tokyo_covid19_3.ipynb

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      "name": "tokyo_covid19_3.ipynb",
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        "<a href=\"https://colab.research.google.com/github/sakasa/notebooks/blob/master/tokyo_covid19_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "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": []
    },
    {
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      "source": [
        ""
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        "id": "ah5ig6fhuYxO"
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      "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": []
    },
    {
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      },
      "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",
          "name": "stdout",
          "text": [
            "<Response [200]>\n",
            "dict_keys(['contacts', 'querents', 'patients_summary', 'inspections_summary', 'lastUpdate', 'main_summary'])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "[{'小計': 3271, '日付': '2022-05-24T08:00:00.000Z'},\n",
              " {'小計': 3929, '日付': '2022-05-25T08:00:00.000Z'},\n",
              " {'小計': 3391, '日付': '2022-05-26T08:00:00.000Z'},\n",
              " {'小計': 2630, '日付': '2022-05-27T08:00:00.000Z'},\n",
              " {'小計': 2549, '日付': '2022-05-28T08:00:00.000Z'},\n",
              " {'小計': 2194, '日付': '2022-05-29T08:00:00.000Z'},\n",
              " {'小計': 1344, '日付': '2022-05-30T08:00:00.000Z'},\n",
              " {'小計': 2362, '日付': '2022-05-31T08:00:00.000Z'},\n",
              " {'小計': 2415, '日付': '2022-06-01T08:00:00.000Z'},\n",
              " {'小計': 2335, '日付': '2022-06-02T08:00:00.000Z'},\n",
              " {'小計': 2111, '日付': '2022-06-03T08:00:00.000Z'},\n",
              " {'小計': 2071, '日付': '2022-06-04T08:00:00.000Z'},\n",
              " {'小計': 1584, '日付': '2022-06-05T08:00:00.000Z'},\n",
              " {'小計': 1013, '日付': '2022-06-06T08:00:00.000Z'},\n",
              " {'小計': 1800, '日付': '2022-06-07T08:00:00.000Z'}]"
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      "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": []
    },
    {
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          "height": 424
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        "outputId": "14fd79b3-4930-4509-b97c-707ae77da8d7"
      },
      "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": [
        {
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              "                           contacts_count  querents_count  patients_count  \\\n",
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              "2020-01-27 17:00:00+09:00               0               0               0   \n",
              "2020-01-28 17:00:00+09:00               0               0               0   \n",
              "...                                   ...             ...             ...   \n",
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              "2022-06-06 17:00:00+09:00             209               0            1013   \n",
              "2022-06-07 17:00:00+09:00               0               0            1800   \n",
              "\n",
              "                                           datetime  \n",
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              "2022-06-06 17:00:00+09:00 2022-06-06 17:00:00+09:00  \n",
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          "metadata": {},
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      ]
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    {
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "0bDKk8pzH3I4",
        "outputId": "b60abf19-5d2a-435c-847b-0b99553e981e"
      },
      "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/plain": [
              "                           contacts_count  querents_count  patients_count  \\\n",
              "2022-06-03 17:00:00+09:00             168               0            2111   \n",
              "2022-06-04 17:00:00+09:00             208               0            2071   \n",
              "2022-06-05 17:00:00+09:00             187               0            1584   \n",
              "2022-06-06 17:00:00+09:00             209               0            1013   \n",
              "2022-06-07 17:00:00+09:00               0               0            1800   \n",
              "\n",
              "                                           datetime  contacts_7d_mov_ave  \\\n",
              "2022-06-03 17:00:00+09:00 2022-06-03 17:00:00+09:00           213.428571   \n",
              "2022-06-04 17:00:00+09:00 2022-06-04 17:00:00+09:00           206.000000   \n",
              "2022-06-05 17:00:00+09:00 2022-06-05 17:00:00+09:00           204.571429   \n",
              "2022-06-06 17:00:00+09:00 2022-06-06 17:00:00+09:00           197.714286   \n",
              "2022-06-07 17:00:00+09:00 2022-06-07 17:00:00+09:00           165.428571   \n",
              "\n",
              "                           contacts_7d_mov_sum  querents_7d_mov_ave  \\\n",
              "2022-06-03 17:00:00+09:00               1494.0                  0.0   \n",
              "2022-06-04 17:00:00+09:00               1442.0                  0.0   \n",
              "2022-06-05 17:00:00+09:00               1432.0                  0.0   \n",
              "2022-06-06 17:00:00+09:00               1384.0                  0.0   \n",
              "2022-06-07 17:00:00+09:00               1158.0                  0.0   \n",
              "\n",
              "                           querents_7d_mov_sum  patients_7d_mov_ave  \\\n",
              "2022-06-03 17:00:00+09:00                  0.0          2187.142857   \n",
              "2022-06-04 17:00:00+09:00                  0.0          2118.857143   \n",
              "2022-06-05 17:00:00+09:00                  0.0          2031.714286   \n",
              "2022-06-06 17:00:00+09:00                  0.0          1984.428571   \n",
              "2022-06-07 17:00:00+09:00                  0.0          1904.142857   \n",
              "\n",
              "                           patients_7d_mov_sum  \n",
              "2022-06-03 17:00:00+09:00              15310.0  \n",
              "2022-06-04 17:00:00+09:00              14832.0  \n",
              "2022-06-05 17:00:00+09:00              14222.0  \n",
              "2022-06-06 17:00:00+09:00              13891.0  \n",
              "2022-06-07 17:00:00+09:00              13329.0  "
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              "      <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",
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              "      <th>querents_7d_mov_sum</th>\n",
              "      <th>patients_7d_mov_ave</th>\n",
              "      <th>patients_7d_mov_sum</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-06-03 17:00:00+09:00</th>\n",
              "      <td>168</td>\n",
              "      <td>0</td>\n",
              "      <td>2111</td>\n",
              "      <td>2022-06-03 17:00:00+09:00</td>\n",
              "      <td>213.428571</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2187.142857</td>\n",
              "      <td>15310.0</td>\n",
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              "      <th>2022-06-04 17:00:00+09:00</th>\n",
              "      <td>208</td>\n",
              "      <td>0</td>\n",
              "      <td>2071</td>\n",
              "      <td>2022-06-04 17:00:00+09:00</td>\n",
              "      <td>206.000000</td>\n",
              "      <td>1442.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2118.857143</td>\n",
              "      <td>14832.0</td>\n",
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              "    <tr>\n",
              "      <th>2022-06-05 17:00:00+09:00</th>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>1584</td>\n",
              "      <td>2022-06-05 17:00:00+09:00</td>\n",
              "      <td>204.571429</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2031.714286</td>\n",
              "      <td>14222.0</td>\n",
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              "    <tr>\n",
              "      <th>2022-06-06 17:00:00+09:00</th>\n",
              "      <td>209</td>\n",
              "      <td>0</td>\n",
              "      <td>1013</td>\n",
              "      <td>2022-06-06 17:00:00+09:00</td>\n",
              "      <td>197.714286</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>1984.428571</td>\n",
              "      <td>13891.0</td>\n",
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              "      <th>2022-06-07 17:00:00+09:00</th>\n",
              "      <td>0</td>\n",
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              "      <td>1904.142857</td>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 844
        },
        "id": "29XdYL6bH4JN",
        "outputId": "67b3727e-0741-43b4-fe94-a4df72e33eb6"
      },
      "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/plain": [
              "                           contacts_count  querents_count  patients_count  \\\n",
              "2022-05-24 17:00:00+09:00             280               0            3271   \n",
              "2022-05-25 17:00:00+09:00             234               0            3929   \n",
              "2022-05-26 17:00:00+09:00             241               0            3391   \n",
              "2022-05-27 17:00:00+09:00             252               0            2630   \n",
              "2022-05-28 17:00:00+09:00             260               0            2549   \n",
              "2022-05-29 17:00:00+09:00             197               0            2194   \n",
              "2022-05-30 17:00:00+09:00             257               0            1344   \n",
              "2022-05-31 17:00:00+09:00             226               0            2362   \n",
              "2022-06-01 17:00:00+09:00             190               0            2415   \n",
              "2022-06-02 17:00:00+09:00             196               0            2335   \n",
              "2022-06-03 17:00:00+09:00             168               0            2111   \n",
              "2022-06-04 17:00:00+09:00             208               0            2071   \n",
              "2022-06-05 17:00:00+09:00             187               0            1584   \n",
              "2022-06-06 17:00:00+09:00             209               0            1013   \n",
              "2022-06-07 17:00:00+09:00               0               0            1800   \n",
              "\n",
              "                                           datetime  contacts_7d_mov_ave  \\\n",
              "2022-05-24 17:00:00+09:00 2022-05-24 17:00:00+09:00           291.000000   \n",
              "2022-05-25 17:00:00+09:00 2022-05-25 17:00:00+09:00           279.857143   \n",
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              "2022-06-07 17:00:00+09:00 2022-06-07 17:00:00+09:00           165.428571   \n",
              "\n",
              "                           contacts_7d_mov_sum  querents_7d_mov_ave  \\\n",
              "2022-05-24 17:00:00+09:00               2037.0                  0.0   \n",
              "2022-05-25 17:00:00+09:00               1959.0                  0.0   \n",
              "2022-05-26 17:00:00+09:00               1924.0                  0.0   \n",
              "2022-05-27 17:00:00+09:00               1905.0                  0.0   \n",
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              "2022-06-07 17:00:00+09:00               1158.0                  0.0   \n",
              "\n",
              "                           querents_7d_mov_sum  patients_7d_mov_ave  \\\n",
              "2022-05-24 17:00:00+09:00                  0.0          3453.857143   \n",
              "2022-05-25 17:00:00+09:00                  0.0          3393.000000   \n",
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              "2022-06-07 17:00:00+09:00                  0.0          1904.142857   \n",
              "\n",
              "                           patients_7d_mov_sum week_num week_day  \n",
              "2022-05-24 17:00:00+09:00              24177.0   '22w21      Tue  \n",
              "2022-05-25 17:00:00+09:00              23751.0   '22w21      Wed  \n",
              "2022-05-26 17:00:00+09:00              22970.0   '22w21      Thu  \n",
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              "2022-05-28 17:00:00+09:00              21112.0   '22w21      Sat  \n",
              "2022-05-29 17:00:00+09:00              19989.0   '22w21      Sun  \n",
              "2022-05-30 17:00:00+09:00              19308.0   '22w22      Mon  \n",
              "2022-05-31 17:00:00+09:00              18399.0   '22w22      Tue  \n",
              "2022-06-01 17:00:00+09:00              16885.0   '22w22      Wed  \n",
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              "      <td>'22w21</td>\n",
              "      <td>Sun</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-05-30 17:00:00+09:00</th>\n",
              "      <td>257</td>\n",
              "      <td>0</td>\n",
              "      <td>1344</td>\n",
              "      <td>2022-05-30 17:00:00+09:00</td>\n",
              "      <td>245.857143</td>\n",
              "      <td>1721.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2758.285714</td>\n",
              "      <td>19308.0</td>\n",
