Pandas 101 - Exploratory Data Analysis in Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "daf68d23",
   "metadata": {},
   "source": [
    "# EDA in Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "14511774",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4fdb05c6",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>CCA3</th>\n",
       "      <th>Country</th>\n",
       "      <th>Capital</th>\n",
       "      <th>Continent</th>\n",
       "      <th>2022 Population</th>\n",
       "      <th>2020 Population</th>\n",
       "      <th>2015 Population</th>\n",
       "      <th>2010 Population</th>\n",
       "      <th>2000 Population</th>\n",
       "      <th>1990 Population</th>\n",
       "      <th>1980 Population</th>\n",
       "      <th>1970 Population</th>\n",
       "      <th>Area (km²)</th>\n",
       "      <th>Density (per km²)</th>\n",
       "      <th>Growth Rate</th>\n",
       "      <th>World Population Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Kabul</td>\n",
       "      <td>Asia</td>\n",
       "      <td>41128771.00</td>\n",
       "      <td>38972230.00</td>\n",
       "      <td>33753499.00</td>\n",
       "      <td>28189672.00</td>\n",
       "      <td>19542982.00</td>\n",
       "      <td>10694796.00</td>\n",
       "      <td>12486631.00</td>\n",
       "      <td>10752971.00</td>\n",
       "      <td>652230.00</td>\n",
       "      <td>63.06</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>138</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Tirana</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2842321.00</td>\n",
       "      <td>2866849.00</td>\n",
       "      <td>2882481.00</td>\n",
       "      <td>2913399.00</td>\n",
       "      <td>3182021.00</td>\n",
       "      <td>3295066.00</td>\n",
       "      <td>2941651.00</td>\n",
       "      <td>2324731.00</td>\n",
       "      <td>28748.00</td>\n",
       "      <td>98.87</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>Algiers</td>\n",
       "      <td>Africa</td>\n",
       "      <td>44903225.00</td>\n",
       "      <td>43451666.00</td>\n",
       "      <td>39543154.00</td>\n",
       "      <td>35856344.00</td>\n",
       "      <td>30774621.00</td>\n",
       "      <td>25518074.00</td>\n",
       "      <td>18739378.00</td>\n",
       "      <td>13795915.00</td>\n",
       "      <td>2381741.00</td>\n",
       "      <td>18.85</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>213</td>\n",
       "      <td>ASM</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>Pago Pago</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>44273.00</td>\n",
       "      <td>46189.00</td>\n",
       "      <td>51368.00</td>\n",
       "      <td>54849.00</td>\n",
       "      <td>58230.00</td>\n",
       "      <td>47818.00</td>\n",
       "      <td>32886.00</td>\n",
       "      <td>27075.00</td>\n",
       "      <td>199.00</td>\n",
       "      <td>222.48</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>203</td>\n",
       "      <td>AND</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>Andorra la Vella</td>\n",
       "      <td>Europe</td>\n",
       "      <td>79824.00</td>\n",
       "      <td>77700.00</td>\n",
       "      <td>71746.00</td>\n",
       "      <td>71519.00</td>\n",
       "      <td>66097.00</td>\n",
       "      <td>53569.00</td>\n",
       "      <td>35611.00</td>\n",
       "      <td>19860.00</td>\n",
       "      <td>468.00</td>\n",
       "      <td>170.56</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>226</td>\n",
       "      <td>WLF</td>\n",
       "      <td>Wallis and Futuna</td>\n",
       "      <td>Mata-Utu</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>11572.00</td>\n",
       "      <td>11655.00</td>\n",
       "      <td>12182.00</td>\n",
       "      <td>13142.00</td>\n",
       "      <td>14723.00</td>\n",
       "      <td>13454.00</td>\n",
       "      <td>11315.00</td>\n",
       "      <td>9377.00</td>\n",
       "      <td>142.00</td>\n",
       "      <td>81.49</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>172</td>\n",
       "      <td>ESH</td>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>El Aaiún</td>\n",
       "      <td>Africa</td>\n",
       "      <td>575986.00</td>\n",
       "      <td>556048.00</td>\n",
       "      <td>491824.00</td>\n",
       "      <td>413296.00</td>\n",
       "      <td>270375.00</td>\n",
       "      <td>178529.00</td>\n",
       "      <td>116775.00</td>\n",
       "      <td>76371.00</td>\n",
       "      <td>266000.00</td>\n",
       "      <td>2.17</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>46</td>\n",
       "      <td>YEM</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>Sanaa</td>\n",
       "      <td>Asia</td>\n",
       "      <td>33696614.00</td>\n",
       "      <td>32284046.00</td>\n",
       "      <td>28516545.00</td>\n",
       "      <td>24743946.00</td>\n",
       "      <td>18628700.00</td>\n",
       "      <td>13375121.00</td>\n",
       "      <td>9204938.00</td>\n",
       "      <td>6843607.00</td>\n",
       "      <td>527968.00</td>\n",
       "      <td>63.82</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>63</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>Lusaka</td>\n",
       "      <td>Africa</td>\n",
       "      <td>20017675.00</td>\n",
       "      <td>18927715.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13792086.00</td>\n",
       "      <td>9891136.00</td>\n",
       "      <td>7686401.00</td>\n",
       "      <td>5720438.00</td>\n",
       "      <td>4281671.00</td>\n",
       "      <td>752612.00</td>\n",
       "      <td>26.60</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>74</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Harare</td>\n",
       "      <td>Africa</td>\n",
       "      <td>16320537.00</td>\n",
       "      <td>15669666.00</td>\n",
       "      <td>14154937.00</td>\n",
       "      <td>12839771.00</td>\n",
       "      <td>11834676.00</td>\n",
       "      <td>10113893.00</td>\n",
       "      <td>7049926.00</td>\n",
       "      <td>5202918.00</td>\n",
       "      <td>390757.00</td>\n",
       "      <td>41.77</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>234 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Rank CCA3            Country           Capital Continent  \\\n",
       "0      36  AFG        Afghanistan             Kabul      Asia   \n",
       "1     138  ALB            Albania            Tirana    Europe   \n",
       "2      34  DZA            Algeria           Algiers    Africa   \n",
       "3     213  ASM     American Samoa         Pago Pago   Oceania   \n",
       "4     203  AND            Andorra  Andorra la Vella    Europe   \n",
       "..    ...  ...                ...               ...       ...   \n",
       "229   226  WLF  Wallis and Futuna          Mata-Utu   Oceania   \n",
       "230   172  ESH     Western Sahara          El Aaiún    Africa   \n",
       "231    46  YEM              Yemen             Sanaa      Asia   \n",
       "232    63  ZMB             Zambia            Lusaka    Africa   \n",
       "233    74  ZWE           Zimbabwe            Harare    Africa   \n",
       "\n",
       "     2022 Population  2020 Population  2015 Population  2010 Population  \\\n",
       "0        41128771.00      38972230.00      33753499.00      28189672.00   \n",
       "1         2842321.00       2866849.00       2882481.00       2913399.00   \n",
       "2        44903225.00      43451666.00      39543154.00      35856344.00   \n",
       "3           44273.00         46189.00         51368.00         54849.00   \n",
       "4           79824.00         77700.00         71746.00         71519.00   \n",
       "..               ...              ...              ...              ...   \n",
       "229         11572.00         11655.00         12182.00         13142.00   \n",
       "230        575986.00        556048.00        491824.00        413296.00   \n",
       "231      33696614.00      32284046.00      28516545.00      24743946.00   \n",
       "232      20017675.00      18927715.00              NaN      13792086.00   \n",
       "233      16320537.00      15669666.00      14154937.00      12839771.00   \n",
       "\n",
       "     2000 Population  1990 Population  1980 Population  1970 Population  \\\n",
       "0        19542982.00      10694796.00      12486631.00      10752971.00   \n",
       "1         3182021.00       3295066.00       2941651.00       2324731.00   \n",
       "2        30774621.00      25518074.00      18739378.00      13795915.00   \n",
       "3           58230.00         47818.00         32886.00         27075.00   \n",
       "4           66097.00         53569.00         35611.00         19860.00   \n",
       "..               ...              ...              ...              ...   \n",
       "229         14723.00         13454.00         11315.00          9377.00   \n",
       "230        270375.00        178529.00        116775.00         76371.00   \n",
       "231      18628700.00      13375121.00       9204938.00       6843607.00   \n",
       "232       9891136.00       7686401.00       5720438.00       4281671.00   \n",
       "233      11834676.00      10113893.00       7049926.00       5202918.00   \n",
       "\n",
       "     Area (km²)  Density (per km²)  Growth Rate  World Population Percentage  \n",
       "0     652230.00              63.06         1.03                         0.52  \n",
       "1      28748.00              98.87         1.00                         0.04  \n",
       "2    2381741.00              18.85         1.02                         0.56  \n",
       "3        199.00             222.48         0.98                         0.00  \n",
       "4        468.00             170.56         1.01                         0.00  \n",
       "..          ...                ...          ...                          ...  \n",
       "229      142.00              81.49         1.00                         0.00  \n",
       "230   266000.00               2.17         1.02                         0.01  \n",
       "231   527968.00              63.82         1.02                         0.42  \n",
       "232   752612.00              26.60         1.03                         0.25  \n",
       "233   390757.00              41.77         1.02                         0.20  \n",
       "\n",
       "[234 rows x 17 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\alexf\\OneDrive\\Documents\\Pandas Tutorial\\world_population.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a1670646",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0dda31a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 234 entries, 0 to 233\n",
      "Data columns (total 17 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   Rank                         234 non-null    int64  \n",
      " 1   CCA3                         234 non-null    object \n",
      " 2   Country                      234 non-null    object \n",
