Data Frame.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats = pd.read_csv('C:\\\\Users\\\\Ravishankar.kumar\\\\Desktop\\\\Python\\\\P4-Demographic-Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "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>Country Code</th>\n",
       "      <th>Birth rate</th>\n",
       "      <th>Internet users</th>\n",
       "      <th>Income Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.9</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.2</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.0</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>YEM</td>\n",
       "      <td>32.947</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>20.850</td>\n",
       "      <td>46.5</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>COD</td>\n",
       "      <td>42.394</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>40.471</td>\n",
       "      <td>15.4</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>35.715</td>\n",
       "      <td>18.5</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Country Name Country Code  Birth rate  Internet users  \\\n",
       "0                   Aruba          ABW      10.244            78.9   \n",
       "1             Afghanistan          AFG      35.253             5.9   \n",
       "2                  Angola          AGO      45.985            19.1   \n",
       "3                 Albania          ALB      12.877            57.2   \n",
       "4    United Arab Emirates          ARE      11.044            88.0   \n",
       "..                    ...          ...         ...             ...   \n",
       "190           Yemen, Rep.          YEM      32.947            20.0   \n",
       "191          South Africa          ZAF      20.850            46.5   \n",
       "192      Congo, Dem. Rep.          COD      42.394             2.2   \n",
       "193                Zambia          ZMB      40.471            15.4   \n",
       "194              Zimbabwe          ZWE      35.715            18.5   \n",
       "\n",
       "            Income Group  \n",
       "0            High income  \n",
       "1             Low income  \n",
       "2    Upper middle income  \n",
       "3    Upper middle income  \n",
       "4            High income  \n",
       "..                   ...  \n",
       "190  Lower middle income  \n",
       "191  Upper middle income  \n",
       "192           Low income  \n",
       "193  Lower middle income  \n",
       "194           Low income  \n",
       "\n",
       "[195 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ravishankar.kumar\\Desktop\\Python Programming Course\n"
     ]
    }
   ],
   "source": [
    "print(os.getcwd())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country Name', 'Country Code', 'Birth rate', 'Internet users',\n",
       "       'Income Group'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Country Code</th>\n",
       "      <th>Birth rate</th>\n",
       "      <th>Internet users</th>\n",
       "      <th>Income Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.9</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.2</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.0</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country Name Country Code  Birth rate  Internet users  \\\n",
       "0                 Aruba          ABW      10.244            78.9   \n",
       "1           Afghanistan          AFG      35.253             5.9   \n",
       "2                Angola          AGO      45.985            19.1   \n",
       "3               Albania          ALB      12.877            57.2   \n",
       "4  United Arab Emirates          ARE      11.044            88.0   \n",
       "\n",
       "          Income Group  \n",
       "0          High income  \n",
       "1           Low income  \n",
       "2  Upper middle income  \n",
       "3  Upper middle income  \n",
       "4          High income  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Country Code</th>\n",
       "      <th>Birth rate</th>\n",
       "      <th>Internet users</th>\n",
       "      <th>Income Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>YEM</td>\n",
       "      <td>32.947</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>20.850</td>\n",
       "      <td>46.5</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>COD</td>\n",
       "      <td>42.394</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>40.471</td>\n",
       "      <td>15.4</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>35.715</td>\n",
       "      <td>18.5</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country Name Country Code  Birth rate  Internet users  \\\n",
       "190       Yemen, Rep.          YEM      32.947            20.0   \n",
       "191      South Africa          ZAF      20.850            46.5   \n",
       "192  Congo, Dem. Rep.          COD      42.394             2.2   \n",
       "193            Zambia          ZMB      40.471            15.4   \n",
       "194          Zimbabwe          ZWE      35.715            18.5   \n",
       "\n",
       "            Income Group  \n",
       "190  Lower middle income  \n",
       "191  Upper middle income  \n",
       "192           Low income  \n",
       "193  Lower middle income  \n",
       "194           Low income  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of              Country Name Country Code  Birth rate  Internet users  \\\n",
       "0                   Aruba          ABW      10.244            78.9   \n",
       "1             Afghanistan          AFG      35.253             5.9   \n",
       "2                  Angola          AGO      45.985            19.1   \n",
