4. Analysis in Week Unit.ipynb

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   "metadata": {},
   "source": [
    "# Date 2 Week "
   ]
  },
  {
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   "id": "00948bd1",
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     "end_time": "2022-05-14T16:50:06.005861Z",
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    {
     "name": "stdout",
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     "text": [
      "2020-w05    4\n",
      "2020-w06    7\n",
      "2020-w07    7\n",
      "2020-w08    7\n",
      "2020-w09    7\n",
      "           ..\n",
      "2022-w04    7\n",
      "2022-w05    7\n",
      "2022-w06    7\n",
      "2022-w07    7\n",
      "2022-w08    4\n",
      "Name: yweek, Length: 109, dtype: int64\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>year</th>\n",
       "      <th>week</th>\n",
       "      <th>weekday</th>\n",
       "      <th>yweek</th>\n",
       "      <th>yweek_Tu</th>\n",
       "      <th>yweek_Wd</th>\n",
       "      <th>yweek_Th</th>\n",
       "      <th>yweek_Fr</th>\n",
       "      <th>yweek_Sa</th>\n",
       "      <th>yweek_Sn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2020-w05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-w05</td>\n",
       "      <td>2020-w05</td>\n",
       "      <td>2020-w05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2020-w05</td>\n",
       "      <td>2020-w05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-w05</td>\n",
       "      <td>2020-w05</td>\n",
       "      <td>2020-w06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2020-w05</td>\n",
       "      <td>2020-w05</td>\n",
       "      <td>2020-w05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-w05</td>\n",
       "      <td>2020-w06</td>\n",
       "      <td>2020-w06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>2020-w05</td>\n",
       "      <td>2020-w05</td>\n",
       "      <td>2020-w05</td>\n",
       "      <td>2020-w05</td>\n",
       "      <td>2020-w06</td>\n",
       "      <td>2020-w06</td>\n",
       "      <td>2020-w06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-w06</td>\n",
       "      <td>2020-w05</td>\n",
       "      <td>2020-w05</td>\n",
       "      <td>2020-w05</td>\n",
       "      <td>2020-w06</td>\n",
       "      <td>2020-w06</td>\n",
       "      <td>2020-w06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>2022-02-20</td>\n",
       "      <td>2022</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2022-w07</td>\n",
       "      <td>2022-w07</td>\n",
       "      <td>2022-w07</td>\n",
       "      <td>2022-w07</td>\n",
       "      <td>2022-w08</td>\n",
       "      <td>2022-w08</td>\n",
       "      <td>2022-w08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>2022-02-21</td>\n",
       "      <td>2022</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-w08</td>\n",
       "      <td>2022-w07</td>\n",
       "      <td>2022-w07</td>\n",
       "      <td>2022-w07</td>\n",
       "      <td>2022-w08</td>\n",
       "      <td>2022-w08</td>\n",
       "      <td>2022-w08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>2022-02-22</td>\n",
       "      <td>2022</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2022-w08</td>\n",
       "      <td>2022-w08</td>\n",
       "      <td>2022-w07</td>\n",
       "      <td>2022-w07</td>\n",
       "      <td>2022-w08</td>\n",
       "      <td>2022-w08</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>2022-02-23</td>\n",
       "      <td>2022</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>2022-w08</td>\n",
       "      <td>2022-w08</td>\n",
       "      <td>2022-w08</td>\n",
       "      <td>2022-w07</td>\n",
       "      <td>2022-w08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>2022-02-24</td>\n",
       "      <td>2022</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2022-w08</td>\n",
       "      <td>2022-w08</td>\n",
       "      <td>2022-w08</td>\n",
       "      <td>2022-w08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>757 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  year  week  weekday     yweek  yweek_Tu  yweek_Wd  yweek_Th  \\\n",
       "0   2020-01-30  2020     5        4  2020-w05       NaN       NaN       NaN   \n",
       "1   2020-01-31  2020     5        5  2020-w05  2020-w05       NaN       NaN   \n",
       "2   2020-02-01  2020     5        6  2020-w05  2020-w05  2020-w05       NaN   \n",
       "3   2020-02-02  2020     5        7  2020-w05  2020-w05  2020-w05  2020-w05   \n",
       "4   2020-02-03  2020     6        1  2020-w06  2020-w05  2020-w05  2020-w05   \n",
       "..         ...   ...   ...      ...       ...       ...       ...       ...   \n",
       "752 2022-02-20  2022     7        7  2022-w07  2022-w07  2022-w07  2022-w07   \n",
       "753 2022-02-21  2022     8        1  2022-w08  2022-w07  2022-w07  2022-w07   \n",
       "754 2022-02-22  2022     8        2  2022-w08  2022-w08  2022-w07  2022-w07   \n",
       "755 2022-02-23  2022     8        3  2022-w08  2022-w08  2022-w08  2022-w07   \n",
       "756 2022-02-24  2022     8        4  2022-w08  2022-w08  2022-w08  2022-w08   \n",
       "\n",
       "     yweek_Fr  yweek_Sa  yweek_Sn  \n",
       "0    2020-w05  2020-w05  2020-w05  \n",
       "1    2020-w05  2020-w05  2020-w06  \n",
       "2    2020-w05  2020-w06  2020-w06  \n",
       "3    2020-w06  2020-w06  2020-w06  \n",
       "4    2020-w06  2020-w06  2020-w06  \n",
       "..        ...       ...       ...  \n",
       "752  2022-w08  2022-w08  2022-w08  \n",
       "753  2022-w08  2022-w08  2022-w08  \n",
       "754  2022-w08  2022-w08       NaN  \n",
       "755  2022-w08       NaN       NaN  \n",
       "756       NaN       NaN       NaN  \n",
       "\n",
       "[757 rows x 11 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def check_year(x):\n",
    "    if x['date'].month == 1 and x['week'] in [52, 53]:\n",
    "        return x['year'] - 1 \n",
    "    else:\n",
    "        return x['year']\n",
    "    \n",
    "    \n",
    "path = 'raw/covidinfo/ltla_2022-02-25.csv'\n",
    "df = pd.read_csv(path)\n",
    "DateSeries = pd.to_datetime(df['date'].drop_duplicates().reset_index(drop = True))\n",
    "WeekSeries = DateSeries.dt.isocalendar().week\n",
    "YearSeries = DateSeries.dt.year\n",
    "\n",
    "DateTable = pd.DataFrame({'date': DateSeries, 'year': YearSeries, 'week':WeekSeries})\n",
    "DateTable = DateTable.sort_values('date').reset_index(drop = True)\n",
    "\n",
    "\n",
    "DateTable['weekday'] = DateTable['date'].dt.weekday + 1\n",
    "DateTable['year'] = DateTable.apply(lambda x: check_year(x), axis = 1)\n",
    "DateTable['yweek'] = DateTable.apply(lambda x: '{}-w{:02d}'.format(x['year'], x['week']), axis = 1)\n",
    "DateTable['yweek_Tu'] = DateTable['yweek'].shift(1)\n",
    "DateTable['yweek_Wd'] = DateTable['yweek'].shift(2)\n",
    "DateTable['yweek_Th'] = DateTable['yweek'].shift(3)\n",
    "DateTable['yweek_Fr'] = DateTable['yweek'].shift(-1)\n",
    "DateTable['yweek_Sa'] = DateTable['yweek'].shift(-2)\n",
    "DateTable['yweek_Sn'] = DateTable['yweek'].shift(-3)\n",
    "\n",
    "yweek = '2022-w07'\n",
    "print(DateTable['yweek'].value_counts().sort_index())\n",
