Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"E:/Data analytics with python/gapminder-FiveYearData.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1977</td>\n",
       "      <td>14880372.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>38.438</td>\n",
       "      <td>786.113360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1982</td>\n",
       "      <td>12881816.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>39.854</td>\n",
       "      <td>978.011439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1987</td>\n",
       "      <td>13867957.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>40.822</td>\n",
       "      <td>852.395945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1992</td>\n",
       "      <td>16317921.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>41.674</td>\n",
       "      <td>649.341395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1997</td>\n",
       "      <td>22227415.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>41.763</td>\n",
       "      <td>635.341351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2002</td>\n",
       "      <td>25268405.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>42.129</td>\n",
       "      <td>726.734055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2007</td>\n",
       "      <td>31889923.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>43.828</td>\n",
       "      <td>974.580338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1952</td>\n",
       "      <td>1282697.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>55.230</td>\n",
       "      <td>1601.056136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1957</td>\n",
       "      <td>1476505.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>59.280</td>\n",
       "      <td>1942.284244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1962</td>\n",
       "      <td>1728137.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>64.820</td>\n",
       "      <td>2312.888958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1967</td>\n",
       "      <td>1984060.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>66.220</td>\n",
       "      <td>2760.196931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1972</td>\n",
       "      <td>2263554.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>67.690</td>\n",
       "      <td>3313.422188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1977</td>\n",
       "      <td>2509048.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>68.930</td>\n",
       "      <td>3533.003910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1982</td>\n",
       "      <td>2780097.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>70.420</td>\n",
       "      <td>3630.880722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>3075321.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>72.000</td>\n",
       "      <td>3738.932735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1992</td>\n",
       "      <td>3326498.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>71.581</td>\n",
       "      <td>2497.437901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1997</td>\n",
       "      <td>3428038.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>72.950</td>\n",
       "      <td>3193.054604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2002</td>\n",
       "      <td>3508512.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>75.651</td>\n",
       "      <td>4604.211737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2007</td>\n",
       "      <td>3600523.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>76.423</td>\n",
       "      <td>5937.029526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1952</td>\n",
       "      <td>9279525.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.077</td>\n",
       "      <td>2449.008185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1957</td>\n",
       "      <td>10270856.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>45.685</td>\n",
       "      <td>3013.976023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1962</td>\n",
       "      <td>11000948.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>48.303</td>\n",
       "      <td>2550.816880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1967</td>\n",
       "      <td>12760499.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>51.407</td>\n",
       "      <td>3246.991771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1972</td>\n",
       "      <td>14760787.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>54.518</td>\n",
       "      <td>4182.663766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1977</td>\n",
       "      <td>17152804.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>58.014</td>\n",
       "      <td>4910.416756</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>1674</th>\n",
       "      <td>Yemen Rep.</td>\n",
       "      <td>1982</td>\n",
       "      <td>9657618.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>49.113</td>\n",
       "      <td>1977.557010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1675</th>\n",
       "      <td>Yemen Rep.</td>\n",
       "      <td>1987</td>\n",
       "      <td>11219340.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>52.922</td>\n",
       "      <td>1971.741538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1676</th>\n",
       "      <td>Yemen Rep.</td>\n",
       "      <td>1992</td>\n",
       "      <td>13367997.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>55.599</td>\n",
       "      <td>1879.496673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677</th>\n",
       "      <td>Yemen Rep.</td>\n",
       "      <td>1997</td>\n",
       "      <td>15826497.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>58.020</td>\n",
       "      <td>2117.484526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678</th>\n",
       "      <td>Yemen Rep.</td>\n",
       "      <td>2002</td>\n",
       "      <td>18701257.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>60.308</td>\n",
       "      <td>2234.820827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1679</th>\n",
       "      <td>Yemen Rep.</td>\n",
       "      <td>2007</td>\n",
       "      <td>22211743.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>62.698</td>\n",
       "      <td>2280.769906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1680</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1952</td>\n",
