Gapminder.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gapminder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div name=\"html-admonition\" style=\"font-size: 0.8em\">\n",
    "<input type=\"button\" onclick=\"location.href='https://translate.google.com/translate?hl=&sl=ja&tl=en&u='+window.location;\" value=\"Google translation\" style=\"color:#ffffff;background-color:#008080; height:25px\" onmouseover=\"this.style.background='#99ccff'\" onmouseout=\"this.style.background='#008080'\"/> in English or the language of your choice.\n",
    "</div><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n"
     ]
    }
   ],
   "source": [
    "import japanize_matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import py4macro\n",
    "from gapminder import gapminder\n",
    "\n",
    "# 警告メッセージを非表示\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Gapminder](https://www.gapminder.org)とは世界規模で見た経済格差をデータで探る有名なサイトであり、一見の価値があるサイトである。そのサイトで使われているデータを整理してパッケージにまとめたのが`gapminder`である。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````{note}\n",
    "MacではTerminal、WindowsではGit Bashを使い、次のコマンドで`gapminder`をインストールできる。\n",
    "```\n",
    "pip install gapminder\n",
    "```\n",
    "````\n",
    "\n",
    "ここでは`gapminder`に含まれるデータを使い`pandas`の`groupby`という`DataFrame`のメソッドの使い方の例を紹介する。両方ともデータをグループ化して扱う場合に非常に重宝するので、覚えておいて損はしないだろう。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## データ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "＜列ラベル＞\n",
    "* `country`：国名\n",
    "* `continent`：大陸\n",
    "* `year`：年\n",
    "* `lifeExp`：平均寿命\n",
    "* `pop`：人口\n",
    "* `gdpPercap`：一人当たりGDP（国内総生産）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "データの読み込みと最初の5行の表示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</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",
       "      <td>779.445314</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",
       "      <td>820.853030</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",
       "      <td>853.100710</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",
       "      <td>836.197138</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",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       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"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = gapminder\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "最後の5行の表示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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",
       "      <td>706.157306</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",
       "      <td>693.420786</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",
       "      <td>792.449960</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",
       "      <td>672.038623</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",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country continent  year  lifeExp       pop   gdpPercap\n",
       "1699  Zimbabwe    Africa  1987   62.351   9216418  706.157306\n",
       "1700  Zimbabwe    Africa  1992   60.377  10704340  693.420786\n",
       "1701  Zimbabwe    Africa  1997   46.809  11404948  792.449960\n",
       "1702  Zimbabwe    Africa  2002   39.989  11926563  672.038623\n",
       "1703  Zimbabwe    Africa  2007   43.487  12311143  469.709298"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "データセットの内容確認"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1704 entries, 0 to 1703\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   country    1704 non-null   object \n",
      " 1   continent  1704 non-null   object \n",
      " 2   year       1704 non-null   int64  \n",
      " 3   lifeExp    1704 non-null   float64\n",
      " 4   pop        1704 non-null   int64  \n",
      " 5   gdpPercap  1704 non-null   float64\n",
      "dtypes: float64(2), int64(2), object(2)\n",
      "memory usage: 80.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "記述統計の表示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1704.00000</td>\n",
       "      <td>1704.000000</td>\n",
       "      <td>1.704000e+03</td>\n",
       "      <td>1704.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1979.50000</td>\n",
       "      <td>59.474439</td>\n",
       "      <td>2.960121e+07</td>\n",
       "      <td>7215.327081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.26533</td>\n",
       "      <td>12.917107</td>\n",
       "      <td>1.061579e+08</td>\n",
       "      <td>9857.454543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1952.00000</td>\n",
       "      <td>23.599000</td>\n",
       "      <td>6.001100e+04</td>\n",
       "      <td>241.165876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1965.75000</td>\n",