    "DateTable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bba91010",
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    "ExecuteTime": {
     "end_time": "2022-05-14T16:50:07.990852Z",
     "start_time": "2022-05-14T16:50:07.965166Z"
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   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yweek</th>\n",
       "      <th>yweek_idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-w05</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-w06</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-w07</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-w08</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-w09</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>2022-w04</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>2022-w05</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>2022-w06</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>2022-w07</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>2022-w08</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        yweek  yweek_idx\n",
       "0    2020-w05          5\n",
       "1    2020-w06          6\n",
       "2    2020-w07          7\n",
       "3    2020-w08          8\n",
       "4    2020-w09          9\n",
       "..        ...        ...\n",
       "104  2022-w04        109\n",
       "105  2022-w05        110\n",
       "106  2022-w06        111\n",
       "107  2022-w07        112\n",
       "108  2022-w08        113\n",
       "\n",
       "[109 rows x 2 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearweek_to_idx = DateTable['yweek'].value_counts().sort_index().reset_index()\n",
    "yearweek_to_idx.columns = ['yweek', 'freq']\n",
    "yearweek_to_idx['yweek_idx'] = yearweek_to_idx.index + int(yearweek_to_idx['yweek'].values[0].split('-w')[-1])\n",
    "yearweek_to_idx = yearweek_to_idx[['yweek', 'yweek_idx']]\n",
    "yearweek_to_idx"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16801ac1",
   "metadata": {},
   "source": [
    "# City Info"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4721d83b",
   "metadata": {},
   "source": [
    "## City Covariates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b0b8d912",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:50:10.780974Z",
     "start_time": "2022-05-14T16:50:10.731221Z"
    }
   },
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>areaCode</th>\n",
       "      <th>areaName</th>\n",
       "      <th>NHSER21CD</th>\n",
       "      <th>NHSER21NM</th>\n",
       "      <th>old_dependency_ratio</th>\n",
       "      <th>young_dependency_ratio</th>\n",
       "      <th>popu_all</th>\n",
       "      <th>Median age</th>\n",
       "      <th>People per sq. km</th>\n",
       "      <th>Average Score</th>\n",
       "      <th>Area (sq km)</th>\n",
       "      <th>male_avg_age</th>\n",
       "      <th>female_avg_age</th>\n",
       "      <th>female_rate</th>\n",
       "      <th>areaidx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>E40000009</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E06000002</td>\n",
       "      <td>Middlesbrough</td>\n",
       "      <td>E40000009</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>0.258597</td>\n",
       "      <td>0.315276</td>\n",
       "      <td>141285</td>\n",
       "      <td>36.3</td>\n",
       "      <td>2616</td>\n",
       "      <td>40.5</td>\n",
       "      <td>53.8816</td>\n",
       "      <td>37.015974</td>\n",
       "      <td>39.302367</td>\n",
       "      <td>0.505057</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E06000003</td>\n",
       "      <td>Redcar and Cleveland</td>\n",
       "      <td>E40000009</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>0.378299</td>\n",
       "      <td>0.280907</td>\n",
       "      <td>137228</td>\n",
       "      <td>45.2</td>\n",
       "      <td>560</td>\n",
       "      <td>29.8</td>\n",
       "      <td>245.0698</td>\n",
       "      <td>42.037639</td>\n",
       "      <td>44.115800</td>\n",
       "      <td>0.514633</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E06000004</td>\n",
       "      <td>Stockton-on-Tees</td>\n",
       "      <td>E40000009</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>0.299317</td>\n",
       "      <td>0.299981</td>\n",
       "      <td>197419</td>\n",
       "      <td>40.7</td>\n",
       "      <td>963</td>\n",
       "      <td>25.8</td>\n",
       "      <td>204.9331</td>\n",
       "      <td>39.646713</td>\n",
       "      <td>41.726862</td>\n",
       "      <td>0.505058</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E06000005</td>\n",
       "      <td>Darlington</td>\n",
       "      <td>E40000009</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>0.333318</td>\n",
       "      <td>0.284279</td>\n",
       "      <td>107402</td>\n",
       "      <td>43.2</td>\n",
       "      <td>541</td>\n",
       "      <td>25.7</td>\n",
       "      <td>197.4758</td>\n",
       "      <td>41.155788</td>\n",
       "      <td>42.778638</td>\n",
       "      <td>0.513445</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>E09000029</td>\n",
       "      <td>Sutton</td>\n",
       "      <td>E40000003</td>\n",
       "      <td>London</td>\n",
       "      <td>0.238610</td>\n",
       "      <td>0.314247</td>\n",
       "      <td>207707</td>\n",
       "      <td>39.5</td>\n",
       "      <td>4706</td>\n",
       "      <td>14.0</td>\n",
       "      <td>43.8479</td>\n",
       "      <td>38.056436</td>\n",
       "      <td>39.842322</td>\n",
       "      <td>0.512202</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>E09000030</td>\n",
       "      <td>Tower Hamlets</td>\n",
       "      <td>E40000003</td>\n",
       "      <td>London</td>\n",
       "      <td>0.088241</td>\n",
       "      <td>0.257283</td>\n",
       "      <td>331969</td>\n",
       "      <td>31.6</td>\n",
       "      <td>16427</td>\n",
       "      <td>27.9</td>\n",
       "      <td>19.7710</td>\n",
       "      <td>32.614445</td>\n",
       "      <td>32.569271</td>\n",
       "      <td>0.474219</td>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>E09000031</td>\n",
       "      <td>Waltham Forest</td>\n",
       "      <td>E40000003</td>\n",
       "      <td>London</td>\n",
       "      <td>0.161879</td>\n",
       "      <td>0.307642</td>\n",
       "      <td>276940</td>\n",
       "      <td>35.2</td>\n",
       "      <td>7137</td>\n",
       "      <td>25.2</td>\n",
       "      <td>38.8102</td>\n",
       "      <td>35.037225</td>\n",
       "      <td>36.734122</td>\n",
       "      <td>0.495299</td>\n",
       "      <td>303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>E09000032</td>\n",
       "      <td>Wandsworth</td>\n",
       "      <td>E40000003</td>\n",
       "      <td>London</td>\n",
       "      <td>0.133132</td>\n",
       "      <td>0.238689</td>\n",
       "      <td>329735</td>\n",
       "      <td>34.0</td>\n",
       "      <td>9624</td>\n",
       "      <td>16.6</td>\n",
       "      <td>34.2619</td>\n",
       "      <td>34.995129</td>\n",
       "      <td>35.965513</td>\n",
       "      <td>0.520603</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>E40000003</td>\n",