       "      <td>2672000.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>42.038</td>\n",
       "      <td>1147.388831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1681</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1957</td>\n",
       "      <td>3016000.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>44.077</td>\n",
       "      <td>1311.956766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1682</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1962</td>\n",
       "      <td>3421000.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.023</td>\n",
       "      <td>1452.725766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1683</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1967</td>\n",
       "      <td>3900000.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>47.768</td>\n",
       "      <td>1777.077318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1684</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1972</td>\n",
       "      <td>4506497.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>50.107</td>\n",
       "      <td>1773.498265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1977</td>\n",
       "      <td>5216550.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>51.386</td>\n",
       "      <td>1588.688299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1686</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1982</td>\n",
       "      <td>6100407.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>51.821</td>\n",
       "      <td>1408.678565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1687</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1987</td>\n",
       "      <td>7272406.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>50.821</td>\n",
       "      <td>1213.315116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1688</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1992</td>\n",
       "      <td>8381163.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.100</td>\n",
       "      <td>1210.884633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1689</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1997</td>\n",
       "      <td>9417789.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>40.238</td>\n",
       "      <td>1071.353818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>2002</td>\n",
       "      <td>10595811.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.193</td>\n",
       "      <td>1071.613938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1691</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>2007</td>\n",
       "      <td>11746035.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>42.384</td>\n",
       "      <td>1271.211593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1692</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1952</td>\n",
       "      <td>3080907.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>48.451</td>\n",
       "      <td>406.884115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1693</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1957</td>\n",
       "      <td>3646340.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>50.469</td>\n",
       "      <td>518.764268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1694</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1962</td>\n",
       "      <td>4277736.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>52.358</td>\n",
       "      <td>527.272182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1695</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1967</td>\n",
       "      <td>4995432.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>53.995</td>\n",
       "      <td>569.795071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1696</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1972</td>\n",
       "      <td>5861135.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>55.635</td>\n",
       "      <td>799.362176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1697</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1977</td>\n",
       "      <td>6642107.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>57.674</td>\n",
       "      <td>685.587682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1698</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1982</td>\n",
       "      <td>7636524.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.363</td>\n",
       "      <td>788.855041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1987</td>\n",
       "      <td>9216418.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>62.351</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>10704340.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year         pop continent  lifeExp    gdpPercap\n",
       "0     Afghanistan  1952   8425333.0      Asia   28.801   779.445314\n",
       "1     Afghanistan  1957   9240934.0      Asia   30.332   820.853030\n",
       "2     Afghanistan  1962  10267083.0      Asia   31.997   853.100710\n",
       "3     Afghanistan  1967  11537966.0      Asia   34.020   836.197138\n",
       "4     Afghanistan  1972  13079460.0      Asia   36.088   739.981106\n",
       "5     Afghanistan  1977  14880372.0      Asia   38.438   786.113360\n",
       "6     Afghanistan  1982  12881816.0      Asia   39.854   978.011439\n",
       "7     Afghanistan  1987  13867957.0      Asia   40.822   852.395945\n",
       "8     Afghanistan  1992  16317921.0      Asia   41.674   649.341395\n",
       "9     Afghanistan  1997  22227415.0      Asia   41.763   635.341351\n",
       "10    Afghanistan  2002  25268405.0      Asia   42.129   726.734055\n",
       "11    Afghanistan  2007  31889923.0      Asia   43.828   974.580338\n",
       "12        Albania  1952   1282697.0    Europe   55.230  1601.056136\n",
       "13        Albania  1957   1476505.0    Europe   59.280  1942.284244\n",
       "14        Albania  1962   1728137.0    Europe   64.820  2312.888958\n",
       "15        Albania  1967   1984060.0    Europe   66.220  2760.196931\n",
       "16        Albania  1972   2263554.0    Europe   67.690  3313.422188\n",
       "17        Albania  1977   2509048.0    Europe   68.930  3533.003910\n",
       "18        Albania  1982   2780097.0    Europe   70.420  3630.880722\n",
       "19        Albania  1987   3075321.0    Europe   72.000  3738.932735\n",
