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Update 2021-05-31
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Update 2021-05-31
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "tokyo_covid19_3.ipynb",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/sakasa/notebooks/blob/master/Update%202021-05-31\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "B9I0otUoM-I_"
},
"source": [
"import requests\n",
"import json\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import datetime\n",
"from pytz import timezone\n",
"import numpy as np\n",
"import seaborn as sns\n",
"\n",
"%matplotlib inline"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "-KCogvsSM-JD"
},
"source": [
"### Tokyo COVID19 Data\n",
"https://github.com/tokyo-metropolitan-gov/covid19"
]
},
{
"cell_type": "code",
"metadata": {
"id": "UAkqklMlM-JD"
},
"source": [
"url = 'https://raw.githubusercontent.com/tokyo-metropolitan-gov/covid19/development/data/data.json'"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Pegky7yeM-JF",
"outputId": "ca2ee22d-5c5f-4f37-9223-043fed24b1ae"
},
"source": [
"response = requests.get(url)\n",
"print(response)\n",
"j_res = json.loads(response.text) # json文字列をdictへ\n",
"print(j_res.keys())\n",
"j_res['patients_summary']['data'][-15:]"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"<Response [200]>\n",
"dict_keys(['contacts', 'querents', 'patients_summary', 'inspections_summary', 'lastUpdate', 'main_summary'])\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'小計': 419, '日付': '2021-05-17T08:00:00.000Z'},\n",
" {'小計': 732, '日付': '2021-05-18T08:00:00.000Z'},\n",
" {'小計': 766, '日付': '2021-05-19T08:00:00.000Z'},\n",
" {'小計': 843, '日付': '2021-05-20T08:00:00.000Z'},\n",
" {'小計': 649, '日付': '2021-05-21T08:00:00.000Z'},\n",
" {'小計': 602, '日付': '2021-05-22T08:00:00.000Z'},\n",
" {'小計': 535, '日付': '2021-05-23T08:00:00.000Z'},\n",
" {'小計': 340, '日付': '2021-05-24T08:00:00.000Z'},\n",
" {'小計': 542, '日付': '2021-05-25T08:00:00.000Z'},\n",
" {'小計': 743, '日付': '2021-05-26T08:00:00.000Z'},\n",
" {'小計': 684, '日付': '2021-05-27T08:00:00.000Z'},\n",
" {'小計': 614, '日付': '2021-05-28T08:00:00.000Z'},\n",
" {'小計': 539, '日付': '2021-05-29T08:00:00.000Z'},\n",
" {'小計': 448, '日付': '2021-05-30T08:00:00.000Z'},\n",
" {'小計': 260, '日付': '2021-05-31T08:00:00.000Z'}]"
]
},
"metadata": {
"tags": []
},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "kxbUYb_HB25G"
},
"source": [
"def utc_datestr_to_jst_date(datestr):\n",
" #return datetime.datetime.fromisoformat(datestr.replace('Z', '+00:00')).astimezone(timezone('Asia/Tokyo'))\n",
" # for ~Python3.6\n",
" return datetime.datetime.strptime(datestr.split('.')[0].replace('-', '').replace(':', ''), '%Y%m%dT%H%M%S').astimezone(timezone('Asia/Tokyo'))"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "PtSjr0JLKm8L"
},
"source": [
"keys = ['contacts', 'querents', 'patients_summary']"
],
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 419
},
"id": "hl0JfBS5HyAG",
"outputId": "2d21114d-c576-46d5-85cc-db4f0c836ad1"
},
"source": [
"d = j_res\n",
"df = pd.DataFrame()\n",
"for key in keys:\n",
" k = f'{key.split(\"_\")[0]}_count'\n",
" for v in d[key].values():\n",
" if type(v) is list:\n",
" index = [_v['日付'] for _v in v]\n",
" tmp_df = pd.DataFrame(_v for _v in v)\n",
" tmp_df.index = [utc_datestr_to_jst_date(dt) for dt in tmp_df['日付']]\n",
" tmp_df[k] = tmp_df['小計']\n",
" df = pd.concat([df, tmp_df[k]], axis=1)\n",
"df = df.fillna(0).astype('int64')\n",
"df.index = [i for i in df.index]\n",
"df['datetime'] = df.index\n",
"df"
],
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"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>contacts_count</th>\n",
" <th>querents_count</th>\n",
" <th>patients_count</th>\n",