       "3                 Albania          ALB      12.877            57.2   \n",
       "4    United Arab Emirates          ARE      11.044            88.0   \n",
       "..                    ...          ...         ...             ...   \n",
       "190           Yemen, Rep.          YEM      32.947            20.0   \n",
       "191          South Africa          ZAF      20.850            46.5   \n",
       "192      Congo, Dem. Rep.          COD      42.394             2.2   \n",
       "193                Zambia          ZMB      40.471            15.4   \n",
       "194              Zimbabwe          ZWE      35.715            18.5   \n",
       "\n",
       "            Income Group  \n",
       "0            High income  \n",
       "1             Low income  \n",
       "2    Upper middle income  \n",
       "3    Upper middle income  \n",
       "4            High income  \n",
       "..                   ...  \n",
       "190  Lower middle income  \n",
       "191  Upper middle income  \n",
       "192           Low income  \n",
       "193  Lower middle income  \n",
       "194           Low income  \n",
       "\n",
       "[195 rows x 5 columns]>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 195 entries, 0 to 194\n",
      "Data columns (total 5 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Country Name    195 non-null    object \n",
      " 1   Country Code    195 non-null    object \n",
      " 2   Birth rate      195 non-null    float64\n",
      " 3   Internet users  195 non-null    float64\n",
      " 4   Income Group    195 non-null    object \n",
      "dtypes: float64(2), object(3)\n",
      "memory usage: 7.7+ KB\n"
     ]
    }
   ],
   "source": [
    "stats.info()"
   ]
  },
  {
   "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>Birth rate</th>\n",
       "      <th>Internet users</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>21.469928</td>\n",
       "      <td>42.076471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.605467</td>\n",
       "      <td>29.030788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.120500</td>\n",
       "      <td>14.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19.680000</td>\n",
       "      <td>41.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>29.759500</td>\n",
       "      <td>66.225000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>49.661000</td>\n",
       "      <td>96.546800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Birth rate  Internet users\n",
       "count  195.000000      195.000000\n",
       "mean    21.469928       42.076471\n",
       "std     10.605467       29.030788\n",
       "min      7.900000        0.900000\n",
       "25%     12.120500       14.520000\n",
       "50%     19.680000       41.000000\n",
       "75%     29.759500       66.225000\n",
       "max     49.661000       96.546800"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Birth rate</th>\n",
       "      <td>195.0</td>\n",
       "      <td>21.469928</td>\n",
       "      <td>10.605467</td>\n",
       "      <td>7.9</td>\n",
       "      <td>12.1205</td>\n",
       "      <td>19.68</td>\n",
       "      <td>29.7595</td>\n",
       "      <td>49.6610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Internet users</th>\n",
       "      <td>195.0</td>\n",
       "      <td>42.076471</td>\n",
       "      <td>29.030788</td>\n",
       "      <td>0.9</td>\n",
       "      <td>14.5200</td>\n",
       "      <td>41.00</td>\n",
       "      <td>66.2250</td>\n",
       "      <td>96.5468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count       mean        std  min      25%    50%      75%  \\\n",
       "Birth rate      195.0  21.469928  10.605467  7.9  12.1205  19.68  29.7595   \n",
       "Internet users  195.0  42.076471  29.030788  0.9  14.5200  41.00  66.2250   \n",
       "\n",
       "                    max  \n",
       "Birth rate      49.6610  \n",
       "Internet users  96.5468  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Country Code</th>\n",
       "      <th>Birth rate</th>\n",
       "      <th>Internet users</th>\n",
       "      <th>Income Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.9</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.2</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.0</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country Name Country Code  Birth rate  Internet users  \\\n",
       "0                 Aruba          ABW      10.244            78.9   \n",
       "1           Afghanistan          AFG      35.253             5.9   \n",
       "2                Angola          AGO      45.985            19.1   \n",
       "3               Albania          ALB      12.877            57.2   \n",
       "4  United Arab Emirates          ARE      11.044            88.0   \n",
       "\n",
       "          Income Group  \n",
       "0          High income  \n",
       "1           Low income  \n",
       "2  Upper middle income  \n",
       "3  Upper middle income  \n",
       "4          High income  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                     Aruba\n",
       "1               Afghanistan\n",
       "2                    Angola\n",
       "3                   Albania\n",
       "4      United Arab Emirates\n",
       "               ...         \n",
       "190             Yemen, Rep.\n",
       "191            South Africa\n",
       "192        Congo, Dem. Rep.\n",
       "193                  Zambia\n",
       "194                Zimbabwe\n",
