land area)  1990  4.97   \n",
       "\n",
       "                         Series name_y Scale Decimals  Order  \\\n",
       "0  Land area below 5m (% of land area)     0        1     11   \n",
       "1  Land area below 5m (% of land area)     0        1     11   \n",
       "2  Land area below 5m (% of land area)     0        1     11   \n",
       "3  Land area below 5m (% of land area)     0        1     11   \n",
       "4  Land area below 5m (% of land area)     0        1     11   \n",
       "\n",
       "                 Topic                                         Definition  \\\n",
       "0  Exposure to impacts  Land area below 5m is the percentage of total ...   \n",
       "1  Exposure to impacts  Land area below 5m is the percentage of total ...   \n",
       "2  Exposure to impacts  Land area below 5m is the percentage of total ...   \n",
       "3  Exposure to impacts  Land area below 5m is the percentage of total ...   \n",
       "4  Exposure to impacts  Land area below 5m is the percentage of total ...   \n",
       "\n",
       "                                              Source  \n",
       "0  Center for International Earth Science Informa...  \n",
       "1  Center for International Earth Science Informa...  \n",
       "2  Center for International Earth Science Informa...  \n",
       "3  Center for International Earth Science Informa...  \n",
       "4  Center for International Earth Science Informa...  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data_series_topic.head()\n",
    "data_series_topic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(297264, 13)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, I can group by Topic for knowing in which area or topic these contries have most issues related to the\n",
    "# climate change.\n",
    "data_series_topic.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_series_topic.drop_duplicates() # There are no duplicated data on this dataframe.\n",
    "# Now, I will filter only the columns I need to work on for showing some graphs.\n",
    "data_series_topic = data_series_topic[['Country name', 'Series name_x', 'Topic', 'Definition','Year', 'Rate']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country name</th>\n",
       "      <th>Series name_x</th>\n",
       "      <th>Topic</th>\n",
       "      <th>Definition</th>\n",
       "      <th>Year</th>\n",
       "      <th>Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>1990</td>\n",
       "      <td>29.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>1990</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>1990</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>1990</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>1990</td>\n",
       "      <td>4.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country name                        Series name_x                Topic  \\\n",
       "0        Aruba  Land area below 5m (% of land area)  Exposure to impacts   \n",
       "1      Andorra  Land area below 5m (% of land area)  Exposure to impacts   \n",
       "2  Afghanistan  Land area below 5m (% of land area)  Exposure to impacts   \n",
       "3       Angola  Land area below 5m (% of land area)  Exposure to impacts   \n",
       "4      Albania  Land area below 5m (% of land area)  Exposure to impacts   \n",
       "\n",
       "                                          Definition  Year  Rate  \n",
       "0  Land area below 5m is the percentage of total ...  1990 29.57  \n",
       "1  Land area below 5m is the percentage of total ...  1990  0.00  \n",
       "2  Land area below 5m is the percentage of total ...  1990  0.00  \n",
       "3  Land area below 5m is the percentage of total ...  1990  0.21  \n",
       "4  Land area below 5m is the percentage of total ...  1990  4.97  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_series_topic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_series_topic_tidy = data_series_topic.pivot_table(index=['Country name', 'Series name_x', 'Definition', 'Year'],\n",
    "      #                                                       columns='Topic', values='Rating')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Country name</th>\n",
       "      <th>Series name_x</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>1990</td>\n",
       "      <td>29.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>1990</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>1990</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>1990</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>1990</td>\n",
       "      <td>4.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297259</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>Urban population</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Urban population is the share of the midyear p...</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297260</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>Urban population</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Urban population is the share of the midyear p...</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297261</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>Urban population</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Urban population is the share of the midyear p...</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297262</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Urban population</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Urban population is the share of the midyear p...</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297263</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Urban population</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Urban population is the share of the midyear p...</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66616 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Country name                        Series name_x  \\\n",