              "      <td>'22w22</td>\n",
              "      <td>Mon</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-05-31 17:00:00+09:00</th>\n",
              "      <td>226</td>\n",
              "      <td>0</td>\n",
              "      <td>2362</td>\n",
              "      <td>2022-05-31 17:00:00+09:00</td>\n",
              "      <td>238.142857</td>\n",
              "      <td>1667.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2628.428571</td>\n",
              "      <td>18399.0</td>\n",
              "      <td>'22w22</td>\n",
              "      <td>Tue</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-01 17:00:00+09:00</th>\n",
              "      <td>190</td>\n",
              "      <td>0</td>\n",
              "      <td>2415</td>\n",
              "      <td>2022-06-01 17:00:00+09:00</td>\n",
              "      <td>231.857143</td>\n",
              "      <td>1623.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2412.142857</td>\n",
              "      <td>16885.0</td>\n",
              "      <td>'22w22</td>\n",
              "      <td>Wed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-02 17:00:00+09:00</th>\n",
              "      <td>196</td>\n",
              "      <td>0</td>\n",
              "      <td>2335</td>\n",
              "      <td>2022-06-02 17:00:00+09:00</td>\n",
              "      <td>225.428571</td>\n",
              "      <td>1578.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2261.285714</td>\n",
              "      <td>15829.0</td>\n",
              "      <td>'22w22</td>\n",
              "      <td>Thu</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-03 17:00:00+09:00</th>\n",
              "      <td>168</td>\n",
              "      <td>0</td>\n",
              "      <td>2111</td>\n",
              "      <td>2022-06-03 17:00:00+09:00</td>\n",
              "      <td>213.428571</td>\n",
              "      <td>1494.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2187.142857</td>\n",
              "      <td>15310.0</td>\n",
              "      <td>'22w22</td>\n",
              "      <td>Fri</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-04 17:00:00+09:00</th>\n",
              "      <td>208</td>\n",
              "      <td>0</td>\n",
              "      <td>2071</td>\n",
              "      <td>2022-06-04 17:00:00+09:00</td>\n",
              "      <td>206.000000</td>\n",
              "      <td>1442.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2118.857143</td>\n",
              "      <td>14832.0</td>\n",
              "      <td>'22w22</td>\n",
              "      <td>Sat</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-05 17:00:00+09:00</th>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>1584</td>\n",
              "      <td>2022-06-05 17:00:00+09:00</td>\n",
              "      <td>204.571429</td>\n",
              "      <td>1432.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2031.714286</td>\n",
              "      <td>14222.0</td>\n",
              "      <td>'22w22</td>\n",
              "      <td>Sun</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-06 17:00:00+09:00</th>\n",
              "      <td>209</td>\n",
              "      <td>0</td>\n",
              "      <td>1013</td>\n",
              "      <td>2022-06-06 17:00:00+09:00</td>\n",
              "      <td>197.714286</td>\n",
              "      <td>1384.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1984.428571</td>\n",
              "      <td>13891.0</td>\n",
              "      <td>'22w23</td>\n",
              "      <td>Mon</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-07 17:00:00+09:00</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1800</td>\n",
              "      <td>2022-06-07 17:00:00+09:00</td>\n",
              "      <td>165.428571</td>\n",
              "      <td>1158.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1904.142857</td>\n",
              "      <td>13329.0</td>\n",
              "      <td>'22w23</td>\n",
              "      <td>Tue</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e91de8e1-5c60-48a1-a2fd-1c43eac1e252')\"\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
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      ]
    },
    {
      "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": 206
        },
        "id": "5sr8B_HwIgEW",
        "outputId": "a0960a61-1dd8-4c96-bef5-8b0981e2dbda"
      },
      "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": {
            "text/plain": [
              "                           patients_week_mean  patients_week_sum\n",
              "2022-05-22 17:00:00+09:00         3560.142857              24921\n",
              "2022-05-29 17:00:00+09:00         2855.571429              19989\n",
              "2022-06-05 17:00:00+09:00         2031.714286              14222\n",
              "2022-06-07 17:00:00+09:00         1406.500000               2813\n",
              "2022-01-02 17:00:00+09:00           80.000000                160"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b4898776-fc32-4b1f-971e-84162591a040\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "    <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>2022-05-22 17:00:00+09:00</th>\n",
              "      <td>3560.142857</td>\n",
              "      <td>24921</td>\n",
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              "    <tr>\n",
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              "      <td>2855.571429</td>\n",
              "      <td>19989</td>\n",
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              "    <tr>\n",
              "      <th>2022-06-05 17:00:00+09:00</th>\n",
              "      <td>2031.714286</td>\n",
              "      <td>14222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-07 17:00:00+09:00</th>\n",
              "      <td>1406.500000</td>\n",
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              "      <th>2022-01-02 17:00:00+09:00</th>\n",
              "      <td>80.000000</td>\n",
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              "  </tbody>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b4898776-fc32-4b1f-971e-84162591a040 button.colab-df-convert');\n",
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              "\n",
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              "          const element = document.querySelector('#df-b4898776-fc32-4b1f-971e-84162591a040');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 844
        },
        "id": "fuHCAMAwH3mX",
        "outputId": "33512a64-ffe3-4bac-d07e-46d94be086b4"
      },
      "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": {
            "text/plain": [
              "                           contacts_count  querents_count  patients_count  \\\n",
              "2022-05-24 17:00:00+09:00             280               0            3271   \n",
              "2022-05-25 17:00:00+09:00             234               0            3929   \n",
              "2022-05-26 17:00:00+09:00             241               0            3391   \n",
              "2022-05-27 17:00:00+09:00             252               0            2630   \n",
              "2022-05-28 17:00:00+09:00             260               0            2549   \n",
              "2022-05-29 17:00:00+09:00             197               0            2194   \n",
              "2022-05-30 17:00:00+09:00             257               0            1344   \n",
              "2022-05-31 17:00:00+09:00             226               0            2362   \n",
              "2022-06-01 17:00:00+09:00             190               0            2415   \n",
              "2022-06-02 17:00:00+09:00             196               0            2335   \n",
              "2022-06-03 17:00:00+09:00             168               0            2111   \n",
              "2022-06-04 17:00:00+09:00             208               0            2071   \n",
              "2022-06-05 17:00:00+09:00             187               0            1584   \n",
              "2022-06-06 17:00:00+09:00             209               0            1013   \n",
              "2022-06-07 17:00:00+09:00               0               0            1800   \n",
              "\n",
              "                                           datetime  contacts_7d_mov_ave  \\\n",
              "2022-05-24 17:00:00+09:00 2022-05-24 17:00:00+09:00           291.000000   \n",
              "2022-05-25 17:00:00+09:00 2022-05-25 17:00:00+09:00           279.857143   \n",
              "2022-05-26 17:00:00+09:00 2022-05-26 17:00:00+09:00           274.857143   \n",
              "2022-05-27 17:00:00+09:00 2022-05-27 17:00:00+09:00           272.142857   \n",
              "2022-05-28 17:00:00+09:00 2022-05-28 17:00:00+09:00           261.142857   \n",
              "2022-05-29 17:00:00+09:00 2022-05-29 17:00:00+09:00           252.000000   \n",
              "2022-05-30 17:00:00+09:00 2022-05-30 17:00:00+09:00           245.857143   \n",
              "2022-05-31 17:00:00+09:00 2022-05-31 17:00:00+09:00           238.142857   \n",
              "2022-06-01 17:00:00+09:00 2022-06-01 17:00:00+09:00           231.857143   \n",
              "2022-06-02 17:00:00+09:00 2022-06-02 17:00:00+09:00           225.428571   \n",
              "2022-06-03 17:00:00+09:00 2022-06-03 17:00:00+09:00           213.428571   \n",
              "2022-06-04 17:00:00+09:00 2022-06-04 17:00:00+09:00           206.000000   \n",
              "2022-06-05 17:00:00+09:00 2022-06-05 17:00:00+09:00           204.571429   \n",
              "2022-06-06 17:00:00+09:00 2022-06-06 17:00:00+09:00           197.714286   \n",
              "2022-06-07 17:00:00+09:00 2022-06-07 17:00:00+09:00           165.428571   \n",
              "\n",
              "                           contacts_7d_mov_sum  querents_7d_mov_ave  \\\n",
              "2022-05-24 17:00:00+09:00               2037.0                  0.0   \n",
              "2022-05-25 17:00:00+09:00               1959.0                  0.0   \n",
              "2022-05-26 17:00:00+09:00               1924.0                  0.0   \n",
              "2022-05-27 17:00:00+09:00               1905.0                  0.0   \n",
              "2022-05-28 17:00:00+09:00               1828.0                  0.0   \n",
              "2022-05-29 17:00:00+09:00               1764.0                  0.0   \n",
              "2022-05-30 17:00:00+09:00               1721.0                  0.0   \n",
              "2022-05-31 17:00:00+09:00               1667.0                  0.0   \n",
              "2022-06-01 17:00:00+09:00               1623.0                  0.0   \n",
              "2022-06-02 17:00:00+09:00               1578.0                  0.0   \n",
              "2022-06-03 17:00:00+09:00               1494.0                  0.0   \n",
              "2022-06-04 17:00:00+09:00               1442.0                  0.0   \n",
              "2022-06-05 17:00:00+09:00               1432.0                  0.0   \n",
              "2022-06-06 17:00:00+09:00               1384.0                  0.0   \n",
              "2022-06-07 17:00:00+09:00               1158.0                  0.0   \n",
              "\n",
              "                           querents_7d_mov_sum  patients_7d_mov_ave  \\\n",
              "2022-05-24 17:00:00+09:00                  0.0          3453.857143   \n",
              "2022-05-25 17:00:00+09:00                  0.0          3393.000000   \n",
              "2022-05-26 17:00:00+09:00                  0.0          3281.428571   \n",
              "2022-05-27 17:00:00+09:00                  0.0          3146.714286   \n",
              "2022-05-28 17:00:00+09:00                  0.0          3016.000000   \n",
              "2022-05-29 17:00:00+09:00                  0.0          2855.571429   \n",
              "2022-05-30 17:00:00+09:00                  0.0          2758.285714   \n",
              "2022-05-31 17:00:00+09:00                  0.0          2628.428571   \n",
              "2022-06-01 17:00:00+09:00                  0.0          2412.142857   \n",
              "2022-06-02 17:00:00+09:00                  0.0          2261.285714   \n",
              "2022-06-03 17:00:00+09:00                  0.0          2187.142857   \n",
              "2022-06-04 17:00:00+09:00                  0.0          2118.857143   \n",
              "2022-06-05 17:00:00+09:00                  0.0          2031.714286   \n",
              "2022-06-06 17:00:00+09:00                  0.0          1984.428571   \n",
              "2022-06-07 17:00:00+09:00                  0.0          1904.142857   \n",
              "\n",
              "                           patients_7d_mov_sum week_num week_day  \\\n",
              "2022-05-24 17:00:00+09:00              24177.0   '22w21      Tue   \n",
              "2022-05-25 17:00:00+09:00              23751.0   '22w21      Wed   \n",
              "2022-05-26 17:00:00+09:00              22970.0   '22w21      Thu   \n",
              "2022-05-27 17:00:00+09:00              22027.0   '22w21      Fri   \n",
              "2022-05-28 17:00:00+09:00              21112.0   '22w21      Sat   \n",
              "2022-05-29 17:00:00+09:00              19989.0   '22w21      Sun   \n",
              "2022-05-30 17:00:00+09:00              19308.0   '22w22      Mon   \n",
              "2022-05-31 17:00:00+09:00              18399.0   '22w22      Tue   \n",
