Week1_Video3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "512ba126-a142-48a4-95f6-92f8113ee959",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_15656\\2151744951.py:1: DeprecationWarning: \n",
      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
      "but was not found to be installed on your system.\n",
      "If this would cause problems for you,\n",
      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
      "        \n",
      "  import pandas as pd\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "006fb303-efc8-4eb1-9b51-e32d89825918",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('D:/Sayli_etc/NEPTEL_DataAnalysisPython/Week_1/08_gap-every-five-years.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4bdc5283-54e4-4fbe-8f4b-6bf78ffc4a4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       country continent  year  lifeExp       pop   gdpPercap\n",
      "1703  Zimbabwe    Africa  2007   43.487  12311143  469.709298\n"
     ]
    }
   ],
   "source": [
    "print(df.tail(n=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "593f7cf6-49ec-4d0b-9b35-448ed0aaf5d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         country continent  year  lifeExp       pop    gdpPercap\n",
      "0    Afghanistan      Asia  1952   28.801   8425333   779.445314\n",
      "99    Bangladesh      Asia  1967   43.453  62821884   721.186086\n",
      "999     Mongolia      Asia  1967   51.253   1149500  1226.041130\n"
     ]
    }
   ],
   "source": [
    "print(df.loc[[0,99,999]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a486afa3-3f75-47fd-96d5-2d3e4a0e7f89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "country      Afghanistan\n",
      "continent           Asia\n",
      "year                1957\n",
      "lifeExp           30.332\n",
      "pop              9240934\n",
      "gdpPercap      820.85303\n",
      "Name: 1, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.iloc[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a1599556-9f3e-4bbf-a519-e7594d8f310f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "country      Bangladesh\n",
      "continent          Asia\n",
      "year               1967\n",
      "lifeExp          43.453\n",
      "pop            62821884\n",
      "gdpPercap    721.186086\n",
      "Name: 99, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.iloc[99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d75ef5fe-976f-456e-b594-ca5d45d5e868",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "country        Zimbabwe\n",
      "continent        Africa\n",
      "year               2007\n",
      "lifeExp          43.487\n",
      "pop            12311143\n",
      "gdpPercap    469.709298\n",
      "Name: 1703, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.iloc[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "74c15afb-eb24-417e-9f2a-140263d128a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         country continent  year  lifeExp       pop    gdpPercap\n",
      "0    Afghanistan      Asia  1952   28.801   8425333   779.445314\n",
      "99    Bangladesh      Asia  1967   43.453  62821884   721.186086\n",
      "999     Mongolia      Asia  1967   51.253   1149500  1226.041130\n"
     ]
    }
   ],
   "source": [
    "print(df.iloc[[0,99,999]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fea73085-597f-45d2-89f4-cee7068bab6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "subset = df.loc[:, ['year', 'pop']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "46fbd566-4912-401b-9fd9-0478a7bd2de3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   year       pop\n",
      "0  1952   8425333\n",
      "1  1957   9240934\n",
      "2  1962  10267083\n",
      "3  1967  11537966\n",
      "4  1972  13079460\n"
     ]
    }
   ],
   "source": [
    "print(subset.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1806ccf2-79ad-495c-956b-1cb4347e1fb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "subset2 = df.iloc[:, [2,4,-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "994200fd-7b0e-4489-9266-d6cea9e2ea54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   year       pop   gdpPercap\n",
      "0  1952   8425333  779.445314\n",
      "1  1957   9240934  820.853030\n",
      "2  1962  10267083  853.100710\n",
      "3  1967  11537966  836.197138\n",
      "4  1972  13079460  739.981106\n"
     ]
    }
   ],
   "source": [
    "print(subset2.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c7928510-1389-418f-a48c-5cc1b709847d",
   "metadata": {},
   "outputs": [],
   "source": [
    "small_r = list(range(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fa43d3ed-b7be-4483-9d0d-96a2a3116a05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "74c4dcad-bb8c-4589-8d97-16d76cd75eb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "subset3 = df.iloc[:, small_r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "49afd936-0e1c-48ed-9249-908af938fe00",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1952</td>\n",
       "      <td>28.801</td>\n",
       "      <td>8425333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1957</td>\n",
       "      <td>30.332</td>\n",
       "      <td>9240934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1962</td>\n",
       "      <td>31.997</td>\n",
       "      <td>10267083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1967</td>\n",
       "      <td>34.020</td>\n",
       "      <td>11537966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1972</td>\n",
       "      <td>36.088</td>\n",
       "      <td>13079460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1987</td>\n",
       "      <td>62.351</td>\n",
       "      <td>9216418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1992</td>\n",