       "0                  Aruba  Land area below 5m (% of land area)   \n",
       "1                Andorra  Land area below 5m (% of land area)   \n",
       "2            Afghanistan  Land area below 5m (% of land area)   \n",
       "3                 Angola  Land area below 5m (% of land area)   \n",
       "4                Albania  Land area below 5m (% of land area)   \n",
       "...                  ...                                  ...   \n",
       "297259       Yemen, Rep.                     Urban population   \n",
       "297260      South Africa                     Urban population   \n",
       "297261  Congo, Dem. Rep.                     Urban population   \n",
       "297262            Zambia                     Urban population   \n",
       "297263          Zimbabwe                     Urban population   \n",
       "\n",
       "                      Topic  \\\n",
       "0       Exposure to impacts   \n",
       "1       Exposure to impacts   \n",
       "2       Exposure to impacts   \n",
       "3       Exposure to impacts   \n",
       "4       Exposure to impacts   \n",
       "...                     ...   \n",
       "297259  Exposure to impacts   \n",
       "297260  Exposure to impacts   \n",
       "297261  Exposure to impacts   \n",
       "297262  Exposure to impacts   \n",
       "297263  Exposure to impacts   \n",
       "\n",
       "                                               Definition  Year  Rate  \n",
       "0       Land area below 5m is the percentage of total ...  1990 29.57  \n",
       "1       Land area below 5m is the percentage of total ...  1990  0.00  \n",
       "2       Land area below 5m is the percentage of total ...  1990  0.00  \n",
       "3       Land area below 5m is the percentage of total ...  1990  0.21  \n",
       "4       Land area below 5m is the percentage of total ...  1990  4.97  \n",
       "...                                                   ...   ...   ...  \n",
       "297259  Urban population is the share of the midyear p...  2011  0.00  \n",
       "297260  Urban population is the share of the midyear p...  2011  0.00  \n",
       "297261  Urban population is the share of the midyear p...  2011  0.00  \n",
       "297262  Urban population is the share of the midyear p...  2011  0.00  \n",
       "297263  Urban population is the share of the midyear p...  2011  0.00  \n",
       "\n",
       "[66616 rows x 6 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_series_topic.loc[data_series_topic['Topic']=='Exposure to impacts']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
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       "      <th>35878</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Size of the economy</td>\n",
       "      <td>2008</td>\n",
       "      <td>1107112965.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35879</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Size of the economy</td>\n",
       "      <td>2009</td>\n",
       "      <td>1462118590.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35880</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Size of the economy</td>\n",
       "      <td>2010</td>\n",
       "      <td>1871642978.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35881</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Size of the economy</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35882 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Country name                Topic  Year          Rate\n",
       "0      Afghanistan       Carbon markets  1990          0.00\n",
       "1      Afghanistan       Carbon markets  1991          0.00\n",
       "2      Afghanistan       Carbon markets  1992          0.00\n",
       "3      Afghanistan       Carbon markets  1993          0.00\n",
       "4      Afghanistan       Carbon markets  1994          0.00\n",
       "...            ...                  ...   ...           ...\n",
       "35877     Zimbabwe  Size of the economy  2007 1257674967.55\n",
       "35878     Zimbabwe  Size of the economy  2008 1107112965.69\n",
       "35879     Zimbabwe  Size of the economy  2009 1462118590.55\n",
       "35880     Zimbabwe  Size of the economy  2010 1871642978.69\n",
       "35881     Zimbabwe  Size of the economy  2011          0.00\n",
       "\n",
       "[35882 rows x 4 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, I want to use the groupby function for calculating the mean of the rate by year and by country.\n",
    "data_series_topic.groupby(['Country name','Topic','Year'])['Rate'].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_series_topic.groupby(['Country name','Topic','Year'])['Rate'].agg(pos=lambda ts: (ts > 0).sum()).reset_index()\n",
    "#sb.heatmap(data_series_topic.corr())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What it was the worst years in average that can possibly cause weather/temperature change between 1990 and 2011.\n",
    "# We can see that  in the past years has been increasing the weather change in avarage.\n",
    "\n",
    "years = list(range(1990,2012))\n",
    "print(years)\n",
    "results = data_series_topic.groupby('Year').mean()\n",
    "plt.bar(years, results['Rate'])\n",
    "#plt.xticks(years)\n",
    "plt.ylabel('Rates')\n",