       "Name: Country Name, Length: 195, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats['Country Name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Country Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>YEM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>ZAF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>COD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Country Name Country Code\n",
       "0                   Aruba          ABW\n",
       "1             Afghanistan          AFG\n",
       "2                  Angola          AGO\n",
       "3                 Albania          ALB\n",
       "4    United Arab Emirates          ARE\n",
       "..                    ...          ...\n",
       "190           Yemen, Rep.          YEM\n",
       "191          South Africa          ZAF\n",
       "192      Congo, Dem. Rep.          COD\n",
       "193                Zambia          ZMB\n",
       "194              Zimbabwe          ZWE\n",
       "\n",
       "[195 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[['Country Name','Country Code']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Country Code</th>\n",
       "      <th>Birth rate</th>\n",
       "      <th>Internet users</th>\n",
       "      <th>Income Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.9</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.2</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.0</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country Name Country Code  Birth rate  Internet users  \\\n",
       "0                 Aruba          ABW      10.244            78.9   \n",
       "1           Afghanistan          AFG      35.253             5.9   \n",
       "2                Angola          AGO      45.985            19.1   \n",
       "3               Albania          ALB      12.877            57.2   \n",
       "4  United Arab Emirates          ARE      11.044            88.0   \n",
       "\n",
       "          Income Group  \n",
       "0          High income  \n",
       "1           Low income  \n",
       "2  Upper middle income  \n",
       "3  Upper middle income  \n",
       "4          High income  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats.columns=['CountryName','CountryCode','Birthrate','Internetusers','IncomeGroup']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>Birthrate</th>\n",
       "      <th>Internetusers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.9</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.2</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.0</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CountryName CountryCode  Birthrate  Internetusers  \\\n",
       "0                 Aruba         ABW     10.244           78.9   \n",
       "1           Afghanistan         AFG     35.253            5.9   \n",
       "2                Angola         AGO     45.985           19.1   \n",
       "3               Albania         ALB     12.877           57.2   \n",
       "4  United Arab Emirates         ARE     11.044           88.0   \n",
       "\n",
       "           IncomeGroup  \n",
       "0          High income  \n",
       "1           Low income  \n",
       "2  Upper middle income  \n",
       "3  Upper middle income  \n",
       "4          High income  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.head()"
   ]
  },
  {
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>ARG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>ARM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>ATG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CountryName CountryCode\n",
       "4  United Arab Emirates         ARE\n",
       "5             Argentina         ARG\n",
       "6               Armenia         ARM\n",
       "7   Antigua and Barbuda         ATG"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[4:8][['CountryName','CountryCode']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = stats.Birthrate * stats.Internetusers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    808.2516\n",
       "1    207.9927\n",
       "2    878.3135\n",
       "3    736.5644\n",
       "4    971.8720\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats['MyCol'] = stats.Birthrate * stats.Internetusers"
   ]
  },
  {
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>Birthrate</th>\n",
       "      <th>Internetusers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "      <th>MyCol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.9</td>\n",
       "      <td>High income</td>\n",
       "      <td>808.2516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Low income</td>\n",
       "      <td>207.9927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>878.3135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.2</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>736.5644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.0</td>\n",
       "      <td>High income</td>\n",
       "      <td>971.8720</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CountryName CountryCode  Birthrate  Internetusers  \\\n",
       "0                 Aruba         ABW     10.244           78.9   \n",
       "1           Afghanistan         AFG     35.253            5.9   \n",
       "2                Angola         AGO     45.985           19.1   \n",
       "3               Albania         ALB     12.877           57.2   \n",
       "4  United Arab Emirates         ARE     11.044           88.0   \n",
       "\n",
       "           IncomeGroup     MyCol  \n",
       "0          High income  808.2516  \n",
       "1           Low income  207.9927  \n",
       "2  Upper middle income  878.3135  \n",
       "3  Upper middle income  736.5644  \n",