      " 3   Capital                      234 non-null    object \n",
      " 4   Continent                    234 non-null    object \n",
      " 5   2022 Population              230 non-null    float64\n",
      " 6   2020 Population              233 non-null    float64\n",
      " 7   2015 Population              230 non-null    float64\n",
      " 8   2010 Population              227 non-null    float64\n",
      " 9   2000 Population              227 non-null    float64\n",
      " 10  1990 Population              229 non-null    float64\n",
      " 11  1980 Population              229 non-null    float64\n",
      " 12  1970 Population              230 non-null    float64\n",
      " 13  Area (km²)                   232 non-null    float64\n",
      " 14  Density (per km²)            230 non-null    float64\n",
      " 15  Growth Rate                  232 non-null    float64\n",
      " 16  World Population Percentage  234 non-null    float64\n",
      "dtypes: float64(12), int64(1), object(4)\n",
      "memory usage: 31.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7eeb46b2",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>2022 Population</th>\n",
       "      <th>2020 Population</th>\n",
       "      <th>2015 Population</th>\n",
       "      <th>2010 Population</th>\n",
       "      <th>2000 Population</th>\n",
       "      <th>1990 Population</th>\n",
       "      <th>1980 Population</th>\n",
       "      <th>1970 Population</th>\n",
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       "      <th>Growth Rate</th>\n",
       "      <th>World Population Percentage</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>234.00</td>\n",
       "      <td>230.00</td>\n",
       "      <td>233.00</td>\n",
       "      <td>230.00</td>\n",
       "      <td>227.00</td>\n",
       "      <td>227.00</td>\n",
       "      <td>229.00</td>\n",
       "      <td>229.00</td>\n",
       "      <td>230.00</td>\n",
       "      <td>232.00</td>\n",
       "      <td>230.00</td>\n",
       "      <td>232.00</td>\n",
       "      <td>234.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>117.50</td>\n",
       "      <td>34632250.88</td>\n",
       "      <td>33600710.95</td>\n",
       "      <td>32066004.16</td>\n",
       "      <td>30270164.48</td>\n",
       "      <td>26840495.26</td>\n",
       "      <td>19330463.93</td>\n",
       "      <td>16282884.78</td>\n",
       "      <td>15866499.13</td>\n",
       "      <td>581663.75</td>\n",
       "      <td>456.81</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>67.69</td>\n",
       "      <td>137889172.44</td>\n",
       "      <td>135873196.61</td>\n",
       "      <td>131507146.34</td>\n",
       "      <td>126074183.54</td>\n",
       "      <td>113352454.57</td>\n",
       "      <td>81309624.96</td>\n",
       "      <td>69345465.54</td>\n",
       "      <td>68355859.75</td>\n",
       "      <td>1769133.06</td>\n",
       "      <td>2083.74</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00</td>\n",
       "      <td>510.00</td>\n",
       "      <td>520.00</td>\n",
       "      <td>564.00</td>\n",
       "      <td>596.00</td>\n",
       "      <td>651.00</td>\n",
       "      <td>700.00</td>\n",
       "      <td>733.00</td>\n",
       "      <td>752.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>59.25</td>\n",
       "      <td>419738.50</td>\n",
       "      <td>406471.00</td>\n",
       "      <td>394295.00</td>\n",
       "      <td>382726.50</td>\n",
       "      <td>329470.00</td>\n",
       "      <td>261928.00</td>\n",
       "      <td>223752.00</td>\n",
       "      <td>145880.50</td>\n",
       "      <td>2567.25</td>\n",
       "      <td>36.60</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>117.50</td>\n",
       "      <td>5762857.00</td>\n",
       "      <td>5456681.00</td>\n",
       "      <td>5244415.00</td>\n",
       "      <td>4889741.00</td>\n",
       "      <td>4491202.00</td>\n",
       "      <td>3785847.00</td>\n",
       "      <td>3135123.00</td>\n",
       "      <td>2511718.00</td>\n",
       "      <td>77141.00</td>\n",
       "      <td>95.35</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>175.75</td>\n",
       "      <td>22653719.00</td>\n",
       "      <td>21522626.00</td>\n",
       "      <td>19730853.75</td>\n",
       "      <td>16825852.50</td>\n",
       "      <td>15625467.00</td>\n",
       "      <td>11882762.00</td>\n",
       "      <td>9817257.00</td>\n",
       "      <td>8817329.00</td>\n",
       "      <td>414643.25</td>\n",
       "      <td>236.88</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>234.00</td>\n",
       "      <td>1425887337.00</td>\n",
       "      <td>1424929781.00</td>\n",
       "      <td>1393715448.00</td>\n",
       "      <td>1348191368.00</td>\n",
       "      <td>1264099069.00</td>\n",
       "      <td>1153704252.00</td>\n",
       "      <td>982372466.00</td>\n",
       "      <td>822534450.00</td>\n",
       "      <td>17098242.00</td>\n",
       "      <td>23172.27</td>\n",
       "      <td>1.07</td>\n",
       "      <td>17.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Rank  2022 Population  2020 Population  2015 Population  \\\n",
       "count 234.00           230.00           233.00           230.00   \n",
       "mean  117.50      34632250.88      33600710.95      32066004.16   \n",
       "std    67.69     137889172.44     135873196.61     131507146.34   \n",
       "min     1.00           510.00           520.00           564.00   \n",
       "25%    59.25        419738.50        406471.00        394295.00   \n",
       "50%   117.50       5762857.00       5456681.00       5244415.00   \n",
       "75%   175.75      22653719.00      21522626.00      19730853.75   \n",
       "max   234.00    1425887337.00    1424929781.00    1393715448.00   \n",
       "\n",
       "       2010 Population  2000 Population  1990 Population  1980 Population  \\\n",
       "count           227.00           227.00           229.00           229.00   \n",
       "mean       30270164.48      26840495.26      19330463.93      16282884.78   \n",
       "std       126074183.54     113352454.57      81309624.96      69345465.54   \n",
       "min             596.00           651.00           700.00           733.00   \n",
       "25%          382726.50        329470.00        261928.00        223752.00   \n",
       "50%         4889741.00       4491202.00       3785847.00       3135123.00   \n",
       "75%        16825852.50      15625467.00      11882762.00       9817257.00   \n",
       "max      1348191368.00    1264099069.00    1153704252.00     982372466.00   \n",
       "\n",
       "       1970 Population  Area (km²)  Density (per km²)  Growth Rate  \\\n",
       "count           230.00      232.00             230.00       232.00   \n",
       "mean       15866499.13   581663.75             456.81         1.01   \n",
       "std        68355859.75  1769133.06            2083.74         0.01   \n",
       "min             752.00        1.00               0.03         0.91   \n",
       "25%          145880.50     2567.25              36.60         1.00   \n",
       "50%         2511718.00    77141.00              95.35         1.01   \n",
       "75%         8817329.00   414643.25             236.88         1.02   \n",
       "max       822534450.00 17098242.00           23172.27         1.07   \n",
       "\n",
       "       World Population Percentage  \n",
       "count                       234.00  \n",
       "mean                          0.43  \n",
       "std                           1.71  \n",
       "min                           0.00  \n",
       "25%                           0.01  \n",
       "50%                           0.07  \n",
       "75%                           0.28  \n",
       "max                          17.88  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d67f6c64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rank                           0\n",
       "CCA3                           0\n",
       "Country                        0\n",
       "Capital                        0\n",
       "Continent                      0\n",
       "2022 Population                4\n",
       "2020 Population                1\n",
       "2015 Population                4\n",
       "2010 Population                7\n",
       "2000 Population                7\n",
       "1990 Population                5\n",
       "1980 Population                5\n",
       "1970 Population                4\n",
       "Area (km²)                     2\n",
       "Density (per km²)              4\n",
       "Growth Rate                    2\n",
       "World Population Percentage    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3c67bffb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rank                           234\n",
       "CCA3                           234\n",
       "Country                        234\n",
       "Capital                        234\n",
       "Continent                        6\n",
       "2022 Population                230\n",
       "2020 Population                233\n",
       "2015 Population                230\n",
       "2010 Population                227\n",
       "2000 Population                227\n",
       "1990 Population                229\n",
       "1980 Population                229\n",
       "1970 Population                230\n",
       "Area (km²)                     231\n",
       "Density (per km²)              230\n",
       "Growth Rate                    178\n",
       "World Population Percentage     70\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "10f4ccde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>CCA3</th>\n",
       "      <th>Country</th>\n",
       "      <th>Capital</th>\n",
       "      <th>Continent</th>\n",
       "      <th>2022 Population</th>\n",
       "      <th>2020 Population</th>\n",
       "      <th>2015 Population</th>\n",
       "      <th>2010 Population</th>\n",
       "      <th>2000 Population</th>\n",
       "      <th>1990 Population</th>\n",
       "      <th>1980 Population</th>\n",
       "      <th>1970 Population</th>\n",
       "      <th>Area (km²)</th>\n",
       "      <th>Density (per km²)</th>\n",
       "      <th>Growth Rate</th>\n",
       "      <th>World Population Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1</td>\n",
       "      <td>CHN</td>\n",
       "      <td>China</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1425887337.00</td>\n",
       "      <td>1424929781.00</td>\n",
       "      <td>1393715448.00</td>\n",
       "      <td>1348191368.00</td>\n",
       "      <td>1264099069.00</td>\n",
       "      <td>1153704252.00</td>\n",
       "      <td>982372466.00</td>\n",
       "      <td>822534450.00</td>\n",
       "      <td>9706961.00</td>\n",
       "      <td>146.89</td>\n",
       "      <td>1.00</td>\n",
       "      <td>17.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2</td>\n",
       "      <td>IND</td>\n",