       "      <td>48.198000</td>\n",
       "      <td>2.793664e+06</td>\n",
       "      <td>1202.060309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1979.50000</td>\n",
       "      <td>60.712500</td>\n",
       "      <td>7.023596e+06</td>\n",
       "      <td>3531.846988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1993.25000</td>\n",
       "      <td>70.845500</td>\n",
       "      <td>1.958522e+07</td>\n",
       "      <td>9325.462346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2007.00000</td>\n",
       "      <td>82.603000</td>\n",
       "      <td>1.318683e+09</td>\n",
       "      <td>113523.132900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             year      lifeExp           pop      gdpPercap\n",
       "count  1704.00000  1704.000000  1.704000e+03    1704.000000\n",
       "mean   1979.50000    59.474439  2.960121e+07    7215.327081\n",
       "std      17.26533    12.917107  1.061579e+08    9857.454543\n",
       "min    1952.00000    23.599000  6.001100e+04     241.165876\n",
       "25%    1965.75000    48.198000  2.793664e+06    1202.060309\n",
       "50%    1979.50000    60.712500  7.023596e+06    3531.846988\n",
       "75%    1993.25000    70.845500  1.958522e+07    9325.462346\n",
       "max    2007.00000    82.603000  1.318683e+09  113523.132900"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "含まれる国名の表示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true,
    "tags": [
     "output_scroll"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Afghanistan', 'Albania', 'Algeria', 'Angola', 'Argentina',\n",
       "       'Australia', 'Austria', 'Bahrain', 'Bangladesh', 'Belgium',\n",
       "       'Benin', 'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil',\n",
       "       'Bulgaria', 'Burkina Faso', 'Burundi', 'Cambodia', 'Cameroon',\n",
       "       'Canada', 'Central African Republic', 'Chad', 'Chile', 'China',\n",
       "       'Colombia', 'Comoros', 'Congo, Dem. Rep.', 'Congo, Rep.',\n",
       "       'Costa Rica', \"Cote d'Ivoire\", 'Croatia', 'Cuba', 'Czech Republic',\n",
       "       'Denmark', 'Djibouti', 'Dominican Republic', 'Ecuador', 'Egypt',\n",
       "       'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Ethiopia',\n",
       "       'Finland', 'France', 'Gabon', 'Gambia', 'Germany', 'Ghana',\n",
       "       'Greece', 'Guatemala', 'Guinea', 'Guinea-Bissau', 'Haiti',\n",
       "       'Honduras', 'Hong Kong, China', 'Hungary', 'Iceland', 'India',\n",
       "       'Indonesia', 'Iran', 'Iraq', 'Ireland', 'Israel', 'Italy',\n",
       "       'Jamaica', 'Japan', 'Jordan', 'Kenya', 'Korea, Dem. Rep.',\n",
       "       'Korea, Rep.', 'Kuwait', 'Lebanon', 'Lesotho', 'Liberia', 'Libya',\n",
       "       'Madagascar', 'Malawi', 'Malaysia', 'Mali', 'Mauritania',\n",
       "       'Mauritius', 'Mexico', 'Mongolia', 'Montenegro', 'Morocco',\n",
       "       'Mozambique', 'Myanmar', 'Namibia', 'Nepal', 'Netherlands',\n",
       "       'New Zealand', 'Nicaragua', 'Niger', 'Nigeria', 'Norway', 'Oman',\n",
       "       'Pakistan', 'Panama', 'Paraguay', 'Peru', 'Philippines', 'Poland',\n",
       "       'Portugal', 'Puerto Rico', 'Reunion', 'Romania', 'Rwanda',\n",
       "       'Sao Tome and Principe', 'Saudi Arabia', 'Senegal', 'Serbia',\n",
       "       'Sierra Leone', 'Singapore', 'Slovak Republic', 'Slovenia',\n",
       "       'Somalia', 'South Africa', 'Spain', 'Sri Lanka', 'Sudan',\n",
       "       'Swaziland', 'Sweden', 'Switzerland', 'Syria', 'Taiwan',\n",
       "       'Tanzania', 'Thailand', 'Togo', 'Trinidad and Tobago', 'Tunisia',\n",
       "       'Turkey', 'Uganda', 'United Kingdom', 'United States', 'Uruguay',\n",
       "       'Venezuela', 'Vietnam', 'West Bank and Gaza', 'Yemen, Rep.',\n",
       "       'Zambia', 'Zimbabwe'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries = df.loc[:,'country'].unique()\n",
    "countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "国の数の確認"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(countries)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`2007`年における`continent`の内訳（国の数）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent\n",
       "Africa      52\n",
       "Asia        33\n",
       "Europe      30\n",
       "Americas    25\n",
       "Oceania      2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond = ( df['year']==df['year'].max() )\n",
    "df.loc[cond,:].value_counts('continent')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "割合で表すと"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent\n",
       "Africa      0.366197\n",