    "plt.xlabel('Years')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_series_topic.Rate.plot(kind='hist') data_series_topic.loc[data_series_topic['Topic']=='Exposure to impacts']\n",
    "name_country = data_series_topic.loc[data_series_topic['Country name']=='Argentina']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country name</th>\n",
       "      <th>Series name_x</th>\n",
       "      <th>Topic</th>\n",
       "      <th>Definition</th>\n",
       "      <th>Year</th>\n",
       "      <th>Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>1990</td>\n",
       "      <td>1.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>1991</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>1992</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>938</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>1994</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296105</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Urban population</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Urban population is the share of the midyear p...</td>\n",
       "      <td>2007</td>\n",
       "      <td>36139884.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296338</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Urban population</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Urban population is the share of the midyear p...</td>\n",
       "      <td>2008</td>\n",
       "      <td>36537154.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296571</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Urban population</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Urban population is the share of the midyear p...</td>\n",
       "      <td>2009</td>\n",
       "      <td>36937597.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296804</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Urban population</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Urban population is the share of the midyear p...</td>\n",
       "      <td>2010</td>\n",
       "      <td>37341035.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297037</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Urban population</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Urban population is the share of the midyear p...</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1276 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country name                        Series name_x                Topic  \\\n",
       "6         Argentina  Land area below 5m (% of land area)  Exposure to impacts   \n",
       "239       Argentina  Land area below 5m (% of land area)  Exposure to impacts   \n",
       "472       Argentina  Land area below 5m (% of land area)  Exposure to impacts   \n",
       "705       Argentina  Land area below 5m (% of land area)  Exposure to impacts   \n",
       "938       Argentina  Land area below 5m (% of land area)  Exposure to impacts   \n",
       "...             ...                                  ...                  ...   \n",
       "296105    Argentina                     Urban population  Exposure to impacts   \n",
       "296338    Argentina                     Urban population  Exposure to impacts   \n",
       "296571    Argentina                     Urban population  Exposure to impacts   \n",
       "296804    Argentina                     Urban population  Exposure to impacts   \n",
       "297037    Argentina                     Urban population  Exposure to impacts   \n",
       "\n",
       "                                               Definition  Year        Rate  \n",
       "6       Land area below 5m is the percentage of total ...  1990        1.23  \n",
       "239     Land area below 5m is the percentage of total ...  1991        0.00  \n",
       "472     Land area below 5m is the percentage of total ...  1992        0.00  \n",
       "705     Land area below 5m is the percentage of total ...  1993        0.00  \n",
       "938     Land area below 5m is the percentage of total ...  1994        0.00  \n",
       "...                                                   ...   ...         ...  \n",
       "296105  Urban population is the share of the midyear p...  2007 36139884.59  \n",
       "296338  Urban population is the share of the midyear p...  2008 36537154.16  \n",
       "296571  Urban population is the share of the midyear p...  2009 36937597.34  \n",
       "296804  Urban population is the share of the midyear p...  2010 37341035.42  \n",
       "297037  Urban population is the share of the midyear p...  2011        0.00  \n",
       "\n",
       "[1276 rows x 6 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#topic = data_series_topic.loc[data_series_topic['Topic']=='Exposure to impacts']\n",
    "name_country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Argentina', ylabel='Density'>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Shows the histogram graphic for Argentina between 1990 and 2011 in rates of change by the topic \"Exposure to impacts\"\n",
    "sb.distplot(name_country.Year, kde=True, axlabel='Argentina', color='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following code line cell I tried to select all values in the column Rate on the dataframe name_country. I will\n",
    "create another temporal rate value for the country Argentina by selecting all values in the column rate that are less than 1.0. As we can see in the cell below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1276 entries, 6 to 297037\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype   \n",
      "---  ------         --------------  -----   \n",
      " 0   Country name   1276 non-null   category\n",
      " 1   Series name_x  1276 non-null   category\n",