       "20        Albania  1992   3326498.0    Europe   71.581  2497.437901\n",
       "21        Albania  1997   3428038.0    Europe   72.950  3193.054604\n",
       "22        Albania  2002   3508512.0    Europe   75.651  4604.211737\n",
       "23        Albania  2007   3600523.0    Europe   76.423  5937.029526\n",
       "24        Algeria  1952   9279525.0    Africa   43.077  2449.008185\n",
       "25        Algeria  1957  10270856.0    Africa   45.685  3013.976023\n",
       "26        Algeria  1962  11000948.0    Africa   48.303  2550.816880\n",
       "27        Algeria  1967  12760499.0    Africa   51.407  3246.991771\n",
       "28        Algeria  1972  14760787.0    Africa   54.518  4182.663766\n",
       "29        Algeria  1977  17152804.0    Africa   58.014  4910.416756\n",
       "...           ...   ...         ...       ...      ...          ...\n",
       "1674   Yemen Rep.  1982   9657618.0      Asia   49.113  1977.557010\n",
       "1675   Yemen Rep.  1987  11219340.0      Asia   52.922  1971.741538\n",
       "1676   Yemen Rep.  1992  13367997.0      Asia   55.599  1879.496673\n",
       "1677   Yemen Rep.  1997  15826497.0      Asia   58.020  2117.484526\n",
       "1678   Yemen Rep.  2002  18701257.0      Asia   60.308  2234.820827\n",
       "1679   Yemen Rep.  2007  22211743.0      Asia   62.698  2280.769906\n",
       "1680       Zambia  1952   2672000.0    Africa   42.038  1147.388831\n",
       "1681       Zambia  1957   3016000.0    Africa   44.077  1311.956766\n",
       "1682       Zambia  1962   3421000.0    Africa   46.023  1452.725766\n",
       "1683       Zambia  1967   3900000.0    Africa   47.768  1777.077318\n",
       "1684       Zambia  1972   4506497.0    Africa   50.107  1773.498265\n",
       "1685       Zambia  1977   5216550.0    Africa   51.386  1588.688299\n",
       "1686       Zambia  1982   6100407.0    Africa   51.821  1408.678565\n",
       "1687       Zambia  1987   7272406.0    Africa   50.821  1213.315116\n",
       "1688       Zambia  1992   8381163.0    Africa   46.100  1210.884633\n",
       "1689       Zambia  1997   9417789.0    Africa   40.238  1071.353818\n",
       "1690       Zambia  2002  10595811.0    Africa   39.193  1071.613938\n",
       "1691       Zambia  2007  11746035.0    Africa   42.384  1271.211593\n",
       "1692     Zimbabwe  1952   3080907.0    Africa   48.451   406.884115\n",
       "1693     Zimbabwe  1957   3646340.0    Africa   50.469   518.764268\n",
       "1694     Zimbabwe  1962   4277736.0    Africa   52.358   527.272182\n",
       "1695     Zimbabwe  1967   4995432.0    Africa   53.995   569.795071\n",
       "1696     Zimbabwe  1972   5861135.0    Africa   55.635   799.362176\n",
       "1697     Zimbabwe  1977   6642107.0    Africa   57.674   685.587682\n",
       "1698     Zimbabwe  1982   7636524.0    Africa   60.363   788.855041\n",
       "1699     Zimbabwe  1987   9216418.0    Africa   62.351   706.157306\n",
       "1700     Zimbabwe  1992  10704340.0    Africa   60.377   693.420786\n",
       "1701     Zimbabwe  1997  11404948.0    Africa   46.809   792.449960\n",
       "1702     Zimbabwe  2002  11926563.0    Africa   39.989   672.038623\n",
       "1703     Zimbabwe  2007  12311143.0    Africa   43.487   469.709298\n",
       "\n",
       "[1704 rows x 6 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year         pop continent  lifeExp   gdpPercap\n",
       "0  Afghanistan  1952   8425333.0      Asia   28.801  779.445314\n",
       "1  Afghanistan  1957   9240934.0      Asia   30.332  820.853030\n",
       "2  Afghanistan  1962  10267083.0      Asia   31.997  853.100710\n",
       "3  Afghanistan  1967  11537966.0      Asia   34.020  836.197138\n",
       "4  Afghanistan  1972  13079460.0      Asia   36.088  739.981106"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#heading: list 1st five rows from dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1704, 6)\n"
     ]
    }
   ],
   "source": [
    "#size of the file: number of rows and coloumns\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['country', 'year', 'pop', 'continent', 'lifeExp', 'gdpPercap'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#name of columns\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "country       object\n",
      "year           int64\n",
      "pop          float64\n",
      "continent     object\n",
      "lifeExp      float64\n",
      "gdpPercap    float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#summary of dataset, nature of data\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1704 entries, 0 to 1703\n",
      "Data columns (total 6 columns):\n",
      "country      1704 non-null object\n",
      "year         1704 non-null int64\n",
      "pop          1704 non-null float64\n",
      "continent    1704 non-null object\n",
      "lifeExp      1704 non-null float64\n",
      "gdpPercap    1704 non-null float64\n",
      "dtypes: float64(3), int64(1), object(2)\n",
      "memory usage: 66.6+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "#full info of columns\n",
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    Afghanistan\n",
      "1    Afghanistan\n",
      "2    Afghanistan\n",
      "3    Afghanistan\n",
      "4    Afghanistan\n",
      "Name: country, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#pick up only country column as save in one variable\n",
    "country_df = df['country']\n",
    "#print only 5 rowsl\n",
    "print(country_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1699    Zimbabwe\n",
      "1700    Zimbabwe\n",
      "1701    Zimbabwe\n",
      "1702    Zimbabwe\n",
      "1703    Zimbabwe\n",
      "Name: country, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#for print last 5 oberservation\n",
    "print(country_df.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       country continent  year\n",
      "0  Afghanistan      Asia  1952\n",
      "1  Afghanistan      Asia  1957\n",
      "2  Afghanistan      Asia  1962\n",
      "3  Afghanistan      Asia  1967\n",
      "4  Afghanistan      Asia  1972\n"
     ]
    }
   ],
   "source": [
    "#take more than one columns\n",
    "subset = df[['country','continent','year']]\n",
    "print(subset.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "country      Afghanistan\n",
      "year                1952\n",
      "pop          8.42533e+06\n",
      "continent           Asia\n",
      "lifeExp           28.801\n",
      "gdpPercap        779.445\n",