       "Asia        0.232394\n",
       "Europe      0.211268\n",
       "Americas    0.176056\n",
       "Oceania     0.014085\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond = ( df['year']==df['year'].max() )\n",
    "df.loc[cond,:].value_counts('continent', normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## groupby()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df_group = df.groupby('continent')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "属性を調べてみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".agg                .aggregate          .all                .any\n",
      ".apply              .bfill              .boxplot            .continent\n",
      ".corr               .corrwith           .count              .country\n",
      ".cov                .cumcount           .cummax             .cummin\n",
      ".cumprod            .cumsum             .describe           .diff\n",
      ".dtypes             .ewm                .expanding          .ffill\n",
      ".fillna             .filter             .first              .gdpPercap\n",
      ".get_group          .groups             .head               .hist\n",
      ".idxmax             .idxmin             .indices            .last\n",
      ".lifeExp            .max                .mean               .median\n",
      ".min                .ndim               .ngroup             .ngroups\n",
      ".nth                .nunique            .ohlc               .pct_change\n",
      ".pipe               .plot               .pop                .prod\n",
      ".quantile           .rank               .resample           .rolling\n",
      ".sample             .sem                .shift              .size\n",
      ".skew               .std                .sum                .tail\n",
      ".take               .transform          .value_counts       .var\n",
      ".year\n"
     ]
    }
   ],
   "source": [
    "py4macro.see(df_group)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### continentの内訳（again）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent\n",
       "Africa      52\n",
       "Americas    25\n",
       "Asia        33\n",
       "Europe      30\n",
       "Oceania      2\n",
       "Name: country, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_names = df_group['country'].nunique()\n",
    "country_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 統計量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "three_vars=['lifeExp','pop','gdpPercap']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### 観測値の数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "大陸別の観測値の数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent\n",
       "Africa      624\n",
       "Americas    300\n",
       "Asia        396\n",
       "Europe      360\n",
       "Oceania      24\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group.size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "棒グラフ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_group.size().plot(kind='bar')\n",
    "ax.set_title('大陸別の観測値の数', size=15)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "変数別での観測値の数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>624</td>\n",
       "      <td>624</td>\n",
       "      <td>624</td>\n",
       "      <td>624</td>\n",
       "      <td>624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>396</td>\n",
       "      <td>396</td>\n",
       "      <td>396</td>\n",
       "      <td>396</td>\n",
       "      <td>396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           country  year  lifeExp  pop  gdpPercap\n",
       "continent                                        \n",
       "Africa         624   624      624  624        624\n",
       "Americas       300   300      300  300        300\n",
       "Asia           396   396      396  396        396\n",
       "Europe         360   360      360  360        360\n",
       "Oceania         24    24       24   24         24"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### 平均"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "それぞれの変数の平均（文字列の`country`を含めるとエラーが発生するため`three_vars`を指定する。）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>48.865330</td>\n",
       "      <td>9.916003e+06</td>\n",
       "      <td>2193.754578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>64.658737</td>\n",
       "      <td>2.450479e+07</td>\n",
       "      <td>7136.110356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>60.064903</td>\n",
       "      <td>7.703872e+07</td>\n",
       "      <td>7902.150428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>71.903686</td>\n",
       "      <td>1.716976e+07</td>\n",