       "      <td>60.377</td>\n",
       "      <td>10704340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1997</td>\n",
       "      <td>46.809</td>\n",
       "      <td>11404948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2002</td>\n",
       "      <td>39.989</td>\n",
       "      <td>11926563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2007</td>\n",
       "      <td>43.487</td>\n",
       "      <td>12311143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country continent  year  lifeExp       pop\n",
       "0     Afghanistan      Asia  1952   28.801   8425333\n",
       "1     Afghanistan      Asia  1957   30.332   9240934\n",
       "2     Afghanistan      Asia  1962   31.997  10267083\n",
       "3     Afghanistan      Asia  1967   34.020  11537966\n",
       "4     Afghanistan      Asia  1972   36.088  13079460\n",
       "...           ...       ...   ...      ...       ...\n",
       "1699     Zimbabwe    Africa  1987   62.351   9216418\n",
       "1700     Zimbabwe    Africa  1992   60.377  10704340\n",
       "1701     Zimbabwe    Africa  1997   46.809  11404948\n",
       "1702     Zimbabwe    Africa  2002   39.989  11926563\n",
       "1703     Zimbabwe    Africa  2007   43.487  12311143\n",
       "\n",
       "[1704 rows x 5 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "23ed0b64-4a1e-40db-9f63-3faaaff36b63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angola\n"
     ]
    }
   ],
   "source": [
    "print(df.loc[42, 'country'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0cbbaf4c-681a-40da-87d8-8a88a28f7b9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angola\n"
     ]
    }
   ],
   "source": [
    "print(df.iloc[42, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fe8f98d4-49ad-44fd-9904-e1601676d2ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         country  lifeExp    gdpPercap\n",
      "0    Afghanistan   28.801   779.445314\n",
      "99    Bangladesh   43.453   721.186086\n",
      "999     Mongolia   51.253  1226.041130\n"
     ]
    }
   ],
   "source": [
    "print(df.iloc[[0,99,999], [0,3,5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8a1afcf8-4cea-4d90-828f-457a93d4bf46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         country  lifeExp    gdpPercap\n",
      "0    Afghanistan   28.801   779.445314\n",
      "99    Bangladesh   43.453   721.186086\n",
      "999     Mongolia   51.253  1226.041130\n"
     ]
    }
   ],
   "source": [
    "print(df.loc[[0,99,999], ['country','lifeExp','gdpPercap']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a4a3add7-4300-4a27-903c-3b1bce95a785",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        country  lifeExp    gdpPercap\n",
      "10  Afghanistan   42.129   726.734055\n",
      "11  Afghanistan   43.828   974.580338\n",
      "12      Albania   55.230  1601.056136\n",
      "13      Albania   59.280  1942.284244\n"
     ]
    }
   ],
   "source": [
    "print(df.loc[10:13, ['country','lifeExp','gdpPercap']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "630f3742-fb5e-4639-ab45-b866a1ce08f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       country continent  year  lifeExp       pop   gdpPercap\n",
      "0  Afghanistan      Asia  1952   28.801   8425333  779.445314\n",
      "1  Afghanistan      Asia  1957   30.332   9240934  820.853030\n",
      "2  Afghanistan      Asia  1962   31.997  10267083  853.100710\n",
      "3  Afghanistan      Asia  1967   34.020  11537966  836.197138\n",
      "4  Afghanistan      Asia  1972   36.088  13079460  739.981106\n",
      "5  Afghanistan      Asia  1977   38.438  14880372  786.113360\n",
      "6  Afghanistan      Asia  1982   39.854  12881816  978.011439\n",
      "7  Afghanistan      Asia  1987   40.822  13867957  852.395945\n",
      "8  Afghanistan      Asia  1992   41.674  16317921  649.341395\n",
      "9  Afghanistan      Asia  1997   41.763  22227415  635.341351\n"
     ]
    }
   ],
   "source": [
    "print(df.head(n=10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a512d96d-0cb7-4e16-9677-7b8b6cd4ca17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year\n",
      "1952    49.057620\n",
      "1957    51.507401\n",
      "1962    53.609249\n",
      "1967    55.678290\n",
      "1972    57.647386\n",
      "1977    59.570157\n",
      "1982    61.533197\n",
      "1987    63.212613\n",
      "1992    64.160338\n",
      "1997    65.014676\n",
      "2002    65.694923\n",
      "2007    67.007423\n",
      "Name: lifeExp, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df.groupby('year')['lifeExp'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8c267bfc-4ab9-4ca9-9515-19698a0534b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "means_ = df.groupby(['year','continent'])[['lifeExp','gdpPercap']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "347fcdb5-e776-431d-8bfb-3e1413671aeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1952</th>\n",
       "      <th>Africa</th>\n",
       "      <td>39.135500</td>\n",
       "      <td>1252.572466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>53.279840</td>\n",
       "      <td>4079.062552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>46.314394</td>\n",
       "      <td>5195.484004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>64.408500</td>\n",
       "      <td>5661.057435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>69.255000</td>\n",
       "      <td>10298.085650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1957</th>\n",
       "      <th>Africa</th>\n",
       "      <td>41.266346</td>\n",
       "      <td>1385.236062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>55.960280</td>\n",