       "      <td>India</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1417173173.00</td>\n",
       "      <td>1396387127.00</td>\n",
       "      <td>1322866505.00</td>\n",
       "      <td>1240613620.00</td>\n",
       "      <td>1059633675.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>557501301.00</td>\n",
       "      <td>3287590.00</td>\n",
       "      <td>431.07</td>\n",
       "      <td>1.01</td>\n",
       "      <td>17.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>3</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>Washington, D.C.</td>\n",
       "      <td>North America</td>\n",
       "      <td>338289857.00</td>\n",
       "      <td>335942003.00</td>\n",
       "      <td>324607776.00</td>\n",
       "      <td>311182845.00</td>\n",
       "      <td>282398554.00</td>\n",
       "      <td>248083732.00</td>\n",
       "      <td>223140018.00</td>\n",
       "      <td>200328340.00</td>\n",
       "      <td>9372610.00</td>\n",
       "      <td>36.09</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>4</td>\n",
       "      <td>IDN</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>Asia</td>\n",
       "      <td>275501339.00</td>\n",
       "      <td>271857970.00</td>\n",
       "      <td>259091970.00</td>\n",
       "      <td>244016173.00</td>\n",
       "      <td>214072421.00</td>\n",
       "      <td>182159874.00</td>\n",
       "      <td>148177096.00</td>\n",
       "      <td>115228394.00</td>\n",
       "      <td>1904569.00</td>\n",
       "      <td>144.65</td>\n",
       "      <td>1.01</td>\n",
       "      <td>3.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>5</td>\n",
       "      <td>PAK</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Islamabad</td>\n",
       "      <td>Asia</td>\n",
       "      <td>235824862.00</td>\n",
       "      <td>227196741.00</td>\n",
       "      <td>210969298.00</td>\n",
       "      <td>194454498.00</td>\n",
       "      <td>154369924.00</td>\n",
       "      <td>115414069.00</td>\n",
       "      <td>80624057.00</td>\n",
       "      <td>59290872.00</td>\n",
       "      <td>881912.00</td>\n",
       "      <td>267.40</td>\n",
       "      <td>1.02</td>\n",
       "      <td>2.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>6</td>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Abuja</td>\n",
       "      <td>Africa</td>\n",
       "      <td>218541212.00</td>\n",
       "      <td>208327405.00</td>\n",
       "      <td>183995785.00</td>\n",
       "      <td>160952853.00</td>\n",
       "      <td>122851984.00</td>\n",
       "      <td>95214257.00</td>\n",
       "      <td>72951439.00</td>\n",
       "      <td>55569264.00</td>\n",
       "      <td>923768.00</td>\n",
       "      <td>236.58</td>\n",
       "      <td>1.02</td>\n",
       "      <td>2.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>7</td>\n",
       "      <td>BRA</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Brasilia</td>\n",
       "      <td>South America</td>\n",
       "      <td>215313498.00</td>\n",
       "      <td>213196304.00</td>\n",
       "      <td>205188205.00</td>\n",
       "      <td>196353492.00</td>\n",
       "      <td>175873720.00</td>\n",
       "      <td>150706446.00</td>\n",
       "      <td>122288383.00</td>\n",
       "      <td>96369875.00</td>\n",
       "      <td>8515767.00</td>\n",
       "      <td>25.28</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8</td>\n",
       "      <td>BGD</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>Asia</td>\n",
       "      <td>171186372.00</td>\n",
       "      <td>167420951.00</td>\n",
       "      <td>157830000.00</td>\n",
       "      <td>148391139.00</td>\n",
       "      <td>129193327.00</td>\n",
       "      <td>107147651.00</td>\n",
       "      <td>83929765.00</td>\n",
       "      <td>67541860.00</td>\n",
       "      <td>147570.00</td>\n",
       "      <td>1160.04</td>\n",
       "      <td>1.01</td>\n",
       "      <td>2.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>9</td>\n",
       "      <td>RUS</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>Europe</td>\n",
       "      <td>144713314.00</td>\n",
       "      <td>145617329.00</td>\n",
       "      <td>144668389.00</td>\n",
       "      <td>143242599.00</td>\n",
       "      <td>146844839.00</td>\n",
       "      <td>148005704.00</td>\n",
       "      <td>138257420.00</td>\n",
       "      <td>130093010.00</td>\n",
       "      <td>17098242.00</td>\n",
       "      <td>8.46</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>10</td>\n",
       "      <td>MEX</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Mexico City</td>\n",
       "      <td>North America</td>\n",
       "      <td>127504125.00</td>\n",
       "      <td>125998302.00</td>\n",
       "      <td>120149897.00</td>\n",
       "      <td>112532401.00</td>\n",
       "      <td>97873442.00</td>\n",
       "      <td>81720428.00</td>\n",
       "      <td>67705186.00</td>\n",
       "      <td>50289306.00</td>\n",
       "      <td>1964375.00</td>\n",
       "      <td>64.91</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Rank CCA3        Country           Capital      Continent  \\\n",
       "41      1  CHN          China           Beijing           Asia   \n",
       "92      2  IND          India         New Delhi           Asia   \n",
       "221     3  USA  United States  Washington, D.C.  North America   \n",
       "93      4  IDN      Indonesia           Jakarta           Asia   \n",
       "156     5  PAK       Pakistan         Islamabad           Asia   \n",
       "149     6  NGA        Nigeria             Abuja         Africa   \n",
       "27      7  BRA         Brazil          Brasilia  South America   \n",
       "16      8  BGD     Bangladesh             Dhaka           Asia   \n",
       "171     9  RUS         Russia            Moscow         Europe   \n",
       "131    10  MEX         Mexico       Mexico City  North America   \n",
       "\n",
       "     2022 Population  2020 Population  2015 Population  2010 Population  \\\n",
       "41     1425887337.00    1424929781.00    1393715448.00    1348191368.00   \n",
       "92     1417173173.00    1396387127.00    1322866505.00    1240613620.00   \n",
       "221     338289857.00     335942003.00     324607776.00     311182845.00   \n",
       "93      275501339.00     271857970.00     259091970.00     244016173.00   \n",
       "156     235824862.00     227196741.00     210969298.00     194454498.00   \n",
       "149     218541212.00     208327405.00     183995785.00     160952853.00   \n",
       "27      215313498.00     213196304.00     205188205.00     196353492.00   \n",
       "16      171186372.00     167420951.00     157830000.00     148391139.00   \n",
       "171     144713314.00     145617329.00     144668389.00     143242599.00   \n",
       "131     127504125.00     125998302.00     120149897.00     112532401.00   \n",
       "\n",
       "     2000 Population  1990 Population  1980 Population  1970 Population  \\\n",
       "41     1264099069.00    1153704252.00     982372466.00     822534450.00   \n",
       "92     1059633675.00              NaN              NaN     557501301.00   \n",
       "221     282398554.00     248083732.00     223140018.00     200328340.00   \n",
       "93      214072421.00     182159874.00     148177096.00     115228394.00   \n",
       "156     154369924.00     115414069.00      80624057.00      59290872.00   \n",
       "149     122851984.00      95214257.00      72951439.00      55569264.00   \n",
       "27      175873720.00     150706446.00     122288383.00      96369875.00   \n",
       "16      129193327.00     107147651.00      83929765.00      67541860.00   \n",
       "171     146844839.00     148005704.00     138257420.00     130093010.00   \n",
       "131      97873442.00      81720428.00      67705186.00      50289306.00   \n",
       "\n",
       "     Area (km²)  Density (per km²)  Growth Rate  World Population Percentage  \n",
       "41   9706961.00             146.89         1.00                        17.88  \n",
       "92   3287590.00             431.07         1.01                        17.77  \n",
       "221  9372610.00              36.09         1.00                         4.24  \n",
       "93   1904569.00             144.65         1.01                         3.45  \n",
       "156   881912.00             267.40         1.02                         2.96  \n",
       "149   923768.00             236.58         1.02                         2.74  \n",
       "27   8515767.00              25.28         1.00                         2.70  \n",
       "16    147570.00            1160.04         1.01                         2.15  \n",
       "171 17098242.00               8.46         1.00                         1.81  \n",
       "131  1964375.00              64.91         1.01                         1.60  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=\"World Population Percentage\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "39245776",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Rank</th>\n",
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       "      <th>2010 Population</th>\n",
       "      <th>2000 Population</th>\n",
       "      <th>1990 Population</th>\n",
       "      <th>1980 Population</th>\n",
       "      <th>1970 Population</th>\n",
       "      <th>Area (km²)</th>\n",
       "      <th>Density (per km²)</th>\n",
       "      <th>Growth Rate</th>\n",
       "      <th>World Population Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rank</th>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022 Population</th>\n",
       "      <td>-0.36</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020 Population</th>\n",
       "      <td>-0.36</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 Population</th>\n",
       "      <td>-0.35</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.46</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010 Population</th>\n",
       "      <td>-0.35</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.46</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000 Population</th>\n",
       "      <td>-0.34</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.47</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990 Population</th>\n",
       "      <td>-0.33</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.52</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980 Population</th>\n",
       "      <td>-0.33</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.53</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970 Population</th>\n",
       "      <td>-0.34</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.51</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Area (km²)</th>\n",
       "      <td>-0.38</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.51</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Density (per km²)</th>\n",