       "      <td>14469.475533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>74.326208</td>\n",
       "      <td>8.874672e+06</td>\n",
       "      <td>18621.609223</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             lifeExp           pop     gdpPercap\n",
       "continent                                       \n",
       "Africa     48.865330  9.916003e+06   2193.754578\n",
       "Americas   64.658737  2.450479e+07   7136.110356\n",
       "Asia       60.064903  7.703872e+07   7902.150428\n",
       "Europe     71.903686  1.716976e+07  14469.475533\n",
       "Oceania    74.326208  8.874672e+06  18621.609223"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group[three_vars].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`gdpPercap`と`lifeExp`の大陸別平均の散布図"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_group[three_vars].mean().plot(kind='scatter', x='gdpPercap', y='lifeExp')\n",
    "ax.set_title('gdpPercapとlifeExpの\\n大陸別平均の散布図', size=20)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### 標準偏差"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "それぞれの変数の標準偏差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>9.150210</td>\n",
       "      <td>1.549092e+07</td>\n",
       "      <td>2827.929863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>9.345088</td>\n",
       "      <td>5.097943e+07</td>\n",
       "      <td>6396.764112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>11.864532</td>\n",
       "      <td>2.068852e+08</td>\n",
       "      <td>14045.373112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>5.433178</td>\n",
       "      <td>2.051944e+07</td>\n",
       "      <td>9355.213498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>3.795611</td>\n",
       "      <td>6.506342e+06</td>\n",
       "      <td>6358.983321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             lifeExp           pop     gdpPercap\n",
       "continent                                       \n",
       "Africa      9.150210  1.549092e+07   2827.929863\n",
       "Americas    9.345088  5.097943e+07   6396.764112\n",
       "Asia       11.864532  2.068852e+08  14045.373112\n",
       "Europe      5.433178  2.051944e+07   9355.213498\n",
       "Oceania     3.795611  6.506342e+06   6358.983321"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group[three_vars].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### 最大値"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>76.442</td>\n",
       "      <td>135031164</td>\n",
       "      <td>21951.21176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>2007</td>\n",
       "      <td>80.653</td>\n",
       "      <td>301139947</td>\n",
       "      <td>42951.65309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>2007</td>\n",
       "      <td>82.603</td>\n",
       "      <td>1318683096</td>\n",
       "      <td>113523.13290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2007</td>\n",
       "      <td>81.757</td>\n",
       "      <td>82400996</td>\n",
       "      <td>49357.19017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2007</td>\n",
       "      <td>81.235</td>\n",
       "      <td>20434176</td>\n",
       "      <td>34435.36744</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  country  year  lifeExp         pop     gdpPercap\n",
       "continent                                                         \n",
       "Africa           Zimbabwe  2007   76.442   135031164   21951.21176\n",
       "Americas        Venezuela  2007   80.653   301139947   42951.65309\n",
       "Asia          Yemen, Rep.  2007   82.603  1318683096  113523.13290\n",
       "Europe     United Kingdom  2007   81.757    82400996   49357.19017\n",
       "Oceania       New Zealand  2007   81.235    20434176   34435.36744"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### 最小値"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1952</td>\n",
       "      <td>23.599</td>\n",
       "      <td>60011</td>\n",
       "      <td>241.165876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1952</td>\n",
       "      <td>37.579</td>\n",
       "      <td>662850</td>\n",
       "      <td>1201.637154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>28.801</td>\n",
       "      <td>120447</td>\n",
       "      <td>331.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1952</td>\n",
       "      <td>43.585</td>\n",
       "      <td>147962</td>\n",
       "      <td>973.533195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>Australia</td>\n",
       "      <td>1952</td>\n",
       "      <td>69.120</td>\n",
       "      <td>1994794</td>\n",