       "      <td>London</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>305 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      areaCode              areaName  NHSER21CD                 NHSER21NM  \\\n",
       "0    E06000001            Hartlepool  E40000009  North East and Yorkshire   \n",
       "1    E06000002         Middlesbrough  E40000009  North East and Yorkshire   \n",
       "2    E06000003  Redcar and Cleveland  E40000009  North East and Yorkshire   \n",
       "3    E06000004      Stockton-on-Tees  E40000009  North East and Yorkshire   \n",
       "4    E06000005            Darlington  E40000009  North East and Yorkshire   \n",
       "..         ...                   ...        ...                       ...   \n",
       "300  E09000029                Sutton  E40000003                    London   \n",
       "301  E09000030         Tower Hamlets  E40000003                    London   \n",
       "302  E09000031        Waltham Forest  E40000003                    London   \n",
       "303  E09000032            Wandsworth  E40000003                    London   \n",
       "304  E09000033           Westminster  E40000003                    London   \n",
       "\n",
       "     old_dependency_ratio  young_dependency_ratio  popu_all  Median age  \\\n",
       "0                0.313702                0.288634     93836        41.7   \n",
       "1                0.258597                0.315276    141285        36.3   \n",
       "2                0.378299                0.280907    137228        45.2   \n",
       "3                0.299317                0.299981    197419        40.7   \n",
       "4                0.333318                0.284279    107402        43.2   \n",
       "..                    ...                     ...       ...         ...   \n",
       "300              0.238610                0.314247    207707        39.5   \n",
       "301              0.088241                0.257283    331969        31.6   \n",
       "302              0.161879                0.307642    276940        35.2   \n",
       "303              0.133132                0.238689    329735        34.0   \n",
       "304              0.175793                0.223543    269848        36.3   \n",
       "\n",
       "     People per sq. km  Average Score  Area (sq km)  male_avg_age  \\\n",
       "0                  999           35.0       93.7125     40.157418   \n",
       "1                 2616           40.5       53.8816     37.015974   \n",
       "2                  560           29.8      245.0698     42.037639   \n",
       "3                  963           25.8      204.9331     39.646713   \n",
       "4                  541           25.7      197.4758     41.155788   \n",
       "..                 ...            ...           ...           ...   \n",
       "300               4706           14.0       43.8479     38.056436   \n",
       "301              16427           27.9       19.7710     32.614445   \n",
       "302               7137           25.2       38.8102     35.037225   \n",
       "303               9624           16.6       34.2619     34.995129   \n",
       "304              12167           20.3       21.4826     36.918846   \n",
       "\n",
       "     female_avg_age  female_rate  areaidx  \n",
       "0         42.190274     0.509730        1  \n",
       "1         39.302367     0.505057        2  \n",
       "2         44.115800     0.514633        3  \n",
       "3         41.726862     0.505058        4  \n",
       "4         42.778638     0.513445        5  \n",
       "..              ...          ...      ...  \n",
       "300       39.842322     0.512202      301  \n",
       "301       32.569271     0.474219      302  \n",
       "302       36.734122     0.495299      303  \n",
       "303       35.965513     0.520603      304  \n",
       "304       38.427939     0.472677      305  \n",
       "\n",
       "[305 rows x 15 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "path = 'data/ltla_neighbours_df.csv'\n",
    "ltla_neighbours_df = pd.read_csv(path)# .rename(columns = {'LAD21NM': 'areaName',})\n",
    "ltla_neighbours_df['neighboors'] = ltla_neighbours_df['neighboors'].apply(eval)\n",
    "# ltla_neighbours_df\n",
    "\n",
    "path = 'data/ltla_region_england.csv'\n",
    "ltla_region_england_df = pd.read_csv(path).rename(columns = {'LAD21CD': 'areaCode',\n",
    "                                                            'LAD21NM': 'areaName',})\n",
    "path = 'data/ltal_covariates_df.csv'\n",
    "tmp = pd.read_csv(path)\n",
    "ltla_df = pd.merge(ltla_region_england_df, tmp)\n",
    "# ltla_df = pd.merge(ltla_df, ltla_neighbours_df)\n",
    "ltla_df['areaidx'] = list(range(1, len(ltla_df) + 1))\n",
    "\n",
    "ltla_covariate_df = ltla_df\n",
    "ltla_covariate_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9414a7e9",
   "metadata": {},
   "source": [
    "## City Covid (Day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bb7d1324",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:50:22.002486Z",
     "start_time": "2022-05-14T16:50:12.104732Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>areaCode</th>\n",
       "      <th>areaName</th>\n",
       "      <th>areaType</th>\n",
       "      <th>date</th>\n",
       "      <th>cumCasesBySpecimenDate</th>\n",
       "      <th>cumCasesBySpecimenDateRate</th>\n",
       "      <th>newCasesBySpecimenDate</th>\n",
       "      <th>dateidx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2020-03-17</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>2</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>2</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>3</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218886</th>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2022-02-20</td>\n",
       "      <td>58626</td>\n",
       "      <td>21725.6</td>\n",
       "      <td>137.0</td>\n",
       "      <td>781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218887</th>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2022-02-21</td>\n",
       "      <td>58767</td>\n",
       "      <td>21777.8</td>\n",
       "      <td>141.0</td>\n",
       "      <td>782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218888</th>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2022-02-22</td>\n",
       "      <td>58913</td>\n",
       "      <td>21831.9</td>\n",
       "      <td>146.0</td>\n",
       "      <td>783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218889</th>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2022-02-23</td>\n",
       "      <td>59035</td>\n",
       "      <td>21877.1</td>\n",
       "      <td>122.0</td>\n",
       "      <td>784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218890</th>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2022-02-24</td>\n",
       "      <td>59072</td>\n",
       "      <td>21890.8</td>\n",
       "      <td>37.0</td>\n",
       "      <td>785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>218891 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         areaCode     areaName areaType       date  cumCasesBySpecimenDate  \\\n",
       "0       E06000001   Hartlepool     ltla 2020-03-16                       1   \n",
       "1       E06000001   Hartlepool     ltla 2020-03-17                       1   \n",