              "2022-06-01 17:00:00+09:00              16885.0   '22w22      Wed   \n",
              "2022-06-02 17:00:00+09:00              15829.0   '22w22      Thu   \n",
              "2022-06-03 17:00:00+09:00              15310.0   '22w22      Fri   \n",
              "2022-06-04 17:00:00+09:00              14832.0   '22w22      Sat   \n",
              "2022-06-05 17:00:00+09:00              14222.0   '22w22      Sun   \n",
              "2022-06-06 17:00:00+09:00              13891.0   '22w23      Mon   \n",
              "2022-06-07 17:00:00+09:00              13329.0   '22w23      Tue   \n",
              "\n",
              "                           patients_week_mean  patients_week_sum  \n",
              "2022-05-24 17:00:00+09:00                 NaN                NaN  \n",
              "2022-05-25 17:00:00+09:00                 NaN                NaN  \n",
              "2022-05-26 17:00:00+09:00                 NaN                NaN  \n",
              "2022-05-27 17:00:00+09:00                 NaN                NaN  \n",
              "2022-05-28 17:00:00+09:00                 NaN                NaN  \n",
              "2022-05-29 17:00:00+09:00         2855.571429            19989.0  \n",
              "2022-05-30 17:00:00+09:00                 NaN                NaN  \n",
              "2022-05-31 17:00:00+09:00                 NaN                NaN  \n",
              "2022-06-01 17:00:00+09:00                 NaN                NaN  \n",
              "2022-06-02 17:00:00+09:00                 NaN                NaN  \n",
              "2022-06-03 17:00:00+09:00                 NaN                NaN  \n",
              "2022-06-04 17:00:00+09:00                 NaN                NaN  \n",
              "2022-06-05 17:00:00+09:00         2031.714286            14222.0  \n",
              "2022-06-06 17:00:00+09:00                 NaN                NaN  \n",
              "2022-06-07 17:00:00+09:00         1406.500000             2813.0  "
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>contacts_count</th>\n",
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              "      <th>patients_week_sum</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-05-24 17:00:00+09:00</th>\n",
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              "      <th>2022-05-25 17:00:00+09:00</th>\n",
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              "      <th>2022-05-26 17:00:00+09:00</th>\n",
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              "      <th>2022-05-27 17:00:00+09:00</th>\n",
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              "      <th>2022-05-28 17:00:00+09:00</th>\n",
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              "      <th>2022-05-29 17:00:00+09:00</th>\n",
              "      <td>197</td>\n",
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              "      <th>2022-05-30 17:00:00+09:00</th>\n",
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              "      <th>2022-06-04 17:00:00+09:00</th>\n",
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              "      <td>2031.714286</td>\n",
              "      <td>14222.0</td>\n",
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              "    <tr>\n",
              "      <th>2022-06-06 17:00:00+09:00</th>\n",
              "      <td>209</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2022-06-07 17:00:00+09:00</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1800</td>\n",
              "      <td>2022-06-07 17:00:00+09:00</td>\n",
              "      <td>165.428571</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1406.500000</td>\n",
              "      <td>2813.0</td>\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0afb47af-18a7-4ad7-b4d8-93bf217492cd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0afb47af-18a7-4ad7-b4d8-93bf217492cd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YYYiYt6sM-JZ"
      },
      "source": [
        "### グラフ出力"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DKYNzasF4Hgv"
      },
      "source": [
        "sns.set()\n",
        "sns.set_style('whitegrid')"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EXiY2UjgM-Ja"
      },
      "source": [
        "### 日毎の数\n",
        "x = df3.index\n",
        "y1_1 = df3['patients_count']\n",
        "y1_2 = df3['patients_7d_mov_ave']\n",
        "#y1_3 = df3['patients_7d_mov_center_ave']\n",
        "# y1_4 = df3['patients_week_mean']"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EaWNEkjWM-Jc"
      },
      "source": [
        "### 週毎の数\n",
        "# y2_1 = df3['patients_7d_mov_sum']\n",
        "# y2_2 = df3['patients_week_sum']"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rq5oJbZsbhW6"
      },
      "source": [
        ""
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "id": "tJ4d547xM-Je",
        "outputId": "21103ecb-ec9b-40a8-89c9-c9e8e427a4ea"
      },
      "source": [
        "fig, ax = plt.subplots(1,1, figsize=(20, 8))\n",
        "\n",
        "### 日毎の数\n",
        "ax.set_title('daily count')\n",
        "ax.set_xlabel('date')\n",
        "ax.set_ylabel('sum')\n",
        "ax.bar(x, y1_1, label='patients_count')\n",
        "ax.plot(x, y1_2, label='patients_7d_mov_ave')\n",
        "#ax[0].plot(x, y1_3, label='patients_7d_mov_center_ave')\n",
        "# ax[0].bar(x, y1_4, label='patients_week_mean')\n",
        "ax.grid(linestyle=':')\n",
        "ax.legend()\n",
        "\n",
        "# ### 週毎の合計\n",
        "# ax[1].set_title('weekly count')\n",
        "# ax[1].set_xlabel('date')\n",
        "# ax[1].set_ylabel('sum')\n",
        "# ax[1].plot(x, y2_1, label='patients_7d_mov_sum')\n",
        "# ax[1].bar(x, y2_2, label='patients_week_sum')\n",
        "# ax[1].grid(linestyle=':')\n",
        "# ax[1].legend()\n",
        "\n",
        "plt.tight_layout()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KoRfqEaewYtQ"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zQq1tD9PK8-j"
      },
      "source": [
        ""
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XTdSG_f3XB9d"
      },
      "source": [
        "def get_date(d: datetime) -> datetime:\n",
        "  \"\"\"時間が入った `datetime` を `%Y-%m-%d 00:00:00` の `datetime` に変換します\"\"\"\n",
        "  return datetime.datetime.strptime(d.strftime('%Y-%m-%d'), '%Y-%m-%d')"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lUBuDLEGVwKo"
      },
      "source": [
        "em_start = datetime.datetime(2020, 4, 7)\n",
        "em_end = datetime.datetime(2020, 5, 25)\n",
        "def em_term() -> list:\n",
        "  \"\"\"緊急事態宣言期間の日付のリスト\"\"\"\n",
        "  em_term = []\n",
        "  for i in range((em_end - em_start).days + 1):\n",
        "    em_term.append(em_start + datetime.timedelta(i))\n",
        "  return em_term"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LP9aknBzVBXP"
      },
      "source": [
        "def emergency(d: datetime) -> int:\n",
        "  \"\"\"緊急事態宣言期間中か\"\"\"\n",
        "  return int(get_date(d) in em_term())"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U__BOyP8Wxna"
      },
      "source": [
        "def em_passed(d: datetime) -> int:\n",
        "  \"\"\"緊急事態宣言から経過日数\"\"\"\n",
        "  d = get_date(d)\n",
        "  return (d - em_start).days if em_start < d else 0"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "id": "HFNgCNh6SrwI",
        "outputId": "b2f7d8d3-2a80-4af3-d18b-240f6f977e04"
      },
      "source": [
        "ds_df = pd.DataFrame({\n",
        "    'timestamp': [d.timestamp() for d in df3.index], # タイムスタンプ\n",
        "    'year': np.array(df3.index.strftime('%Y'), dtype=np.int64), # 年\n",
        "    'month': np.array(df3.index.strftime('%m'), dtype=np.int64), # 月\n",
        "    'day': np.array(df3.index.strftime('%d'), dtype=np.int64), # 日\n",
        "    'week_day': df3['week_day'], # 曜日\n",
        "})\n",
        "# 曜日をダミー変数化\n",
        "ds_df = pd.concat([ds_df, pd.get_dummies(ds_df['week_day'])], axis=1)\n",
        "ds_df = ds_df.drop(columns=['week_day'])\n",
        "\n",
        "# 経過日数\n",
        "# ds_df['passed'] = [(d - df3['datetime'][0]).days for d in df3['datetime']]\n",
        "ds_df['passed'] = [(d - df3.index[0]).days for d in df3.index]\n",
        "# 緊急事態宣言期間中\n",
        "ds_df['emergency'] = [emergency(d) for d in df3.index]\n",
        "# 緊急事態宣言経過日数\n",
        "ds_df['em_passed'] = [em_passed(d) for d in df3.index]\n",
        "for key in keys:\n",
        "  k = key.split('_')[0]\n",
        "  # 日毎の数\n",
        "  ds_df[f'{k}_count'] = df3[f'{k}_count']\n",
        "  # 7日間移動平均\n",
        "  ds_df[f'{k}_7d_mov_ave'] = df3[f'{k}_7d_mov_ave']\n",
        "  #ds_df[f'{k}_7d_mov_center_ave'] = df3[f'{k}_7d_mov_center_ave']\n",
        "  # 7日間移動合計\n",
        "  ds_df[f'{k}_7d_mov_sum'] = df3[f'{k}_7d_mov_sum']\n",
        "\n",
        "# 日毎の数\n",
        "# ds_df['patients_count'] = df3['patients_count']\n",
        "# 7日間移動平均\n",
        "# ds_df['patients_7d_mov_ave'] = df3['patients_7d_mov_ave']\n",
        "#ds_df['7d_mov_center_ave'] = df3['patients_7d_mov_center_ave']\n",
        "# 7日間移動合計\n",
        "# ds_df['patients_7d_mov_sum'] = df3['patients_7d_mov_sum']\n",
        "\n",
        "ds_df = ds_df.reset_index(drop=True)\n",
        "ds_df.tail(15)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        timestamp  year  month  day  Fri  Mon  Sat  Sun  Thu  Tue  ...  \\\n",
              "851  1.653379e+09  2022      5   24    0    0    0    0    0    1  ...   \n",
              "852  1.653466e+09  2022      5   25    0    0    0    0    0    0  ...   \n",
              "853  1.653552e+09  2022      5   26    0    0    0    0    1    0  ...   \n",
              "854  1.653638e+09  2022      5   27    1    0    0    0    0    0  ...   \n",
              "855  1.653725e+09  2022      5   28    0    0    1    0    0    0  ...   \n",
              "856  1.653811e+09  2022      5   29    0    0    0    1    0    0  ...   \n",
              "857  1.653898e+09  2022      5   30    0    1    0    0    0    0  ...   \n",
              "858  1.653984e+09  2022      5   31    0    0    0    0    0    1  ...   \n",
              "859  1.654070e+09  2022      6    1    0    0    0    0    0    0  ...   \n",
              "860  1.654157e+09  2022      6    2    0    0    0    0    1    0  ...   \n",
              "861  1.654243e+09  2022      6    3    1    0    0    0    0    0  ...   \n",
              "862  1.654330e+09  2022      6    4    0    0    1    0    0    0  ...   \n",
              "863  1.654416e+09  2022      6    5    0    0    0    1    0    0  ...   \n",
              "864  1.654502e+09  2022      6    6    0    1    0    0    0    0  ...   \n",
              "865  1.654589e+09  2022      6    7    0    0    0    0    0    1  ...   \n",
              "\n",
              "     em_passed  contacts_count  contacts_7d_mov_ave  contacts_7d_mov_sum  \\\n",
              "851        777             280           291.000000               2037.0   \n",
              "852        778             234           279.857143               1959.0   \n",
              "853        779             241           274.857143               1924.0   \n",
              "854        780             252           272.142857               1905.0   \n",
              "855        781             260           261.142857               1828.0   \n",
              "856        782             197           252.000000               1764.0   \n",
              "857        783             257           245.857143               1721.0   \n",
              "858        784             226           238.142857               1667.0   \n",
              "859        785             190           231.857143               1623.0   \n",
              "860        786             196           225.428571               1578.0   \n",
              "861        787             168           213.428571               1494.0   \n",
              "862        788             208           206.000000               1442.0   \n",
              "863        789             187           204.571429               1432.0   \n",
              "864        790             209           197.714286               1384.0   \n",
              "865        791               0           165.428571               1158.0   \n",
              "\n",
              "     querents_count  querents_7d_mov_ave  querents_7d_mov_sum  patients_count  \\\n",
              "851               0                  0.0                  0.0            3271   \n",
              "852               0                  0.0                  0.0            3929   \n",
              "853               0                  0.0                  0.0            3391   \n",
              "854               0                  0.0                  0.0            2630   \n",
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              "  </div>\n",
              "  "
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          "metadata": {},
          "execution_count": 20
        }
      ]
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    {
      "cell_type": "code",
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        "id": "QYKqB8CSm3_0",
        "outputId": "24841c11-a89c-4751-e876-68d0b6af146f"
      },
      "source": [
        "ds_df.info()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 866 entries, 0 to 865\n",