       "      <td>10039.595640</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  year  lifeExp      pop     gdpPercap\n",
       "continent                                                   \n",
       "Africa         Algeria  1952   23.599    60011    241.165876\n",
       "Americas     Argentina  1952   37.579   662850   1201.637154\n",
       "Asia       Afghanistan  1952   28.801   120447    331.000000\n",
       "Europe         Albania  1952   43.585   147962    973.533195\n",
       "Oceania      Australia  1952   69.120  1994794  10039.595640"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### 3変数の記述統計"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "hidden": true,
    "tags": [
     "output_scroll"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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>continent</th>\n",
       "      <th>Africa</th>\n",
       "      <th>Americas</th>\n",
       "      <th>Asia</th>\n",
       "      <th>Europe</th>\n",
       "      <th>Oceania</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">lifeExp</th>\n",
       "      <th>count</th>\n",
       "      <td>624.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>48.9</td>\n",
       "      <td>64.7</td>\n",
       "      <td>60.1</td>\n",
       "      <td>71.9</td>\n",
       "      <td>74.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.2</td>\n",
       "      <td>9.3</td>\n",
       "      <td>11.9</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>23.6</td>\n",
       "      <td>37.6</td>\n",
       "      <td>28.8</td>\n",
       "      <td>43.6</td>\n",
       "      <td>69.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>42.4</td>\n",
       "      <td>58.4</td>\n",
       "      <td>51.4</td>\n",
       "      <td>69.6</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>47.8</td>\n",
       "      <td>67.0</td>\n",
       "      <td>61.8</td>\n",
       "      <td>72.2</td>\n",
       "      <td>73.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>54.4</td>\n",
       "      <td>71.7</td>\n",
       "      <td>69.5</td>\n",
       "      <td>75.5</td>\n",
       "      <td>77.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>76.4</td>\n",
       "      <td>80.7</td>\n",
       "      <td>82.6</td>\n",
       "      <td>81.8</td>\n",
       "      <td>81.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">pop</th>\n",
       "      <th>count</th>\n",
       "      <td>624.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9916003.1</td>\n",
       "      <td>24504795.0</td>\n",
       "      <td>77038722.0</td>\n",
       "      <td>17169764.7</td>\n",
       "      <td>8874672.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15490923.3</td>\n",
       "      <td>50979430.2</td>\n",
       "      <td>206885204.6</td>\n",
       "      <td>20519437.6</td>\n",
       "      <td>6506342.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>60011.0</td>\n",
       "      <td>662850.0</td>\n",
       "      <td>120447.0</td>\n",
       "      <td>147962.0</td>\n",
       "      <td>1994794.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1342075.0</td>\n",
       "      <td>2962358.8</td>\n",
       "      <td>3844393.0</td>\n",
       "      <td>4331500.0</td>\n",
       "      <td>3199212.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4579311.0</td>\n",
       "      <td>6227510.0</td>\n",
       "      <td>14530830.5</td>\n",
       "      <td>8551125.0</td>\n",
       "      <td>6403491.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10801489.8</td>\n",
       "      <td>18340309.0</td>\n",
       "      <td>46300348.0</td>\n",
       "      <td>21802867.0</td>\n",
       "      <td>14351625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>135031164.0</td>\n",
       "      <td>301139947.0</td>\n",
       "      <td>1318683096.0</td>\n",
       "      <td>82400996.0</td>\n",
       "      <td>20434176.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">gdpPercap</th>\n",
       "      <th>count</th>\n",
       "      <td>624.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2193.8</td>\n",
       "      <td>7136.1</td>\n",
       "      <td>7902.2</td>\n",
       "      <td>14469.5</td>\n",
       "      <td>18621.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2827.9</td>\n",
       "      <td>6396.8</td>\n",
       "      <td>14045.4</td>\n",
       "      <td>9355.2</td>\n",
       "      <td>6359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>241.2</td>\n",
       "      <td>1201.6</td>\n",
       "      <td>331.0</td>\n",
       "      <td>973.5</td>\n",
       "      <td>10039.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>761.2</td>\n",