       "2       E06000001   Hartlepool     ltla 2020-03-18                       2   \n",
       "3       E06000001   Hartlepool     ltla 2020-03-19                       2   \n",
       "4       E06000001   Hartlepool     ltla 2020-03-20                       3   \n",
       "...           ...          ...      ...        ...                     ...   \n",
       "218886  E09000033  Westminster     ltla 2022-02-20                   58626   \n",
       "218887  E09000033  Westminster     ltla 2022-02-21                   58767   \n",
       "218888  E09000033  Westminster     ltla 2022-02-22                   58913   \n",
       "218889  E09000033  Westminster     ltla 2022-02-23                   59035   \n",
       "218890  E09000033  Westminster     ltla 2022-02-24                   59072   \n",
       "\n",
       "        cumCasesBySpecimenDateRate  newCasesBySpecimenDate  dateidx  \n",
       "0                              1.1                     1.0       75  \n",
       "1                              1.1                     0.0       76  \n",
       "2                              2.1                     1.0       77  \n",
       "3                              2.1                     0.0       78  \n",
       "4                              3.2                     1.0       79  \n",
       "...                            ...                     ...      ...  \n",
       "218886                     21725.6                   137.0      781  \n",
       "218887                     21777.8                   141.0      782  \n",
       "218888                     21831.9                   146.0      783  \n",
       "218889                     21877.1                   122.0      784  \n",
       "218890                     21890.8                    37.0      785  \n",
       "\n",
       "[218891 rows x 8 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "\n",
    "path = 'raw/covidinfo/ltla_2022-02-25.csv'\n",
    "df = pd.read_csv(path)\n",
    "df['date'] = pd.to_datetime(df['date'])\n",
    "df = df.sort_values(['areaCode', 'date']).reset_index(drop = True)\n",
    "ltla_covid_df = df[df['areaCode'].isin(ltla_df['areaCode'])].reset_index(drop = True)\n",
    "ltla_covid_df['dateidx'] = ltla_covid_df['date'].apply(lambda x: (x-pd.to_datetime('2020-01-01')).days)\n",
    "\n",
    "ltla_covid_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "241b2a73",
   "metadata": {},
   "source": [
    "## City Covid (Week)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecb3ba7c",
   "metadata": {},
   "source": [
    "### [JUMP HERE] yweek col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ea2b5f63",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:50:22.005509Z",
     "start_time": "2022-05-14T16:50:22.003586Z"
    }
   },
   "outputs": [],
   "source": [
    "yweek_col = 'yweek'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "73126cee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:50:33.488072Z",
     "start_time": "2022-05-14T16:50:22.006151Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>areaCode</th>\n",
       "      <th>areaName</th>\n",
       "      <th>areaType</th>\n",
       "      <th>yweek</th>\n",
       "      <th>cumCasesWeek</th>\n",
       "      <th>newCasesWeek</th>\n",
       "      <th>records</th>\n",
       "      <th>yweek_idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2020-w12</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2020-w13</td>\n",
       "      <td>12</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2020-w14</td>\n",
       "      <td>39</td>\n",
       "      <td>27.0</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2020-w15</td>\n",
       "      <td>88</td>\n",
       "      <td>49.0</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2020-w16</td>\n",
       "      <td>161</td>\n",
       "      <td>73.0</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30951</th>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2022-w03</td>\n",
       "      <td>52780</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>7</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30952</th>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2022-w04</td>\n",
       "      <td>54712</td>\n",
       "      <td>1932.0</td>\n",
       "      <td>7</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30953</th>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2022-w05</td>\n",
       "      <td>56278</td>\n",
       "      <td>1566.0</td>\n",
       "      <td>7</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30954</th>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2022-w06</td>\n",
       "      <td>57580</td>\n",
       "      <td>1302.0</td>\n",
       "      <td>7</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30955</th>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2022-w07</td>\n",
       "      <td>58626</td>\n",
       "      <td>1046.0</td>\n",
       "      <td>7</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30956 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        areaCode     areaName areaType     yweek  cumCasesWeek  newCasesWeek  \\\n",
       "0      E06000001   Hartlepool     ltla  2020-w12             3           3.0   \n",
       "1      E06000001   Hartlepool     ltla  2020-w13            12           9.0   \n",
       "2      E06000001   Hartlepool     ltla  2020-w14            39          27.0   \n",
       "3      E06000001   Hartlepool     ltla  2020-w15            88          49.0   \n",
       "4      E06000001   Hartlepool     ltla  2020-w16           161          73.0   \n",
       "...          ...          ...      ...       ...           ...           ...   \n",
       "30951  E09000033  Westminster     ltla  2022-w03         52780        2010.0   \n",
       "30952  E09000033  Westminster     ltla  2022-w04         54712        1932.0   \n",
       "30953  E09000033  Westminster     ltla  2022-w05         56278        1566.0   \n",
       "30954  E09000033  Westminster     ltla  2022-w06         57580        1302.0   \n",
       "30955  E09000033  Westminster     ltla  2022-w07         58626        1046.0   \n",
       "\n",
       "       records  yweek_idx  \n",
       "0            7         12  \n",
       "1            7         13  \n",
       "2            7         14  \n",
       "3            7         15  \n",
       "4            7         16  \n",
       "...        ...        ...  \n",
       "30951        7        108  \n",
       "30952        7        109  \n",
       "30953        7        110  \n",
       "30954        7        111  \n",
       "30955        7        112  \n",
       "\n",
       "[30956 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_weekdf_info(week_df):\n",
    "    d = week_df[['areaCode', 'areaName', 'areaType', yweek_col]].iloc[0].to_dict()\n",
    "    d['cumCasesWeek'] =  week_df['cumCasesBySpecimenDate'].max()\n",
    "    d['newCasesWeek'] = week_df['newCasesBySpecimenDate'].sum()\n",
    "    d['records'] = len(week_df)\n",
    "    return d\n",
    "\n",
    "tmp = pd.merge(ltla_covid_df, DateTable[['date', yweek_col]])\n",
    "ltla_covid_df_weekly = pd.DataFrame()\n",
    "for city, city_df in tmp.groupby('areaName'):\n",
    "    df = city_df\n",