            "Data columns (total 23 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   timestamp            866 non-null    float64\n",
            " 1   year                 866 non-null    int64  \n",
            " 2   month                866 non-null    int64  \n",
            " 3   day                  866 non-null    int64  \n",
            " 4   Fri                  866 non-null    uint8  \n",
            " 5   Mon                  866 non-null    uint8  \n",
            " 6   Sat                  866 non-null    uint8  \n",
            " 7   Sun                  866 non-null    uint8  \n",
            " 8   Thu                  866 non-null    uint8  \n",
            " 9   Tue                  866 non-null    uint8  \n",
            " 10  Wed                  866 non-null    uint8  \n",
            " 11  passed               866 non-null    int64  \n",
            " 12  emergency            866 non-null    int64  \n",
            " 13  em_passed            866 non-null    int64  \n",
            " 14  contacts_count       866 non-null    int64  \n",
            " 15  contacts_7d_mov_ave  866 non-null    float64\n",
            " 16  contacts_7d_mov_sum  866 non-null    float64\n",
            " 17  querents_count       866 non-null    int64  \n",
            " 18  querents_7d_mov_ave  866 non-null    float64\n",
            " 19  querents_7d_mov_sum  866 non-null    float64\n",
            " 20  patients_count       866 non-null    int64  \n",
            " 21  patients_7d_mov_ave  866 non-null    float64\n",
            " 22  patients_7d_mov_sum  866 non-null    float64\n",
            "dtypes: float64(7), int64(9), uint8(7)\n",
            "memory usage: 114.3 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "aTUpPtDM97uY",
        "outputId": "1d8a63b8-c555-43d3-cf35-221f69778e31"
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      "source": [
        "ds_df.describe()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "          timestamp         year       month         day         Fri  \\\n",
              "count  8.660000e+02   866.000000  866.000000  866.000000  866.000000   \n",
              "mean   1.617221e+09  2020.786374    6.050808   15.722864    0.143187   \n",
              "std    2.161183e+07     0.730412    3.395298    8.858782    0.350466   \n",
              "min    1.579853e+09  2020.000000    1.000000    1.000000    0.000000   \n",
              "25%    1.598537e+09  2020.000000    3.000000    8.000000    0.000000   \n",
              "50%    1.617221e+09  2021.000000    6.000000   16.000000    0.000000   \n",
              "75%    1.635905e+09  2021.000000    9.000000   23.000000    0.000000   \n",
              "max    1.654589e+09  2022.000000   12.000000   31.000000    1.000000   \n",
              "\n",
              "              Mon         Sat         Sun         Thu         Tue  ...  \\\n",
              "count  866.000000  866.000000  866.000000  866.000000  866.000000  ...   \n",
              "mean     0.143187    0.143187    0.143187    0.142032    0.143187  ...   \n",
              "std      0.350466    0.350466    0.350466    0.349285    0.350466  ...   \n",
              "min      0.000000    0.000000    0.000000    0.000000    0.000000  ...   \n",
              "25%      0.000000    0.000000    0.000000    0.000000    0.000000  ...   \n",
              "50%      0.000000    0.000000    0.000000    0.000000    0.000000  ...   \n",
              "75%      0.000000    0.000000    0.000000    0.000000    0.000000  ...   \n",
              "max      1.000000    1.000000    1.000000    1.000000    1.000000  ...   \n",
              "\n",
              "        em_passed  contacts_count  contacts_7d_mov_ave  contacts_7d_mov_sum  \\\n",
              "count  866.000000      866.000000           866.000000           866.000000   \n",
              "mean   361.704388      664.359122           663.873282          4647.108545   \n",
              "std    245.150326      540.364413           528.175109          3697.231305   \n",
              "min      0.000000        0.000000             0.000000             0.000000   \n",
              "25%    142.250000      322.250000           325.714286          2280.000000   \n",
              "50%    358.500000      508.000000           511.857143          3583.000000   \n",
              "75%    574.750000      790.000000           782.500000          5477.500000   \n",
              "max    791.000000     3277.000000          2723.000000         19061.000000   \n",
              "\n",
              "       querents_count  querents_7d_mov_ave  querents_7d_mov_sum  \\\n",
              "count      866.000000           866.000000           866.000000   \n",
              "mean       425.541570           425.541570          2978.790993   \n",
              "std        754.764465           695.744818          4870.213727   \n",
              "min          0.000000             0.000000             0.000000   \n",
              "25%          0.000000             0.000000             0.000000   \n",
              "50%          0.000000             0.000000             0.000000   \n",
              "75%        742.000000           964.250000          6749.750000   \n",
              "max       4060.000000          2574.142857         18019.000000   \n",
              "\n",
              "       patients_count  patients_7d_mov_ave  patients_7d_mov_sum  \n",
              "count      866.000000           866.000000           866.000000  \n",
              "mean      1794.038106          1788.271907         12517.884527  \n",
              "std       3542.491443          3461.563310         24230.952901  \n",
              "min          0.000000             0.000000             0.000000  \n",
              "25%         78.000000            90.142857           631.000000  \n",
              "50%        330.500000           344.071429          2408.500000  \n",
              "75%       1240.250000          1274.964286          8924.750000  \n",
              "max      21562.000000         18562.285714        129936.000000  \n",
              "\n",
              "[8 rows x 23 columns]"
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              "\n",
              "  <div id=\"df-8cd78ca6-6c0c-458c-b7d2-0212b74704ba\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>timestamp</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>Fri</th>\n",
              "      <th>Mon</th>\n",
              "      <th>Sat</th>\n",
              "      <th>Sun</th>\n",
              "      <th>Thu</th>\n",
              "      <th>Tue</th>\n",
              "      <th>...</th>\n",
              "      <th>em_passed</th>\n",
              "      <th>contacts_count</th>\n",
              "      <th>contacts_7d_mov_ave</th>\n",
              "      <th>contacts_7d_mov_sum</th>\n",
              "      <th>querents_count</th>\n",
              "      <th>querents_7d_mov_ave</th>\n",
              "      <th>querents_7d_mov_sum</th>\n",
              "      <th>patients_count</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>count</th>\n",
              "      <td>8.660000e+02</td>\n",
              "      <td>866.000000</td>\n",
              "      <td>866.000000</td>\n",
              "      <td>866.000000</td>\n",
              "      <td>866.000000</td>\n",
              "      <td>866.000000</td>\n",
              "      <td>866.000000</td>\n",
              "      <td>866.000000</td>\n",
              "      <td>866.000000</td>\n",
              "      <td>866.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>866.000000</td>\n",
              "      <td>866.000000</td>\n",
              "      <td>866.000000</td>\n",
              "      <td>866.000000</td>\n",
              "      <td>866.000000</td>\n",
              "      <td>866.000000</td>\n",
              "      <td>866.000000</td>\n",
              "      <td>866.000000</td>\n",
              "      <td>866.000000</td>\n",
              "      <td>866.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.617221e+09</td>\n",
              "      <td>2020.786374</td>\n",
              "      <td>6.050808</td>\n",
              "      <td>15.722864</td>\n",
              "      <td>0.143187</td>\n",
              "      <td>0.143187</td>\n",
              "      <td>0.143187</td>\n",
              "      <td>0.143187</td>\n",
              "      <td>0.142032</td>\n",
              "      <td>0.143187</td>\n",
              "      <td>...</td>\n",
              "      <td>361.704388</td>\n",
              "      <td>664.359122</td>\n",
              "      <td>663.873282</td>\n",
              "      <td>4647.108545</td>\n",
              "      <td>425.541570</td>\n",
              "      <td>425.541570</td>\n",
              "      <td>2978.790993</td>\n",
              "      <td>1794.038106</td>\n",
              "      <td>1788.271907</td>\n",
              "      <td>12517.884527</td>\n",
              "    </tr>\n",
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              "      <th>std</th>\n",
              "      <td>2.161183e+07</td>\n",
              "      <td>0.730412</td>\n",
              "      <td>3.395298</td>\n",
              "      <td>8.858782</td>\n",
              "      <td>0.350466</td>\n",
              "      <td>0.350466</td>\n",
              "      <td>0.350466</td>\n",
              "      <td>0.350466</td>\n",
              "      <td>0.349285</td>\n",
              "      <td>0.350466</td>\n",
              "      <td>...</td>\n",
              "      <td>245.150326</td>\n",
              "      <td>540.364413</td>\n",
              "      <td>528.175109</td>\n",
              "      <td>3697.231305</td>\n",
              "      <td>754.764465</td>\n",
              "      <td>695.744818</td>\n",
              "      <td>4870.213727</td>\n",
              "      <td>3542.491443</td>\n",
              "      <td>3461.563310</td>\n",
              "      <td>24230.952901</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.579853e+09</td>\n",
              "      <td>2020.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>1.598537e+09</td>\n",
              "      <td>2020.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>142.250000</td>\n",
              "      <td>322.250000</td>\n",
              "      <td>325.714286</td>\n",
              "      <td>2280.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>90.142857</td>\n",
              "      <td>631.000000</td>\n",
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              "      <th>50%</th>\n",
              "      <td>1.617221e+09</td>\n",
              "      <td>2021.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>358.500000</td>\n",
              "      <td>508.000000</td>\n",
              "      <td>511.857143</td>\n",
              "      <td>3583.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>330.500000</td>\n",
              "      <td>344.071429</td>\n",
              "      <td>2408.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.635905e+09</td>\n",
              "      <td>2021.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>574.750000</td>\n",
              "      <td>790.000000</td>\n",
              "      <td>782.500000</td>\n",
              "      <td>5477.500000</td>\n",
              "      <td>742.000000</td>\n",
              "      <td>964.250000</td>\n",
              "      <td>6749.750000</td>\n",
              "      <td>1240.250000</td>\n",
              "      <td>1274.964286</td>\n",
              "      <td>8924.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.654589e+09</td>\n",
              "      <td>2022.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>791.000000</td>\n",
              "      <td>3277.000000</td>\n",
              "      <td>2723.000000</td>\n",
              "      <td>19061.000000</td>\n",
              "      <td>4060.000000</td>\n",
              "      <td>2574.142857</td>\n",
              "      <td>18019.000000</td>\n",
              "      <td>21562.000000</td>\n",
              "      <td>18562.285714</td>\n",
              "      <td>129936.000000</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 23 columns</p>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8cd78ca6-6c0c-458c-b7d2-0212b74704ba');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Na_sHq871CpJ"
      },
      "source": [
        ""
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ugaJ-BeYlqzB",
        "outputId": "1e2ed3b0-ab0b-450b-c841-f810198757a7"
      },
      "source": [
        "ds_df.corr()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        timestamp      year     month       day       Fri  \\\n",
              "timestamp            1.000000e+00  0.921464  0.042948 -0.004158 -0.003270   \n",
              "year                 9.214641e-01  1.000000 -0.347105 -0.034888 -0.002305   \n",
              "month                4.294783e-02 -0.347105  1.000000 -0.005604 -0.002235   \n",
              "day                 -4.158056e-03 -0.034888 -0.005604  1.000000  0.003115   \n",
              "Fri                 -3.270483e-03 -0.002305 -0.002235  0.003115  1.000000   \n",
              "Mon                  1.635242e-03  0.002211 -0.002235  0.005721 -0.167116   \n",
              "Sat                 -1.635242e-03  0.002211 -0.010007  0.003487 -0.167116   \n",
              "Sun                 -3.132984e-16  0.002211 -0.006121  0.004604 -0.167116   \n",
              "Thu                  8.137691e-04 -0.003281  0.010480 -0.000341 -0.166329   \n",