" <th>datetime</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2020-01-24 17:00:00+09:00</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2020-01-24 17:00:00+09:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-01-25 17:00:00+09:00</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2020-01-25 17:00:00+09:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-01-26 17:00:00+09:00</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2020-01-26 17:00:00+09:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-01-27 17:00:00+09:00</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2020-01-27 17:00:00+09:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-01-28 17:00:00+09:00</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2020-01-28 17:00:00+09:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-27 17:00:00+09:00</th>\n",
" <td>426</td>\n",
" <td>0</td>\n",
" <td>684</td>\n",
" <td>2021-05-27 17:00:00+09:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-28 17:00:00+09:00</th>\n",
" <td>410</td>\n",
" <td>0</td>\n",
" <td>614</td>\n",
" <td>2021-05-28 17:00:00+09:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-29 17:00:00+09:00</th>\n",
" <td>419</td>\n",
" <td>0</td>\n",
" <td>539</td>\n",
" <td>2021-05-29 17:00:00+09:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-30 17:00:00+09:00</th>\n",
" <td>326</td>\n",
" <td>0</td>\n",
" <td>448</td>\n",
" <td>2021-05-30 17:00:00+09:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-31 17:00:00+09:00</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>260</td>\n",
" <td>2021-05-31 17:00:00+09:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>494 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" contacts_count ... datetime\n",
"2020-01-24 17:00:00+09:00 0 ... 2020-01-24 17:00:00+09:00\n",
"2020-01-25 17:00:00+09:00 0 ... 2020-01-25 17:00:00+09:00\n",
"2020-01-26 17:00:00+09:00 0 ... 2020-01-26 17:00:00+09:00\n",
"2020-01-27 17:00:00+09:00 0 ... 2020-01-27 17:00:00+09:00\n",
"2020-01-28 17:00:00+09:00 0 ... 2020-01-28 17:00:00+09:00\n",
"... ... ... ...\n",
"2021-05-27 17:00:00+09:00 426 ... 2021-05-27 17:00:00+09:00\n",
"2021-05-28 17:00:00+09:00 410 ... 2021-05-28 17:00:00+09:00\n",
"2021-05-29 17:00:00+09:00 419 ... 2021-05-29 17:00:00+09:00\n",
"2021-05-30 17:00:00+09:00 326 ... 2021-05-30 17:00:00+09:00\n",
"2021-05-31 17:00:00+09:00 0 ... 2021-05-31 17:00:00+09:00\n",
"\n",
"[494 rows x 4 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 309
},
"id": "0bDKk8pzH3I4",
"outputId": "92db9d9f-ce1a-4c9b-f066-b60f06328bba"
},
"source": [
"# 7日移動平均\n",
"df1 = df\n",
"for key in keys:\n",
" k = key.split('_')[0]\n",
" df1[f'{k}_7d_mov_ave'] = df[f'{k}_count'].rolling('7D').mean()\n",
" # df1[f'{k}_7d_mov_center_ave'] = df[f'{k}_count'].rolling(7, center=True).mean()\n",
" df1[f'{k}_7d_mov_sum'] = df[f'{k}_count'].rolling('7D').sum()\n",
"df1.tail()"
],
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"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>contacts_count</th>\n",
" <th>querents_count</th>\n",
" <th>patients_count</th>\n",
" <th>datetime</th>\n",
" <th>contacts_7d_mov_ave</th>\n",
" <th>contacts_7d_mov_sum</th>\n",
" <th>querents_7d_mov_ave</th>\n",
" <th>querents_7d_mov_sum</th>\n",
" <th>patients_7d_mov_ave</th>\n",
" <th>patients_7d_mov_sum</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2021-05-27 17:00:00+09:00</th>\n",
" <td>426</td>\n",
" <td>0</td>\n",
" <td>684</td>\n",
" <td>2021-05-27 17:00:00+09:00</td>\n",
" <td>459.000000</td>\n",
" <td>3213.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>585.000000</td>\n",
" <td>4095.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-28 17:00:00+09:00</th>\n",
" <td>410</td>\n",
" <td>0</td>\n",
" <td>614</td>\n",
" <td>2021-05-28 17:00:00+09:00</td>\n",
" <td>445.571429</td>\n",
" <td>3119.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>580.000000</td>\n",
" <td>4060.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-29 17:00:00+09:00</th>\n",
" <td>419</td>\n",
" <td>0</td>\n",
" <td>539</td>\n",
" <td>2021-05-29 17:00:00+09:00</td>\n",