      " 2   Topic          1276 non-null   object  \n",
      " 3   Definition     1276 non-null   object  \n",
      " 4   Year           1276 non-null   object  \n",
      " 5   Rate           1276 non-null   float64 \n",
      "dtypes: category(2), float64(1), object(3)\n",
      "memory usage: 68.4+ KB\n"
     ]
    }
   ],
   "source": [
    "name_country.head()\n",
    "name_country.info()\n",
    "\n",
    "\n",
    "rat = name_country.loc[name_country['Rate'] > 1.0]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country name</th>\n",
       "      <th>Series name_x</th>\n",
       "      <th>Topic</th>\n",
       "      <th>Definition</th>\n",
       "      <th>Year</th>\n",
       "      <th>Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>1990</td>\n",
       "      <td>1.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2336</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7928</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Agricultural land under irrigation (% of total...</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Agricultural irrigated land refers to agricult...</td>\n",
       "      <td>2002</td>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10258</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Cereal yield (kg per hectare)</td>\n",
       "      <td>Resilience</td>\n",
       "      <td>Cereal yield, measured as kilograms per hectar...</td>\n",
       "      <td>1990</td>\n",
       "      <td>2232.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10491</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Cereal yield (kg per hectare)</td>\n",
       "      <td>Resilience</td>\n",
       "      <td>Cereal yield, measured as kilograms per hectar...</td>\n",
       "      <td>1991</td>\n",
       "      <td>2666.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295872</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Urban population</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Urban population is the share of the midyear p...</td>\n",
       "      <td>2006</td>\n",
       "      <td>35745846.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296105</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Urban population</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Urban population is the share of the midyear p...</td>\n",
       "      <td>2007</td>\n",
       "      <td>36139884.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296338</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Urban population</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Urban population is the share of the midyear p...</td>\n",
       "      <td>2008</td>\n",
       "      <td>36537154.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296571</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Urban population</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Urban population is the share of the midyear p...</td>\n",
       "      <td>2009</td>\n",
       "      <td>36937597.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296804</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Urban population</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Urban population is the share of the midyear p...</td>\n",
       "      <td>2010</td>\n",
       "      <td>37341035.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>473 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country name                                      Series name_x  \\\n",
       "6         Argentina                Land area below 5m (% of land area)   \n",
       "2336      Argentina                Land area below 5m (% of land area)   \n",
       "7928      Argentina  Agricultural land under irrigation (% of total...   \n",
       "10258     Argentina                      Cereal yield (kg per hectare)   \n",
       "10491     Argentina                      Cereal yield (kg per hectare)   \n",
       "...             ...                                                ...   \n",
       "295872    Argentina                                   Urban population   \n",
       "296105    Argentina                                   Urban population   \n",
       "296338    Argentina                                   Urban population   \n",
       "296571    Argentina                                   Urban population   \n",
       "296804    Argentina                                   Urban population   \n",
       "\n",
       "                      Topic  \\\n",
       "6       Exposure to impacts   \n",
       "2336    Exposure to impacts   \n",
       "7928    Exposure to impacts   \n",
       "10258            Resilience   \n",
       "10491            Resilience   \n",
       "...                     ...   \n",
       "295872  Exposure to impacts   \n",
       "296105  Exposure to impacts   \n",
       "296338  Exposure to impacts   \n",
       "296571  Exposure to impacts   \n",
       "296804  Exposure to impacts   \n",
       "\n",
       "                                               Definition  Year        Rate  \n",
       "6       Land area below 5m is the percentage of total ...  1990        1.23  \n",
       "2336    Land area below 5m is the percentage of total ...  2000        1.23  \n",
       "7928    Agricultural irrigated land refers to agricult...  2002        1.05  \n",
       "10258   Cereal yield, measured as kilograms per hectar...  1990     2232.20  \n",
       "10491   Cereal yield, measured as kilograms per hectar...  1991     2666.10  \n",
       "...                                                   ...   ...         ...  \n",