      "Name: 0, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#subset file by location\n",
    "#here we are priting first row only\n",
    "print(df.loc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "country       Bangladesh\n",
      "year                1972\n",
      "pop          7.07593e+07\n",
      "continent           Asia\n",
      "lifeExp           45.252\n",
      "gdpPercap        630.234\n",
      "Name: 100, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#printing 100th row\n",
    "print(df.loc[100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         country  year         pop continent  lifeExp    gdpPercap\n",
      "0    Afghanistan  1952   8425333.0      Asia   28.801   779.445314\n",
      "99    Bangladesh  1967  62821884.0      Asia   43.453   721.186086\n",
      "999     Mongolia  1967   1149500.0      Asia   51.253  1226.041130\n"
     ]
    }
   ],
   "source": [
    "#printing specific row\n",
    "print(df.loc[[0,99,999]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "country      Afghanistan\n",
      "year                1957\n",
      "pop          9.24093e+06\n",
      "continent           Asia\n",
      "lifeExp           30.332\n",
      "gdpPercap        820.853\n",
      "Name: 1, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#another way of to find location, for 2nd row\n",
    "print(df.iloc[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "country         Zimbabwe\n",
      "year                2007\n",
      "pop          1.23111e+07\n",
      "continent         Africa\n",
      "lifeExp           43.487\n",
      "gdpPercap        469.709\n",
      "Name: 1703, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#last row\n",
    "print(df.iloc[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         country  year         pop continent  lifeExp    gdpPercap\n",
      "0    Afghanistan  1952   8425333.0      Asia   28.801   779.445314\n",
      "99    Bangladesh  1967  62821884.0      Asia   43.453   721.186086\n",
      "999     Mongolia  1967   1149500.0      Asia   51.253  1226.041130\n"
     ]
    }
   ],
   "source": [
    "#print first, 100, and 1000th row\n",
    "print(df.iloc[[0,99,999]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   year         pop\n",
      "0  1952   8425333.0\n",
      "1  1957   9240934.0\n",
      "2  1962  10267083.0\n",
      "3  1967  11537966.0\n",
      "4  1972  13079460.0\n"
     ]
    }
   ],
   "source": [
    "#slicing columns, here we are selecting all rows for year and pop columns\n",
    "subset = df.loc[:,['year','pop']]\n",
    "print(subset.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          pop  lifeExp   gdpPercap\n",
      "0   8425333.0   28.801  779.445314\n",
      "1   9240934.0   30.332  820.853030\n",
      "2  10267083.0   31.997  853.100710\n",
      "3  11537966.0   34.020  836.197138\n",
      "4  13079460.0   36.088  739.981106\n"
     ]
    }
   ],
   "source": [
    "#with iloc -1 is for last column\n",
    "subset = df.iloc[:,[2,4,-1]]\n",
    "print(subset.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4]\n"
     ]
    }
   ],
   "source": [
    "#creating a range\n",
    "small_range = list(range(5))\n",
    "print(small_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       country  year         pop continent  lifeExp\n",
      "0  Afghanistan  1952   8425333.0      Asia   28.801\n",
      "1  Afghanistan  1957   9240934.0      Asia   30.332\n",
      "2  Afghanistan  1962  10267083.0      Asia   31.997\n",
      "3  Afghanistan  1967  11537966.0      Asia   34.020\n",
      "4  Afghanistan  1972  13079460.0      Asia   36.088\n"
     ]
    }
   ],
   "source": [
    "#subsetting columns with range\n",
    "subset = df.iloc[:,small_range]\n",
    "print(subset.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angola\n"
     ]
    }
   ],
   "source": [
    "#subsetting rows and column both, 43rd row in country column, speicific cell\n",
    "print(df.loc[42,'country'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angola\n"
     ]
    }
   ],
   "source": [
    "#with iloc, 0th column i.e. first column\n",
    "#with loc we use columns by name and with iloc refer columns by number, thats \"i\" represents the index\n",
    "print(df.iloc[42,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         country continent    gdpPercap\n",
      "0    Afghanistan      Asia   779.445314\n",
      "99    Bangladesh      Asia   721.186086\n",
      "999     Mongolia      Asia  1226.041130\n"
     ]
    }
   ],
   "source": [
    "#get 1st, 100th and 1000th row from 1st 4th and 6th column\n",
    "print(df.iloc[[0,99,999],[0,3,5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         country continent    gdpPercap\n",
      "0    Afghanistan      Asia   779.445314\n",
      "99    Bangladesh      Asia   721.186086\n",
      "999     Mongolia      Asia  1226.041130\n"
     ]
    }
   ],
   "source": [
    "#with loc, i.e. with columns name, it is more easy to understand\n",
    "print(df.loc[[0,99,999],['country','continent','gdpPercap']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        country continent    gdpPercap\n",
      "10  Afghanistan      Asia   726.734055\n",
      "11  Afghanistan      Asia   974.580338\n",
      "12      Albania    Europe  1601.056136\n",
      "13      Albania    Europe  1942.284244\n"
     ]
    }
   ],
   "source": [
    "#form continous range of rows\n",
    "print(df.loc[10:13,['country','continent','gdpPercap']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       country  year         pop continent  lifeExp   gdpPercap\n",
      "0  Afghanistan  1952   8425333.0      Asia   28.801  779.445314\n",
      "1  Afghanistan  1957   9240934.0      Asia   30.332  820.853030\n",
      "2  Afghanistan  1962  10267083.0      Asia   31.997  853.100710\n",
      "3  Afghanistan  1967  11537966.0      Asia   34.020  836.197138\n",
      "4  Afghanistan  1972  13079460.0      Asia   36.088  739.981106\n",
      "5  Afghanistan  1977  14880372.0      Asia   38.438  786.113360\n",
      "6  Afghanistan  1982  12881816.0      Asia   39.854  978.011439\n",
      "7  Afghanistan  1987  13867957.0      Asia   40.822  852.395945\n",
      "8  Afghanistan  1992  16317921.0      Asia   41.674  649.341395\n",
      "9  Afghanistan  1997  22227415.0      Asia   41.763  635.341351\n"
     ]
    }
   ],
   "source": [
    "#printing first 10 rows\n",