" <td>432.714286</td>\n",
" <td>3029.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>571.000000</td>\n",
" <td>3997.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-30 17:00:00+09:00</th>\n",
" <td>326</td>\n",
" <td>0</td>\n",
" <td>448</td>\n",
" <td>2021-05-30 17:00:00+09:00</td>\n",
" <td>427.571429</td>\n",
" <td>2993.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>558.571429</td>\n",
" <td>3910.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-31 17:00:00+09:00</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>260</td>\n",
" <td>2021-05-31 17:00:00+09:00</td>\n",
" <td>351.428571</td>\n",
" <td>2460.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>547.142857</td>\n",
" <td>3830.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" contacts_count ... patients_7d_mov_sum\n",
"2021-05-27 17:00:00+09:00 426 ... 4095.0\n",
"2021-05-28 17:00:00+09:00 410 ... 4060.0\n",
"2021-05-29 17:00:00+09:00 419 ... 3997.0\n",
"2021-05-30 17:00:00+09:00 326 ... 3910.0\n",
"2021-05-31 17:00:00+09:00 0 ... 3830.0\n",
"\n",
"[5 rows x 10 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 789
},
"id": "29XdYL6bH4JN",
"outputId": "9ca1e640-9319-4bce-afed-0f04f4a540c4"
},
"source": [
"df2 = df1\n",
"# 日付が年の何週目かを列にセット `'{yy}w{week nnum}`\n",
"df2['week_num'] = [f'\\'{d.strftime(\"%y\")}w{d.strftime(\"%V\")}' for d in df1.index]\n",
"# 曜日の列をセット\n",
"df2['week_day'] = [d.strftime('%a') for d in df1.index]\n",
"df2.tail(15)"
],
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\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>contacts_count</th>\n",
" <th>querents_count</th>\n",
" <th>patients_count</th>\n",
" <th>datetime</th>\n",
" <th>contacts_7d_mov_ave</th>\n",
" <th>contacts_7d_mov_sum</th>\n",
" <th>querents_7d_mov_ave</th>\n",
" <th>querents_7d_mov_sum</th>\n",
" <th>patients_7d_mov_ave</th>\n",
" <th>patients_7d_mov_sum</th>\n",
" <th>week_num</th>\n",
" <th>week_day</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2021-05-17 17:00:00+09:00</th>\n",
" <td>703</td>\n",
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" <td>784.428571</td>\n",
" <td>5491.0</td>\n",
" <td>'21w20</td>\n",
" <td>Mon</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-18 17:00:00+09:00</th>\n",
" <td>569</td>\n",
" <td>0</td>\n",
" <td>732</td>\n",
" <td>2021-05-18 17:00:00+09:00</td>\n",
" <td>612.000000</td>\n",
" <td>4284.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>756.857143</td>\n",
" <td>5298.0</td>\n",
" <td>'21w20</td>\n",
" <td>Tue</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-19 17:00:00+09:00</th>\n",
" <td>559</td>\n",
" <td>0</td>\n",
" <td>766</td>\n",
" <td>2021-05-19 17:00:00+09:00</td>\n",
" <td>603.000000</td>\n",
" <td>4221.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>727.857143</td>\n",
" <td>5095.0</td>\n",
" <td>'21w20</td>\n",
" <td>Wed</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-20 17:00:00+09:00</th>\n",
" <td>489</td>\n",
" <td>0</td>\n",
" <td>843</td>\n",
" <td>2021-05-20 17:00:00+09:00</td>\n",
" <td>581.857143</td>\n",
" <td>4073.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>704.000000</td>\n",
" <td>4928.0</td>\n",
" <td>'21w20</td>\n",
" <td>Thu</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-21 17:00:00+09:00</th>\n",
" <td>504</td>\n",
" <td>0</td>\n",
" <td>649</td>\n",
" <td>2021-05-21 17:00:00+09:00</td>\n",
" <td>568.571429</td>\n",
" <td>3980.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>674.714286</td>\n",
" <td>4723.0</td>\n",
" <td>'21w20</td>\n",
" <td>Fri</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-22 17:00:00+09:00</th>\n",
" <td>509</td>\n",
" <td>0</td>\n",
" <td>602</td>\n",
" <td>2021-05-22 17:00:00+09:00</td>\n",
" <td>549.142857</td>\n",
" <td>3844.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>650.428571</td>\n",
" <td>4553.0</td>\n",
" <td>'21w20</td>\n",