    "    city_df_by_week = pd.DataFrame(df.groupby(yweek_col).apply(get_weekdf_info).to_list())\n",
    "    unfull = city_df_by_week[city_df_by_week['records'] != 7][yweek_col].values\n",
    "    assert len(unfull) <= 2\n",
    "    city_df_by_week = city_df_by_week[city_df_by_week['records'] == 7]\n",
    "    ltla_covid_df_weekly  = pd.concat([ltla_covid_df_weekly, city_df_by_week]) \n",
    "    \n",
    "ltla_covid_df_w = ltla_covid_df_weekly.reset_index(drop = True)\n",
    "ltla_covid_df_w = pd.merge(ltla_covid_df_w, yearweek_to_idx)\n",
    "ltla_covid_df_w = ltla_covid_df_w.sort_values(['areaCode', yweek_col]).reset_index(drop = True)\n",
    "ltla_covid_df_w"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48c54807",
   "metadata": {},
   "source": [
    "# City Covid Covariates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a538bcfc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:50:33.491892Z",
     "start_time": "2022-05-14T16:50:33.489479Z"
    }
   },
   "outputs": [],
   "source": [
    "areaName_areaidx_df = ltla_df[['areaName', 'areaidx']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7691ea0e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:50:33.497305Z",
     "start_time": "2022-05-14T16:50:33.493672Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['areaCode', 'areaName', 'areaType', 'yweek', 'cumCasesWeek',\n",
       "       'newCasesWeek', 'records', 'yweek_idx'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ltla_covid_df_w.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9bdff815",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:50:33.525158Z",
     "start_time": "2022-05-14T16:50:33.498124Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>areaidx</th>\n",
       "      <th>areaCode</th>\n",
       "      <th>areaName</th>\n",
       "      <th>areaType</th>\n",
       "      <th>NHSER21NM</th>\n",
       "      <th>yweek_idx</th>\n",
       "      <th>yweek</th>\n",
       "      <th>new_case_per_10k</th>\n",
       "      <th>cumCasesWeek</th>\n",
       "      <th>newCasesWeek</th>\n",
       "      <th>old_dependency_ratio</th>\n",
       "      <th>young_dependency_ratio</th>\n",
       "      <th>popu_all</th>\n",
       "      <th>Median age</th>\n",
       "      <th>People per sq. km</th>\n",
       "      <th>Average Score</th>\n",
       "      <th>Area (sq km)</th>\n",
       "      <th>male_avg_age</th>\n",
       "      <th>female_avg_age</th>\n",
       "      <th>female_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>12</td>\n",
       "      <td>2020-w12</td>\n",
       "      <td>0.319707</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>13</td>\n",
       "      <td>2020-w13</td>\n",
       "      <td>0.959120</td>\n",
       "      <td>12</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>14</td>\n",
       "      <td>2020-w14</td>\n",
       "      <td>2.877361</td>\n",
       "      <td>39</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>15</td>\n",
       "      <td>2020-w15</td>\n",
       "      <td>5.221876</td>\n",
       "      <td>88</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>16</td>\n",
       "      <td>2020-w16</td>\n",
       "      <td>7.779530</td>\n",
       "      <td>161</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>30951</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>108</td>\n",
       "      <td>2022-w03</td>\n",
       "      <td>74.486378</td>\n",
       "      <td>52780</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30952</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>109</td>\n",
       "      <td>2022-w04</td>\n",
       "      <td>71.595861</td>\n",
       "      <td>54712</td>\n",
       "      <td>1932.0</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30953</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>110</td>\n",
       "      <td>2022-w05</td>\n",
       "      <td>58.032670</td>\n",
       "      <td>56278</td>\n",
       "      <td>1566.0</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30954</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>111</td>\n",
       "      <td>2022-w06</td>\n",
       "      <td>48.249385</td>\n",
       "      <td>57580</td>\n",
       "      <td>1302.0</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30955</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>112</td>\n",
       "      <td>2022-w07</td>\n",
       "      <td>38.762563</td>\n",
       "      <td>58626</td>\n",
       "      <td>1046.0</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30956 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       areaidx   areaCode     areaName areaType                 NHSER21NM  \\\n",
       "0            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "1            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "2            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "3            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "4            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "...        ...        ...          ...      ...                       ...   \n",
       "30951      305  E09000033  Westminster     ltla                    London   \n",
       "30952      305  E09000033  Westminster     ltla                    London   \n",
       "30953      305  E09000033  Westminster     ltla                    London   \n",
       "30954      305  E09000033  Westminster     ltla                    London   \n",
       "30955      305  E09000033  Westminster     ltla                    London   \n",
       "\n",
       "       yweek_idx     yweek  new_case_per_10k  cumCasesWeek  newCasesWeek  \\\n",
       "0             12  2020-w12          0.319707             3           3.0   \n",
       "1             13  2020-w13          0.959120            12           9.0   \n",
       "2             14  2020-w14          2.877361            39          27.0   \n",
       "3             15  2020-w15          5.221876            88          49.0   \n",
       "4             16  2020-w16          7.779530           161          73.0   \n",
       "...          ...       ...               ...           ...           ...   \n",
       "30951        108  2022-w03         74.486378         52780        2010.0   \n",
       "30952        109  2022-w04         71.595861         54712        1932.0   \n",
       "30953        110  2022-w05         58.032670         56278        1566.0   \n",
       "30954        111  2022-w06         48.249385         57580        1302.0   \n",
       "30955        112  2022-w07         38.762563         58626        1046.0   \n",
       "\n",
       "       old_dependency_ratio  young_dependency_ratio  popu_all  Median age  \\\n",
       "0                  0.313702                0.288634     93836        41.7   \n",
       "1                  0.313702                0.288634     93836        41.7   \n",
       "2                  0.313702                0.288634     93836        41.7   \n",
       "3                  0.313702                0.288634     93836        41.7   \n",
       "4                  0.313702                0.288634     93836        41.7   \n",
       "...                     ...                     ...       ...         ...   \n",
       "30951              0.175793                0.223543    269848        36.3   \n",