       "      <td>0.13</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Growth Rate</th>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>World Population Percentage</th>\n",
       "      <td>-0.36</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Rank  2022 Population  2020 Population  \\\n",
       "Rank                         1.00            -0.36            -0.36   \n",
       "2022 Population             -0.36             1.00             1.00   \n",
       "2020 Population             -0.36             1.00             1.00   \n",
       "2015 Population             -0.35             1.00             1.00   \n",
       "2010 Population             -0.35             1.00             1.00   \n",
       "2000 Population             -0.34             0.99             1.00   \n",
       "1990 Population             -0.33             0.99             0.99   \n",
       "1980 Population             -0.33             0.99             0.99   \n",
       "1970 Population             -0.34             0.97             0.98   \n",
       "Area (km²)                  -0.38             0.45             0.45   \n",
       "Density (per km²)            0.13            -0.03            -0.03   \n",
       "Growth Rate                 -0.22            -0.02            -0.03   \n",
       "World Population Percentage -0.36             1.00             1.00   \n",
       "\n",
       "                             2015 Population  2010 Population  \\\n",
       "Rank                                   -0.35            -0.35   \n",
       "2022 Population                         1.00             1.00   \n",
       "2020 Population                         1.00             1.00   \n",
       "2015 Population                         1.00             1.00   \n",
       "2010 Population                         1.00             1.00   \n",
       "2000 Population                         1.00             1.00   \n",
       "1990 Population                         0.99             1.00   \n",
       "1980 Population                         0.99             0.99   \n",
       "1970 Population                         0.98             0.98   \n",
       "Area (km²)                              0.46             0.46   \n",
       "Density (per km²)                      -0.03            -0.03   \n",
       "Growth Rate                            -0.03            -0.04   \n",
       "World Population Percentage             1.00             1.00   \n",
       "\n",
       "                             2000 Population  1990 Population  \\\n",
       "Rank                                   -0.34            -0.33   \n",
       "2022 Population                         0.99             0.99   \n",
       "2020 Population                         1.00             0.99   \n",
       "2015 Population                         1.00             0.99   \n",
       "2010 Population                         1.00             1.00   \n",
       "2000 Population                         1.00             1.00   \n",
       "1990 Population                         1.00             1.00   \n",
       "1980 Population                         1.00             1.00   \n",
       "1970 Population                         0.99             1.00   \n",
       "Area (km²)                              0.47             0.52   \n",
       "Density (per km²)                      -0.03            -0.03   \n",
       "Growth Rate                            -0.05            -0.07   \n",
       "World Population Percentage             0.99             0.99   \n",
       "\n",
       "                             1980 Population  1970 Population  Area (km²)  \\\n",
       "Rank                                   -0.33            -0.34       -0.38   \n",
       "2022 Population                         0.99             0.97        0.45   \n",
       "2020 Population                         0.99             0.98        0.45   \n",
       "2015 Population                         0.99             0.98        0.46   \n",
       "2010 Population                         0.99             0.98        0.46   \n",
       "2000 Population                         1.00             0.99        0.47   \n",
       "1990 Population                         1.00             1.00        0.52   \n",
       "1980 Population                         1.00             1.00        0.53   \n",
       "1970 Population                         1.00             1.00        0.51   \n",
       "Area (km²)                              0.53             0.51        1.00   \n",
       "Density (per km²)                      -0.03            -0.03       -0.06   \n",
       "Growth Rate                            -0.08            -0.08       -0.01   \n",
       "World Population Percentage             0.99             0.97        0.45   \n",
       "\n",
       "                             Density (per km²)  Growth Rate  \\\n",
       "Rank                                      0.13        -0.22   \n",
       "2022 Population                          -0.03        -0.02   \n",
       "2020 Population                          -0.03        -0.03   \n",
       "2015 Population                          -0.03        -0.03   \n",
       "2010 Population                          -0.03        -0.04   \n",
       "2000 Population                          -0.03        -0.05   \n",
       "1990 Population                          -0.03        -0.07   \n",
       "1980 Population                          -0.03        -0.08   \n",
       "1970 Population                          -0.03        -0.08   \n",
       "Area (km²)                               -0.06        -0.01   \n",
       "Density (per km²)                         1.00        -0.07   \n",
       "Growth Rate                              -0.07         1.00   \n",
       "World Population Percentage              -0.03        -0.02   \n",
       "\n",
       "                             World Population Percentage  \n",
       "Rank                                               -0.36  \n",
       "2022 Population                                     1.00  \n",
       "2020 Population                                     1.00  \n",
       "2015 Population                                     1.00  \n",
       "2010 Population                                     1.00  \n",
       "2000 Population                                     0.99  \n",
       "1990 Population                                     0.99  \n",
       "1980 Population                                     0.99  \n",
       "1970 Population                                     0.97  \n",
       "Area (km²)                                          0.45  \n",
       "Density (per km²)                                  -0.03  \n",
       "Growth Rate                                        -0.02  \n",
       "World Population Percentage                         1.00  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "27b1f5ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(), annot = True)\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (20,7)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ea317332",
   "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>Rank</th>\n",
       "      <th>2022 Population</th>\n",
       "      <th>2020 Population</th>\n",
       "      <th>2015 Population</th>\n",
       "      <th>2010 Population</th>\n",
       "      <th>2000 Population</th>\n",
       "      <th>1990 Population</th>\n",
       "      <th>1980 Population</th>\n",
       "      <th>1970 Population</th>\n",
       "      <th>Area (km²)</th>\n",
       "      <th>Density (per km²)</th>\n",
       "      <th>Growth Rate</th>\n",
       "      <th>World Population Percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>77.56</td>\n",
       "      <td>96327387.31</td>\n",
       "      <td>94955134.37</td>\n",
       "      <td>89165003.64</td>\n",
       "      <td>89087770.00</td>\n",
       "      <td>80580835.11</td>\n",
       "      <td>48639995.33</td>\n",
       "      <td>40278333.33</td>\n",
       "      <td>43839877.83</td>\n",
       "      <td>642762.82</td>\n",
       "      <td>1025.02</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South America</th>\n",
       "      <td>97.57</td>\n",
       "      <td>31201186.29</td>\n",
       "      <td>30823574.50</td>\n",
       "      <td>29509599.71</td>\n",
       "      <td>26789395.54</td>\n",
       "      <td>25015888.69</td>\n",
       "      <td>21224743.93</td>\n",
       "      <td>17270643.29</td>\n",
       "      <td>13781939.71</td>\n",
       "      <td>1301302.85</td>\n",
       "      <td>20.97</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>92.16</td>\n",
       "      <td>25455879.68</td>\n",
       "      <td>23871435.26</td>\n",
       "      <td>21419703.57</td>\n",
       "      <td>18898197.31</td>\n",
       "      <td>14598365.95</td>\n",
       "      <td>11376964.52</td>\n",
       "      <td>8586031.98</td>\n",
       "      <td>6567175.27</td>\n",
       "      <td>537879.30</td>\n",
       "      <td>126.41</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>124.50</td>\n",
       "      <td>15055371.82</td>\n",
       "      <td>14915843.92</td>\n",
       "      <td>15027454.12</td>\n",
       "      <td>14712278.68</td>\n",
       "      <td>14817685.71</td>\n",
       "      <td>14785203.94</td>\n",
       "      <td>14200004.52</td>\n",
       "      <td>13118479.82</td>\n",
       "      <td>460208.22</td>\n",
       "      <td>663.32</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>160.93</td>\n",
       "      <td>15007403.40</td>\n",
       "      <td>14855914.82</td>\n",
       "      <td>14259596.25</td>\n",
       "      <td>13568016.28</td>\n",
       "      <td>12151739.60</td>\n",
       "      <td>10531660.62</td>\n",
       "      <td>9207334.03</td>\n",
       "      <td>7885865.15</td>\n",
       "      <td>606104.45</td>\n",
       "      <td>272.49</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>188.52</td>\n",
       "      <td>2046386.32</td>\n",
       "      <td>1910148.96</td>\n",
       "      <td>1756664.48</td>\n",
       "      <td>1613163.65</td>\n",
       "      <td>1357512.09</td>\n",
       "      <td>1162774.87</td>\n",
       "      <td>996532.17</td>\n",
       "      <td>846968.26</td>\n",
       "      <td>370220.91</td>\n",
       "      <td>132.54</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Rank  2022 Population  2020 Population  2015 Population  \\\n",
       "Continent                                                                 \n",
       "Asia           77.56      96327387.31      94955134.37      89165003.64   \n",
       "South America  97.57      31201186.29      30823574.50      29509599.71   \n",
       "Africa         92.16      25455879.68      23871435.26      21419703.57   \n",
       "Europe        124.50      15055371.82      14915843.92      15027454.12   \n",
       "North America 160.93      15007403.40      14855914.82      14259596.25   \n",
       "Oceania       188.52       2046386.32       1910148.96       1756664.48   \n",
