国内年代別死亡数.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/nagae/CoVid-19/blob/master/%E5%9B%BD%E5%86%85%E5%B9%B4%E4%BB%A3%E5%88%A5%E6%AD%BB%E4%BA%A1%E6%95%B0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RSt5hY9aSY65",
    "outputId": "3f6b47bf-5c34-4b1d-df20-edb32d253214"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zsh:1: command not found: pip\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "!pip install japanize-matplotlib\n",
    "import japanize_matplotlib\n",
    "import matplotlib.dates as mdates\n",
    "from matplotlib.dates import MO, TU, WE, TH, FR, SA, SU\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "beoLXHYDSitY",
    "outputId": "57d3928b-ce81-42f7-e628-9f778d00b18b"
   },
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "Missing optional dependency 'openpyxl'.  Use pip or conda to install openpyxl.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/86/rd7l37z13n1c5q6chfpmtbq40000gn/T/ipykernel_2763/396015167.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# 国立社会保障・人口問題研究所のデータを読込む\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mfname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'http://www.ipss.go.jp/projects/j/Choju/covid19/data/japan_deaths.xlsx'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0morig_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_excel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msheet_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0musecols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"年代\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"男性\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"女性\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0morig_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Applications/JupyterLab.app/Contents/Resources/jlab_server/lib/python3.8/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 )\n\u001b[0;32m--> 311\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Applications/JupyterLab.app/Contents/Resources/jlab_server/lib/python3.8/site-packages/pandas/io/excel/_base.py\u001b[0m in \u001b[0;36mread_excel\u001b[0;34m(io, sheet_name, header, names, index_col, usecols, squeeze, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, parse_dates, date_parser, thousands, comment, skipfooter, convert_float, mangle_dupe_cols, storage_options)\u001b[0m\n\u001b[1;32m    362\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mExcelFile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    363\u001b[0m         \u001b[0mshould_close\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 364\u001b[0;31m         \u001b[0mio\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExcelFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstorage_options\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstorage_options\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    365\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    366\u001b[0m         raise ValueError(\n",
      "\u001b[0;32m/Applications/JupyterLab.app/Contents/Resources/jlab_server/lib/python3.8/site-packages/pandas/io/excel/_base.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, path_or_buffer, engine, storage_options)\u001b[0m\n\u001b[1;32m   1231\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstorage_options\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstorage_options\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1232\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1233\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engines\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstorage_options\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstorage_options\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1235\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__fspath__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Applications/JupyterLab.app/Contents/Resources/jlab_server/lib/python3.8/site-packages/pandas/io/excel/_openpyxl.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, filepath_or_buffer, storage_options)\u001b[0m\n\u001b[1;32m    519\u001b[0m             \u001b[0mpassed\u001b[0m \u001b[0mto\u001b[0m \u001b[0mfsspec\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mappropriate\u001b[0m \u001b[0mURLs\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0msee\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0m_get_filepath_or_buffer\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    520\u001b[0m         \"\"\"\n\u001b[0;32m--> 521\u001b[0;31m         \u001b[0mimport_optional_dependency\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"openpyxl\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    522\u001b[0m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstorage_options\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstorage_options\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    523\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Applications/JupyterLab.app/Contents/Resources/jlab_server/lib/python3.8/site-packages/pandas/compat/_optional.py\u001b[0m in \u001b[0;36mimport_optional_dependency\u001b[0;34m(name, extra, errors, min_version)\u001b[0m\n\u001b[1;32m    116\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    119\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: Missing optional dependency 'openpyxl'.  Use pip or conda to install openpyxl."