       "4          High income  971.8720  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>Birthrate</th>\n",
       "      <th>Internetusers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.9</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.2</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.0</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>YEM</td>\n",
       "      <td>32.947</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>20.850</td>\n",
       "      <td>46.5</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>COD</td>\n",
       "      <td>42.394</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>40.471</td>\n",
       "      <td>15.4</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>35.715</td>\n",
       "      <td>18.5</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              CountryName CountryCode  Birthrate  Internetusers  \\\n",
       "0                   Aruba         ABW     10.244           78.9   \n",
       "1             Afghanistan         AFG     35.253            5.9   \n",
       "2                  Angola         AGO     45.985           19.1   \n",
       "3                 Albania         ALB     12.877           57.2   \n",
       "4    United Arab Emirates         ARE     11.044           88.0   \n",
       "..                    ...         ...        ...            ...   \n",
       "190           Yemen, Rep.         YEM     32.947           20.0   \n",
       "191          South Africa         ZAF     20.850           46.5   \n",
       "192      Congo, Dem. Rep.         COD     42.394            2.2   \n",
       "193                Zambia         ZMB     40.471           15.4   \n",
       "194              Zimbabwe         ZWE     35.715           18.5   \n",
       "\n",
       "             IncomeGroup  \n",
       "0            High income  \n",
       "1             Low income  \n",
       "2    Upper middle income  \n",
       "3    Upper middle income  \n",
       "4            High income  \n",
       "..                   ...  \n",
       "190  Lower middle income  \n",
       "191  Upper middle income  \n",
       "192           Low income  \n",
       "193  Lower middle income  \n",
       "194           Low income  \n",
       "\n",
       "[195 rows x 5 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.drop('MyCol', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "Filter = stats.Internetusers < 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "       ...  \n",
       "190    False\n",
       "191    False\n",
       "192    False\n",
       "193    False\n",
       "194    False\n",
       "Name: Internetusers, Length: 195, dtype: bool"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>Birthrate</th>\n",
       "      <th>Internetusers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "      <th>MyCol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>BDI</td>\n",
       "      <td>44.151</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Low income</td>\n",
       "      <td>57.3963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Eritrea</td>\n",
       "      <td>ERI</td>\n",
       "      <td>34.800</td>\n",
       "      <td>0.9</td>\n",
       "      <td>Low income</td>\n",
       "      <td>31.3200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>ETH</td>\n",
       "      <td>32.925</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Low income</td>\n",
       "      <td>62.5575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Guinea</td>\n",
       "      <td>GIN</td>\n",
       "      <td>37.337</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Low income</td>\n",
       "      <td>59.7392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>MMR</td>\n",
       "      <td>18.119</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>28.9904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>Niger</td>\n",
       "      <td>NER</td>\n",
       "      <td>49.661</td>\n",
       "      <td>1.7</td>\n",
       "      <td>Low income</td>\n",
       "      <td>84.4237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>SLE</td>\n",
       "      <td>36.729</td>\n",
       "      <td>1.7</td>\n",
       "      <td>Low income</td>\n",
       "      <td>62.4393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Somalia</td>\n",
       "      <td>SOM</td>\n",
       "      <td>43.891</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Low income</td>\n",
       "      <td>65.8365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>TLS</td>\n",
       "      <td>35.755</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>39.3305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CountryName CountryCode  Birthrate  Internetusers          IncomeGroup  \\\n",
       "11        Burundi         BDI     44.151            1.3           Low income   \n",
       "52        Eritrea         ERI     34.800            0.9           Low income   \n",
       "55       Ethiopia         ETH     32.925            1.9           Low income   \n",
       "64         Guinea         GIN     37.337            1.6           Low income   \n",
       "117       Myanmar         MMR     18.119            1.6  Lower middle income   \n",
       "127         Niger         NER     49.661            1.7           Low income   \n",
       "154  Sierra Leone         SLE     36.729            1.7           Low income   \n",
       "156       Somalia         SOM     43.891            1.5           Low income   \n",
       "172   Timor-Leste         TLS     35.755            1.1  Lower middle income   \n",
       "\n",
       "       MyCol  \n",
       "11   57.3963  \n",
       "52   31.3200  \n",
       "55   62.5575  \n",
       "64   59.7392  \n",