       "\n",
       "               2010 Population  2000 Population  1990 Population  \\\n",
       "Continent                                                          \n",
       "Asia               89087770.00      80580835.11      48639995.33   \n",
       "South America      26789395.54      25015888.69      21224743.93   \n",
       "Africa             18898197.31      14598365.95      11376964.52   \n",
       "Europe             14712278.68      14817685.71      14785203.94   \n",
       "North America      13568016.28      12151739.60      10531660.62   \n",
       "Oceania             1613163.65       1357512.09       1162774.87   \n",
       "\n",
       "               1980 Population  1970 Population  Area (km²)  \\\n",
       "Continent                                                     \n",
       "Asia               40278333.33      43839877.83   642762.82   \n",
       "South America      17270643.29      13781939.71  1301302.85   \n",
       "Africa              8586031.98       6567175.27   537879.30   \n",
       "Europe             14200004.52      13118479.82   460208.22   \n",
       "North America       9207334.03       7885865.15   606104.45   \n",
       "Oceania              996532.17        846968.26   370220.91   \n",
       "\n",
       "               Density (per km²)  Growth Rate  World Population Percentage  \n",
       "Continent                                                                   \n",
       "Asia                     1025.02         1.01                         1.18  \n",
       "South America              20.97         1.01                         0.39  \n",
       "Africa                    126.41         1.02                         0.31  \n",
       "Europe                    663.32         1.00                         0.19  \n",
       "North America             272.49         1.00                         0.19  \n",
       "Oceania                   132.54         1.01                         0.02  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Continent').mean().sort_values(by=\"2022 Population\",ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4dbbe83b",
   "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>Rank</th>\n",
       "      <th>CCA3</th>\n",
       "      <th>Country</th>\n",
       "      <th>Capital</th>\n",
       "      <th>Continent</th>\n",
       "      <th>2022 Population</th>\n",
       "      <th>2020 Population</th>\n",
       "      <th>2015 Population</th>\n",
       "      <th>2010 Population</th>\n",
       "      <th>2000 Population</th>\n",
       "      <th>1990 Population</th>\n",
       "      <th>1980 Population</th>\n",
       "      <th>1970 Population</th>\n",
       "      <th>Area (km²)</th>\n",
       "      <th>Density (per km²)</th>\n",
       "      <th>Growth Rate</th>\n",
       "      <th>World Population Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>213</td>\n",
       "      <td>ASM</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>Pago Pago</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>44273.00</td>\n",
       "      <td>46189.00</td>\n",
       "      <td>51368.00</td>\n",
       "      <td>54849.00</td>\n",
       "      <td>58230.00</td>\n",
       "      <td>47818.00</td>\n",
       "      <td>32886.00</td>\n",
       "      <td>27075.00</td>\n",
       "      <td>199.00</td>\n",
       "      <td>222.48</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>55</td>\n",
       "      <td>AUS</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Canberra</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>26177413.00</td>\n",
       "      <td>25670051.00</td>\n",
       "      <td>23820236.00</td>\n",
       "      <td>22019168.00</td>\n",
       "      <td>19017963.00</td>\n",
       "      <td>17048003.00</td>\n",
       "      <td>14706322.00</td>\n",
       "      <td>12595034.00</td>\n",
       "      <td>7692024.00</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>223</td>\n",
       "      <td>COK</td>\n",
       "      <td>Cook Islands</td>\n",
       "      <td>Avarua</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>17011.00</td>\n",
       "      <td>17029.00</td>\n",
       "      <td>17695.00</td>\n",
       "      <td>17212.00</td>\n",
       "      <td>15897.00</td>\n",
       "      <td>17123.00</td>\n",
       "      <td>17651.00</td>\n",
       "      <td>20470.00</td>\n",
       "      <td>236.00</td>\n",
       "      <td>72.08</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>162</td>\n",
       "      <td>FJI</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>Suva</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>929766.00</td>\n",
       "      <td>920422.00</td>\n",
       "      <td>917200.00</td>\n",
       "      <td>905169.00</td>\n",
       "      <td>832509.00</td>\n",
       "      <td>780430.00</td>\n",
       "      <td>644582.00</td>\n",
       "      <td>527634.00</td>\n",
       "      <td>18272.00</td>\n",
       "      <td>50.88</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>183</td>\n",
       "      <td>PYF</td>\n",
       "      <td>French Polynesia</td>\n",
       "      <td>Papeete</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>306279.00</td>\n",
       "      <td>301920.00</td>\n",
       "      <td>291787.00</td>\n",
       "      <td>283788.00</td>\n",
       "      <td>250927.00</td>\n",
       "      <td>211089.00</td>\n",
       "      <td>163591.00</td>\n",
       "      <td>117891.00</td>\n",
       "      <td>4167.00</td>\n",
       "      <td>73.50</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>191</td>\n",
       "      <td>GUM</td>\n",
       "      <td>Guam</td>\n",
       "      <td>Hagåtña</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>171774.00</td>\n",
       "      <td>169231.00</td>\n",
       "      <td>167978.00</td>\n",
       "      <td>164905.00</td>\n",
       "      <td>160188.00</td>\n",
       "      <td>138263.00</td>\n",
       "      <td>110286.00</td>\n",
       "      <td>88300.00</td>\n",
       "      <td>549.00</td>\n",
       "      <td>312.89</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>192</td>\n",
       "      <td>KIR</td>\n",
       "      <td>Kiribati</td>\n",
       "      <td>Tarawa</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>131232.00</td>\n",
       "      <td>126463.00</td>\n",
       "      <td>116707.00</td>\n",
       "      <td>107995.00</td>\n",
       "      <td>88826.00</td>\n",
       "      <td>75124.00</td>\n",
       "      <td>60813.00</td>\n",
       "      <td>57437.00</td>\n",
       "      <td>811.00</td>\n",
       "      <td>161.81</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>215</td>\n",
       "      <td>MHL</td>\n",
       "      <td>Marshall Islands</td>\n",
       "      <td>Majuro</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>41569.00</td>\n",
       "      <td>43413.00</td>\n",
       "      <td>49410.00</td>\n",
       "      <td>53416.00</td>\n",
       "      <td>54224.00</td>\n",
       "      <td>46047.00</td>\n",
       "      <td>31988.00</td>\n",
       "      <td>23969.00</td>\n",
       "      <td>181.00</td>\n",
       "      <td>229.66</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>194</td>\n",
       "      <td>FSM</td>\n",
       "      <td>Micronesia</td>\n",
       "      <td>Palikir</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>114164.00</td>\n",
       "      <td>112106.00</td>\n",
       "      <td>109462.00</td>\n",
       "      <td>107588.00</td>\n",
       "      <td>111709.00</td>\n",
       "      <td>98603.00</td>\n",
       "      <td>76299.00</td>\n",
       "      <td>58989.00</td>\n",
       "      <td>702.00</td>\n",
       "      <td>162.63</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>225</td>\n",
       "      <td>NRU</td>\n",
       "      <td>Nauru</td>\n",
       "      <td>Yaren</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>12668.00</td>\n",
       "      <td>12315.00</td>\n",
       "      <td>11185.00</td>\n",
       "      <td>10241.00</td>\n",
       "      <td>10377.00</td>\n",
       "      <td>9598.00</td>\n",
       "      <td>7635.00</td>\n",
       "      <td>6663.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>603.24</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>185</td>\n",
       "      <td>NCL</td>\n",
       "      <td>New Caledonia</td>\n",
       "      <td>Nouméa</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>289950.00</td>\n",
       "      <td>286403.00</td>\n",
       "      <td>283032.00</td>\n",
       "      <td>261426.00</td>\n",
       "      <td>221537.00</td>\n",
       "      <td>177264.00</td>\n",
       "      <td>148599.00</td>\n",
       "      <td>110982.00</td>\n",
       "      <td>18575.00</td>\n",
       "      <td>15.61</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>123</td>\n",
       "      <td>NZL</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Wellington</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>5185288.00</td>\n",
       "      <td>5061133.00</td>\n",
       "      <td>4590590.00</td>\n",
       "      <td>4346338.00</td>\n",
       "      <td>3855266.00</td>\n",
       "      <td>3397389.00</td>\n",
       "      <td>3147168.00</td>\n",
       "      <td>2824061.00</td>\n",
       "      <td>270467.00</td>\n",
       "      <td>19.17</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>232</td>\n",
       "      <td>NIU</td>\n",
       "      <td>Niue</td>\n",
       "      <td>Alofi</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1934.00</td>\n",
       "      <td>1942.00</td>\n",
       "      <td>1847.00</td>\n",
       "      <td>1812.00</td>\n",
       "      <td>2074.00</td>\n",
       "      <td>2533.00</td>\n",
       "      <td>3637.00</td>\n",
       "      <td>5185.00</td>\n",
       "      <td>260.00</td>\n",
       "      <td>7.44</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>210</td>\n",
       "      <td>NFK</td>\n",
       "      <td>Northern Mariana Islands</td>\n",
       "      <td>Saipan</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>49551.00</td>\n",
       "      <td>49587.00</td>\n",
       "      <td>51514.00</td>\n",
       "      <td>54087.00</td>\n",
       "      <td>80338.00</td>\n",
       "      <td>48002.00</td>\n",
       "      <td>17613.00</td>\n",
       "      <td>10143.00</td>\n",
       "      <td>464.00</td>\n",
       "      <td>106.79</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>222</td>\n",
       "      <td>PLW</td>\n",
       "      <td>Palau</td>\n",
       "      <td>Ngerulmud</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17972.00</td>\n",
       "      <td>17794.00</td>\n",
       "      <td>18540.00</td>\n",
       "      <td>19726.00</td>\n",
       "      <td>15293.00</td>\n",
       "      <td>12252.00</td>\n",
       "      <td>11366.00</td>\n",
       "      <td>459.00</td>\n",
       "      <td>39.34</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>93</td>\n",