       "      <td>3427.8</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>7213.1</td>\n",
       "      <td>14141.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1192.1</td>\n",
       "      <td>5465.5</td>\n",
       "      <td>2646.8</td>\n",
       "      <td>12081.7</td>\n",
       "      <td>17983.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2377.4</td>\n",
       "      <td>7830.2</td>\n",
       "      <td>8549.3</td>\n",
       "      <td>20461.4</td>\n",
       "      <td>22214.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>21951.2</td>\n",
       "      <td>42951.7</td>\n",
       "      <td>113523.1</td>\n",
       "      <td>49357.2</td>\n",
       "      <td>34435.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "continent             Africa     Americas          Asia      Europe  \\\n",
       "lifeExp   count        624.0        300.0         396.0       360.0   \n",
       "          mean          48.9         64.7          60.1        71.9   \n",
       "          std            9.2          9.3          11.9         5.4   \n",
       "          min           23.6         37.6          28.8        43.6   \n",
       "          25%           42.4         58.4          51.4        69.6   \n",
       "          50%           47.8         67.0          61.8        72.2   \n",
       "          75%           54.4         71.7          69.5        75.5   \n",
       "          max           76.4         80.7          82.6        81.8   \n",
       "pop       count        624.0        300.0         396.0       360.0   \n",
       "          mean     9916003.1   24504795.0    77038722.0  17169764.7   \n",
       "          std     15490923.3   50979430.2   206885204.6  20519437.6   \n",
       "          min        60011.0     662850.0      120447.0    147962.0   \n",
       "          25%      1342075.0    2962358.8     3844393.0   4331500.0   \n",
       "          50%      4579311.0    6227510.0    14530830.5   8551125.0   \n",
       "          75%     10801489.8   18340309.0    46300348.0  21802867.0   \n",
       "          max    135031164.0  301139947.0  1318683096.0  82400996.0   \n",
       "gdpPercap count        624.0        300.0         396.0       360.0   \n",
       "          mean        2193.8       7136.1        7902.2     14469.5   \n",
       "          std         2827.9       6396.8       14045.4      9355.2   \n",
       "          min          241.2       1201.6         331.0       973.5   \n",
       "          25%          761.2       3427.8        1057.0      7213.1   \n",
       "          50%         1192.1       5465.5        2646.8     12081.7   \n",
       "          75%         2377.4       7830.2        8549.3     20461.4   \n",
       "          max        21951.2      42951.7      113523.1     49357.2   \n",
       "\n",
       "continent           Oceania  \n",
       "lifeExp   count        24.0  \n",
       "          mean         74.3  \n",
       "          std           3.8  \n",
       "          min          69.1  \n",
       "          25%          71.2  \n",
       "          50%          73.7  \n",
       "          75%          77.6  \n",
       "          max          81.2  \n",
       "pop       count        24.0  \n",
       "          mean    8874672.3  \n",
       "          std     6506342.5  \n",
       "          min     1994794.0  \n",
       "          25%     3199212.5  \n",
       "          50%     6403491.5  \n",
       "          75%    14351625.0  \n",
       "          max    20434176.0  \n",
       "gdpPercap count        24.0  \n",
       "          mean      18621.6  \n",
       "          std        6359.0  \n",
       "          min       10039.6  \n",
       "          25%       14141.9  \n",
       "          50%       17983.3  \n",
       "          75%       22214.1  \n",
       "          max       34435.4  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group[three_vars].describe().map(\"{0:.1f}\".format).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### groupby.agg()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`agg()`を使うと複数のメソッドを同時に使うことができる。この場合は，メソッド名を文字列として引数として使う。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>48.865330</td>\n",
       "      <td>9.916003e+06</td>\n",
       "      <td>2193.754578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>64.658737</td>\n",
       "      <td>2.450479e+07</td>\n",
       "      <td>7136.110356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>60.064903</td>\n",
       "      <td>7.703872e+07</td>\n",
       "      <td>7902.150428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>71.903686</td>\n",