    "print(df.head(n=10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year\n",
      "1952    49.057620\n",
      "1957    51.507401\n",
      "1962    53.609249\n",
      "1967    55.678290\n",
      "1972    57.647386\n",
      "1977    59.570157\n",
      "1982    61.533197\n",
      "1987    63.212613\n",
      "1992    64.160338\n",
      "1997    65.014676\n",
      "2002    65.694923\n",
      "2007    67.007423\n",
      "Name: lifeExp, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#for each year of data, what is life expectancy\n",
    "#Group data by years and then find out mean on that lifeExp\n",
    "print(df.groupby('year')['lifeExp'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  lifeExp     gdpPercap\n",
      "year continent                         \n",
      "1952 Africa     39.135500   1252.572466\n",
      "     Americas   53.279840   4079.062552\n",
      "     Asia       46.314394   5195.484004\n",
      "     Europe     64.408500   5661.057435\n",
      "     Oceania    69.255000  10298.085650\n",
      "1957 Africa     41.266346   1385.236062\n",
      "     Americas   55.960280   4616.043733\n",
      "     Asia       49.318544   5787.732940\n",
      "     Europe     66.703067   6963.012816\n",
      "     Oceania    70.295000  11598.522455\n",
      "1962 Africa     43.319442   1598.078825\n",
      "     Americas   58.398760   4901.541870\n",
      "     Asia       51.563223   5729.369625\n",
      "     Europe     68.539233   8365.486814\n",
      "     Oceania    71.085000  12696.452430\n",
      "1967 Africa     45.334538   2050.363801\n",
      "     Americas   60.410920   5668.253496\n",
      "     Asia       54.663640   5971.173374\n",
      "     Europe     69.737600  10143.823757\n",
      "     Oceania    71.310000  14495.021790\n",
      "1972 Africa     47.450942   2339.615674\n",
      "     Americas   62.394920   6491.334139\n",
      "     Asia       57.319269   8187.468699\n",
      "     Europe     70.775033  12479.575246\n",
      "     Oceania    71.910000  16417.333380\n",
      "1977 Africa     49.580423   2585.938508\n",
      "     Americas   64.391560   7352.007126\n",
      "     Asia       59.610556   7791.314020\n",
      "     Europe     71.937767  14283.979110\n",
      "     Oceania    72.855000  17283.957605\n",
      "1982 Africa     51.592865   2481.592960\n",
      "     Americas   66.228840   7506.737088\n",
      "     Asia       62.617939   7434.135157\n",
      "     Europe     72.806400  15617.896551\n",
      "     Oceania    74.290000  18554.709840\n",
      "1987 Africa     53.344788   2282.668991\n",
      "     Americas   68.090720   7793.400261\n",
      "     Asia       64.851182   7608.226508\n",
      "     Europe     73.642167  17214.310727\n",
      "     Oceania    75.320000  20448.040160\n",
      "1992 Africa     53.629577   2281.810333\n",
      "     Americas   69.568360   8044.934406\n",
      "     Asia       66.537212   8639.690248\n",
      "     Europe     74.440100  17061.568084\n",
      "     Oceania    76.945000  20894.045885\n",
      "1997 Africa     53.598269   2378.759555\n",
      "     Americas   71.150480   8889.300863\n",
      "     Asia       68.020515   9834.093295\n",
      "     Europe     75.505167  19076.781802\n",
      "     Oceania    78.190000  24024.175170\n",
      "2002 Africa     53.325231   2599.385159\n",
      "     Americas   72.422040   9287.677107\n",
      "     Asia       69.233879  10174.090397\n",
      "     Europe     76.700600  21711.732422\n",
      "     Oceania    79.740000  26938.778040\n",
      "2007 Africa     54.806038   3089.032605\n",
      "     Americas   73.608120  11003.031625\n",
      "     Asia       70.728485  12473.026870\n",
      "     Europe     77.648600  25054.481636\n",
      "     Oceania    80.719500  29810.188275\n"
     ]
    }
   ],
   "source": [
    "#groupby on multiple coloumns\n",
    "multi_grp_var = df.groupby(['year', 'continent'])[['lifeExp','gdpPercap']].mean()\n",
    "print(multi_grp_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  lifeExp     gdpPercap\n",
      "continent year                         \n",
      "Africa    1952  39.135500   1252.572466\n",
      "          1957  41.266346   1385.236062\n",
      "          1962  43.319442   1598.078825\n",
      "          1967  45.334538   2050.363801\n",
      "          1972  47.450942   2339.615674\n",
      "          1977  49.580423   2585.938508\n",
      "          1982  51.592865   2481.592960\n",
      "          1987  53.344788   2282.668991\n",
      "          1992  53.629577   2281.810333\n",
      "          1997  53.598269   2378.759555\n",
      "          2002  53.325231   2599.385159\n",
      "          2007  54.806038   3089.032605\n",
      "Americas  1952  53.279840   4079.062552\n",
      "          1957  55.960280   4616.043733\n",
      "          1962  58.398760   4901.541870\n",
      "          1967  60.410920   5668.253496\n",
      "          1972  62.394920   6491.334139\n",
      "          1977  64.391560   7352.007126\n",
      "          1982  66.228840   7506.737088\n",
      "          1987  68.090720   7793.400261\n",
      "          1992  69.568360   8044.934406\n",
      "          1997  71.150480   8889.300863\n",
      "          2002  72.422040   9287.677107\n",
      "          2007  73.608120  11003.031625\n",
      "Asia      1952  46.314394   5195.484004\n",
      "          1957  49.318544   5787.732940\n",
      "          1962  51.563223   5729.369625\n",
      "          1967  54.663640   5971.173374\n",
      "          1972  57.319269   8187.468699\n",
      "          1977  59.610556   7791.314020\n",
      "          1982  62.617939   7434.135157\n",
      "          1987  64.851182   7608.226508\n",
      "          1992  66.537212   8639.690248\n",
      "          1997  68.020515   9834.093295\n",
      "          2002  69.233879  10174.090397\n",
      "          2007  70.728485  12473.026870\n",
      "Europe    1952  64.408500   5661.057435\n",
      "          1957  66.703067   6963.012816\n",
      "          1962  68.539233   8365.486814\n",
      "          1967  69.737600  10143.823757\n",
      "          1972  70.775033  12479.575246\n",
      "          1977  71.937767  14283.979110\n",
      "          1982  72.806400  15617.896551\n",
      "          1987  73.642167  17214.310727\n",
      "          1992  74.440100  17061.568084\n",
      "          1997  75.505167  19076.781802\n",