       "30952              0.175793                0.223543    269848        36.3   \n",
       "30953              0.175793                0.223543    269848        36.3   \n",
       "30954              0.175793                0.223543    269848        36.3   \n",
       "30955              0.175793                0.223543    269848        36.3   \n",
       "\n",
       "       People per sq. km  Average Score  Area (sq km)  male_avg_age  \\\n",
       "0                    999           35.0       93.7125     40.157418   \n",
       "1                    999           35.0       93.7125     40.157418   \n",
       "2                    999           35.0       93.7125     40.157418   \n",
       "3                    999           35.0       93.7125     40.157418   \n",
       "4                    999           35.0       93.7125     40.157418   \n",
       "...                  ...            ...           ...           ...   \n",
       "30951              12167           20.3       21.4826     36.918846   \n",
       "30952              12167           20.3       21.4826     36.918846   \n",
       "30953              12167           20.3       21.4826     36.918846   \n",
       "30954              12167           20.3       21.4826     36.918846   \n",
       "30955              12167           20.3       21.4826     36.918846   \n",
       "\n",
       "       female_avg_age  female_rate  \n",
       "0           42.190274     0.509730  \n",
       "1           42.190274     0.509730  \n",
       "2           42.190274     0.509730  \n",
       "3           42.190274     0.509730  \n",
       "4           42.190274     0.509730  \n",
       "...               ...          ...  \n",
       "30951       38.427939     0.472677  \n",
       "30952       38.427939     0.472677  \n",
       "30953       38.427939     0.472677  \n",
       "30954       38.427939     0.472677  \n",
       "30955       38.427939     0.472677  \n",
       "\n",
       "[30956 rows x 20 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ltla_covid_covariate_df = pd.merge(ltla_covid_df_w, ltla_covariate_df[['areaCode', 'NHSER21NM',\n",
    "       'old_dependency_ratio', 'young_dependency_ratio', 'popu_all',\n",
    "       'Median age', 'People per sq. km', 'Average Score', 'Area (sq km)',\n",
    "       'male_avg_age', 'female_avg_age', 'female_rate', 'areaidx']], on = 'areaCode')\n",
    "\n",
    "ltla_covid_covariate_df['new_case_per_10k'] = ltla_covid_covariate_df['newCasesWeek'] / ltla_covid_covariate_df['popu_all'] * 10000\n",
    "\n",
    "\n",
    "cols = [# areaidx\n",
    "        'areaidx', 'areaCode', 'areaName', 'areaType', 'NHSER21NM',\n",
    "        # date\n",
    "        'yweek_idx', yweek_col, #'yweek_d', \n",
    "        # y\n",
    "        'new_case_per_10k', 'cumCasesWeek', 'newCasesWeek', \n",
    "        # features\n",
    "        'old_dependency_ratio', 'young_dependency_ratio', 'popu_all',\n",
    "       'Median age', 'People per sq. km', 'Average Score', 'Area (sq km)',\n",
    "       'male_avg_age', 'female_avg_age', 'female_rate', \n",
    "       ]\n",
    "\n",
    "\n",
    "ltla_covid_covariate_df = ltla_covid_covariate_df[cols]\n",
    "# ltla_covid_covariate_df.to_clipboard()\n",
    "ltla_covid_covariate_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7250b6f4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:50:33.872940Z",
     "start_time": "2022-05-14T16:50:33.526138Z"
    }
   },
   "outputs": [],
   "source": [
    "ltla_covid_covariate_df.to_csv('data/week/all_cities_yx_{}.csv'.format(yweek_col), index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "869aa9cc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:50:33.877556Z",
     "start_time": "2022-05-14T16:50:33.873884Z"
    }
   },
   "outputs": [],
   "source": [
    "# dateidx = 10\n",
    "# convert_dateidx_to_date(dateidx)\n",
    "\n",
    "\n",
    "def convert_dateidx_to_date(dateidx):\n",
    "    return (pd.to_timedelta(dateidx, unit = 'D') + pd.to_datetime('2020-01-01')).date()\n",
    "\n",
    "def convert_date_to_dateidx(date):\n",
    "    return (pd.to_datetime(date) - pd.to_datetime('2020-01-01')).days\n",
    "\n",
    "def convert_areaName_to_areaidx(areaName):\n",
    "    df = areaName_areaidx_df\n",
    "    return df[df['areaName'] == areaName].iloc[0]['areaidx']\n",
    "\n",
    "def convert_areaidx_to_areaName(areaidx):\n",
    "    df = areaName_areaidx_df\n",
    "    return df[df['areaidx'] == areaidx].iloc[0]['areaName']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7bd3e51",
   "metadata": {},
   "source": [
    "# Game Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "92a7e139",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:50:33.899145Z",
     "start_time": "2022-05-14T16:50:33.878405Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>city</th>\n",
       "      <th>score</th>\n",
       "      <th>scoreB</th>\n",
       "      <th>host</th>\n",
       "      <th>win</th>\n",
       "      <th>tie</th>\n",
       "      <th>loss</th>\n",
       "      <th>max</th>\n",
       "      <th>gap</th>\n",
       "      <th>game</th>\n",
       "      <th>last_game_date</th>\n",
       "      <th>last_host_game_date</th>\n",
       "      <th>next_game_date</th>\n",
       "      <th>next_host_game_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-12-28</td>\n",
       "      <td>2019-12-26</td>\n",
       "      <td>2020-01-12</td>\n",
       "      <td>2020-01-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-12</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>2019-12-26</td>\n",
       "      <td>2020-01-18</td>\n",
       "      <td>2020-01-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-18</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-01-12</td>\n",
       "      <td>2020-01-12</td>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>2020-01-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-01-18</td>\n",
       "      <td>2020-01-12</td>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>2020-02-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>2020-02-16</td>\n",
       "      <td>2020-02-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>Wolverhampton</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2021-11-27</td>\n",
       "      <td>2021-11-20</td>\n",
       "      <td>2021-12-04</td>\n",
       "      <td>2021-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1321</th>\n",
       "      <td>2021-12-04</td>\n",
       "      <td>Wolverhampton</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>2021-12-11</td>\n",
       "      <td>2021-12-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1322</th>\n",
       "      <td>2021-12-11</td>\n",
       "      <td>Wolverhampton</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2021-12-04</td>\n",
       "      <td>2021-12-04</td>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>2021-12-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1323</th>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>Wolverhampton</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2021-12-11</td>\n",
       "      <td>2021-12-04</td>\n",
       "      <td>2021-12-19</td>\n",
       "      <td>2021-12-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1324</th>\n",