       "      <td>PNG</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>Port Moresby</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>10142619.00</td>\n",
       "      <td>9749640.00</td>\n",
       "      <td>8682174.00</td>\n",
       "      <td>7583269.00</td>\n",
       "      <td>5508297.00</td>\n",
       "      <td>3864972.00</td>\n",
       "      <td>3104788.00</td>\n",
       "      <td>2489059.00</td>\n",
       "      <td>462840.00</td>\n",
       "      <td>21.91</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>188</td>\n",
       "      <td>WSM</td>\n",
       "      <td>Samoa</td>\n",
       "      <td>Apia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>222382.00</td>\n",
       "      <td>214929.00</td>\n",
       "      <td>203571.00</td>\n",
       "      <td>194672.00</td>\n",
       "      <td>184008.00</td>\n",
       "      <td>168186.00</td>\n",
       "      <td>164905.00</td>\n",
       "      <td>142771.00</td>\n",
       "      <td>2842.00</td>\n",
       "      <td>78.25</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>166</td>\n",
       "      <td>SLB</td>\n",
       "      <td>Solomon Islands</td>\n",
       "      <td>Honiara</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>724273.00</td>\n",
       "      <td>691191.00</td>\n",
       "      <td>612660.00</td>\n",
       "      <td>540394.00</td>\n",
       "      <td>429978.00</td>\n",
       "      <td>324171.00</td>\n",
       "      <td>233668.00</td>\n",
       "      <td>172833.00</td>\n",
       "      <td>28896.00</td>\n",
       "      <td>25.06</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>233</td>\n",
       "      <td>TKL</td>\n",
       "      <td>Tokelau</td>\n",
       "      <td>Nukunonu</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1871.00</td>\n",
       "      <td>1827.00</td>\n",
       "      <td>1454.00</td>\n",
       "      <td>1367.00</td>\n",
       "      <td>1666.00</td>\n",
       "      <td>1669.00</td>\n",
       "      <td>1647.00</td>\n",
       "      <td>1714.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>155.92</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>197</td>\n",
       "      <td>TON</td>\n",
       "      <td>Tonga</td>\n",
       "      <td>Nuku‘alofa</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>106858.00</td>\n",
       "      <td>105254.00</td>\n",
       "      <td>106122.00</td>\n",
       "      <td>107383.00</td>\n",
       "      <td>102603.00</td>\n",
       "      <td>98727.00</td>\n",
       "      <td>96708.00</td>\n",
       "      <td>86484.00</td>\n",
       "      <td>747.00</td>\n",
       "      <td>143.05</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>227</td>\n",
       "      <td>TUV</td>\n",
       "      <td>Tuvalu</td>\n",
       "      <td>Funafuti</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>11312.00</td>\n",
       "      <td>11069.00</td>\n",
       "      <td>10877.00</td>\n",
       "      <td>10550.00</td>\n",
       "      <td>9638.00</td>\n",
       "      <td>9182.00</td>\n",
       "      <td>7731.00</td>\n",
       "      <td>5814.00</td>\n",
       "      <td>26.00</td>\n",
       "      <td>435.08</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>181</td>\n",
       "      <td>VUT</td>\n",
       "      <td>Vanuatu</td>\n",
       "      <td>Port-Vila</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>326740.00</td>\n",
       "      <td>311685.00</td>\n",
       "      <td>276438.00</td>\n",
       "      <td>245453.00</td>\n",
       "      <td>192074.00</td>\n",
       "      <td>150882.00</td>\n",
       "      <td>118156.00</td>\n",
       "      <td>87019.00</td>\n",
       "      <td>12189.00</td>\n",
       "      <td>26.81</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>226</td>\n",
       "      <td>WLF</td>\n",
       "      <td>Wallis and Futuna</td>\n",
       "      <td>Mata-Utu</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>11572.00</td>\n",
       "      <td>11655.00</td>\n",
       "      <td>12182.00</td>\n",
       "      <td>13142.00</td>\n",
       "      <td>14723.00</td>\n",
       "      <td>13454.00</td>\n",
       "      <td>11315.00</td>\n",
       "      <td>9377.00</td>\n",
       "      <td>142.00</td>\n",
       "      <td>81.49</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Rank CCA3                   Country       Capital Continent  \\\n",
       "3     213  ASM            American Samoa     Pago Pago   Oceania   \n",
       "11     55  AUS                 Australia      Canberra   Oceania   \n",
       "44    223  COK              Cook Islands        Avarua   Oceania   \n",
       "66    162  FJI                      Fiji          Suva   Oceania   \n",
       "70    183  PYF          French Polynesia       Papeete   Oceania   \n",
       "81    191  GUM                      Guam       Hagåtña   Oceania   \n",
       "107   192  KIR                  Kiribati        Tarawa   Oceania   \n",
       "126   215  MHL          Marshall Islands        Majuro   Oceania   \n",
       "132   194  FSM                Micronesia       Palikir   Oceania   \n",
       "142   225  NRU                     Nauru         Yaren   Oceania   \n",
       "145   185  NCL             New Caledonia        Nouméa   Oceania   \n",
       "146   123  NZL               New Zealand    Wellington   Oceania   \n",
       "150   232  NIU                      Niue         Alofi   Oceania   \n",
       "153   210  NFK  Northern Mariana Islands        Saipan   Oceania   \n",
       "157   222  PLW                     Palau     Ngerulmud   Oceania   \n",
       "160    93  PNG          Papua New Guinea  Port Moresby   Oceania   \n",
       "179   188  WSM                     Samoa          Apia   Oceania   \n",
       "191   166  SLB           Solomon Islands       Honiara   Oceania   \n",
       "209   233  TKL                   Tokelau      Nukunonu   Oceania   \n",
       "210   197  TON                     Tonga    Nuku‘alofa   Oceania   \n",
       "216   227  TUV                    Tuvalu      Funafuti   Oceania   \n",
       "225   181  VUT                   Vanuatu     Port-Vila   Oceania   \n",
       "229   226  WLF         Wallis and Futuna      Mata-Utu   Oceania   \n",
       "\n",
       "     2022 Population  2020 Population  2015 Population  2010 Population  \\\n",
       "3           44273.00         46189.00         51368.00         54849.00   \n",
       "11       26177413.00      25670051.00      23820236.00      22019168.00   \n",
       "44          17011.00         17029.00         17695.00         17212.00   \n",
       "66         929766.00        920422.00        917200.00        905169.00   \n",
       "70         306279.00        301920.00        291787.00        283788.00   \n",
       "81         171774.00        169231.00        167978.00        164905.00   \n",
       "107        131232.00        126463.00        116707.00        107995.00   \n",
       "126         41569.00         43413.00         49410.00         53416.00   \n",
       "132        114164.00        112106.00        109462.00        107588.00   \n",
       "142         12668.00         12315.00         11185.00         10241.00   \n",
       "145        289950.00        286403.00        283032.00        261426.00   \n",
       "146       5185288.00       5061133.00       4590590.00       4346338.00   \n",
       "150          1934.00          1942.00          1847.00          1812.00   \n",
       "153         49551.00         49587.00         51514.00         54087.00   \n",
       "157              NaN         17972.00         17794.00         18540.00   \n",
       "160      10142619.00       9749640.00       8682174.00       7583269.00   \n",
       "179        222382.00        214929.00        203571.00        194672.00   \n",
       "191        724273.00        691191.00        612660.00        540394.00   \n",
       "209          1871.00          1827.00          1454.00          1367.00   \n",
       "210        106858.00        105254.00        106122.00        107383.00   \n",
       "216         11312.00         11069.00         10877.00         10550.00   \n",
       "225        326740.00        311685.00        276438.00        245453.00   \n",
       "229         11572.00         11655.00         12182.00         13142.00   \n",
       "\n",
       "     2000 Population  1990 Population  1980 Population  1970 Population  \\\n",
       "3           58230.00         47818.00         32886.00         27075.00   \n",
       "11       19017963.00      17048003.00      14706322.00      12595034.00   \n",
       "44          15897.00         17123.00         17651.00         20470.00   \n",
       "66         832509.00        780430.00        644582.00        527634.00   \n",
       "70         250927.00        211089.00        163591.00        117891.00   \n",
       "81         160188.00        138263.00        110286.00         88300.00   \n",
       "107         88826.00         75124.00         60813.00         57437.00   \n",
       "126         54224.00         46047.00         31988.00         23969.00   \n",
       "132        111709.00         98603.00         76299.00         58989.00   \n",
       "142         10377.00          9598.00          7635.00          6663.00   \n",
       "145        221537.00        177264.00        148599.00        110982.00   \n",
       "146       3855266.00       3397389.00       3147168.00       2824061.00   \n",
       "150          2074.00          2533.00          3637.00          5185.00   \n",
       "153         80338.00         48002.00         17613.00         10143.00   \n",
       "157         19726.00         15293.00         12252.00         11366.00   \n",
       "160       5508297.00       3864972.00       3104788.00       2489059.00   \n",
       "179        184008.00        168186.00        164905.00        142771.00   \n",
       "191        429978.00        324171.00        233668.00        172833.00   \n",
       "209          1666.00          1669.00          1647.00          1714.00   \n",
       "210        102603.00         98727.00         96708.00         86484.00   \n",
       "216          9638.00          9182.00          7731.00          5814.00   \n",
       "225        192074.00        150882.00        118156.00         87019.00   \n",
       "229         14723.00         13454.00         11315.00          9377.00   \n",
       "\n",
       "     Area (km²)  Density (per km²)  Growth Rate  World Population Percentage  \n",
       "3        199.00             222.48         0.98                         0.00  \n",
       "11   7692024.00               3.40         1.01                         0.33  \n",
       "44       236.00              72.08         1.00                         0.00  \n",
       "66     18272.00              50.88         1.01                         0.01  \n",
       "70      4167.00              73.50         1.01                         0.00  \n",
       "81       549.00             312.89         1.01                         0.00  \n",