              "Tue                  3.270483e-03  0.002211  0.002623 -0.004332 -0.167116   \n",
              "Wed                 -8.137691e-04 -0.003281  0.007555 -0.012297 -0.166329   \n",
              "passed               1.000000e+00  0.921464  0.042948 -0.004158 -0.003270   \n",
              "emergency           -3.276847e-01 -0.263815 -0.111186 -0.000802 -0.000231   \n",
              "em_passed            9.989874e-01  0.926119  0.028431 -0.003756 -0.002911   \n",
              "contacts_count      -9.002749e-02  0.012831 -0.252886  0.015833 -0.017163   \n",
              "contacts_7d_mov_ave -7.860704e-02  0.024106 -0.253332  0.002883 -0.001348   \n",
              "contacts_7d_mov_sum -7.860487e-02  0.024107 -0.253330  0.002881 -0.001348   \n",
              "querents_count      -6.167394e-01 -0.607706  0.075715  0.007024  0.023316   \n",
              "querents_7d_mov_ave -6.617130e-01 -0.659257  0.100730 -0.002525 -0.000576   \n",
              "querents_7d_mov_sum -6.617130e-01 -0.659257  0.100730 -0.002525 -0.000576   \n",
              "patients_count       5.413401e-01  0.644349 -0.352421 -0.012031  0.017060   \n",
              "patients_7d_mov_ave  5.573158e-01  0.658050 -0.348655 -0.016557 -0.000687   \n",
              "patients_7d_mov_sum  5.573169e-01  0.658050 -0.348653 -0.016558 -0.000687   \n",
              "\n",
              "                          Mon       Sat           Sun       Thu       Tue  \\\n",
              "timestamp            0.001635 -0.001635 -3.132984e-16  0.000814  0.003270   \n",
              "year                 0.002211  0.002211  2.211149e-03 -0.003281  0.002211   \n",
              "month               -0.002235 -0.010007 -6.120919e-03  0.010480  0.002623   \n",
              "day                  0.005721  0.003487  4.604204e-03 -0.000341 -0.004332   \n",
              "Fri                 -0.167116 -0.167116 -1.671159e-01 -0.166329 -0.167116   \n",
              "Mon                  1.000000 -0.167116 -1.671159e-01 -0.166329 -0.167116   \n",
              "Sat                 -0.167116  1.000000 -1.671159e-01 -0.166329 -0.167116   \n",
              "Sun                 -0.167116 -0.167116  1.000000e+00 -0.166329 -0.167116   \n",
              "Thu                 -0.166329 -0.166329 -1.663286e-01  1.000000 -0.166329   \n",
              "Tue                 -0.167116 -0.167116 -1.671159e-01 -0.166329  1.000000   \n",
              "Wed                 -0.166329 -0.166329 -1.663286e-01 -0.165545 -0.166329   \n",
              "passed               0.001635 -0.001635 -3.156832e-16  0.000814  0.003270   \n",
              "emergency           -0.000231 -0.000231 -2.306780e-04  0.000579 -0.000231   \n",
              "em_passed            0.001650 -0.001391  1.299265e-04  0.000437  0.003171   \n",
              "contacts_count       0.038351  0.046165 -2.542856e-02 -0.012172 -0.018451   \n",
              "contacts_7d_mov_ave -0.000809 -0.001163 -9.957025e-04  0.002657 -0.000809   \n",
              "contacts_7d_mov_sum -0.000809 -0.001162 -9.952113e-04  0.002658 -0.000809   \n",
              "querents_count       0.093759 -0.109371 -1.008096e-01  0.028935  0.037087   \n",
              "querents_7d_mov_ave -0.000576 -0.000576 -5.764506e-04  0.001446 -0.000576   \n",
              "querents_7d_mov_sum -0.000576 -0.000576 -5.764506e-04  0.001446 -0.000576   \n",
              "patients_count      -0.070995  0.011876 -1.126784e-02  0.033541 -0.010420   \n",
              "patients_7d_mov_ave -0.000052 -0.000405 -1.896826e-04  0.000732  0.000193   \n",
              "patients_7d_mov_sum -0.000052 -0.000405 -1.897278e-04  0.000732  0.000193   \n",
              "\n",
              "                     ...  em_passed  contacts_count  contacts_7d_mov_ave  \\\n",
              "timestamp            ...   0.998987       -0.090027            -0.078607   \n",
              "year                 ...   0.926119        0.012831             0.024106   \n",
              "month                ...   0.028431       -0.252886            -0.253332   \n",
              "day                  ...  -0.003756        0.015833             0.002883   \n",
              "Fri                  ...  -0.002911       -0.017163            -0.001348   \n",
              "Mon                  ...   0.001650        0.038351            -0.000809   \n",
              "Sat                  ...  -0.001391        0.046165            -0.001163   \n",
              "Sun                  ...   0.000130       -0.025429            -0.000996   \n",
              "Thu                  ...   0.000437       -0.012172             0.002657   \n",
              "Tue                  ...   0.003171       -0.018451            -0.000809   \n",
              "Wed                  ...  -0.001089       -0.011382             0.002485   \n",
              "passed               ...   0.998987       -0.090027            -0.078607   \n",
              "emergency            ...  -0.337553        0.320128             0.340280   \n",
              "em_passed            ...   1.000000       -0.099849            -0.089267   \n",
              "contacts_count       ...  -0.099849        1.000000             0.964667   \n",
              "contacts_7d_mov_ave  ...  -0.089267        0.964667             1.000000   \n",
              "contacts_7d_mov_sum  ...  -0.089266        0.964667             1.000000   \n",
              "querents_count       ...  -0.626906        0.245632             0.247075   \n",
              "querents_7d_mov_ave  ...  -0.673839        0.268851             0.272494   \n",
              "querents_7d_mov_sum  ...  -0.673839        0.268851             0.272494   \n",
              "patients_count       ...   0.545738        0.479365             0.533924   \n",
              "patients_7d_mov_ave  ...   0.561902        0.447448             0.506776   \n",
              "patients_7d_mov_sum  ...   0.561903        0.447449             0.506777   \n",
              "\n",
              "                     contacts_7d_mov_sum  querents_count  querents_7d_mov_ave  \\\n",
              "timestamp                      -0.078605       -0.616739            -0.661713   \n",
              "year                            0.024107       -0.607706            -0.659257   \n",
              "month                          -0.253330        0.075715             0.100730   \n",
              "day                             0.002881        0.007024            -0.002525   \n",
              "Fri                            -0.001348        0.023316            -0.000576   \n",
              "Mon                            -0.000809        0.093759            -0.000576   \n",
              "Sat                            -0.001162       -0.109371            -0.000576   \n",
              "Sun                            -0.000995       -0.100810            -0.000576   \n",
              "Thu                             0.002658        0.028935             0.001446   \n",
              "Tue                            -0.000809        0.037087            -0.000576   \n",
              "Wed                             0.002482        0.027273             0.001446   \n",
              "passed                         -0.078605       -0.616739            -0.661713   \n",
              "emergency                       0.340279        0.241423             0.275418   \n",
              "em_passed                      -0.089266       -0.626906            -0.673839   \n",
              "contacts_count                  0.964667        0.245632             0.268851   \n",
              "contacts_7d_mov_ave             1.000000        0.247075             0.272494   \n",
              "contacts_7d_mov_sum             1.000000        0.247076             0.272494   \n",
              "querents_count                  0.247076        1.000000             0.919475   \n",
              "querents_7d_mov_ave             0.272494        0.919475             1.000000   \n",
              "querents_7d_mov_sum             0.272494        0.919475             1.000000   \n",
              "patients_count                  0.533923       -0.263829            -0.284867   \n",
              "patients_7d_mov_ave             0.506776       -0.268817            -0.291026   \n",
              "patients_7d_mov_sum             0.506777       -0.268817            -0.291025   \n",
              "\n",
              "                     querents_7d_mov_sum  patients_count  patients_7d_mov_ave  \\\n",
              "timestamp                      -0.661713        0.541340             0.557316   \n",
              "year                           -0.659257        0.644349             0.658050   \n",
              "month                           0.100730       -0.352421            -0.348655   \n",
              "day                            -0.002525       -0.012031            -0.016557   \n",
              "Fri                            -0.000576        0.017060            -0.000687   \n",
              "Mon                            -0.000576       -0.070995            -0.000052   \n",
              "Sat                            -0.000576        0.011876            -0.000405   \n",
              "Sun                            -0.000576       -0.011268            -0.000190   \n",
              "Thu                             0.001446        0.033541             0.000732   \n",
              "Tue                            -0.000576       -0.010420             0.000193   \n",
              "Wed                             0.001446        0.030420             0.000413   \n",
              "passed                         -0.661713        0.541340             0.557316   \n",
              "emergency                       0.275418       -0.118411            -0.120345   \n",
              "em_passed                      -0.673839        0.545738             0.561902   \n",
              "contacts_count                  0.268851        0.479365             0.447448   \n",
              "contacts_7d_mov_ave             0.272494        0.533924             0.506776   \n",
              "contacts_7d_mov_sum             0.272494        0.533923             0.506776   \n",
              "querents_count                  0.919475       -0.263829            -0.268817   \n",
              "querents_7d_mov_ave             1.000000       -0.284867            -0.291026   \n",
              "querents_7d_mov_sum             1.000000       -0.284867            -0.291026   \n",
              "patients_count                 -0.284867        1.000000             0.971832   \n",
              "patients_7d_mov_ave            -0.291026        0.971832             1.000000   \n",
              "patients_7d_mov_sum            -0.291025        0.971832             1.000000   \n",
              "\n",
              "                     patients_7d_mov_sum  \n",
              "timestamp                       0.557317  \n",
              "year                            0.658050  \n",
              "month                          -0.348653  \n",
              "day                            -0.016558  \n",
              "Fri                            -0.000687  \n",
              "Mon                            -0.000052  \n",
              "Sat                            -0.000405  \n",
              "Sun                            -0.000190  \n",
              "Thu                             0.000732  \n",
              "Tue                             0.000193  \n",
              "Wed                             0.000413  \n",
              "passed                          0.557317  \n",
              "emergency                      -0.120345  \n",
              "em_passed                       0.561903  \n",
              "contacts_count                  0.447449  \n",
              "contacts_7d_mov_ave             0.506777  \n",
              "contacts_7d_mov_sum             0.506777  \n",
              "querents_count                 -0.268817  \n",
              "querents_7d_mov_ave            -0.291025  \n",
              "querents_7d_mov_sum            -0.291025  \n",
              "patients_count                  0.971832  \n",
              "patients_7d_mov_ave             1.000000  \n",
              "patients_7d_mov_sum             1.000000  \n",
              "\n",
              "[23 rows x 23 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>timestamp</th>\n",
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              "      <th>day</th>\n",
              "      <th>Fri</th>\n",
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              "      <th>Thu</th>\n",
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              "      <th>querents_count</th>\n",
              "      <th>querents_7d_mov_ave</th>\n",
              "      <th>querents_7d_mov_sum</th>\n",
              "      <th>patients_count</th>\n",
              "      <th>patients_7d_mov_ave</th>\n",
              "      <th>patients_7d_mov_sum</th>\n",
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              "      <th>timestamp</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.921464</td>\n",
              "      <td>0.042948</td>\n",
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              "      <td>0.998987</td>\n",
              "      <td>-0.090027</td>\n",
              "      <td>-0.078607</td>\n",
              "      <td>-0.078605</td>\n",
              "      <td>-0.616739</td>\n",
              "      <td>-0.661713</td>\n",