       "      <td>2021-12-19</td>\n",
       "      <td>Wolverhampton</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>2021-12-04</td>\n",
       "      <td>2022-01-03</td>\n",
       "      <td>2022-01-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1325 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date           city  score  scoreB  host  win  tie  loss  max  gap  \\\n",
       "0    2020-01-01     Birmingham    2.0     1.0   0.0  1.0  0.0   0.0  2.0  1.0   \n",
       "1    2020-01-12     Birmingham    1.0     6.0   1.0  0.0  0.0   1.0  6.0 -5.0   \n",
       "2    2020-01-18     Birmingham    1.0     1.0   0.0  0.0  1.0   0.0  1.0  0.0   \n",
       "3    2020-01-21     Birmingham    2.0     1.0   1.0  1.0  0.0   0.0  2.0  1.0   \n",
       "4    2020-02-01     Birmingham    1.0     2.0   0.0  0.0  0.0   1.0  2.0 -1.0   \n",
       "...         ...            ...    ...     ...   ...  ...  ...   ...  ...  ...   \n",
       "1320 2021-12-01  Wolverhampton    0.0     0.0   1.0  0.0  1.0   0.0  0.0  0.0   \n",
       "1321 2021-12-04  Wolverhampton    0.0     1.0   1.0  0.0  0.0   1.0  1.0 -1.0   \n",
       "1322 2021-12-11  Wolverhampton    0.0     1.0   0.0  0.0  0.0   1.0  1.0 -1.0   \n",
       "1323 2021-12-15  Wolverhampton    1.0     0.0   0.0  1.0  0.0   0.0  1.0  1.0   \n",
       "1324 2021-12-19  Wolverhampton    0.0     0.0   1.0  0.0  1.0   0.0  0.0  0.0   \n",
       "\n",
       "      game last_game_date last_host_game_date next_game_date  \\\n",
       "0      1.0     2019-12-28          2019-12-26     2020-01-12   \n",
       "1      1.0     2020-01-01          2019-12-26     2020-01-18   \n",
       "2      1.0     2020-01-12          2020-01-12     2020-01-21   \n",
       "3      1.0     2020-01-18          2020-01-12     2020-02-01   \n",
       "4      1.0     2020-01-21          2020-01-21     2020-02-16   \n",
       "...    ...            ...                 ...            ...   \n",
       "1320   1.0     2021-11-27          2021-11-20     2021-12-04   \n",
       "1321   1.0     2021-12-01          2021-12-01     2021-12-11   \n",
       "1322   1.0     2021-12-04          2021-12-04     2021-12-15   \n",
       "1323   1.0     2021-12-11          2021-12-04     2021-12-19   \n",
       "1324   1.0     2021-12-15          2021-12-04     2022-01-03   \n",
       "\n",
       "     next_host_game_date  \n",
       "0             2020-01-12  \n",
       "1             2020-01-21  \n",
       "2             2020-01-21  \n",
       "3             2020-02-16  \n",
       "4             2020-02-16  \n",
       "...                  ...  \n",
       "1320          2021-12-04  \n",
       "1321          2021-12-19  \n",
       "1322          2021-12-19  \n",
       "1323          2021-12-19  \n",
       "1324          2022-01-15  \n",
       "\n",
       "[1325 rows x 15 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ltla_game_df = pd.read_csv('data/ltla_game_df.csv')\n",
    "ltla_game_df['date'] = pd.to_datetime(ltla_game_df['date'])\n",
    "ltla_game_df[ltla_game_df['date'].dt.year.isin([2020, 2021])].reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83d95f4b",
   "metadata": {},
   "source": [
    "## Weekday Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a3fbd1a3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:50:33.905739Z",
     "start_time": "2022-05-14T16:50:33.901244Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6    738\n",
       "7    476\n",
       "3    153\n",
       "1    120\n",
       "2     99\n",
       "4     73\n",
       "5     64\n",
       "Name: date, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ltla_game_df['date'].dt.weekday + 1).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8175a4a0",
   "metadata": {},
   "source": [
    "# YWeek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ce782905",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:50:33.915397Z",
     "start_time": "2022-05-14T16:50:33.906575Z"
    }
   },
   "outputs": [],
   "source": [
    "ltla_game_df = pd.read_csv('data/ltla_game_df.csv')\n",
    "ltla_game_df['date'] = pd.to_datetime(ltla_game_df['date'])\n",
    "\n",
    "date_to_yweek = DateTable[['date', yweek_col]].drop_duplicates().sort_values('date').reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fad22349",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:50:33.933858Z",
     "start_time": "2022-05-14T16:50:33.916168Z"
    }
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   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>city</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Bournemouth, Christchurch and Poole</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>2020-w05</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Brighton and Hove</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-w05</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Hammersmith and Fulham</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>2020-w05</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Leicester</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-w05</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>Watford</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-w26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>2020-07-13</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-w29</td>\n",
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       "    <tr>\n",
       "      <th>1310</th>\n",
       "      <td>2020-07-13</td>\n",
       "      <td>Trafford</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-w29</td>\n",
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       "    <tr>\n",
       "      <th>1311</th>\n",
       "      <td>2022-01-03</td>\n",
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       "      <th>1312</th>\n",
       "      <td>2022-01-03</td>\n",
       "      <td>Wolverhampton</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "<p>1313 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "           date                                 city  score  scoreB  host  \\\n",
       "0    2020-02-01                           Birmingham    1.0     2.0   0.0   \n",
       "1    2020-02-01  Bournemouth, Christchurch and Poole    2.0     1.0   1.0   \n",
       "2    2020-02-01                    Brighton and Hove    3.0     3.0   0.0   \n",
       "3    2020-02-01               Hammersmith and Fulham    2.0     2.0   0.0   \n",
       "4    2020-02-01                            Leicester    2.0     2.0   1.0   \n",
       "...         ...                                  ...    ...     ...   ...   \n",
       "1308 2020-06-28                              Watford    1.0     3.0   1.0   \n",
       "1309 2020-07-13                          Southampton    2.0     2.0   0.0   \n",
       "1310 2020-07-13                             Trafford    2.0     2.0   1.0   \n",
       "1311 2022-01-03                             Trafford    0.0     1.0   1.0   \n",
       "1312 2022-01-03                        Wolverhampton    1.0     0.0   0.0   \n",
       "\n",
       "      win  tie  loss  max  gap  game     yweek  \n",
       "0     0.0  0.0   1.0  2.0 -1.0   1.0  2020-w05  \n",
       "1     1.0  0.0   0.0  2.0  1.0   1.0  2020-w05  \n",
       "2     0.0  1.0   0.0  3.0  0.0   1.0  2020-w05  \n",