       "107      811.00             161.81         1.02                         0.00  \n",
       "126      181.00             229.66         0.99                         0.00  \n",
       "132      702.00             162.63         1.01                         0.00  \n",
       "142       21.00             603.24         1.01                         0.00  \n",
       "145    18575.00              15.61         1.01                         0.00  \n",
       "146   270467.00              19.17         1.01                         0.07  \n",
       "150      260.00               7.44         1.00                         0.00  \n",
       "153      464.00             106.79         1.00                         0.00  \n",
       "157      459.00              39.34         1.00                         0.00  \n",
       "160   462840.00              21.91         1.02                         0.13  \n",
       "179     2842.00              78.25         1.02                         0.00  \n",
       "191    28896.00              25.06         1.02                         0.01  \n",
       "209       12.00             155.92         1.01                         0.00  \n",
       "210      747.00             143.05         1.01                         0.00  \n",
       "216       26.00             435.08         1.01                         0.00  \n",
       "225    12189.00              26.81         1.02                         0.00  \n",
       "229      142.00              81.49         1.00                         0.00  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Continent'].str.contains('Oceania')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8b69d58f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1970 Population</th>\n",
       "      <th>1980 Population</th>\n",
       "      <th>1990 Population</th>\n",
       "      <th>2000 Population</th>\n",
       "      <th>2010 Population</th>\n",
       "      <th>2015 Population</th>\n",
       "      <th>2020 Population</th>\n",
       "      <th>2022 Population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>43839877.83</td>\n",
       "      <td>40278333.33</td>\n",
       "      <td>48639995.33</td>\n",
       "      <td>80580835.11</td>\n",
       "      <td>89087770.00</td>\n",
       "      <td>89165003.64</td>\n",
       "      <td>94955134.37</td>\n",
       "      <td>96327387.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South America</th>\n",
       "      <td>13781939.71</td>\n",
       "      <td>17270643.29</td>\n",
       "      <td>21224743.93</td>\n",
       "      <td>25015888.69</td>\n",
       "      <td>26789395.54</td>\n",
       "      <td>29509599.71</td>\n",
       "      <td>30823574.50</td>\n",
       "      <td>31201186.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>6567175.27</td>\n",
       "      <td>8586031.98</td>\n",
       "      <td>11376964.52</td>\n",
       "      <td>14598365.95</td>\n",
       "      <td>18898197.31</td>\n",
       "      <td>21419703.57</td>\n",
       "      <td>23871435.26</td>\n",
       "      <td>25455879.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>13118479.82</td>\n",
       "      <td>14200004.52</td>\n",
       "      <td>14785203.94</td>\n",
       "      <td>14817685.71</td>\n",
       "      <td>14712278.68</td>\n",
       "      <td>15027454.12</td>\n",
       "      <td>14915843.92</td>\n",
       "      <td>15055371.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>7885865.15</td>\n",
       "      <td>9207334.03</td>\n",
       "      <td>10531660.62</td>\n",
       "      <td>12151739.60</td>\n",
       "      <td>13568016.28</td>\n",
       "      <td>14259596.25</td>\n",
       "      <td>14855914.82</td>\n",
       "      <td>15007403.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>846968.26</td>\n",
       "      <td>996532.17</td>\n",
       "      <td>1162774.87</td>\n",
       "      <td>1357512.09</td>\n",
       "      <td>1613163.65</td>\n",
       "      <td>1756664.48</td>\n",
       "      <td>1910148.96</td>\n",
       "      <td>2046386.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               1970 Population  1980 Population  1990 Population  \\\n",
       "Continent                                                          \n",
       "Asia               43839877.83      40278333.33      48639995.33   \n",
       "South America      13781939.71      17270643.29      21224743.93   \n",
       "Africa              6567175.27       8586031.98      11376964.52   \n",
       "Europe             13118479.82      14200004.52      14785203.94   \n",
       "North America       7885865.15       9207334.03      10531660.62   \n",
       "Oceania              846968.26        996532.17       1162774.87   \n",
       "\n",
       "               2000 Population  2010 Population  2015 Population  \\\n",
       "Continent                                                          \n",
       "Asia               80580835.11      89087770.00      89165003.64   \n",
       "South America      25015888.69      26789395.54      29509599.71   \n",
       "Africa             14598365.95      18898197.31      21419703.57   \n",
       "Europe             14817685.71      14712278.68      15027454.12   \n",
       "North America      12151739.60      13568016.28      14259596.25   \n",
       "Oceania             1357512.09       1613163.65       1756664.48   \n",
       "\n",
       "               2020 Population  2022 Population  \n",
       "Continent                                        \n",
       "Asia               94955134.37      96327387.31  \n",
       "South America      30823574.50      31201186.29  \n",
       "Africa             23871435.26      25455879.68  \n",
       "Europe             14915843.92      15055371.82  \n",
       "North America      14855914.82      15007403.40  \n",
       "Oceania             1910148.96       2046386.32  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.groupby('Continent')[['1970 Population',\n",
    "       '1980 Population', '1990 Population', '2000 Population',\n",
    "       '2010 Population', '2015 Population', '2020 Population',\n",
    "       '2022 Population']].mean().sort_values(by=\"2022 Population\",ascending=False)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "553ca41c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Rank', 'CCA3', 'Country', 'Capital', 'Continent', '2022 Population',\n",
       "       '2020 Population', '2015 Population', '2010 Population',\n",
       "       '2000 Population', '1990 Population', '1980 Population',\n",
       "       '1970 Population', 'Area (km²)', 'Density (per km²)', 'Growth Rate',\n",
       "       'World Population Percentage'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "938d691b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Continent</th>\n",
       "      <th>Asia</th>\n",
       "      <th>South America</th>\n",
       "      <th>Africa</th>\n",
       "      <th>Europe</th>\n",
       "      <th>North America</th>\n",
       "      <th>Oceania</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1970 Population</th>\n",
       "      <td>43839877.83</td>\n",
       "      <td>13781939.71</td>\n",
       "      <td>6567175.27</td>\n",
       "      <td>13118479.82</td>\n",
       "      <td>7885865.15</td>\n",
       "      <td>846968.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980 Population</th>\n",
       "      <td>40278333.33</td>\n",
       "      <td>17270643.29</td>\n",
       "      <td>8586031.98</td>\n",
       "      <td>14200004.52</td>\n",
       "      <td>9207334.03</td>\n",
       "      <td>996532.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990 Population</th>\n",
       "      <td>48639995.33</td>\n",
       "      <td>21224743.93</td>\n",
       "      <td>11376964.52</td>\n",
       "      <td>14785203.94</td>\n",
       "      <td>10531660.62</td>\n",
       "      <td>1162774.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000 Population</th>\n",
       "      <td>80580835.11</td>\n",
       "      <td>25015888.69</td>\n",
       "      <td>14598365.95</td>\n",
       "      <td>14817685.71</td>\n",
       "      <td>12151739.60</td>\n",
       "      <td>1357512.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010 Population</th>\n",
       "      <td>89087770.00</td>\n",
       "      <td>26789395.54</td>\n",
       "      <td>18898197.31</td>\n",
       "      <td>14712278.68</td>\n",
       "      <td>13568016.28</td>\n",
       "      <td>1613163.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 Population</th>\n",
       "      <td>89165003.64</td>\n",
       "      <td>29509599.71</td>\n",
       "      <td>21419703.57</td>\n",
       "      <td>15027454.12</td>\n",
       "      <td>14259596.25</td>\n",
       "      <td>1756664.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020 Population</th>\n",
       "      <td>94955134.37</td>\n",
       "      <td>30823574.50</td>\n",
       "      <td>23871435.26</td>\n",
       "      <td>14915843.92</td>\n",
       "      <td>14855914.82</td>\n",
       "      <td>1910148.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022 Population</th>\n",
       "      <td>96327387.31</td>\n",
       "      <td>31201186.29</td>\n",
       "      <td>25455879.68</td>\n",
       "      <td>15055371.82</td>\n",
       "      <td>15007403.40</td>\n",
       "      <td>2046386.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Continent              Asia  South America      Africa      Europe  \\\n",
       "1970 Population 43839877.83    13781939.71  6567175.27 13118479.82   \n",
       "1980 Population 40278333.33    17270643.29  8586031.98 14200004.52   \n",
       "1990 Population 48639995.33    21224743.93 11376964.52 14785203.94   \n",
       "2000 Population 80580835.11    25015888.69 14598365.95 14817685.71   \n",
       "2010 Population 89087770.00    26789395.54 18898197.31 14712278.68   \n",
       "2015 Population 89165003.64    29509599.71 21419703.57 15027454.12   \n",
       "2020 Population 94955134.37    30823574.50 23871435.26 14915843.92   \n",
       "2022 Population 96327387.31    31201186.29 25455879.68 15055371.82   \n",
       "\n",
       "Continent        North America    Oceania  \n",
       "1970 Population     7885865.15  846968.26  \n",
       "1980 Population     9207334.03  996532.17  \n",
       "1990 Population    10531660.62 1162774.87  \n",
       "2000 Population    12151739.60 1357512.09  \n",
       "2010 Population    13568016.28 1613163.65  \n",
       "2015 Population    14259596.25 1756664.48  \n",
       "2020 Population    14855914.82 1910148.96  \n",