" <td>Sat</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-23 17:00:00+09:00</th>\n",
" <td>362</td>\n",
" <td>0</td>\n",
" <td>535</td>\n",
" <td>2021-05-23 17:00:00+09:00</td>\n",
" <td>527.857143</td>\n",
" <td>3695.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>649.428571</td>\n",
" <td>4546.0</td>\n",
" <td>'21w20</td>\n",
" <td>Sun</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-24 17:00:00+09:00</th>\n",
" <td>533</td>\n",
" <td>0</td>\n",
" <td>340</td>\n",
" <td>2021-05-24 17:00:00+09:00</td>\n",
" <td>503.571429</td>\n",
" <td>3525.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>638.142857</td>\n",
" <td>4467.0</td>\n",
" <td>'21w21</td>\n",
" <td>Mon</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-25 17:00:00+09:00</th>\n",
" <td>455</td>\n",
" <td>0</td>\n",
" <td>542</td>\n",
" <td>2021-05-25 17:00:00+09:00</td>\n",
" <td>487.285714</td>\n",
" <td>3411.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>611.000000</td>\n",
" <td>4277.0</td>\n",
" <td>'21w21</td>\n",
" <td>Tue</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-26 17:00:00+09:00</th>\n",
" <td>424</td>\n",
" <td>0</td>\n",
" <td>743</td>\n",
" <td>2021-05-26 17:00:00+09:00</td>\n",
" <td>468.000000</td>\n",
" <td>3276.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>607.714286</td>\n",
" <td>4254.0</td>\n",
" <td>'21w21</td>\n",
" <td>Wed</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-27 17:00:00+09:00</th>\n",
" <td>426</td>\n",
" <td>0</td>\n",
" <td>684</td>\n",
" <td>2021-05-27 17:00:00+09:00</td>\n",
" <td>459.000000</td>\n",
" <td>3213.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>585.000000</td>\n",
" <td>4095.0</td>\n",
" <td>'21w21</td>\n",
" <td>Thu</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-28 17:00:00+09:00</th>\n",
" <td>410</td>\n",
" <td>0</td>\n",
" <td>614</td>\n",
" <td>2021-05-28 17:00:00+09:00</td>\n",
" <td>445.571429</td>\n",
" <td>3119.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>580.000000</td>\n",
" <td>4060.0</td>\n",
" <td>'21w21</td>\n",
" <td>Fri</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-29 17:00:00+09:00</th>\n",
" <td>419</td>\n",
" <td>0</td>\n",
" <td>539</td>\n",
" <td>2021-05-29 17:00:00+09:00</td>\n",
" <td>432.714286</td>\n",
" <td>3029.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>571.000000</td>\n",
" <td>3997.0</td>\n",
" <td>'21w21</td>\n",
" <td>Sat</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-30 17:00:00+09:00</th>\n",
" <td>326</td>\n",
" <td>0</td>\n",
" <td>448</td>\n",
" <td>2021-05-30 17:00:00+09:00</td>\n",
" <td>427.571429</td>\n",
" <td>2993.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>558.571429</td>\n",
" <td>3910.0</td>\n",
" <td>'21w21</td>\n",
" <td>Sun</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-31 17:00:00+09:00</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0.0</td>\n",
" <td>547.142857</td>\n",
" <td>3830.0</td>\n",
" <td>'21w22</td>\n",
" <td>Mon</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" contacts_count querents_count ... week_num week_day\n",
"2021-05-17 17:00:00+09:00 703 0 ... '21w20 Mon\n",
"2021-05-18 17:00:00+09:00 569 0 ... '21w20 Tue\n",
"2021-05-19 17:00:00+09:00 559 0 ... '21w20 Wed\n",
"2021-05-20 17:00:00+09:00 489 0 ... '21w20 Thu\n",
"2021-05-21 17:00:00+09:00 504 0 ... '21w20 Fri\n",
"2021-05-22 17:00:00+09:00 509 0 ... '21w20 Sat\n",
"2021-05-23 17:00:00+09:00 362 0 ... '21w20 Sun\n",
"2021-05-24 17:00:00+09:00 533 0 ... '21w21 Mon\n",
"2021-05-25 17:00:00+09:00 455 0 ... '21w21 Tue\n",
"2021-05-26 17:00:00+09:00 424 0 ... '21w21 Wed\n",
"2021-05-27 17:00:00+09:00 426 0 ... '21w21 Thu\n",
"2021-05-28 17:00:00+09:00 410 0 ... '21w21 Fri\n",
"2021-05-29 17:00:00+09:00 419 0 ... '21w21 Sat\n",
"2021-05-30 17:00:00+09:00 326 0 ... '21w21 Sun\n",
"2021-05-31 17:00:00+09:00 0 0 ... '21w22 Mon\n",
"\n",
"[15 rows x 12 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "M2Ud9-WfIgct"