       "      <td>1.716976e+07</td>\n",
       "      <td>14469.475533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>74.326208</td>\n",
       "      <td>8.874672e+06</td>\n",
       "      <td>18621.609223</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             lifeExp           pop     gdpPercap\n",
       "continent                                       \n",
       "Africa     48.865330  9.916003e+06   2193.754578\n",
       "Americas   64.658737  2.450479e+07   7136.110356\n",
       "Asia       60.064903  7.703872e+07   7902.150428\n",
       "Europe     71.903686  1.716976e+07  14469.475533\n",
       "Oceania    74.326208  8.874672e+06  18621.609223"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group[three_vars].agg(\"mean\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "hidden": true,
    "tags": [
     "output_scroll"
    ]
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">lifeExp</th>\n",
       "      <th colspan=\"3\" halign=\"left\">pop</th>\n",
       "      <th colspan=\"3\" halign=\"left\">gdpPercap</th>\n",
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
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       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>76.442</td>\n",
       "      <td>23.599</td>\n",
       "      <td>48.865330</td>\n",
       "      <td>135031164</td>\n",
       "      <td>60011</td>\n",
       "      <td>9.916003e+06</td>\n",
       "      <td>21951.21176</td>\n",
       "      <td>241.165876</td>\n",
       "      <td>2193.754578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>80.653</td>\n",
       "      <td>37.579</td>\n",
       "      <td>64.658737</td>\n",
       "      <td>301139947</td>\n",
       "      <td>662850</td>\n",
       "      <td>2.450479e+07</td>\n",
       "      <td>42951.65309</td>\n",
       "      <td>1201.637154</td>\n",
       "      <td>7136.110356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>82.603</td>\n",
       "      <td>28.801</td>\n",
       "      <td>60.064903</td>\n",
       "      <td>1318683096</td>\n",
       "      <td>120447</td>\n",
       "      <td>7.703872e+07</td>\n",
       "      <td>113523.13290</td>\n",
       "      <td>331.000000</td>\n",
       "      <td>7902.150428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>81.757</td>\n",
       "      <td>43.585</td>\n",
       "      <td>71.903686</td>\n",
       "      <td>82400996</td>\n",
       "      <td>147962</td>\n",
       "      <td>1.716976e+07</td>\n",
       "      <td>49357.19017</td>\n",
       "      <td>973.533195</td>\n",
       "      <td>14469.475533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>81.235</td>\n",
       "      <td>69.120</td>\n",
       "      <td>74.326208</td>\n",
       "      <td>20434176</td>\n",
       "      <td>1994794</td>\n",
       "      <td>8.874672e+06</td>\n",
       "      <td>34435.36744</td>\n",
       "      <td>10039.595640</td>\n",
       "      <td>18621.609223</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          lifeExp                            pop                         \\\n",
       "              max     min       mean         max      min          mean   \n",
       "continent                                                                 \n",
       "Africa     76.442  23.599  48.865330   135031164    60011  9.916003e+06   \n",
       "Americas   80.653  37.579  64.658737   301139947   662850  2.450479e+07   \n",
       "Asia       82.603  28.801  60.064903  1318683096   120447  7.703872e+07   \n",
       "Europe     81.757  43.585  71.903686    82400996   147962  1.716976e+07   \n",
       "Oceania    81.235  69.120  74.326208    20434176  1994794  8.874672e+06   \n",
       "\n",
       "              gdpPercap                              \n",
       "                    max           min          mean  \n",
       "continent                                            \n",
       "Africa      21951.21176    241.165876   2193.754578  \n",
       "Americas    42951.65309   1201.637154   7136.110356  \n",
       "Asia       113523.13290    331.000000   7902.150428  \n",
       "Europe      49357.19017    973.533195  14469.475533  \n",
       "Oceania     34435.36744  10039.595640  18621.609223  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group[three_vars].agg([\"max\", \"min\", \"mean\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "自作の関数も使用することができる。この場合は，関数名だけを使う（文字列ではない）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>1.081401</td>\n",
       "      <td>13.611447</td>\n",
       "      <td>9.896297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>0.666174</td>\n",