       "2022 Population    15007403.40 2046386.32  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df2.transpose()\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8a94ac42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0ccece08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R6eFjFH+O5d6o4D3rv8J+/JOCEHQkf2JmP1WwT1wX1n582bH2nQoClaKcFIxGtWANtuNHedzhTCO/9g+lVUFIKOfcf5rZNh18nxwtLxjubjPbqyBUS0m6SsHr8qfh9TMU9HK7pG3OuUfC7f9LQei1J3z+nRW8RtY55wYlbQl/1iMZ7X2/VcFrbFDS/7NgdKEU/PxbJD0cPmejpF+P8tiSNKmDp73OubeEL+YHFPyi/rKCNDfvnPszC4bidZbdZ3/Z5QEd7F+/gvBqsYLk/qhgZscoOCi62zlXPrz1OTM72Tn363BI3PNl95kr6duSLnHO/fewh7xN0n855750iKe91zm3vOzx3jJGmW6Ey/0KF763YE+eMsL9Pq5g2Oebw9tWMt3qUH/cjfazfeWD1K6vPRo6ZXGupGc1Mvo6Ql9H45zbEf7/sgXD69+hkYMn+lp5X9lfyx/k8PvaI+l1Zbcr9o++VsA5958KQ3sze72CT37lnOsP61V43WYFnwDvEn0d02h9VbDG03edcwckPW9m/6bg4O2Hoq+HdIieFl2kg4GQxHtA1cxsloLRIS1m5hQcADkzWxHepHdYnSnn3MZhj3GpghDpTOfcATN7RsGIheH6zawhPAiTgtFBv1Iwza88eCr/ngfLvh7Uob//aZL2HuL6I2bBAt4DCn4vjdaHNo3w83LO9VswfaldwT68XEHPD+XrCgK3fQoOXPtHuM1ejdznIqdgH3zROfeWUW7TO8p2KZyloeAg+tGy7eU/j+E/q1r+fJ5Q2XRb59zHzOzEYbUM3z+HcwpGGH1MwQiktIJRXBcoeE8eTaWvu3hYwx0KRhF9QsF7wC8lbVEQPLxJQfj49wqCp38rqzXlnNtoZu0KBmE8FAYRo/1cRgvWh/uhc+5Pi1+UBdmjGf64TmXvd6GR9rWLVdlrv9yISV+o0r5f7Jwr7Qfh+/HnnHNfHfJEQR4xfB8Z/r2O9Ropr2m02kd73x/t9ibpCefcH41y/StM+jWewkTyLyVdHR5IHCdpR3j1pZU+jIJPuN5oZtfWvMgjEO68WUkF59wXhl19v4IF8hT+/y/hfY6XtF7BkN9/G/Z4/6CgN391mKX8p6R5Fq4toGDkSXk4d2HZ/z8KLz8j6czw8nkaOuqs6DhJvw5/8X9QwR8ZUjD/eOYotfxAwZtL8Rfrbw939Ect++qc+7WCT8L+MHzcSzTCugGjmPR9PcRjNYW/1IvhwJ8qGFpbCfo6CvbXg46wr/dLusiC9d3mK/gU6z/oa2UsPDOlBWdk+rSCaQey4KyrzeHlRZL6nXNb6GtlRuurgk9Yz7JAs6Q/lPSf9HVsh+hpcdv7FYzIkcR7a41cIOku59xpzrl5zrnXSdqq4IB5uI2Srgj/PpCZvT7cx4+T9Hx44BlXMEphJE8qCDKK+hSsyXWJ1eYMUa9X5X+zVMyCtZJuVTAdy2n0Pox2/xmSjnPOPajgWOAtI9xsyP4Rjup6VsHr4I5RHrqgcH2bUIOCn6cUBOBd4X601czeH9ZiZvbmQ32/ZbYpWEfoLgtmHhyx8PX5WgWvoUp9T8HarleUbTv2ELcf8XXknPuVgmlgp7tg1kmXpKt1MHg61GtzTM65vQp+rpdYsAbZcQpOiPKnCkbONIajso5VMKrpRwrefxp08Bj6gwpG1Iy6H4X3mW9mvx9+XfGaWeEx+y4z+5Nw04jvdxaMCn0pvP0zCk4CITN7m4JpgMON9tqv9P3u9QoCwScr/V5GsVHSsvC1JjM7xUY4K7ekh8LbHRve7tVH+BoZ/v2N9r7fJel9Fqz1NFvBFD0p+H5PsoNnfTxmrNfYpA+epGDxZwUvrosUnJHocxZ8utd4yDsOfYyB8P5xKzv1YoT+WMFOc5YdPHVv8TSb10laZGb/JWlR+LUUfHrxPyT9n7L7vMaC0TppBfNCfxJu/3AlRTjn9kn6CwVD/x5X8EnCrWU3mWpm/65geGHxU+vbJb3bzP5DwRzVkRLzmyV9yMweUfBLunibXyj4NOrnZvbxYff5jIJFZ38Rfs8f0uGrWV/D665QMH/8aQVrPmyopAj6GrDgU4kvSLrUzHrMbIGCX5Abw3p+piBIvr2SIuhrYJS+SuyvRYfdVxdMwfiGgk8Pv6vgU8Hi1AD6GjKznII/aN8Q7nvJ8KqEmT2l4I/WZxV8ki4FU1V+YmYFBWtPfLDs4ehr6Aj6+hUFw/y7Fawn8nXn3C/C6+irjqinUjBVpse9crkCelqdhIJR5eW+pSC4GO5rCt6Hf2LBQsFfVTAi4W4F9T+q4IDyP0d5rvU6eOAlqbT48Z9K+riNvij5mCw4eP8fGjoaphrTw99PTyg4o91DCs5cJo3eh9HMlPRA+PP9vspGmpa5Q9Kt4XNOD7fdLelXzrktozzu8H72SjrDzB5TMKLq78LtF0tKmtnPFYwiqrjPzrknw/vfZwcDjyNxpqRHRhm5NdpzOwXB5LstWGj+PxRMEVs5yl0+o9FfR/+u4Cy6UhA4naIgFJCkvILFxH9mBxcXPyxhCJ5TMLLqZgV/w5yqYO3E4nvAf0g6oGCJmSsUhD1/oCDEOE9B2DvqfhS+Z10mab0Fi4tvO8wyPyTphrA/b9HB/UMKQqnNCt4Di+/H35L0agumjF6hg/0rN+Jr3zn3gqR/s2BB8huG3edmSY3h++69ki51wbTGI+ace0jBWmg/Ch/3mxoh+HLOfVfBh5mPht9XcfH0w32NDH8/H+19/1sKRuYW3yf+XUGw16cgJL4+fM6fKZjuN6riAnHAuAsPbhc6534bdS0TCX31g776QV/9oK9+0Fc/6Gvt0VM/LJhCfZdzbpGHx/4zBYui/59aP3ZUzOwmST91zmVHuX66gtDkj51zA2a22zk3Y1yLrJCZrVZw2vtNUdeCocysU8Ei4rUKbVHGzGa4YC2xWQrCxz92zg1f/25Mk3mNJwAAAACoSLh+3+1m9ioPUwmbJN1Y48eMTDhqqVfBGcBG5Jzba2Z/q2D0zvbxqu0IdRM6YZJ6wIKlY6ZI+vsjCZ0kRjwBAAAAAADAE9Z4AgAAAAAAgBcETwAAAAAAAPCC4AkAAAAAAABeEDwBAAAAAADAC4InAAAAAAAAePH/AwhIHJymWxVWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2771472b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0456a5f9",
   "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>2022 Population</th>\n",
       "      <th>2020 Population</th>\n",
       "      <th>2015 Population</th>\n",
       "      <th>2010 Population</th>\n",
       "      <th>2000 Population</th>\n",
       "      <th>1990 Population</th>\n",
       "      <th>1980 Population</th>\n",
       "      <th>1970 Population</th>\n",
       "      <th>Area (km²)</th>\n",
       "      <th>Density (per km²)</th>\n",
       "      <th>Growth Rate</th>\n",
       "      <th>World Population Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41128771.00</td>\n",
       "      <td>38972230.00</td>\n",
       "      <td>33753499.00</td>\n",
       "      <td>28189672.00</td>\n",
       "      <td>19542982.00</td>\n",
       "      <td>10694796.00</td>\n",
       "      <td>12486631.00</td>\n",
       "      <td>10752971.00</td>\n",
       "      <td>652230.00</td>\n",
       "      <td>63.06</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2842321.00</td>\n",
       "      <td>2866849.00</td>\n",
       "      <td>2882481.00</td>\n",
       "      <td>2913399.00</td>\n",
       "      <td>3182021.00</td>\n",
       "      <td>3295066.00</td>\n",
       "      <td>2941651.00</td>\n",
       "      <td>2324731.00</td>\n",
       "      <td>28748.00</td>\n",
       "      <td>98.87</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44903225.00</td>\n",
       "      <td>43451666.00</td>\n",
       "      <td>39543154.00</td>\n",
       "      <td>35856344.00</td>\n",
       "      <td>30774621.00</td>\n",
       "      <td>25518074.00</td>\n",
       "      <td>18739378.00</td>\n",
       "      <td>13795915.00</td>\n",
       "      <td>2381741.00</td>\n",
       "      <td>18.85</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44273.00</td>\n",
       "      <td>46189.00</td>\n",
       "      <td>51368.00</td>\n",
       "      <td>54849.00</td>\n",
       "      <td>58230.00</td>\n",
       "      <td>47818.00</td>\n",
       "      <td>32886.00</td>\n",
       "      <td>27075.00</td>\n",
       "      <td>199.00</td>\n",
       "      <td>222.48</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79824.00</td>\n",
       "      <td>77700.00</td>\n",
       "      <td>71746.00</td>\n",
       "      <td>71519.00</td>\n",
       "      <td>66097.00</td>\n",
       "      <td>53569.00</td>\n",
       "      <td>35611.00</td>\n",
       "      <td>19860.00</td>\n",
       "      <td>468.00</td>\n",
       "      <td>170.56</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>11572.00</td>\n",
       "      <td>11655.00</td>\n",
       "      <td>12182.00</td>\n",
       "      <td>13142.00</td>\n",
       "      <td>14723.00</td>\n",
       "      <td>13454.00</td>\n",
       "      <td>11315.00</td>\n",
       "      <td>9377.00</td>\n",
       "      <td>142.00</td>\n",
       "      <td>81.49</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>575986.00</td>\n",
       "      <td>556048.00</td>\n",
       "      <td>491824.00</td>\n",
       "      <td>413296.00</td>\n",
       "      <td>270375.00</td>\n",
       "      <td>178529.00</td>\n",
       "      <td>116775.00</td>\n",
       "      <td>76371.00</td>\n",
       "      <td>266000.00</td>\n",
       "      <td>2.17</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>33696614.00</td>\n",
       "      <td>32284046.00</td>\n",
       "      <td>28516545.00</td>\n",
       "      <td>24743946.00</td>\n",
       "      <td>18628700.00</td>\n",
       "      <td>13375121.00</td>\n",
       "      <td>9204938.00</td>\n",
       "      <td>6843607.00</td>\n",
       "      <td>527968.00</td>\n",
       "      <td>63.82</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>20017675.00</td>\n",
       "      <td>18927715.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13792086.00</td>\n",
       "      <td>9891136.00</td>\n",
       "      <td>7686401.00</td>\n",
       "      <td>5720438.00</td>\n",
       "      <td>4281671.00</td>\n",
       "      <td>752612.00</td>\n",
       "      <td>26.60</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>16320537.00</td>\n",
       "      <td>15669666.00</td>\n",
       "      <td>14154937.00</td>\n",
       "      <td>12839771.00</td>\n",
       "      <td>11834676.00</td>\n",
       "      <td>10113893.00</td>\n",
       "      <td>7049926.00</td>\n",
       "      <td>5202918.00</td>\n",
       "      <td>390757.00</td>\n",
       "      <td>41.77</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>234 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     2022 Population  2020 Population  2015 Population  2010 Population  \\\n",
       "0        41128771.00      38972230.00      33753499.00      28189672.00   \n",
       "1         2842321.00       2866849.00       2882481.00       2913399.00   \n",
       "2        44903225.00      43451666.00      39543154.00      35856344.00   \n",
       "3           44273.00         46189.00         51368.00         54849.00   \n",
       "4           79824.00         77700.00         71746.00         71519.00   \n",
       "..               ...              ...              ...              ...   \n",
       "229         11572.00         11655.00         12182.00         13142.00   \n",
       "230        575986.00        556048.00        491824.00        413296.00   \n",
       "231      33696614.00      32284046.00      28516545.00      24743946.00   \n",
       "232      20017675.00      18927715.00              NaN      13792086.00   \n",
       "233      16320537.00      15669666.00      14154937.00      12839771.00   \n",
       "\n",
       "     2000 Population  1990 Population  1980 Population  1970 Population  \\\n",
       "0        19542982.00      10694796.00      12486631.00      10752971.00   \n",
       "1         3182021.00       3295066.00       2941651.00       2324731.00   \n",
       "2        30774621.00      25518074.00      18739378.00      13795915.00   \n",
       "3           58230.00         47818.00         32886.00         27075.00   \n",
       "4           66097.00         53569.00         35611.00         19860.00   \n",
       "..               ...              ...              ...              ...   \n",
       "229         14723.00         13454.00         11315.00          9377.00   \n",
       "230        270375.00        178529.00        116775.00         76371.00   \n",
       "231      18628700.00      13375121.00       9204938.00       6843607.00   \n",
       "232       9891136.00       7686401.00       5720438.00       4281671.00   \n",
       "233      11834676.00      10113893.00       7049926.00       5202918.00   \n",
       "\n",
       "     Area (km²)  Density (per km²)  Growth Rate  World Population Percentage  \n",
       "0     652230.00              63.06         1.03                         0.52  \n",
       "1      28748.00              98.87         1.00                         0.04  \n",
       "2    2381741.00              18.85         1.02                         0.56  \n",
       "3        199.00             222.48         0.98                         0.00  \n",
       "4        468.00             170.56         1.01                         0.00  \n",
       "..          ...                ...          ...                          ...  \n",
       "229      142.00              81.49         1.00                         0.00  \n",
       "230   266000.00               2.17         1.02                         0.01  \n",
       "231   527968.00              63.82         1.02                         0.42  \n",
       "232   752612.00              26.60         1.03                         0.25  \n",
       "233   390757.00              41.77         1.02                         0.20  \n",
       "\n",
       "[234 rows x 12 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(include='float')"
   ]
  }
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