       "      <td>4616.043733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>49.318544</td>\n",
       "      <td>5787.732940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>66.703067</td>\n",
       "      <td>6963.012816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>70.295000</td>\n",
       "      <td>11598.522455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1962</th>\n",
       "      <th>Africa</th>\n",
       "      <td>43.319442</td>\n",
       "      <td>1598.078825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>58.398760</td>\n",
       "      <td>4901.541870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>51.563223</td>\n",
       "      <td>5729.369625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>68.539233</td>\n",
       "      <td>8365.486814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>71.085000</td>\n",
       "      <td>12696.452430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1967</th>\n",
       "      <th>Africa</th>\n",
       "      <td>45.334538</td>\n",
       "      <td>2050.363801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>60.410920</td>\n",
       "      <td>5668.253496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>54.663640</td>\n",
       "      <td>5971.173374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>69.737600</td>\n",
       "      <td>10143.823757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>71.310000</td>\n",
       "      <td>14495.021790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1972</th>\n",
       "      <th>Africa</th>\n",
       "      <td>47.450942</td>\n",
       "      <td>2339.615674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>62.394920</td>\n",
       "      <td>6491.334139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>57.319269</td>\n",
       "      <td>8187.468699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>70.775033</td>\n",
       "      <td>12479.575246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>71.910000</td>\n",
       "      <td>16417.333380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1977</th>\n",
       "      <th>Africa</th>\n",
       "      <td>49.580423</td>\n",
       "      <td>2585.938508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>64.391560</td>\n",
       "      <td>7352.007126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>59.610556</td>\n",
       "      <td>7791.314020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>71.937767</td>\n",
       "      <td>14283.979110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>72.855000</td>\n",
       "      <td>17283.957605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1982</th>\n",
       "      <th>Africa</th>\n",
       "      <td>51.592865</td>\n",
       "      <td>2481.592960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>66.228840</td>\n",
       "      <td>7506.737088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>62.617939</td>\n",
       "      <td>7434.135157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>72.806400</td>\n",
       "      <td>15617.896551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>74.290000</td>\n",
       "      <td>18554.709840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1987</th>\n",
       "      <th>Africa</th>\n",
       "      <td>53.344788</td>\n",
       "      <td>2282.668991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>68.090720</td>\n",
       "      <td>7793.400261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>64.851182</td>\n",
       "      <td>7608.226508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>73.642167</td>\n",
       "      <td>17214.310727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>75.320000</td>\n",
       "      <td>20448.040160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1992</th>\n",
       "      <th>Africa</th>\n",
       "      <td>53.629577</td>\n",
       "      <td>2281.810333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>69.568360</td>\n",
       "      <td>8044.934406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>66.537212</td>\n",
       "      <td>8639.690248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>74.440100</td>\n",
       "      <td>17061.568084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>76.945000</td>\n",
       "      <td>20894.045885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1997</th>\n",
       "      <th>Africa</th>\n",
       "      <td>53.598269</td>\n",
       "      <td>2378.759555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>71.150480</td>\n",
       "      <td>8889.300863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>68.020515</td>\n",
       "      <td>9834.093295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>75.505167</td>\n",
       "      <td>19076.781802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>78.190000</td>\n",
       "      <td>24024.175170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2002</th>\n",
       "      <th>Africa</th>\n",
       "      <td>53.325231</td>\n",
       "      <td>2599.385159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>72.422040</td>\n",
       "      <td>9287.677107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>69.233879</td>\n",
       "      <td>10174.090397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>76.700600</td>\n",
       "      <td>21711.732422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>79.740000</td>\n",
       "      <td>26938.778040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2007</th>\n",
       "      <th>Africa</th>\n",
       "      <td>54.806038</td>\n",
       "      <td>3089.032605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>73.608120</td>\n",