     ]
    }
   ],
   "source": [
    "# 国立社会保障・人口問題研究所のデータを読込む\n",
    "fname = 'http://www.ipss.go.jp/projects/j/Choju/covid19/data/japan_deaths.xlsx'\n",
    "orig_df = pd.read_excel(fname, sheet_name=None, header=3, usecols=[0,1,2], names=[\"年代\", \"男性\", \"女性\"])\n",
    "orig_df.keys()"
   ]
  },
  {
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   "execution_count": 13,
   "metadata": {
    "colab": {
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    },
    "id": "FxuJBygXSy-B",
    "outputId": "97038360-8c3b-4b99-f3d9-e7745c465016"
   },
   "outputs": [
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   "source": [
    "# シート名で日付がおかしくなっているものがあるので，それを訂正\n",
    "caution_keys = ['2009028', '2009022', '2009014']\n",
    "keys = [k if k not in caution_keys else k.replace('090', '09') for k in orig_df.keys()]\n",
    "dates = [pd.to_datetime(k, format='%y%m%d') for k in keys]\n",
    "keys"
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  {
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    "outputId": "2b17fa68-e006-4729-f612-558887954b15"
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>40s</th>\n",
       "      <td>40s</td>\n",
       "      <td>91.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50s</th>\n",
       "      <td>50s</td>\n",
       "      <td>278.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>349.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60s</th>\n",
       "      <td>60s</td>\n",
       "      <td>713.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>915.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70s</th>\n",
       "      <td>70s</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>881.0</td>\n",
       "      <td>2878.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80s</th>\n",
       "      <td>80s</td>\n",
       "      <td>2790.0</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>5010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90+</th>\n",
       "      <td>90+</td>\n",
       "      <td>948.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>2575.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>小計 \\nSubtotal</th>\n",
       "      <td>小計 \\nSubtotal</td>\n",
       "      <td>6850.0</td>\n",
       "      <td>5040.0</td>\n",
       "      <td>11890.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>高齢者 \\nOld Person</th>\n",
       "      <td>高齢者 \\nOld Person</td>\n",
       "      <td>61.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>非公表 \\nNot Disclosed</th>\n",
       "      <td>非公表 \\nNot Disclosed</td>\n",
       "      <td>64.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>合計 \\nTotal</th>\n",
       "      <td>合計 \\nTotal</td>\n",
       "      <td>6975.0</td>\n",
       "      <td>5118.0</td>\n",
       "      <td>12093.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      年代      男性      女性       合計\n",
       "年代                                                               \n",
       "<10                                  <10     0.0     0.0      0.0\n",
       "10s                                  10s     0.0     0.0      0.0\n",
       "20s                                  20s     6.0     2.0      8.0\n",
       "30s                                  30s    27.0     8.0     35.0\n",
       "40s                                  40s    91.0    29.0    120.0\n",
       "50s                                  50s   278.0    71.0    349.0\n",
       "60s                                  60s   713.0   202.0    915.0\n",
       "70s                                  70s  1997.0   881.0   2878.0\n",
       "80s                                  80s  2790.0  2220.0   5010.0\n",
       "90+                                  90+   948.0  1627.0   2575.0\n",
       "小計 \\nSubtotal              小計 \\nSubtotal  6850.0  5040.0  11890.0\n",
       "高齢者 \\nOld Person        高齢者 \\nOld Person    61.0    49.0    110.0\n",
       "非公表 \\nNot Disclosed  非公表 \\nNot Disclosed    64.0    29.0     93.0\n",
       "合計 \\nTotal                    合計 \\nTotal  6975.0  5118.0  12093.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# データフレームを作成\n",
    "df = {k: orig_df[or_k].dropna() for or_k,k in zip(orig_df.keys(), keys)}\n",
    "for k in df:\n",
    "    df[k] = df[k].set_index(df[k][\"年代\"])\n",
    "    df[k][\"合計\"] = df[k][\"男性\"]+df[k][\"女性\"]\n",
    "df[keys[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "id": "k7lG1tanmIv_",
    "outputId": "d2a1034e-cf90-453e-b1fb-bc7a31e97424"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>年代</th>\n",
       "      <th>&lt;10</th>\n",
       "      <th>10s</th>\n",
       "      <th>20s</th>\n",
       "      <th>30s</th>\n",
       "      <th>40s</th>\n",
       "      <th>50s</th>\n",
       "      <th>60s</th>\n",
       "      <th>70s</th>\n",
       "      <th>80s</th>\n",
       "      <th>90+</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-30</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>46</td>\n",