       "117  28.9904  \n",
       "127  84.4237  \n",
       "154  62.4393  \n",
       "156  65.8365  \n",
       "172  39.3305  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[Filter]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "Filter1 = stats.Birthrate < 40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>Birthrate</th>\n",
       "      <th>Internetusers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "      <th>MyCol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Eritrea</td>\n",
       "      <td>ERI</td>\n",
       "      <td>34.800</td>\n",
       "      <td>0.9</td>\n",
       "      <td>Low income</td>\n",
       "      <td>31.3200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>ETH</td>\n",
       "      <td>32.925</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Low income</td>\n",
       "      <td>62.5575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Guinea</td>\n",
       "      <td>GIN</td>\n",
       "      <td>37.337</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Low income</td>\n",
       "      <td>59.7392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>MMR</td>\n",
       "      <td>18.119</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>28.9904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>SLE</td>\n",
       "      <td>36.729</td>\n",
       "      <td>1.7</td>\n",
       "      <td>Low income</td>\n",
       "      <td>62.4393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>TLS</td>\n",
       "      <td>35.755</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>39.3305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CountryName CountryCode  Birthrate  Internetusers          IncomeGroup  \\\n",
       "52        Eritrea         ERI     34.800            0.9           Low income   \n",
       "55       Ethiopia         ETH     32.925            1.9           Low income   \n",
       "64         Guinea         GIN     37.337            1.6           Low income   \n",
       "117       Myanmar         MMR     18.119            1.6  Lower middle income   \n",
       "154  Sierra Leone         SLE     36.729            1.7           Low income   \n",
       "172   Timor-Leste         TLS     35.755            1.1  Lower middle income   \n",
       "\n",
       "       MyCol  \n",
       "52   31.3200  \n",
       "55   62.5575  \n",
       "64   59.7392  \n",
       "117  28.9904  \n",
       "154  62.4393  \n",
       "172  39.3305  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[Filter & Filter1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['High income', 'Low income', 'Upper middle income',\n",
       "       'Lower middle income'], dtype=object)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.IncomeGroup.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>Birthrate</th>\n",
       "      <th>Internetusers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "      <th>MyCol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>MMR</td>\n",
       "      <td>18.119</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>28.9904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CountryName CountryCode  Birthrate  Internetusers          IncomeGroup  \\\n",
       "117     Myanmar         MMR     18.119            1.6  Lower middle income   \n",
       "\n",
       "       MyCol  \n",
       "117  28.9904  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[stats.CountryName=='Myanmar']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>Birthrate</th>\n",
       "      <th>Internetusers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "      <th>MyCol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.9</td>\n",
       "      <td>High income</td>\n",
       "      <td>808.2516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Low income</td>\n",
       "      <td>207.9927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>878.3135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.2</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>736.5644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.0</td>\n",
       "      <td>High income</td>\n",
       "      <td>971.8720</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CountryName CountryCode  Birthrate  Internetusers  \\\n",
       "0                 Aruba         ABW     10.244           78.9   \n",
       "1           Afghanistan         AFG     35.253            5.9   \n",
       "2                Angola         AGO     45.985           19.1   \n",
       "3               Albania         ALB     12.877           57.2   \n",
       "4  United Arab Emirates         ARE     11.044           88.0   \n",
       "\n",
       "           IncomeGroup     MyCol  \n",
       "0          High income  808.2516  \n",
       "1           Low income  207.9927  \n",
       "2  Upper middle income  878.3135  \n",
       "3  Upper middle income  736.5644  \n",
       "4          High income  971.8720  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>Birthrate</th>\n",
       "      <th>Internetusers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.9</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.2</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.0</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>YEM</td>\n",
       "      <td>32.947</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>20.850</td>\n",
       "      <td>46.5</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>COD</td>\n",
       "      <td>42.394</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>40.471</td>\n",
       "      <td>15.4</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>35.715</td>\n",