       "      <td>12.261971</td>\n",
       "      <td>5.850528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>0.895731</td>\n",
       "      <td>17.115583</td>\n",
       "      <td>14.324219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>0.530877</td>\n",
       "      <td>4.790574</td>\n",
       "      <td>3.343843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>0.162998</td>\n",
       "      <td>2.077754</td>\n",
       "      <td>1.310079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            lifeExp        pop  gdpPercap\n",
       "continent                                \n",
       "Africa     1.081401  13.611447   9.896297\n",
       "Americas   0.666174  12.261971   5.850528\n",
       "Asia       0.895731  17.115583  14.324219\n",
       "Europe     0.530877   4.790574   3.343843\n",
       "Oceania    0.162998   2.077754   1.310079"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "func = lambda x: ( np.max(x)-np.min(x) )/np.mean(x)\n",
    "\n",
    "df_group[['lifeExp','pop','gdpPercap']].agg(func)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`continent`の内訳の割合を計算"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### 図"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**`continent`平均寿命**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df_lifeExp_continent = df_group['lifeExp'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_lifeExp_continent.plot(kind='bar')\n",
    "ax.set_title('大陸別平均寿命', size=15)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### 3つの変数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df_mean = df_group[three_vars].mean()\n",
    "df_mean['ln_pop'] = np.log( df_mean['pop'] )\n",
    "df_mean['ln_gdpPercap'] = df_mean['gdpPercap'].apply(np.log)\n",
    "df_mean['lifeExp_10'] = df_mean['lifeExp']/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_mean[['ln_pop','lifeExp_10', 'ln_gdpPercap']].plot(kind='bar')\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### 複数階層の`groupby()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`continent`別の平均時系列を考えるときに有用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df_group2 = df.groupby(['continent','year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Africa</th>\n",
       "      <th>1952</th>\n",
       "      <td>39.135500</td>\n",
       "      <td>4.570010e+06</td>\n",
       "      <td>1252.572466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1957</th>\n",
       "      <td>41.266346</td>\n",
       "      <td>5.093033e+06</td>\n",
       "      <td>1385.236062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>43.319442</td>\n",
       "      <td>5.702247e+06</td>\n",
       "      <td>1598.078825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>45.334538</td>\n",
       "      <td>6.447875e+06</td>\n",
       "      <td>2050.363801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>47.450942</td>\n",
       "      <td>7.305376e+06</td>\n",
       "      <td>2339.615674</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  lifeExp           pop    gdpPercap\n",
       "continent year                                      \n",
       "Africa    1952  39.135500  4.570010e+06  1252.572466\n",
       "          1957  41.266346  5.093033e+06  1385.236062\n",
       "          1962  43.319442  5.702247e+06  1598.078825\n",
       "          1967  45.334538  6.447875e+06  2050.363801\n",
       "          1972  47.450942  7.305376e+06  2339.615674"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group2[three_vars].mean().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`lifeExp`の列だけを選択した後，`.unstack()`を使ってyearが行ラベルになるDataFrameに変換してみる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df_lifeExp_group = df_group2[three_vars].mean().loc[:,'lifeExp'].unstack(level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_lifeExp_group.plot()\n",
    "ax.set_title('大陸別平均寿命の推移', size=15)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**世界平均との比較**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df_group_year = df.groupby('year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "world_lifeExp = df_group_year[three_vars].mean()['lifeExp'].to_numpy().reshape(1,12).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_lifeExp_diff = df_lifeExp_group - world_lifeExp\n",
    "\n",
    "ax = df_lifeExp_diff.plot()\n",
    "ax.set_title('大陸別平均寿命の世界平均との差の推移', size=15)\n",
    "pass"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "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"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
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   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}