              "      <td>-0.661713</td>\n",
              "      <td>0.541340</td>\n",
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              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>9.214641e-01</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.347105</td>\n",
              "      <td>-0.034888</td>\n",
              "      <td>-0.002305</td>\n",
              "      <td>0.002211</td>\n",
              "      <td>0.002211</td>\n",
              "      <td>2.211149e-03</td>\n",
              "      <td>-0.003281</td>\n",
              "      <td>0.002211</td>\n",
              "      <td>...</td>\n",
              "      <td>0.926119</td>\n",
              "      <td>0.012831</td>\n",
              "      <td>0.024106</td>\n",
              "      <td>0.024107</td>\n",
              "      <td>-0.607706</td>\n",
              "      <td>-0.659257</td>\n",
              "      <td>-0.659257</td>\n",
              "      <td>0.644349</td>\n",
              "      <td>0.658050</td>\n",
              "      <td>0.658050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>month</th>\n",
              "      <td>4.294783e-02</td>\n",
              "      <td>-0.347105</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.005604</td>\n",
              "      <td>-0.002235</td>\n",
              "      <td>-0.002235</td>\n",
              "      <td>-0.010007</td>\n",
              "      <td>-6.120919e-03</td>\n",
              "      <td>0.010480</td>\n",
              "      <td>0.002623</td>\n",
              "      <td>...</td>\n",
              "      <td>0.028431</td>\n",
              "      <td>-0.252886</td>\n",
              "      <td>-0.253332</td>\n",
              "      <td>-0.253330</td>\n",
              "      <td>0.075715</td>\n",
              "      <td>0.100730</td>\n",
              "      <td>0.100730</td>\n",
              "      <td>-0.352421</td>\n",
              "      <td>-0.348655</td>\n",
              "      <td>-0.348653</td>\n",
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              "    <tr>\n",
              "      <th>day</th>\n",
              "      <td>-4.158056e-03</td>\n",
              "      <td>-0.034888</td>\n",
              "      <td>-0.005604</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.003115</td>\n",
              "      <td>0.005721</td>\n",
              "      <td>0.003487</td>\n",
              "      <td>4.604204e-03</td>\n",
              "      <td>-0.000341</td>\n",
              "      <td>-0.004332</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.003756</td>\n",
              "      <td>0.015833</td>\n",
              "      <td>0.002883</td>\n",
              "      <td>0.002881</td>\n",
              "      <td>0.007024</td>\n",
              "      <td>-0.002525</td>\n",
              "      <td>-0.002525</td>\n",
              "      <td>-0.012031</td>\n",
              "      <td>-0.016557</td>\n",
              "      <td>-0.016558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fri</th>\n",
              "      <td>-3.270483e-03</td>\n",
              "      <td>-0.002305</td>\n",
              "      <td>-0.002235</td>\n",
              "      <td>0.003115</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.167116</td>\n",
              "      <td>-0.167116</td>\n",
              "      <td>-1.671159e-01</td>\n",
              "      <td>-0.166329</td>\n",
              "      <td>-0.167116</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.002911</td>\n",
              "      <td>-0.017163</td>\n",
              "      <td>-0.001348</td>\n",
              "      <td>-0.001348</td>\n",
              "      <td>0.023316</td>\n",
              "      <td>-0.000576</td>\n",
              "      <td>-0.000576</td>\n",
              "      <td>0.017060</td>\n",
              "      <td>-0.000687</td>\n",
              "      <td>-0.000687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mon</th>\n",
              "      <td>1.635242e-03</td>\n",
              "      <td>0.002211</td>\n",
              "      <td>-0.002235</td>\n",
              "      <td>0.005721</td>\n",
              "      <td>-0.167116</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.167116</td>\n",
              "      <td>-1.671159e-01</td>\n",
              "      <td>-0.166329</td>\n",
              "      <td>-0.167116</td>\n",
              "      <td>...</td>\n",
              "      <td>0.001650</td>\n",
              "      <td>0.038351</td>\n",
              "      <td>-0.000809</td>\n",
              "      <td>-0.000809</td>\n",
              "      <td>0.093759</td>\n",
              "      <td>-0.000576</td>\n",
              "      <td>-0.000576</td>\n",
              "      <td>-0.070995</td>\n",
              "      <td>-0.000052</td>\n",
              "      <td>-0.000052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sat</th>\n",
              "      <td>-1.635242e-03</td>\n",
              "      <td>0.002211</td>\n",
              "      <td>-0.010007</td>\n",
              "      <td>0.003487</td>\n",
              "      <td>-0.167116</td>\n",
              "      <td>-0.167116</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.671159e-01</td>\n",
              "      <td>-0.166329</td>\n",
              "      <td>-0.167116</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.001391</td>\n",
              "      <td>0.046165</td>\n",
              "      <td>-0.001163</td>\n",
              "      <td>-0.001162</td>\n",
              "      <td>-0.109371</td>\n",
              "      <td>-0.000576</td>\n",
              "      <td>-0.000576</td>\n",
              "      <td>0.011876</td>\n",
              "      <td>-0.000405</td>\n",
              "      <td>-0.000405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sun</th>\n",
              "      <td>-3.132984e-16</td>\n",
              "      <td>0.002211</td>\n",
              "      <td>-0.006121</td>\n",
              "      <td>0.004604</td>\n",
              "      <td>-0.167116</td>\n",
              "      <td>-0.167116</td>\n",
              "      <td>-0.167116</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>-0.166329</td>\n",
              "      <td>-0.167116</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000130</td>\n",
              "      <td>-0.025429</td>\n",
              "      <td>-0.000996</td>\n",
              "      <td>-0.000995</td>\n",
              "      <td>-0.100810</td>\n",
              "      <td>-0.000576</td>\n",
              "      <td>-0.000576</td>\n",
              "      <td>-0.011268</td>\n",
              "      <td>-0.000190</td>\n",
              "      <td>-0.000190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thu</th>\n",
              "      <td>8.137691e-04</td>\n",
              "      <td>-0.003281</td>\n",
              "      <td>0.010480</td>\n",
              "      <td>-0.000341</td>\n",
              "      <td>-0.166329</td>\n",
              "      <td>-0.166329</td>\n",
              "      <td>-0.166329</td>\n",
              "      <td>-1.663286e-01</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.166329</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000437</td>\n",
              "      <td>-0.012172</td>\n",
              "      <td>0.002657</td>\n",
              "      <td>0.002658</td>\n",
              "      <td>0.028935</td>\n",
              "      <td>0.001446</td>\n",
              "      <td>0.001446</td>\n",
              "      <td>0.033541</td>\n",
              "      <td>0.000732</td>\n",
              "      <td>0.000732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue</th>\n",
              "      <td>3.270483e-03</td>\n",
              "      <td>0.002211</td>\n",
              "      <td>0.002623</td>\n",
              "      <td>-0.004332</td>\n",
              "      <td>-0.167116</td>\n",
              "      <td>-0.167116</td>\n",
              "      <td>-0.167116</td>\n",
              "      <td>-1.671159e-01</td>\n",
              "      <td>-0.166329</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.003171</td>\n",
              "      <td>-0.018451</td>\n",
              "      <td>-0.000809</td>\n",
              "      <td>-0.000809</td>\n",
              "      <td>0.037087</td>\n",
              "      <td>-0.000576</td>\n",
              "      <td>-0.000576</td>\n",
              "      <td>-0.010420</td>\n",
              "      <td>0.000193</td>\n",
              "      <td>0.000193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed</th>\n",
              "      <td>-8.137691e-04</td>\n",
              "      <td>-0.003281</td>\n",
              "      <td>0.007555</td>\n",
              "      <td>-0.012297</td>\n",
              "      <td>-0.166329</td>\n",
              "      <td>-0.166329</td>\n",
              "      <td>-0.166329</td>\n",
              "      <td>-1.663286e-01</td>\n",
              "      <td>-0.165545</td>\n",
              "      <td>-0.166329</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.001089</td>\n",
              "      <td>-0.011382</td>\n",
              "      <td>0.002485</td>\n",
              "      <td>0.002482</td>\n",
              "      <td>0.027273</td>\n",
              "      <td>0.001446</td>\n",
              "      <td>0.001446</td>\n",
              "      <td>0.030420</td>\n",
              "      <td>0.000413</td>\n",
              "      <td>0.000413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>passed</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.921464</td>\n",
              "      <td>0.042948</td>\n",
              "      <td>-0.004158</td>\n",
              "      <td>-0.003270</td>\n",
              "      <td>0.001635</td>\n",
              "      <td>-0.001635</td>\n",
              "      <td>-3.156832e-16</td>\n",
              "      <td>0.000814</td>\n",
              "      <td>0.003270</td>\n",
              "      <td>...</td>\n",
              "      <td>0.998987</td>\n",
              "      <td>-0.090027</td>\n",
              "      <td>-0.078607</td>\n",
              "      <td>-0.078605</td>\n",
              "      <td>-0.616739</td>\n",
              "      <td>-0.661713</td>\n",
              "      <td>-0.661713</td>\n",
              "      <td>0.541340</td>\n",
              "      <td>0.557316</td>\n",
              "      <td>0.557317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>emergency</th>\n",
              "      <td>-3.276847e-01</td>\n",
              "      <td>-0.263815</td>\n",
              "      <td>-0.111186</td>\n",
              "      <td>-0.000802</td>\n",
              "      <td>-0.000231</td>\n",
              "      <td>-0.000231</td>\n",
              "      <td>-0.000231</td>\n",
              "      <td>-2.306780e-04</td>\n",
              "      <td>0.000579</td>\n",
              "      <td>-0.000231</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.337553</td>\n",
              "      <td>0.320128</td>\n",
              "      <td>0.340280</td>\n",
              "      <td>0.340279</td>\n",
              "      <td>0.241423</td>\n",
              "      <td>0.275418</td>\n",
              "      <td>0.275418</td>\n",
              "      <td>-0.118411</td>\n",
              "      <td>-0.120345</td>\n",
              "      <td>-0.120345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>em_passed</th>\n",
              "      <td>9.989874e-01</td>\n",
              "      <td>0.926119</td>\n",
              "      <td>0.028431</td>\n",
              "      <td>-0.003756</td>\n",
              "      <td>-0.002911</td>\n",
              "      <td>0.001650</td>\n",
              "      <td>-0.001391</td>\n",
              "      <td>1.299265e-04</td>\n",
              "      <td>0.000437</td>\n",
              "      <td>0.003171</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.099849</td>\n",
              "      <td>-0.089267</td>\n",
              "      <td>-0.089266</td>\n",
              "      <td>-0.626906</td>\n",
              "      <td>-0.673839</td>\n",
              "      <td>-0.673839</td>\n",
              "      <td>0.545738</td>\n",
              "      <td>0.561902</td>\n",
              "      <td>0.561903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>contacts_count</th>\n",
              "      <td>-9.002749e-02</td>\n",
              "      <td>0.012831</td>\n",
              "      <td>-0.252886</td>\n",
              "      <td>0.015833</td>\n",
              "      <td>-0.017163</td>\n",
              "      <td>0.038351</td>\n",
              "      <td>0.046165</td>\n",
              "      <td>-2.542856e-02</td>\n",
              "      <td>-0.012172</td>\n",
              "      <td>-0.018451</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.099849</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.964667</td>\n",
              "      <td>0.964667</td>\n",
              "      <td>0.245632</td>\n",
              "      <td>0.268851</td>\n",
              "      <td>0.268851</td>\n",
              "      <td>0.479365</td>\n",
              "      <td>0.447448</td>\n",
              "      <td>0.447449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>contacts_7d_mov_ave</th>\n",
              "      <td>-7.860704e-02</td>\n",
              "      <td>0.024106</td>\n",
              "      <td>-0.253332</td>\n",
              "      <td>0.002883</td>\n",
              "      <td>-0.001348</td>\n",