      "          2002  76.700600  21711.732422\n",
      "          2007  77.648600  25054.481636\n",
      "Oceania   1952  69.255000  10298.085650\n",
      "          1957  70.295000  11598.522455\n",
      "          1962  71.085000  12696.452430\n",
      "          1967  71.310000  14495.021790\n",
      "          1972  71.910000  16417.333380\n",
      "          1977  72.855000  17283.957605\n",
      "          1982  74.290000  18554.709840\n",
      "          1987  75.320000  20448.040160\n",
      "          1992  76.945000  20894.045885\n",
      "          1997  78.190000  24024.175170\n",
      "          2002  79.740000  26938.778040\n",
      "          2007  80.719500  29810.188275\n"
     ]
    }
   ],
   "source": [
    "multi_grp_var = df.groupby(['continent','year'])[['lifeExp','gdpPercap']].mean()\n",
    "print(multi_grp_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   continent  year    lifeExp    gdpPercap\n",
      "0     Africa  1952  39.135500  1252.572466\n",
      "1     Africa  1957  41.266346  1385.236062\n",
      "2     Africa  1962  43.319442  1598.078825\n",
      "3     Africa  1967  45.334538  2050.363801\n",
      "4     Africa  1972  47.450942  2339.615674\n",
      "5     Africa  1977  49.580423  2585.938508\n",
      "6     Africa  1982  51.592865  2481.592960\n",
      "7     Africa  1987  53.344788  2282.668991\n",
      "8     Africa  1992  53.629577  2281.810333\n",
      "9     Africa  1997  53.598269  2378.759555\n",
      "10    Africa  2002  53.325231  2599.385159\n",
      "11    Africa  2007  54.806038  3089.032605\n",
      "12  Americas  1952  53.279840  4079.062552\n",
      "13  Americas  1957  55.960280  4616.043733\n",
      "14  Americas  1962  58.398760  4901.541870\n"
     ]
    }
   ],
   "source": [
    "#flatten the index of this multi_grp_var\n",
    "flat = multi_grp_var.reset_index()\n",
    "print(flat.head(15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "continent\n",
      "Africa      52\n",
      "Americas    25\n",
      "Asia        33\n",
      "Europe      30\n",
      "Oceania      2\n",
      "Name: country, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#finding unique values in groupby command: Frequency count\n",
    "print(df.groupby('continent')['country'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year\n",
      "1952    49.057620\n",
      "1957    51.507401\n",
      "1962    53.609249\n",
      "1967    55.678290\n",
      "1972    57.647386\n",
      "1977    59.570157\n",
      "1982    61.533197\n",
      "1987    63.212613\n",
      "1992    64.160338\n",
      "1997    65.014676\n",
      "2002    65.694923\n",
      "2007    67.007423\n",
      "Name: lifeExp, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#globla yearly life expectancy\n",
    "glb_yr_lf_exp_var = df.groupby('year')['lifeExp'].mean()\n",
    "print(glb_yr_lf_exp_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x6aa1e50>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0pVZR8ItUkd15BfzqrWTeSspgZPc2PDJpGN1im0W6LJHvUfCLVIEF67O59ZUktuzO46en9uXHJ/QiRjdHkVpKwS9SCQVFxfz5ozU8/kkqXds047XrxzIivk2kyxI5JAW/SAWt376XW2YksXRTDheM7Mq9PxxIC02qJnWAfkpFysndeW1ROve+s4KYBsbjl47gzCGdIl2WSNgU/CLlkLMvn7veXM6HyVsYkxDLny4apvveSp2j4BcJ01ep2/npq0vZvucAU8/oz7XHJ9BQUy5IHaTgFzmMA4VF/GnOt0z7Io2e7Zoz8/JjGdxVE6tJ3aXgFzmE1K253PJKEisydnPp6HjuOXMAzRrrv43UbfoJFimFu/Pi/I088H4KzRrHMO1/RjJhYMdIlyVSJcK6wsTMWpvZ62a2ysxWmtlYM4s1s7lmtiZ4LPXkZTObHGyzxswmV235IlVv+54DXPP8Qn75VjJH94hl1i3HK/SlXgl3j/9RYJa7X2BmjYFmwN3AR+7+ezObCkwF7izZyMxigV8TulG7A4vM7B1331llPRCpQp+s3srPX1vG7v0F/OoHR3HFMT00Z77UO4cNfjNrCYwDrgBw93wg38zOBk4INnse+JTvBD9wGjDX3bOD95oLnA68XPnSRapOXkERv/9wFc99tZ5+HY7khatHMaBTy0iXJVItwtnjTwC2Ac+a2VBgEXAL0MHdMwHcPdPM2pfStguwqcTz9GDd95jZFGAKQHx8fNgdEKmslZm7ueWVJXybtYcrjunB1DP607RRw0iXJVJtwhnjjwFGAE+6+3BgL6FhnXCU9jeyl7ahu09z90R3T4yLiwvz7UUqrrjY+fuX6zj7L/8ie28Bz115NPf+cKBCX+q9cPb404F0d58fPH+dUPBnmVmnYG+/E7C1jLYnlHjeldCQkEhEbd2dx+2vLeWLNds5ZUB7Hjx/CG1bNIl0WSI14rB7/O6+BdhkZv2CVScDKcA7wMGzdCYDb5fSfDYwwczaBGf9TAjWiUTMnBVbOO2Rz1mwPpvfnjOIpy5PVOhLVAn3rJ6bgZeCM3rSgCsJ/dJ41cyuBjYCFwKYWSJwvbtf4+7ZZnY/sCB4n/sOHugVqWl7DxTy2/dX8vI3GxnYuSWPXjyc3u1bRLoskRpn7qUOuUdUYmKiL1y4MNJlSD2yeONOfjojiQ3Z+5hyfAK3T+hH4xjdKEXqDzNb5O6J4WyrK3elXisoKuaxj9bwl09S6dTqCKZfM4axvdpGuiyRiFLwS72VunUPt81IYvnmXZw3ogv3/nAgLZs2inRZIhGn4Jd6x935x9cb+N0HKzmicUOe+NEIJg7WjVJEDlLwS72StTuPnwWnaY7vG8cfLhhC+5ZNI12WSK2i4Jd64/1lmdw9czkHCou4/5xBXDY6HjPNsyPyXQp+qfN27S/g3ndWMHPJZoZ2bcWfJg2jV5xO0xQpi4Jf6rSv1+7g9leTyMo9wC0n9+Gmk3rTqKFO0xQ5FAW/1El5BUU8NGc1T3+5jh5tm/P69WMZHl/qLSFE5DsU/FLnpGTs5rYZSazOyuWyMfHcPVG3QxQpD/1vkTqjqNh56os0HpqzmtbNGvPslUdzYr/SZgMXkUNR8EudsCl7H7e/upRv1mdz+sCO/O68wcQ2bxzpskTqJAW/1GruzuuL0vnNuykAPHThUM4b0UWnaYpUgoJfaq0dew5w98zlzF6RxaiesTx04VC6xTaLdFkidZ6CX2qlj1dlccfry9m9v4C7J/bn6uMSaKibnotUCQW/1Cr78gt54P2VvDR/I/076qbnItVBwS+1xpKNO7nt4Jz54xL46al9df9bkWqg4JeIKygq5rGPU3n8k1Q6tmyqOfNFqpmCXyJq7bbQnPnL0jVnvkhNCSv4zWw9kAsUAYXunmhmM4CDN2BvDeS4+7Bw2lZB3VLHuTsvztvAAx+spGkjzZkvUpPKs8d/ortvP/jE3ScdXDazh4Bd4baV6LZ1dx4/e30Zn3+7TXPmi0RApYd6LHQlzUXASZUvR+q7OSu2cOcby9hfUMT9Zw/ksjHddTGWSA0LN/gdmGNmDvzN3aeVeO14IMvd11SgrUSJ/flF3P9+CtPnb2Rg55Y8evFwerfXnPkikRBu8B/r7hlm1h6Ya2ar3P3z4LVLgJcr2PbfzGwKMAUgPj6+HF2Q2i558y5+8soS1m3fy3XjE7j91H40jtGc+SKREtb/PnfPCB63AjOBUQBmFgOcB8wob9tStpvm7onunhgXF1eePkgtVVzs/O2ztZz7xL/Ye6CQl64ezV1nDFDoi0TYYff4zaw50MDdc4PlCcB9wcunAKvcPb0CbaUe27Irj5++msRXa3dw2sAO/P68IbTRbJoitUI4Qz0dgJnBAbgYYLq7zwpeu5jvDPOYWWfgaXefeJi2Uk/NSs7kzjeWk19YzIPnD+aixG46gCtSixw2+N09DRhaxmtXlLIuA5h4uLZS/+w9UMh976YwY+EmhnRtxSOThpGgm56L1Dq6cleqxNJNOdw6I4n1O/Zywwm9uO2UvhrLF6mlFPxSKUXFzl8/W8vDc78l7sgmmmdHpA5Q8EuFZeTs57YZScxfl82Zgzvxu3MH06qZ5tkRqe0U/FIh7y/L5K43l1FY7PzhgiFcMLKrDuCK1BEKfimXPQcKufedFby+KJ2h3Vrz6KRh9GjXPNJliUg5KPglbEs27uTWGUlsyt7HzSf15icn96FRQx3AFalrFPxyWEXFzhOfpPLIR2vo2LIpr0wZy6iesZEuS0QqSMEvh5S+cx+3zUhiwfqdnDW0M789ZxCtjtABXJG6TMEvZXo7aTP3vJWMO/zpoqGcO7yLDuCK1AMKfvme3LwCfvX2CmYu2cyI+NY8Mmk48W2bRbosEakiCn75L4s2ZHPrjCQ279zPLSf34eaTehOjA7gi9YqCXwAoLCrmL5+k8tjHqXRq1ZRXrxtLYg8dwBWpjxT8wqbsfdzyyhIWb8zh3OFd+M3ZA2nZVAdwReorBX+Um7kknV++tQIDHr14GGcP6xLpkkSkmin4o9SeA4XcM3M5byVlkNi9DQ9PGka3WB3AFYkGCv4olJKxmxunL2bDjr3cdkpfbjyxlw7gikQRBX8UcXemf7OR37ybQusjGjH92jGMSdAUyiLRRsEfJXLzCrjrzeW8tyyT4/u04+FJw2jXokmkyxKRCFDwR4Hkzbu4afpiNmbv4+en9eOG8b1o0EBX4IpEq7AGds1svZktN7MkM1sYrLvXzDYH65LMbGIZbU83s9VmlmpmU6uyeDk0d+eFeRs478mv2F9QxMvXjuHGE3sr9EWiXHn2+E909+3fWfewu/+xrAZm1hB4HDgVSAcWmNk77p5S/lKlPHLzCpj65nLeX5bJuL5xPHzRUNpqaEdEqP6hnlFAqrunAZjZK8DZgIK/GiVv3sWN0xeTvnM/d5zej+vHaWhHRP4j3HP4HJhjZovMbEqJ9TeZ2TIze8bM2pTSrguwqcTz9GDd95jZFDNbaGYLt23bFmZZUpK788LX6znvia84UFDMK1PG8OMTNLQjIv8t3OA/1t1HAGcAN5rZOOBJoBcwDMgEHiqlXWmJ46V9A3ef5u6J7p4YFxcXZlly0O68Am6cvphfvr2CY3q35YNbjudozbUjIqUIa6jH3TOCx61mNhMY5e6fH3zdzJ4C3iulaTrQrcTzrkBGxcuV0ixPDw3tbM7Zz52n9+e6cQnayxeRMh12j9/MmpvZkQeXgQlAspl1KrHZuUByKc0XAH3MrKeZNQYuBt6pfNkCoaGd579az/lPfkVBUTEzpozhhhM0ni8ihxbOHn8HYGZw56UYYLq7zzKzF8xsGKGhm/XAdQBm1hl42t0nunuhmd0EzAYaAs+4+4pq6EfU2Z1XwJ2vL+PD5C2c2C+Ohy4aRmzzxpEuS0TqAHMvdcg9ohITE33hwoWRLqPWWpaew03Tl7A5Zz93nNaPa4/X0I5ItDOzRe6eGM62unK3Djk4tPPAByuJa9GEV68bw8juOoArIuWj4K8jdu0PDe3MWrGFk/q356ELh9JGQzsiUgEK/jpgWXoON05fTGZOHndP7M81x2loR0QqTsFfi7k7z321nt8FQzszrhvLyO6lXScnIhI+BX8ttWt/AXe8vpTZK7I4ZUB7/njhUFo309COiFSegr8WStqUw03TF7NlVx73nDmAq4/rSXA6rYhIpSn4axF359l/red/P1xJ+yOb8ur1YxkRr6EdEalaCv5aYte+An7++lLmpGRxyoAO/PHCIRraEZFqoeCvBZI25XDjS4vJ2q2hHRGpfgr+CPru0M5r149luIZ2RKSaKfgjpORcO6cM6MBDFw6lVbNGkS5LRKKAgj8CVmTs4saXFrNp537untifa49P0NCOiNQYBX8NcndeWbCJX7+zgthmjZkxZQyJulmKiNQwBX8N2ZdfyD0zk3lzyWaO79OORyYN083PRSQiFPw1IHVrLje8uJjUbXu49ZQ+3HxSHxpqrh0RiRAFfzV7O2kzd725nCMaNeSFq0ZzXJ92kS5JRKKcgr+a5BUUcf97Kbw0fyNH92jDY5eMoGOrppEuS0REwV8dNu7Yx4+nLyJ5826uG5/Azyb0o1HDw97eWESkRoQV/Ga2HsgFioBCd080sz8AZwH5wFrgSnfPCadt1ZReO81esYWfvbYUA566PJFTj+oQ6ZJERP5Lefb4T3T37SWezwXuCm6o/iBwF3BnmG3rnYKiYh78cBVPf7mOIV1b8filI+gW2yzSZYmIfE+Fh3rcfU6Jp/OACypfTt2UuWs/N01fwqINO7l8bHd+ceYAmsQ0jHRZIiKlCjf4HZhjZg78zd2nfef1q4AZFWxbp33+7TZunZHEgYIiHrtkOGcN7RzpkkREDinc4D/W3TPMrD0w18xWufvnAGb2C6AQeKm8bUsysynAFID4+Phyd6SmFRU7j/7zWx77JJW+7Y/kictG0CuuRaTLEhE5rLBONXH3jOBxKzATGAVgZpOBHwA/cncvT9tStpvm7onunhgXF1feftSobbkHuPyZ+fz541TOH9GVt248VqEvInXGYff4zaw50MDdc4PlCcB9ZnY6oYO54919X3naVl35Ne+bddncNH0xu/YX8H/nD+Gio7tFuiQRkXIJZ6inAzAzmD0yBpju7rPMLBVoQmj4BmCeu19vZp2Bp919Ylltq6Ef1a642Jn2RRp/mL2a+NhmPHflKI7q3DLSZYmIlNthg9/d04ChpazvXcb2GcDEQ7Wta3L25XP7q0v5aNVWzhzcid+fP5gjm2rufBGpm3Tl7mEs3ZTDj19azNbcPH7zw4FcPra75s4XkTpNwV8Gd+cfX2/gt++nBLdFPIZh3VpHuiwRkUpT8Jdiz4FC7nxjGe8vy+Sk/u3500VDad2scaTLEhGpEgr+71iZuZsbX1rM+h17ueP0flw/rhcNNHe+iNQjCv4SXl24iV++lUyrIxox/doxjEloG+mSRESqnIKf0ARrv3o7mZe/2cQxvdry6MXDiTtSt0UUkfop6oN/74FCbpy+mE9Xb+OGE3rxswn9dFtEEanXojr4t+Ue4KrnFrAiYxe/O3cwl46u/XMEiYhUVtQGf9q2PUx+9hu25+bz1OWJnDxAN0wRkegQlcG/aMNOrnl+AQ3MeHnKGJ2fLyJRJeqCf86KLdz88hI6tWrKc1eOoke75pEuSUSkRkVV8L8wbwO/fjuZwV1b88zkRNq20Jk7IhJ9oiL43Z0/zF7NE5+u5eT+7Xns0uE0axwVXRcR+Z56n375hcVMfWMZby7ZzCWj4rn/7IHENAzr/jMiIvVSvQ7+3LwCbnhxMV+mbuf2U/ty00m9NbOmiES9ehv8WbvzuOLZBazJyuUPFwzhwkTdKUtEBOpp8KduzWXyMwvI2ZfP3684mvF9a/c9fEVEalK9C/4F67O55vmFNGrYgBnXjWVQl1aRLklEpFapV8H/4fJMbpmRRNc2R/D8laPoFtss0iWJiNQ6YZ3eYmbrzWy5mSWZ2cJgXayZzTWzNcFjmzLaTg62WWNmk6uy+JKe+XIdP56+mMFdWvHG9cco9EVEylCe8xpPdPdh7p4YPJ8KfOTufYCPguf/xcxigV8Do4FRwK/L+gVRUcV/iMhMAAAIy0lEQVTFzgPvp3DfeylMOKoDL10zmjbNdbcsEZGyVOaE9rOB54Pl54FzStnmNGCuu2e7+05gLnB6Jb7nfzlQWMQtM5J46ot1TB7bnSd+NJKmjRpW1duLiNRL4Y7xOzDHzBz4m7tPAzq4eyaAu2eaWftS2nUBNpV4nh6sq7Rd+wu47oWFzEvLZuoZ/bluXILO0RcRCUO4wX+su2cE4T7XzFaF2a60JPZSNzSbAkwBiI8/9Lz4GTn7ufLZBaRt38Mjk4ZxzvAq+V0iIhIVwhrqcfeM4HErMJPQeH2WmXUCCB63ltI0HSh55VRXIKOM7zHN3RPdPTEuruzz7ldt2c15T3xFRs5+nrtylEJfRKScDhv8ZtbczI48uAxMAJKBd4CDZ+lMBt4upflsYIKZtQkO6k4I1lXIV2u3c+GTX+M4r14/lmN7t6voW4mIRK1whno6ADOD8fMYYLq7zzKzBcCrZnY1sBG4EMDMEoHr3f0ad882s/uBBcF73efu2RUp9O2kzfzstaX0aNuc564aRZfWR1TkbUREop65lzrkHlGJiYm+cOFCIDSl8lNfpPG7D1Yxumcs0/4nkVbNGkW4QhGR2sXMFpU43f6QavWVu0XFzv3vpfDcV+s5c0gn/nTRUJrE6HRNEZHKqLXBn1dQxG0zkvgweQtXH9eTX0wcQIMGOl1TRKSyamXwFxU7lz09n0Ubd3LPmQO45viESJckIlJv1MrgX7ttD3vTd/GXS0Zw5pBOkS5HRKReqZXBX1jkvHD1KEYntI10KSIi9U6tvPlsQlwLhb6ISDWplcHftFGtLEtEpF5QwoqIRBkFv4hIlFHwi4hEGQW/iEiUUfCLiEQZBb+ISJRR8IuIRBkFv4hIlKmV8/Gb2TZgQ6TrqIR2wPZIF1HD1OfooD7XXt3dvez71pZQK4O/rjOzheHeEKG+UJ+jg/pcP2ioR0Qkyij4RUSijIK/ekyLdAERoD5HB/W5HtAYv4hIlNEev4hIlFHwh8nMnjGzrWaWXGLdUDP72syWm9m7ZtayxGtDgtdWBK83DdaPDJ6nmtmfzazW3kG+PH02sx+ZWVKJr2IzGxa8Vl/73MjMng/WrzSzu0q0Od3MVgd9nhqJvoSjnP1tbGbPBuuXmtkJJdrUpc+4m5l9EnxmK8zslmB9rJnNNbM1wWObYL0FfUo1s2VmNqLEe00Otl9jZpMj1adyc3d9hfEFjANGAMkl1i0AxgfLVwH3B8sxwDJgaPC8LdAwWP4GGAsY8CFwRqT7VhV9/k67wUBaief1ss/ApcArwXIzYD3QA2gIrAUSgMbAUuCoSPetCvp7I/BssNweWAQ0qIOfcSdgRLB8JPAtcBTwf8DUYP1U4MFgeWLQJwPGAPOD9bFAWvDYJlhuE+n+hfOlPf4wufvnQPZ3VvcDPg+W5wLnB8sTgGXuvjRou8Pdi8ysE9DS3b/20E/OP4Bzqr/6iilnn0u6BHgZoJ732YHmZhYDHAHkA7uBUUCqu6e5ez7wCnB2dddeEeXs71HAR0G7rUAOkFgHP+NMd18cLOcCK4EuhD6j54PNnuc/fTgb+IeHzANaB30+DZjr7tnuvpPQv9XpNdiVClPwV04y8MNg+UKgW7DcF3Azm21mi83sjmB9FyC9RPv0YF1dUlafS5pEEPzU7z6/DuwFMoGNwB/dPZtQ/zaVaF/X+lxWf5cCZ5tZjJn1BEYGr9XZz9jMegDDgflAB3fPhNAvB0J/1UDZn2ed/ZwV/JVzFXCjmS0i9CdjfrA+BjgO+FHweK6ZnUzoT8XvqmunVZXVZwDMbDSwz90PjhnX5z6PAoqAzkBP4HYzS6Du97ms/j5DKNwWAo8AXwGF1NH+mlkL4A3gVnfffahNS1nnh1hf68VEuoC6zN1XERrWwcz6AmcGL6UDn7n79uC1DwiNo74IdC3xFl2BjBoruAocos8HXcx/9vYh9G9RX/t8KTDL3QuArWb2LyCR0F5gyb+E6lSfy+qvuxcCtx3czsy+AtYAO6ljn7GZNSIU+i+5+5vB6iwz6+TumcFQztZgfTqlf57pwAnfWf9pddZdVbTHXwlm1j54bADcA/w1eGk2MMTMmgXjv+OBlODPx1wzGxOc9XA58HYESq+wQ/T54LoLCY1pA//+k7m+9nkjcFJw1kdzQgf+VhE6ONrHzHqaWWNCvwzfqfnKK6as/gY/z82D5VOBQnevcz/XQY1/B1a6+59KvPQOcPDMnMn8pw/vAJcHn/MYYFfQ59nABDNrE5wBNCFYV/tF+uhyXfkitBebCRQQ+k1/NXALoTMCvgV+T3BBXLD9ZcAKQuOl/1difWKwbi3wl5JtattXBfp8AjCvlPepl30GWgCvBZ9zCvDzEu8zMdh+LfCLSPerivrbA1hN6GDoPwnNBlkXP+PjCA3JLAOSgq+JhM6++4jQXzEfAbHB9gY8HvRtOZBY4r2uAlKDrysj3bdwv3TlrohIlNFQj4hIlFHwi4hEGQW/iEiUUfCLiEQZBb+ISJRR8IuIRBkFv0g1MLOGka5BpCwKfol6Znb/wTnZg+cPmNlPzOznZrYgmIP9NyVef8vMFgVzuU8psX6Pmd1nZvMJTVEsUisp+EVCl+9Phn9PU3AxkAX0ITQR2zBgpJmNC7a/yt1HErpa9Sdm1jZY35zQvPaj3f3LmuyASHlokjaJeu6+3sx2mNlwoAOwBDia0NwrS4LNWhD6RfA5obA/N1jfLVi/g9BMnW/UZO0iFaHgFwl5GrgC6Eho+uGTgf9197+V3Ci43eApwFh332dmnwJNg5fz3L2opgoWqSgN9YiEzCR096SjCc2wOBu4KpizHTPrEsxa2QrYGYR+f0IzcorUKdrjFwHcPd/MPgFygr32OWY2APg6uG/4HkIzrs4CrjezZYRmqpwXqZpFKkqzc4rw74O6i4EL3X1NpOsRqU4a6pGoZ2ZHEZpP/SOFvkQD7fGLiEQZ7fGLiEQZBb+ISJRR8IuIRBkFv4hIlFHwi4hEGQW/iEiU+X9ksiiMpEkRXQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot\n",
    "glb_yr_lf_exp_var.plot()\n"
   ]
  },
  {
   "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</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year         pop continent  lifeExp   gdpPercap\n",
       "0  Afghanistan  1952   8425333.0      Asia   28.801  779.445314\n",
       "1  Afghanistan  1957   9240934.0      Asia   30.332  820.853030\n",
       "2  Afghanistan  1962  10267083.0      Asia   31.997  853.100710\n",
       "3  Afghanistan  1967  11537966.0      Asia   34.020  836.197138\n",
       "4  Afghanistan  1972  13079460.0      Asia   36.088  739.981106"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"E:/Padai/nptel/Data analytics with python/IBM-313 Marks.xlsx\"\n",
    "table = pd.read_excel(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    S.No.  MTE (25)  Mini Project (25)  Total (50)  ETE (50)  Total\n",