},
"source": [
"# 週毎に集計\n",
"groupby_week_num = df2.groupby('week_num', as_index=True)"
],
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"id": "5sr8B_HwIgEW",
"outputId": "886ce1b2-f895-4089-da83-8e3d2389dcc4"
},
"source": [
"# 集計結果から平均と合計を取得しデータフレームにセット\n",
"patients_week_num_df = pd.DataFrame({\n",
" # 'patients_week_num': df2['week_num'].unique(),\n",
" 'patients_week_mean': [m for m in groupby_week_num.agg({'patients_count': 'mean'})['patients_count']], \n",
" 'patients_week_sum': [s for s in groupby_week_num.agg({'patients_count': 'sum'})['patients_count']]\n",
" }, index=[d for d in groupby_week_num.agg({'datetime': 'max'})['datetime']])\n",
"patients_week_num_df.tail()"
],
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>patients_week_mean</th>\n",
" <th>patients_week_sum</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2021-05-16 17:00:00+09:00</th>\n",
" <td>806.428571</td>\n",
" <td>5645</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-23 17:00:00+09:00</th>\n",
" <td>649.428571</td>\n",
" <td>4546</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-30 17:00:00+09:00</th>\n",
" <td>558.571429</td>\n",
" <td>3910</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-31 17:00:00+09:00</th>\n",
" <td>260.000000</td>\n",
" <td>260</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-01-03 17:00:00+09:00</th>\n",
" <td>816.000000</td>\n",
" <td>2448</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" patients_week_mean patients_week_sum\n",
"2021-05-16 17:00:00+09:00 806.428571 5645\n",
"2021-05-23 17:00:00+09:00 649.428571 4546\n",
"2021-05-30 17:00:00+09:00 558.571429 3910\n",
"2021-05-31 17:00:00+09:00 260.000000 260\n",
"2021-01-03 17:00:00+09:00 816.000000 2448"
]
},
"metadata": {
"tags": []
},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 789
},
"id": "fuHCAMAwH3mX",
"outputId": "a1afb08b-7a28-4c0e-d4cc-a1b82964c9f1"
},
"source": [
"df3 = pd.concat([\n",
" df2,\n",
" patients_week_num_df.loc[:, ['patients_week_mean', 'patients_week_sum']]\n",
" ], axis=1)\n",
"df3.tail(15)"
],
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
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"\n",
" .dataframe thead th {\n",
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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>contacts_count</th>\n",
" <th>querents_count</th>\n",
" <th>patients_count</th>\n",
" <th>datetime</th>\n",
" <th>contacts_7d_mov_ave</th>\n",
" <th>contacts_7d_mov_sum</th>\n",
" <th>querents_7d_mov_ave</th>\n",
" <th>querents_7d_mov_sum</th>\n",
" <th>patients_7d_mov_ave</th>\n",
" <th>patients_7d_mov_sum</th>\n",
" <th>week_num</th>\n",
" <th>week_day</th>\n",
" <th>patients_week_mean</th>\n",
" <th>patients_week_sum</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2021-05-17 17:00:00+09:00</th>\n",
" <td>703</td>\n",
" <td>0</td>\n",
" <td>419</td>\n",
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" <td>0.0</td>\n",
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" <td>5491.0</td>\n",
" <td>'21w20</td>\n",
" <td>Mon</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-18 17:00:00+09:00</th>\n",
" <td>569</td>\n",
" <td>0</td>\n",
" <td>732</td>\n",
" <td>2021-05-18 17:00:00+09:00</td>\n",
" <td>612.000000</td>\n",
" <td>4284.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>756.857143</td>\n",
" <td>5298.0</td>\n",
" <td>'21w20</td>\n",
" <td>Tue</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-19 17:00:00+09:00</th>\n",
" <td>559</td>\n",
" <td>0</td>\n",
" <td>766</td>\n",
" <td>2021-05-19 17:00:00+09:00</td>\n",
" <td>603.000000</td>\n",
" <td>4221.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>727.857143</td>\n",
" <td>5095.0</td>\n",
" <td>'21w20</td>\n",
" <td>Wed</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-05-20 17:00:00+09:00</th>\n",
" <td>489</td>\n",
" <td>0</td>\n",
" <td>843</td>\n",