       "      <td>11003.031625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>70.728485</td>\n",
       "      <td>12473.026870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>77.648600</td>\n",
       "      <td>25054.481636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>80.719500</td>\n",
       "      <td>29810.188275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  lifeExp     gdpPercap\n",
       "year continent                         \n",
       "1952 Africa     39.135500   1252.572466\n",
       "     Americas   53.279840   4079.062552\n",
       "     Asia       46.314394   5195.484004\n",
       "     Europe     64.408500   5661.057435\n",
       "     Oceania    69.255000  10298.085650\n",
       "1957 Africa     41.266346   1385.236062\n",
       "     Americas   55.960280   4616.043733\n",
       "     Asia       49.318544   5787.732940\n",
       "     Europe     66.703067   6963.012816\n",
       "     Oceania    70.295000  11598.522455\n",
       "1962 Africa     43.319442   1598.078825\n",
       "     Americas   58.398760   4901.541870\n",
       "     Asia       51.563223   5729.369625\n",
       "     Europe     68.539233   8365.486814\n",
       "     Oceania    71.085000  12696.452430\n",
       "1967 Africa     45.334538   2050.363801\n",
       "     Americas   60.410920   5668.253496\n",
       "     Asia       54.663640   5971.173374\n",
       "     Europe     69.737600  10143.823757\n",
       "     Oceania    71.310000  14495.021790\n",
       "1972 Africa     47.450942   2339.615674\n",
       "     Americas   62.394920   6491.334139\n",
       "     Asia       57.319269   8187.468699\n",
       "     Europe     70.775033  12479.575246\n",
       "     Oceania    71.910000  16417.333380\n",
       "1977 Africa     49.580423   2585.938508\n",
       "     Americas   64.391560   7352.007126\n",
       "     Asia       59.610556   7791.314020\n",
       "     Europe     71.937767  14283.979110\n",
       "     Oceania    72.855000  17283.957605\n",
       "1982 Africa     51.592865   2481.592960\n",
       "     Americas   66.228840   7506.737088\n",
       "     Asia       62.617939   7434.135157\n",
       "     Europe     72.806400  15617.896551\n",
       "     Oceania    74.290000  18554.709840\n",
       "1987 Africa     53.344788   2282.668991\n",
       "     Americas   68.090720   7793.400261\n",
       "     Asia       64.851182   7608.226508\n",
       "     Europe     73.642167  17214.310727\n",
       "     Oceania    75.320000  20448.040160\n",
       "1992 Africa     53.629577   2281.810333\n",
       "     Americas   69.568360   8044.934406\n",
       "     Asia       66.537212   8639.690248\n",
       "     Europe     74.440100  17061.568084\n",
       "     Oceania    76.945000  20894.045885\n",
       "1997 Africa     53.598269   2378.759555\n",
       "     Americas   71.150480   8889.300863\n",
       "     Asia       68.020515   9834.093295\n",
       "     Europe     75.505167  19076.781802\n",
       "     Oceania    78.190000  24024.175170\n",
       "2002 Africa     53.325231   2599.385159\n",
       "     Americas   72.422040   9287.677107\n",
       "     Asia       69.233879  10174.090397\n",
       "     Europe     76.700600  21711.732422\n",
       "     Oceania    79.740000  26938.778040\n",
       "2007 Africa     54.806038   3089.032605\n",
       "     Americas   73.608120  11003.031625\n",
       "     Asia       70.728485  12473.026870\n",
       "     Europe     77.648600  25054.481636\n",
       "     Oceania    80.719500  29810.188275"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "means_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2faadc5e-ad1f-40cc-800f-5148da9295c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "flat_data = means_.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1b9c6dfd-d007-4250-ae1e-1c75a77fbaf4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    year continent    lifeExp     gdpPercap\n",
      "0   1952    Africa  39.135500   1252.572466\n",
      "1   1952  Americas  53.279840   4079.062552\n",
      "2   1952      Asia  46.314394   5195.484004\n",
      "3   1952    Europe  64.408500   5661.057435\n",
      "4   1952   Oceania  69.255000  10298.085650\n",
      "5   1957    Africa  41.266346   1385.236062\n",
      "6   1957  Americas  55.960280   4616.043733\n",
      "7   1957      Asia  49.318544   5787.732940\n",
      "8   1957    Europe  66.703067   6963.012816\n",
      "9   1957   Oceania  70.295000  11598.522455\n",
      "10  1962    Africa  43.319442   1598.078825\n",
      "11  1962  Americas  58.398760   4901.541870\n",
      "12  1962      Asia  51.563223   5729.369625\n",
      "13  1962    Europe  68.539233   8365.486814\n",
      "14  1962   Oceania  71.085000  12696.452430\n"
     ]
    }
   ],
   "source": [
    "print(flat_data.head(15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "86ebe673-849e-4e6f-be7f-b89a715a2090",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "continent\n",
      "Africa      52\n",
      "Americas    25\n",
      "Asia        33\n",
      "Europe      30\n",
      "Oceania      2\n",
      "Name: country, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.groupby('continent')['country'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7a8f07af-7ee2-4790-950b-9582d0ea9664",
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = df.groupby('year')['lifeExp'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "181619f8-2d5f-4e01-8d44-fd2434b97134",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='year'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54f6264e-73df-4346-90bc-b1d4cf0bb710",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}