              "      <td>-0.000809</td>\n",
              "      <td>-0.001163</td>\n",
              "      <td>-9.957025e-04</td>\n",
              "      <td>0.002657</td>\n",
              "      <td>-0.000809</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.089267</td>\n",
              "      <td>0.964667</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.247075</td>\n",
              "      <td>0.272494</td>\n",
              "      <td>0.272494</td>\n",
              "      <td>0.533924</td>\n",
              "      <td>0.506776</td>\n",
              "      <td>0.506777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>contacts_7d_mov_sum</th>\n",
              "      <td>-7.860487e-02</td>\n",
              "      <td>0.024107</td>\n",
              "      <td>-0.253330</td>\n",
              "      <td>0.002881</td>\n",
              "      <td>-0.001348</td>\n",
              "      <td>-0.000809</td>\n",
              "      <td>-0.001162</td>\n",
              "      <td>-9.952113e-04</td>\n",
              "      <td>0.002658</td>\n",
              "      <td>-0.000809</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.089266</td>\n",
              "      <td>0.964667</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.247076</td>\n",
              "      <td>0.272494</td>\n",
              "      <td>0.272494</td>\n",
              "      <td>0.533923</td>\n",
              "      <td>0.506776</td>\n",
              "      <td>0.506777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>querents_count</th>\n",
              "      <td>-6.167394e-01</td>\n",
              "      <td>-0.607706</td>\n",
              "      <td>0.075715</td>\n",
              "      <td>0.007024</td>\n",
              "      <td>0.023316</td>\n",
              "      <td>0.093759</td>\n",
              "      <td>-0.109371</td>\n",
              "      <td>-1.008096e-01</td>\n",
              "      <td>0.028935</td>\n",
              "      <td>0.037087</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.626906</td>\n",
              "      <td>0.245632</td>\n",
              "      <td>0.247075</td>\n",
              "      <td>0.247076</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.919475</td>\n",
              "      <td>0.919475</td>\n",
              "      <td>-0.263829</td>\n",
              "      <td>-0.268817</td>\n",
              "      <td>-0.268817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>querents_7d_mov_ave</th>\n",
              "      <td>-6.617130e-01</td>\n",
              "      <td>-0.659257</td>\n",
              "      <td>0.100730</td>\n",
              "      <td>-0.002525</td>\n",
              "      <td>-0.000576</td>\n",
              "      <td>-0.000576</td>\n",
              "      <td>-0.000576</td>\n",
              "      <td>-5.764506e-04</td>\n",
              "      <td>0.001446</td>\n",
              "      <td>-0.000576</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.673839</td>\n",
              "      <td>0.268851</td>\n",
              "      <td>0.272494</td>\n",
              "      <td>0.272494</td>\n",
              "      <td>0.919475</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.284867</td>\n",
              "      <td>-0.291026</td>\n",
              "      <td>-0.291025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>querents_7d_mov_sum</th>\n",
              "      <td>-6.617130e-01</td>\n",
              "      <td>-0.659257</td>\n",
              "      <td>0.100730</td>\n",
              "      <td>-0.002525</td>\n",
              "      <td>-0.000576</td>\n",
              "      <td>-0.000576</td>\n",
              "      <td>-0.000576</td>\n",
              "      <td>-5.764506e-04</td>\n",
              "      <td>0.001446</td>\n",
              "      <td>-0.000576</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.673839</td>\n",
              "      <td>0.268851</td>\n",
              "      <td>0.272494</td>\n",
              "      <td>0.272494</td>\n",
              "      <td>0.919475</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.284867</td>\n",
              "      <td>-0.291026</td>\n",
              "      <td>-0.291025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>patients_count</th>\n",
              "      <td>5.413401e-01</td>\n",
              "      <td>0.644349</td>\n",
              "      <td>-0.352421</td>\n",
              "      <td>-0.012031</td>\n",
              "      <td>0.017060</td>\n",
              "      <td>-0.070995</td>\n",
              "      <td>0.011876</td>\n",
              "      <td>-1.126784e-02</td>\n",
              "      <td>0.033541</td>\n",
              "      <td>-0.010420</td>\n",
              "      <td>...</td>\n",
              "      <td>0.545738</td>\n",
              "      <td>0.479365</td>\n",
              "      <td>0.533924</td>\n",
              "      <td>0.533923</td>\n",
              "      <td>-0.263829</td>\n",
              "      <td>-0.284867</td>\n",
              "      <td>-0.284867</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.971832</td>\n",
              "      <td>0.971832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>patients_7d_mov_ave</th>\n",
              "      <td>5.573158e-01</td>\n",
              "      <td>0.658050</td>\n",
              "      <td>-0.348655</td>\n",
              "      <td>-0.016557</td>\n",
              "      <td>-0.000687</td>\n",
              "      <td>-0.000052</td>\n",
              "      <td>-0.000405</td>\n",
              "      <td>-1.896826e-04</td>\n",
              "      <td>0.000732</td>\n",
              "      <td>0.000193</td>\n",
              "      <td>...</td>\n",
              "      <td>0.561902</td>\n",
              "      <td>0.447448</td>\n",
              "      <td>0.506776</td>\n",
              "      <td>0.506776</td>\n",
              "      <td>-0.268817</td>\n",
              "      <td>-0.291026</td>\n",
              "      <td>-0.291026</td>\n",
              "      <td>0.971832</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>patients_7d_mov_sum</th>\n",
              "      <td>5.573169e-01</td>\n",
              "      <td>0.658050</td>\n",
              "      <td>-0.348653</td>\n",
              "      <td>-0.016558</td>\n",
              "      <td>-0.000687</td>\n",
              "      <td>-0.000052</td>\n",
              "      <td>-0.000405</td>\n",
              "      <td>-1.897278e-04</td>\n",
              "      <td>0.000732</td>\n",
              "      <td>0.000193</td>\n",
              "      <td>...</td>\n",
              "      <td>0.561903</td>\n",
              "      <td>0.447449</td>\n",
              "      <td>0.506777</td>\n",
              "      <td>0.506777</td>\n",
              "      <td>-0.268817</td>\n",
              "      <td>-0.291025</td>\n",
              "      <td>-0.291025</td>\n",
              "      <td>0.971832</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>23 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-85fd0af9-047d-4d4b-b711-ebf0189b27e8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-85fd0af9-047d-4d4b-b711-ebf0189b27e8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-85fd0af9-047d-4d4b-b711-ebf0189b27e8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F2MMo2XSZtgu"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EmMjAKb3Z0fJ",
        "outputId": "da4493cf-259f-4e9b-b781-526d89993884"
      },
      "source": [
        "ds_df.columns"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['timestamp', 'year', 'month', 'day', 'Fri', 'Mon', 'Sat', 'Sun', 'Thu',\n",
              "       'Tue', 'Wed', 'passed', 'emergency', 'em_passed', 'contacts_count',\n",
              "       'contacts_7d_mov_ave', 'contacts_7d_mov_sum', 'querents_count',\n",
              "       'querents_7d_mov_ave', 'querents_7d_mov_sum', 'patients_count',\n",
              "       'patients_7d_mov_ave', 'patients_7d_mov_sum'],\n",
              "      dtype='object')"
            ]
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          "metadata": {},
          "execution_count": 24
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 424
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        "id": "E7NmtA3QdTgj",
        "outputId": "3a4e6f8e-dc9a-4654-b8b6-df1a7a9f38fe"
      },
      "source": [
        "_df = ds_df.copy()\n",
        "_df = _df.drop(['year', 'month', 'day', \n",
        "          'contacts_7d_mov_ave', 'contacts_7d_mov_sum', \n",
        "          'querents_7d_mov_ave', 'querents_7d_mov_sum', \n",
        "          ], axis=1)\n",
        "_df"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        timestamp  Fri  Mon  Sat  Sun  Thu  Tue  Wed  passed  emergency  \\\n",
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              "4    1.580198e+09    0    0    0    0    0    1    0       4          0   \n",
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              "865  1.654589e+09    0    0    0    0    0    1    0     865          0   \n",
              "\n",
              "     em_passed  contacts_count  querents_count  patients_count  \\\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
          "execution_count": 25
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    {
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        "_df2 = _df[['timestamp', 'passed', 'emergency', 'patients_count', 'patients_7d_mov_ave', 'patients_7d_mov_sum']]\n",
        "_df2"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        timestamp  passed  emergency  patients_count  patients_7d_mov_ave  \\\n",
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              "1    1.579939e+09       1          0               1             1.000000   \n",
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              "      <td>0.666667</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.580112e+09</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.580198e+09</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>2.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>861</th>\n",
              "      <td>1.654243e+09</td>\n",
              "      <td>861</td>\n",
              "      <td>0</td>\n",
              "      <td>2111</td>\n",
              "      <td>2187.142857</td>\n",
              "      <td>15310.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>862</th>\n",
              "      <td>1.654330e+09</td>\n",
              "      <td>862</td>\n",
              "      <td>0</td>\n",
              "      <td>2071</td>\n",
              "      <td>2118.857143</td>\n",
              "      <td>14832.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>863</th>\n",
              "      <td>1.654416e+09</td>\n",
              "      <td>863</td>\n",
              "      <td>0</td>\n",
              "      <td>1584</td>\n",
              "      <td>2031.714286</td>\n",
              "      <td>14222.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>864</th>\n",
              "      <td>1.654502e+09</td>\n",
              "      <td>864</td>\n",
              "      <td>0</td>\n",
              "      <td>1013</td>\n",
              "      <td>1984.428571</td>\n",
              "      <td>13891.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>865</th>\n",
              "      <td>1.654589e+09</td>\n",
              "      <td>865</td>\n",
              "      <td>0</td>\n",
              "      <td>1800</td>\n",
              "      <td>1904.142857</td>\n",
              "      <td>13329.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>866 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-29881739-d59f-4b82-ae64-e772db9beacb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-29881739-d59f-4b82-ae64-e772db9beacb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-29881739-d59f-4b82-ae64-e772db9beacb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PBG2UIHpZldo"
      },
      "source": [
        "sns.set(style=\"ticks\", color_codes=True)\n",
        "#sns.pairplot(data=_df)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "GvL38TUTbjZA",
        "outputId": "142ac476-ecc6-4e66-a1f2-5deb940aee6e"
      },
      "source": [
        "sns.pairplot(data=_df2)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f98bcc41d90>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 42 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i-Wi3OuJmeyE",
        "outputId": "543776f2-7336-49e4-e8dd-5c0f884620e2"
      },
      "source": [
        "!python -V"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python 3.7.13\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i88Cw7JEL8pG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38a5ff0a-7e19-47a1-e786-cc808043ce95"
      },
      "source": [
        "!pip freeze"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "absl-py==1.0.0\n",
            "alabaster==0.7.12\n",
            "albumentations==0.1.12\n",
            "altair==4.2.0\n",
            "appdirs==1.4.4\n",
            "argon2-cffi==21.3.0\n",
            "argon2-cffi-bindings==21.2.0\n",
            "arviz==0.12.1\n",
            "astor==0.8.1\n",
            "astropy==4.3.1\n",
            "astunparse==1.6.3\n",
            "atari-py==0.2.9\n",
            "atomicwrites==1.4.0\n",
            "attrs==21.4.0\n",
            "audioread==2.1.9\n",
            "autograd==1.4\n",
            "Babel==2.10.1\n",
            "backcall==0.2.0\n",
            "beautifulsoup4==4.6.3\n",