       "      <td>109</td>\n",
       "      <td>164</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-31</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>84</td>\n",
       "      <td>213</td>\n",
       "      <td>286</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-15</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>84</td>\n",
       "      <td>225</td>\n",
       "      <td>302</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "      <td>87</td>\n",
       "      <td>231</td>\n",
       "      <td>308</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>29</td>\n",
       "      <td>109</td>\n",
       "      <td>315</td>\n",
       "      <td>877</td>\n",
       "      <td>2811</td>\n",
       "      <td>4935</td>\n",
       "      <td>2539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-26</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "      <td>110</td>\n",
       "      <td>318</td>\n",
       "      <td>884</td>\n",
       "      <td>2827</td>\n",
       "      <td>4953</td>\n",
       "      <td>2547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-02</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>110</td>\n",
       "      <td>322</td>\n",
       "      <td>895</td>\n",
       "      <td>2839</td>\n",
       "      <td>4970</td>\n",
       "      <td>2553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-09</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>113</td>\n",
       "      <td>337</td>\n",
       "      <td>901</td>\n",
       "      <td>2856</td>\n",
       "      <td>4981</td>\n",
       "      <td>2563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-16</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>120</td>\n",
       "      <td>349</td>\n",
       "      <td>915</td>\n",
       "      <td>2878</td>\n",
       "      <td>5010</td>\n",
       "      <td>2575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "年代          <10  10s  20s  30s  40s  50s  60s   70s   80s   90+\n",
       "2020-03-31    0    0    0    0    0    2    4    17    23     4\n",
       "2020-04-30    0    0    0    4    5   14   46   109   164    65\n",
       "2020-05-31    0    0    1    4    8   23   84   213   286   147\n",
       "2020-06-15    0    0    1    4    8   24   84   225   302   149\n",
       "2020-06-22    0    0    1    4    9   24   87   231   308   149\n",
       "...         ...  ...  ...  ...  ...  ...  ...   ...   ...   ...\n",
       "2021-07-19    0    0    7   29  109  315  877  2811  4935  2539\n",
       "2021-07-26    0    0    7   30  110  318  884  2827  4953  2547\n",
       "2021-08-02    0    0    7   31  110  322  895  2839  4970  2553\n",
       "2021-08-09    0    0    8   32  113  337  901  2856  4981  2563\n",
       "2021-08-16    0    0    8   35  120  349  915  2878  5010  2575\n",
       "\n",
       "[64 rows x 10 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 必要な部分だけを抽出\n",
    "ndf = pd.DataFrame(index=df[keys[0]].index, columns = dates)\n",
    "for k, date in zip(df.keys(), dates):\n",
    "    ndf[date] = df[k][\"合計\"].astype(int)\n",
    "ndf = ndf.T[-1::-1].iloc[:,0:10]\n",
    "ndf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "NmeV042sTY1A"
   },
   "outputs": [],
   "source": [
    "cases = pd.read_csv('https://covid19.mhlw.go.jp/public/opendata/newly_confirmed_cases_daily.csv')\n",
    "cases[\"Date\"] = pd.to_datetime(cases[\"Date\"])\n",
    "cases = cases.set_index(\"Date\")\n",
    "cases = cases[cases[\"Prefecture\"] == \"ALL\"][\"Newly confirmed cases\"].cumsum().astype(int)\n",
    "cases_weekly = cases.loc[ndf.index].diff()[1:].astype(int)\n",
    "ndf_weekly = ndf.diff()[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 875
    },
    "id": "bzrafjivIsGZ",
    "outputId": "dd30853d-6873-4c46-e548-bd29b72eb011"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1152 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# グラフを描画\n",
    "fig, ax = plt.subplots(2,1,figsize=(12,16))\n",
    "ndf.plot.bar(stacked=True, ax=ax[0])\n",
    "xticklabels = [date.strftime('%y-%m-%d') if i % 4 ==0 or i == len(ndf.index)-1 else \"\" for (i,date) in enumerate(ndf.index)]\n",
    "xticklabels2 = [date.strftime('%y-%m-%d') if i % 4 ==0 or i == len(ndf.index)-2 else \"\" for (i,date) in enumerate(ndf.index[1:])]\n",
    "ax[0].set_xticklabels(xticklabels, rotation=45, ha=\"right\")\n",
    "ax[0].set_title(\"年代別の累積死亡数\")\n",
    "ndf_weekly.plot.bar(stacked=True, ax=ax[1])\n",
    "ax[1].set_title(\"週次の陽性数と年代別の死亡数\")\n",
    "ax[1].set_xticklabels(xticklabels2, rotation=45, ha=\"right\")\n",
    "ax3 = ax[1].twinx()\n",
    "cases_weekly.plot(ax = ax3, use_index=False, linewidth=2, marker='o', color=\"red\")\n",
    "plt.savefig('国内年代別死亡数.pdf')\n",
    "plt.savefig('国内年代別死亡数.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lcD7CHVEUyQM"
   },
   "outputs": [],
   "source": []
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