       "295872  Urban population is the share of the midyear p...  2006 35745846.60  \n",
       "296105  Urban population is the share of the midyear p...  2007 36139884.59  \n",
       "296338  Urban population is the share of the midyear p...  2008 36537154.16  \n",
       "296571  Urban population is the share of the midyear p...  2009 36937597.34  \n",
       "296804  Urban population is the share of the midyear p...  2010 37341035.42  \n",
       "\n",
       "[473 rows x 6 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, I can see the rate column shows us only the values that are greater than 1.0.\n",
    "rat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7ff0812b2a60>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ploting the city Argentina among the years in which the rate of climate change is prsented in different topics or\n",
    "# categories such as \"Exposure to impacts\", \" Resilience\", GhG emissions and energy use, National level actions, Carbon\n",
    "# markets, climate, and Size of economy.\n",
    "#sb.lmplot(x='Rate', y='Year', data=name_country, fit_reg=False, col='Topic')\n",
    "sb.lmplot(x='Rate', y='Year', data=rat, fit_reg=False, col='Topic')\n",
    "# As we can see in the plot below I tried to show all the values on Rate column, but I have some issues for\n",
    "# showing the different categories on the column Topic that contains 7 different sub categories. That is why it shows\n",
    "#7 different graphs based on Topic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>The following figure will show the topic of the impact of the climate change in Argentina based on rate.\n",
    "    It will take some time by executing the lines below, that is why I uncommented it, If you want to execute it,\n",
    "    it migh be uncomment the cell below</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#facet = sb.FacetGrid(name_country, col='Year', row='Topic', hue='Series name_x')\n",
    "#facet.map(plt.scatter, 'Rate', 'Year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I will just check if my dataset : data_Series_topic that I created is a dataframe type.\n",
    "type(data_series_topic)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country name     category\n",
       "Series name_x    category\n",
       "Topic              object\n",
       "Definition         object\n",
       "Year               object\n",
       "Rate              float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_series_topic.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1280x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# I will plot the rate and year of the climate change in Argentina based on the topic of the changes. \n",
    "\n",
    "name_country.columns\n",
    "coun_topic = name_country['Topic'].unique()\n",
    "coun_topic\n",
    "\n",
    "# I will select colors for each category on column Topic\n",
    "colors = [plt.cm.tab10(i/float(len(coun_topic)-1)) for i in range(len(coun_topic))]\n",
    "colors\n",
    "\n",
    "plt.figure(figsize=(16, 10), dpi=80, facecolor=\"w\", edgecolor=\"k\")\n",
    "for i, cat in enumerate(coun_topic):\n",
    "    plt.scatter(\"Year\", \"Rate\", data=name_country.loc[name_country.Topic == cat, :],\n",
    "               s = 20, c=colors[i], label=str(cat))\n",
    "plt.gca().set(xlabel='Year', ylabel='Rate')\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.title(\"Topic of clima change in Argentina based on year and rate\")\n",
    "plt.legend(fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country name</th>\n",
       "      <th>Series name_x</th>\n",
       "      <th>Topic</th>\n",
       "      <th>Definition</th>\n",
       "      <th>Year</th>\n",
       "      <th>Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>1990</td>\n",
       "      <td>1.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>1991</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>1992</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>938</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>1994</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country name                        Series name_x                Topic  \\\n",
       "6      Argentina  Land area below 5m (% of land area)  Exposure to impacts   \n",
       "239    Argentina  Land area below 5m (% of land area)  Exposure to impacts   \n",
       "472    Argentina  Land area below 5m (% of land area)  Exposure to impacts   \n",
       "705    Argentina  Land area below 5m (% of land area)  Exposure to impacts   \n",
       "938    Argentina  Land area below 5m (% of land area)  Exposure to impacts   \n",
       "\n",
       "                                            Definition  Year  Rate  \n",
       "6    Land area below 5m is the percentage of total ...  1990  1.23  \n",
       "239  Land area below 5m is the percentage of total ...  1991  0.00  \n",
       "472  Land area below 5m is the percentage of total ...  1992  0.00  \n",
       "705  Land area below 5m is the percentage of total ...  1993  0.00  \n",
       "938  Land area below 5m is the percentage of total ...  1994  0.00  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_country.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>I am creating my own function called \"mean_of_rate\" for computing and returning the mean of the column \"Rate\" on the dataframe \"name_country\" that contains all the values related to Argentina (Country name). By doing this I am adding a new column on that dataframe with the mean of the rate by year.</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country name</th>\n",
       "      <th>Series name_x</th>\n",
       "      <th>Topic</th>\n",