            "bleach==5.0.0\n",
            "blis==0.4.1\n",
            "bokeh==2.3.3\n",
            "Bottleneck==1.3.4\n",
            "branca==0.5.0\n",
            "bs4==0.0.1\n",
            "CacheControl==0.12.11\n",
            "cached-property==1.5.2\n",
            "cachetools==4.2.4\n",
            "catalogue==1.0.0\n",
            "certifi==2022.5.18.1\n",
            "cffi==1.15.0\n",
            "cftime==1.6.0\n",
            "chardet==3.0.4\n",
            "charset-normalizer==2.0.12\n",
            "click==7.1.2\n",
            "cloudpickle==1.3.0\n",
            "cmake==3.22.4\n",
            "cmdstanpy==0.9.5\n",
            "colorcet==3.0.0\n",
            "colorlover==0.3.0\n",
            "community==1.0.0b1\n",
            "contextlib2==0.5.5\n",
            "convertdate==2.4.0\n",
            "coverage==3.7.1\n",
            "coveralls==0.5\n",
            "crcmod==1.7\n",
            "cufflinks==0.17.3\n",
            "cvxopt==1.2.7\n",
            "cvxpy==1.0.31\n",
            "cycler==0.11.0\n",
            "cymem==2.0.6\n",
            "Cython==0.29.30\n",
            "daft==0.0.4\n",
            "dask==2.12.0\n",
            "datascience==0.10.6\n",
            "debugpy==1.0.0\n",
            "decorator==4.4.2\n",
            "defusedxml==0.7.1\n",
            "descartes==1.1.0\n",
            "dill==0.3.5.1\n",
            "distributed==1.25.3\n",
            "dlib==19.18.0+zzzcolab20220513001918\n",
            "dm-tree==0.1.7\n",
            "docopt==0.6.2\n",
            "docutils==0.17.1\n",
            "dopamine-rl==1.0.5\n",
            "earthengine-api==0.1.311\n",
            "easydict==1.9\n",
            "ecos==2.0.10\n",
            "editdistance==0.5.3\n",
            "en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.5/en_core_web_sm-2.2.5.tar.gz\n",
            "entrypoints==0.4\n",
            "ephem==4.1.3\n",
            "et-xmlfile==1.1.0\n",
            "fa2==0.3.5\n",
            "fastai==1.0.61\n",
            "fastdtw==0.3.4\n",
            "fastjsonschema==2.15.3\n",
            "fastprogress==1.0.2\n",
            "fastrlock==0.8\n",
            "fbprophet==0.7.1\n",
            "feather-format==0.4.1\n",
            "filelock==3.7.0\n",
            "firebase-admin==4.4.0\n",
            "fix-yahoo-finance==0.0.22\n",
            "Flask==1.1.4\n",
            "flatbuffers==2.0\n",
            "folium==0.8.3\n",
            "future==0.16.0\n",
            "gast==0.5.3\n",
            "GDAL==2.2.2\n",
            "gdown==4.4.0\n",
            "gensim==3.6.0\n",
            "geographiclib==1.52\n",
            "geopy==1.17.0\n",
            "gin-config==0.5.0\n",
            "glob2==0.7\n",
            "google==2.0.3\n",
            "google-api-core==1.31.6\n",
            "google-api-python-client==1.12.11\n",
            "google-auth==1.35.0\n",
            "google-auth-httplib2==0.0.4\n",
            "google-auth-oauthlib==0.4.6\n",
            "google-cloud-bigquery==1.21.0\n",
            "google-cloud-bigquery-storage==1.1.1\n",
            "google-cloud-core==1.0.3\n",
            "google-cloud-datastore==1.8.0\n",
            "google-cloud-firestore==1.7.0\n",
            "google-cloud-language==1.2.0\n",
            "google-cloud-storage==1.18.1\n",
            "google-cloud-translate==1.5.0\n",
            "google-colab @ file:///colabtools/dist/google-colab-1.0.0.tar.gz\n",
            "google-pasta==0.2.0\n",
            "google-resumable-media==0.4.1\n",
            "googleapis-common-protos==1.56.2\n",
            "googledrivedownloader==0.4\n",
            "graphviz==0.10.1\n",
            "greenlet==1.1.2\n",
            "grpcio==1.46.3\n",
            "gspread==3.4.2\n",
            "gspread-dataframe==3.0.8\n",
            "gym==0.17.3\n",
            "h5py==3.1.0\n",
            "HeapDict==1.0.1\n",
            "hijri-converter==2.2.4\n",
            "holidays==0.10.5.2\n",
            "holoviews==1.14.9\n",
            "html5lib==1.0.1\n",
            "httpimport==0.5.18\n",
            "httplib2==0.17.4\n",
            "httplib2shim==0.0.3\n",
            "humanize==0.5.1\n",
            "hyperopt==0.1.2\n",
            "ideep4py==2.0.0.post3\n",
            "idna==2.10\n",
            "imageio==2.4.1\n",
            "imagesize==1.3.0\n",
            "imbalanced-learn==0.8.1\n",
            "imblearn==0.0\n",
            "imgaug==0.2.9\n",
            "importlib-metadata==4.11.4\n",
            "importlib-resources==5.7.1\n",
            "imutils==0.5.4\n",
            "inflect==2.1.0\n",
            "iniconfig==1.1.1\n",
            "intel-openmp==2022.1.0\n",
            "intervaltree==2.1.0\n",
            "ipykernel==4.10.1\n",
            "ipython==5.5.0\n",
            "ipython-genutils==0.2.0\n",
            "ipython-sql==0.3.9\n",
            "ipywidgets==7.7.0\n",
            "itsdangerous==1.1.0\n",
            "jax==0.3.8\n",
            "jaxlib @ https://storage.googleapis.com/jax-releases/cuda11/jaxlib-0.3.7+cuda11.cudnn805-cp37-none-manylinux2014_x86_64.whl\n",
            "jedi==0.18.1\n",
            "jieba==0.42.1\n",
            "Jinja2==2.11.3\n",
            "joblib==1.1.0\n",
            "jpeg4py==0.1.4\n",
            "jsonschema==4.3.3\n",
            "jupyter==1.0.0\n",
            "jupyter-client==5.3.5\n",
            "jupyter-console==5.2.0\n",
            "jupyter-core==4.10.0\n",
            "jupyterlab-pygments==0.2.2\n",
            "jupyterlab-widgets==1.1.0\n",
            "kaggle==1.5.12\n",
            "kapre==0.3.7\n",
            "keras==2.8.0\n",
            "Keras-Preprocessing==1.1.2\n",
            "keras-vis==0.4.1\n",
            "kiwisolver==1.4.2\n",
            "korean-lunar-calendar==0.2.1\n",
            "libclang==14.0.1\n",
            "librosa==0.8.1\n",
            "lightgbm==2.2.3\n",
            "llvmlite==0.34.0\n",
            "lmdb==0.99\n",
            "LunarCalendar==0.0.9\n",
            "lxml==4.2.6\n",
            "Markdown==3.3.7\n",
            "MarkupSafe==2.0.1\n",
            "matplotlib==3.2.2\n",
            "matplotlib-inline==0.1.3\n",
            "matplotlib-venn==0.11.7\n",
            "missingno==0.5.1\n",
            "mistune==0.8.4\n",
            "mizani==0.6.0\n",
            "mkl==2019.0\n",
            "mlxtend==0.14.0\n",
            "more-itertools==8.13.0\n",
            "moviepy==0.2.3.5\n",
            "mpmath==1.2.1\n",
            "msgpack==1.0.3\n",
            "multiprocess==0.70.13\n",
            "multitasking==0.0.10\n",
            "murmurhash==1.0.7\n",
            "music21==5.5.0\n",
            "natsort==5.5.0\n",
            "nbclient==0.6.4\n",
            "nbconvert==5.6.1\n",
            "nbformat==5.4.0\n",
            "nest-asyncio==1.5.5\n",
            "netCDF4==1.5.8\n",
            "networkx==2.6.3\n",
            "nibabel==3.0.2\n",
            "nltk==3.2.5\n",
            "notebook==5.3.1\n",
            "numba==0.51.2\n",
            "numexpr==2.8.1\n",
            "numpy==1.21.6\n",
            "nvidia-ml-py3==7.352.0\n",
            "oauth2client==4.1.3\n",
            "oauthlib==3.2.0\n",
            "okgrade==0.4.3\n",
            "opencv-contrib-python==4.1.2.30\n",
            "opencv-python==4.1.2.30\n",
            "openpyxl==3.0.10\n",
            "opt-einsum==3.3.0\n",
            "osqp==0.6.2.post0\n",
            "packaging==21.3\n",
            "palettable==3.3.0\n",
            "pandas==1.3.5\n",
            "pandas-datareader==0.9.0\n",
            "pandas-gbq==0.13.3\n",
            "pandas-profiling==1.4.1\n",
            "pandocfilters==1.5.0\n",
            "panel==0.12.1\n",
            "param==1.12.1\n",
            "parso==0.8.3\n",
            "pathlib==1.0.1\n",
            "patsy==0.5.2\n",
            "pep517==0.12.0\n",
            "pexpect==4.8.0\n",
            "pickleshare==0.7.5\n",
            "Pillow==7.1.2\n",
            "pip-tools==6.2.0\n",
            "plac==1.1.3\n",
            "plotly==5.5.0\n",
            "plotnine==0.6.0\n",
            "pluggy==0.7.1\n",
            "pooch==1.6.0\n",
            "portpicker==1.3.9\n",
            "prefetch-generator==1.0.1\n",
            "preshed==3.0.6\n",
            "prettytable==3.3.0\n",
            "progressbar2==3.38.0\n",
            "prometheus-client==0.14.1\n",
            "promise==2.3\n",
            "prompt-toolkit==1.0.18\n",
            "protobuf==3.17.3\n",
            "psutil==5.4.8\n",
            "psycopg2==2.7.6.1\n",
            "ptyprocess==0.7.0\n",
            "py==1.11.0\n",
            "pyarrow==6.0.1\n",
            "pyasn1==0.4.8\n",
            "pyasn1-modules==0.2.8\n",
            "pycocotools==2.0.4\n",
            "pycparser==2.21\n",
            "pyct==0.4.8\n",
            "pydata-google-auth==1.4.0\n",
            "pydot==1.3.0\n",
            "pydot-ng==2.0.0\n",
            "pydotplus==2.0.2\n",
            "PyDrive==1.3.1\n",
            "pyemd==0.5.1\n",
            "pyerfa==2.0.0.1\n",
            "pyglet==1.5.0\n",
            "Pygments==2.6.1\n",
            "pygobject==3.26.1\n",
            "pymc3==3.11.4\n",
            "PyMeeus==0.5.11\n",
            "pymongo==4.1.1\n",
            "pymystem3==0.2.0\n",
            "PyOpenGL==3.1.6\n",
            "pyparsing==3.0.9\n",
            "pyrsistent==0.18.1\n",
            "pysndfile==1.3.8\n",
            "PySocks==1.7.1\n",
            "pystan==2.19.1.1\n",
            "pytest==3.6.4\n",
            "python-apt==0.0.0\n",
            "python-chess==0.23.11\n",
            "python-dateutil==2.8.2\n",
            "python-louvain==0.16\n",
            "python-slugify==6.1.2\n",
            "python-utils==3.2.3\n",
            "pytz==2022.1\n",
            "pyviz-comms==2.2.0\n",
            "PyWavelets==1.3.0\n",
            "PyYAML==3.13\n",
            "pyzmq==23.0.0\n",
            "qdldl==0.1.5.post2\n",
            "qtconsole==5.3.0\n",
            "QtPy==2.1.0\n",
            "regex==2019.12.20\n",
            "requests==2.23.0\n",
            "requests-oauthlib==1.3.1\n",
            "resampy==0.2.2\n",
            "rpy2==3.4.5\n",
            "rsa==4.8\n",
            "scikit-image==0.18.3\n",
            "scikit-learn==1.0.2\n",
            "scipy==1.4.1\n",
            "screen-resolution-extra==0.0.0\n",
            "scs==3.2.0\n",
            "seaborn==0.11.2\n",
            "semver==2.13.0\n",
            "Send2Trash==1.8.0\n",
            "setuptools-git==1.2\n",
            "Shapely==1.8.2\n",
            "simplegeneric==0.8.1\n",
            "six==1.15.0\n",
            "sklearn==0.0\n",
            "sklearn-pandas==1.8.0\n",
            "smart-open==6.0.0\n",
            "snowballstemmer==2.2.0\n",
            "sortedcontainers==2.4.0\n",
            "SoundFile==0.10.3.post1\n",
            "soupsieve==2.3.2.post1\n",
            "spacy==2.2.4\n",
            "Sphinx==1.8.6\n",
            "sphinxcontrib-serializinghtml==1.1.5\n",
            "sphinxcontrib-websupport==1.2.4\n",
            "SQLAlchemy==1.4.36\n",
            "sqlparse==0.4.2\n",
            "srsly==1.0.5\n",
            "statsmodels==0.10.2\n",
            "sympy==1.7.1\n",
            "tables==3.7.0\n",
            "tabulate==0.8.9\n",
            "tblib==1.7.0\n",
            "tenacity==8.0.1\n",
            "tensorboard==2.8.0\n",
            "tensorboard-data-server==0.6.1\n",
            "tensorboard-plugin-wit==1.8.1\n",
            "tensorflow==2.8.2+zzzcolab20220527125636\n",
            "tensorflow-datasets==4.0.1\n",
            "tensorflow-estimator==2.8.0\n",
            "tensorflow-gcs-config==2.8.0\n",
            "tensorflow-hub==0.12.0\n",
            "tensorflow-io-gcs-filesystem==0.26.0\n",
            "tensorflow-metadata==1.8.0\n",
            "tensorflow-probability==0.16.0\n",
            "termcolor==1.1.0\n",
            "terminado==0.13.3\n",
            "testpath==0.6.0\n",
            "text-unidecode==1.3\n",
            "textblob==0.15.3\n",
            "Theano-PyMC==1.1.2\n",
            "thinc==7.4.0\n",
            "threadpoolctl==3.1.0\n",
            "tifffile==2021.11.2\n",
            "tinycss2==1.1.1\n",
            "tomli==2.0.1\n",
            "toolz==0.11.2\n",
            "torch @ https://download.pytorch.org/whl/cu113/torch-1.11.0%2Bcu113-cp37-cp37m-linux_x86_64.whl\n",
            "torchaudio @ https://download.pytorch.org/whl/cu113/torchaudio-0.11.0%2Bcu113-cp37-cp37m-linux_x86_64.whl\n",
            "torchsummary==1.5.1\n",
            "torchtext==0.12.0\n",
            "torchvision @ https://download.pytorch.org/whl/cu113/torchvision-0.12.0%2Bcu113-cp37-cp37m-linux_x86_64.whl\n",
            "tornado==5.1.1\n",
            "tqdm==4.64.0\n",
            "traitlets==5.1.1\n",
            "tweepy==3.10.0\n",
            "typeguard==2.7.1\n",
            "typing-extensions==4.2.0\n",
            "tzlocal==1.5.1\n",
            "uritemplate==3.0.1\n",
            "urllib3==1.24.3\n",
            "vega-datasets==0.9.0\n",
            "wasabi==0.9.1\n",
            "wcwidth==0.2.5\n",
            "webencodings==0.5.1\n",
            "Werkzeug==1.0.1\n",
            "widgetsnbextension==3.6.0\n",
            "wordcloud==1.5.0\n",
            "wrapt==1.14.1\n",
            "xarray==0.20.2\n",
            "xarray-einstats==0.2.2\n",
            "xgboost==0.90\n",
            "xkit==0.0.0\n",
            "xlrd==1.1.0\n",
            "xlwt==1.3.0\n",
            "yellowbrick==1.4\n",
            "zict==2.2.0\n",
            "zipp==3.8.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "kEy1ZsxHBGXY"
      },
      "execution_count": 30,
      "outputs": []
    }
  ]
}