       "      <td>18.5</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              CountryName CountryCode  Birthrate  Internetusers  \\\n",
       "0                   Aruba         ABW     10.244           78.9   \n",
       "1             Afghanistan         AFG     35.253            5.9   \n",
       "2                  Angola         AGO     45.985           19.1   \n",
       "3                 Albania         ALB     12.877           57.2   \n",
       "4    United Arab Emirates         ARE     11.044           88.0   \n",
       "..                    ...         ...        ...            ...   \n",
       "190           Yemen, Rep.         YEM     32.947           20.0   \n",
       "191          South Africa         ZAF     20.850           46.5   \n",
       "192      Congo, Dem. Rep.         COD     42.394            2.2   \n",
       "193                Zambia         ZMB     40.471           15.4   \n",
       "194              Zimbabwe         ZWE     35.715           18.5   \n",
       "\n",
       "             IncomeGroup  \n",
       "0            High income  \n",
       "1             Low income  \n",
       "2    Upper middle income  \n",
       "3    Upper middle income  \n",
       "4            High income  \n",
       "..                   ...  \n",
       "190  Lower middle income  \n",
       "191  Upper middle income  \n",
       "192           Low income  \n",
       "193  Lower middle income  \n",
       "194           Low income  \n",
       "\n",
       "[195 rows x 5 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.drop('MyCol',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>Birthrate</th>\n",
       "      <th>Internetusers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "      <th>MyCol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.9</td>\n",
       "      <td>High income</td>\n",
       "      <td>808.2516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Low income</td>\n",
       "      <td>207.9927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>878.3135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.2</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>736.5644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.0</td>\n",
       "      <td>High income</td>\n",
       "      <td>971.8720</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CountryName CountryCode  Birthrate  Internetusers  \\\n",
       "0                 Aruba         ABW     10.244           78.9   \n",
       "1           Afghanistan         AFG     35.253            5.9   \n",
       "2                Angola         AGO     45.985           19.1   \n",
       "3               Albania         ALB     12.877           57.2   \n",
       "4  United Arab Emirates         ARE     11.044           88.0   \n",
       "\n",
       "           IncomeGroup     MyCol  \n",
       "0          High income  808.2516  \n",
       "1           Low income  207.9927  \n",
       "2  Upper middle income  878.3135  \n",
       "3  Upper middle income  736.5644  \n",
       "4          High income  971.8720  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'High income'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.iat[0,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45.985"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.at[2,'Birthrate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>Birthrate</th>\n",
       "      <th>Internetusers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "      <th>MyCol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.9</td>\n",
       "      <td>High income</td>\n",
       "      <td>808.2516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Low income</td>\n",
       "      <td>207.9927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>878.3135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.2</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>736.5644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.0</td>\n",
       "      <td>High income</td>\n",
       "      <td>971.8720</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CountryName CountryCode  Birthrate  Internetusers  \\\n",
       "0                 Aruba         ABW     10.244           78.9   \n",
       "1           Afghanistan         AFG     35.253            5.9   \n",
       "2                Angola         AGO     45.985           19.1   \n",
       "3               Albania         ALB     12.877           57.2   \n",
       "4  United Arab Emirates         ARE     11.044           88.0   \n",
       "\n",
       "           IncomeGroup     MyCol  \n",
       "0          High income  808.2516  \n",
       "1           Low income  207.9927  \n",
       "2  Upper middle income  878.3135  \n",
       "3  Upper middle income  736.5644  \n",
       "4          High income  971.8720  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Internetusers' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-54-4355b5f21baa>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mvis1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstats\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mInternetusers\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'Internetusers' is not defined"
     ]
    }
   ],
   "source": [
    "vis1 = sns.distplot(stats[Internetusers])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "vis1 = sns.boxplot(data= stats,x = 'IncomeGroup', y = 'Birthrate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 503.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis3 = sns.lmplot(data = stats, x = 'Internetusers', y = 'Birthrate',fit_reg= False, hue='IncomeGroup')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}