       "      <th>Definition</th>\n",
       "      <th>Year</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>1990</td>\n",
       "      <td>1.23</td>\n",
       "      <td>2495799694.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>1991</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3322743327.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>1992</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4065367476.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4255983226.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>938</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Land area below 5m (% of land area)</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Land area below 5m is the percentage of total ...</td>\n",
       "      <td>1994</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4529189778.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296105</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Urban population</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Urban population is the share of the midyear p...</td>\n",
       "      <td>2007</td>\n",
       "      <td>36139884.59</td>\n",
       "      <td>4537470827.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296338</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Urban population</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Urban population is the share of the midyear p...</td>\n",
       "      <td>2008</td>\n",
       "      <td>36537154.16</td>\n",
       "      <td>5678997948.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296571</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Urban population</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Urban population is the share of the midyear p...</td>\n",
       "      <td>2009</td>\n",
       "      <td>36937597.34</td>\n",
       "      <td>5324626344.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296804</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Urban population</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Urban population is the share of the midyear p...</td>\n",
       "      <td>2010</td>\n",
       "      <td>37341035.42</td>\n",
       "      <td>6380567748.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297037</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Urban population</td>\n",
       "      <td>Exposure to impacts</td>\n",
       "      <td>Urban population is the share of the midyear p...</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1276 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country name                        Series name_x                Topic  \\\n",
       "6         Argentina  Land area below 5m (% of land area)  Exposure to impacts   \n",
       "239       Argentina  Land area below 5m (% of land area)  Exposure to impacts   \n",
       "472       Argentina  Land area below 5m (% of land area)  Exposure to impacts   \n",
       "705       Argentina  Land area below 5m (% of land area)  Exposure to impacts   \n",
       "938       Argentina  Land area below 5m (% of land area)  Exposure to impacts   \n",
       "...             ...                                  ...                  ...   \n",
       "296105    Argentina                     Urban population  Exposure to impacts   \n",
       "296338    Argentina                     Urban population  Exposure to impacts   \n",
       "296571    Argentina                     Urban population  Exposure to impacts   \n",
       "296804    Argentina                     Urban population  Exposure to impacts   \n",
       "297037    Argentina                     Urban population  Exposure to impacts   \n",
       "\n",
       "                                               Definition  Year        Rate  \\\n",
       "6       Land area below 5m is the percentage of total ...  1990        1.23   \n",
       "239     Land area below 5m is the percentage of total ...  1991        0.00   \n",
       "472     Land area below 5m is the percentage of total ...  1992        0.00   \n",
       "705     Land area below 5m is the percentage of total ...  1993        0.00   \n",
       "938     Land area below 5m is the percentage of total ...  1994        0.00   \n",
       "...                                                   ...   ...         ...   \n",
       "296105  Urban population is the share of the midyear p...  2007 36139884.59   \n",
       "296338  Urban population is the share of the midyear p...  2008 36537154.16   \n",
       "296571  Urban population is the share of the midyear p...  2009 36937597.34   \n",
       "296804  Urban population is the share of the midyear p...  2010 37341035.42   \n",
       "297037  Urban population is the share of the midyear p...  2011        0.00   \n",
       "\n",
       "                Mean  \n",
       "6      2495799694.11  \n",
       "239    3322743327.02  \n",
       "472    4065367476.63  \n",
       "705    4255983226.55  \n",
       "938    4529189778.89  \n",
       "...              ...  \n",
       "296105 4537470827.49  \n",
       "296338 5678997948.47  \n",
       "296571 5324626344.41  \n",
       "296804 6380567748.97  \n",
       "297037         12.61  \n",
       "\n",
       "[1276 rows x 7 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def mean_of_rate(name_country):\n",
    "    name_country['Mean']= name_country['Rate'].mean()\n",
    "    return name_country\n",
    "\n",
    "plot_mean_per_year =name_country.groupby('Year').apply(mean_of_rate)\n",
    "plot_mean_per_year\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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