      "0       1      5.00                 20       25.00     12.00  37.00\n",
      "1       2     11.05                 20       31.05     26.00  57.05\n",
      "2       3      8.10                 20       28.10     14.00  42.10\n",
      "3       4      6.00                 10       16.00     13.00  29.00\n",
      "4       5     11.35                 20       31.35     17.00  48.35\n",
      "5       6     11.00                 20       31.00     24.00  55.00\n",
      "6       7     10.50                 15       25.50     18.00  43.50\n",
      "7       8      9.00                 15       24.00     14.00  38.00\n",
      "8       9      4.00                 15       19.00     19.00  38.00\n",
      "9      14      8.50                 10       18.50     13.00  31.50\n",
      "10     10     12.50                 15       27.50     18.00  45.50\n",
      "11     11     16.55                 15       31.55     11.00  42.55\n",
      "12     12      8.20                 22       30.20     21.00  51.20\n",
      "13     13     11.00                 22       33.00     18.25  51.25\n",
      "14     16      9.50                 10       19.50     22.00  41.50\n",
      "15     17     12.75                 22       34.75     29.00  63.75\n",
      "16     18      6.00                 10       16.00     12.00  28.00\n",
      "17     19      1.70                 10       11.70     14.00  25.70\n",
      "18     20      5.05                 10       15.05     11.00  26.05\n",
      "19     21      3.70                 10       13.70     19.00  32.70\n",
      "20     22      9.70                 10       19.70     23.00  42.70\n",
      "21     15     19.10                 22       41.10     33.00  74.10\n",
      "22     23     17.00                 22       39.00     22.00  61.00\n",
      "23     24     12.80                 10       22.80     35.00  57.80\n",
      "24     26     22.50                 22       44.50     50.00  94.50\n",
      "25     27     23.50                 22       45.50     47.00  92.50\n",
      "26     44     19.00                 22       41.00     30.00  71.00\n",
      "27     28     13.05                 22       35.05     24.00  59.05\n",
      "28     29     14.80                 22       36.80     27.00  63.80\n",
      "29     45     12.00                 12       24.00     20.00  44.00\n",
      "..    ...       ...                ...         ...       ...    ...\n",
      "49     50     13.10                 15       28.10     22.00  50.10\n",
      "50     51      8.65                 15       23.65     22.00  45.65\n",
      "51     52     16.50                 15       31.50     23.00  54.50\n",
      "52     53     12.90                 22       34.90     20.00  54.90\n",
      "53     54      9.50                 15       24.50     19.00  43.50\n",
      "54     55      3.50                 15       18.50     17.00  35.50\n",
      "55     56      7.70                 15       22.70     17.00  39.70\n",
      "56     57      5.55                 15       20.55     16.00  36.55\n",
      "57     58      7.30                 15       22.30     22.00  44.30\n",
      "58     59     10.15                 10       20.15     22.00  42.15\n",
      "59     60     14.35                 15       29.35     19.00  48.35\n",
      "60     61      4.50                 15       19.50     18.00  37.50\n",
      "61     62     14.00                 15       29.00     20.00  49.00\n",
      "62     63      4.70                 15       19.70     23.00  42.70\n",
      "63     64      8.00                 15       23.00     12.00  35.00\n",
      "64     65     10.30                 15       25.30     17.00  42.30\n",
      "65     66      5.85                 10       15.85     24.00  39.85\n",
      "66     67      7.00                 22       29.00     12.00  41.00\n",
      "67     68      7.00                 10       17.00     16.00  33.00\n",
      "68     69      9.50                 10       19.50     11.00  30.50\n",
      "69     70      8.00                 10       18.00     23.00  41.00\n",
      "70     71     15.40                 22       37.40     18.00  55.40\n",
      "71     72      1.20                 10       11.20      9.00  20.20\n",
      "72     73      4.90                 15       19.90     20.00  39.90\n",
      "73     74      1.00                 16       17.00      7.00  24.00\n",
      "74     75     12.05                 10       22.05     20.00  42.05\n",
      "75     76     12.25                 10       22.25     28.00  50.25\n",
      "76     77      1.75                 10       11.75       NaN   0.00\n",
      "77     78      3.00                 10       13.00       NaN   0.00\n",
      "78     79      5.80                 10       15.80     12.00  27.80\n",
      "\n",
      "[79 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "print(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46.90632911392405\n",
      "45.0\n"
     ]
    }
   ],
   "source": [
    "#mean and median\n",
    "x = table['Total']\n",
    "print(np.mean(x))\n",
    "print(np.median(x))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ModeResult(mode=array([0.]), count=array([2]))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy\n",
    "from scipy import stats\n",
    "stats.mode(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.0\n"
     ]
    }
   ],
   "source": [
    "#percentile\n",
    "a = np.array([1,2,3,4,5])\n",
    "p = np.percentile(a,50) #50 percentile\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "262.78147892965876\n"
     ]
    }
   ],
   "source": [
    "#variance\n",
    "print(np.var(x))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.210536046955966"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#population std deviation\n",
    "import statistics\n",
    "statistics.pstdev(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.210536046955966"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sample std deviation\n",
    "np.std(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10226407464884266"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#skewness\n",
    "stats.skew(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": [
    "plt.boxplot(x, sym='*')\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.7.3"
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 },
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