       "3     0.0  1.0   0.0  2.0  0.0   1.0  2020-w05  \n",
       "4     0.0  1.0   0.0  2.0  0.0   1.0  2020-w05  \n",
       "...   ...  ...   ...  ...  ...   ...       ...  \n",
       "1308  0.0  0.0   1.0  3.0 -2.0   1.0  2020-w26  \n",
       "1309  0.0  1.0   0.0  2.0  0.0   1.0  2020-w29  \n",
       "1310  0.0  1.0   0.0  2.0  0.0   1.0  2020-w29  \n",
       "1311  0.0  0.0   1.0  1.0 -1.0   1.0  2022-w01  \n",
       "1312  1.0  0.0   0.0  1.0  1.0   1.0  2022-w01  \n",
       "\n",
       "[1313 rows x 12 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ltla_game_df = pd.merge(ltla_game_df, date_to_yweek, on = 'date')\n",
    "\n",
    "cols = ['date', 'city', 'score', 'scoreB', 'host', 'win', 'tie', 'loss', 'max',\n",
    "       'gap', 'game', yweek_col]\n",
    "\n",
    "ltla_game_df = ltla_game_df[cols]\n",
    "ltla_game_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1ca740a8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:50:49.427837Z",
     "start_time": "2022-05-14T16:50:48.279599Z"
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   },
   "outputs": [
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       "  </tbody>\n",
       "</table>\n",
       "<p>1016 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               city     yweek  games_num  weekday1  weekday2  weekday3  \\\n",
       "0        Birmingham  2020-w05          1         0         0         0   \n",
       "1        Birmingham  2020-w07          1         0         0         0   \n",
       "2        Birmingham  2020-w08          1         0         0         0   \n",
       "3        Birmingham  2020-w25          1         0         0         0   \n",
       "4        Birmingham  2020-w26          2         0         0         1   \n",
       "...             ...       ...        ...       ...       ...       ...   \n",
       "1011  Wolverhampton  2021-w49          1         0         0         0   \n",
       "1012  Wolverhampton  2021-w50          2         0         0         1   \n",
       "1013  Wolverhampton  2022-w01          1         1         0         0   \n",
       "1014  Wolverhampton  2022-w02          1         0         0         0   \n",
       "1015  Wolverhampton  2022-w03          1         0         0         0   \n",
       "\n",
       "      weekday4  weekday5  weekday6  weekday7  score  scoreB  host  win  tie  \\\n",
       "0            0         0         1         0    1.0     2.0   0.0  0.0  0.0   \n",
       "1            0         0         0         1    2.0     3.0   1.0  0.0  0.0   \n",
       "2            0         0         1         0    0.0     2.0   0.0  0.0  0.0   \n",
       "3            0         0         0         1    1.0     2.0   1.0  0.0  0.0   \n",
       "4            0         0         1         0    0.5     1.0   0.5  0.0  0.5   \n",
       "...        ...       ...       ...       ...    ...     ...   ...  ...  ...   \n",
       "1011         0         0         1         0    0.0     1.0   0.0  0.0  0.0   \n",
       "1012         0         0         0         1    0.5     0.0   0.5  0.5  0.5   \n",
       "1013         0         0         0         0    1.0     0.0   0.0  1.0  0.0   \n",
       "1014         0         0         1         0    3.0     1.0   1.0  1.0  0.0   \n",
       "1015         0         0         1         0    2.0     1.0   0.0  1.0  0.0   \n",
       "\n",
       "      loss  max  gap  \n",
       "0      1.0  2.0  1.0  \n",
       "1      1.0  3.0  1.0  \n",
       "2      1.0  2.0  2.0  \n",
       "3      1.0  2.0  1.0  \n",
       "4      0.5  1.0  1.0  \n",
       "...    ...  ...  ...  \n",
       "1011   1.0  1.0  1.0  \n",
       "1012   0.0  1.0  1.0  \n",
       "1013   0.0  1.0  1.0  \n",
       "1014   0.0  3.0  2.0  \n",
       "1015   0.0  2.0  1.0  \n",
       "\n",
       "[1016 rows x 18 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_info_df_city_yweek(df_city_yweek):\n",
    "    df_city_yweek['weekday'] = df_city_yweek['date'].dt.weekday + 1\n",
    "    df_city_yweek = df_city_yweek.sort_values('date')\n",
    "\n",
    "    d = {} \n",
    "    d['city'] = df_city_yweek['city'].values[0]\n",
    "    d[yweek_col] = df_city_yweek[yweek_col].values[0]\n",
    "    d['games_num'] = len(df_city_yweek)\n",
    "    for i in range(1, 8):\n",
    "        if i in df_city_yweek['weekday'].values:\n",
    "            d['weekday{}'.format(i)] = 1\n",
    "        else:\n",
    "            d['weekday{}'.format(i)] = 0\n",
    "            \n",
    "    for i in ['score', 'scoreB', 'host', 'win', 'tie', 'loss']:\n",
    "        d[i] = df_city_yweek[i].mean()\n",
    "\n",
    "    for i in ['max', 'gap']:\n",
    "        d[i] = df_city_yweek[i].abs().max()\n",
    "        \n",
    "    return d\n",
    "\n",
    "ltla_game_df_w = pd.DataFrame()\n",
    "for city, df_city in ltla_game_df.groupby('city'):\n",
    "    result = df_city.groupby(yweek_col).apply(get_info_df_city_yweek)\n",
    "    result = pd.DataFrame(result.to_list())\n",
    "    # if city == 'Liverpool':\n",
    "    #     break\n",
    "    ltla_game_df_w = pd.concat([ltla_game_df_w, result])\n",
    "    \n",
    "    \n",
    "ltla_game_df_w = ltla_game_df_w.reset_index(drop = True)\n",
    "ltla_game_df_w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6da18c0d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:50:52.565731Z",
     "start_time": "2022-05-14T16:50:52.510986Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>city</th>\n",
       "      <th>Birmingham</th>\n",
       "      <th>Bournemouth, Christchurch and Poole</th>\n",
       "      <th>Brighton and Hove</th>\n",
       "      <th>Burnley</th>\n",
       "      <th>Croydon</th>\n",
       "      <th>East Hampshire</th>\n",
       "      <th>Hammersmith and Fulham</th>\n",
       "      <th>Haringey</th>\n",
       "      <th>Hounslow</th>\n",
       "      <th>Islington</th>\n",
       "      <th>...</th>\n",
       "      <th>Liverpool</th>\n",
       "      <th>Manchester</th>\n",
       "      <th>Newcastle upon Tyne</th>\n",
       "      <th>Newham</th>\n",
       "      <th>Norwich</th>\n",
       "      <th>Sheffield</th>\n",
       "      <th>Southampton</th>\n",
       "      <th>Trafford</th>\n",
       "      <th>Watford</th>\n",
       "      <th>Wolverhampton</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yweek</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-w05</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-w06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-w07</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-w08</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-w09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-w51</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-w52</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-w01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-w02</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-w03</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "city      Birmingham  Bournemouth, Christchurch and Poole  Brighton and Hove  \\\n",
       "yweek                                                                          \n",
       "2020-w05         1.0                                  1.0                1.0   \n",
       "2020-w06         NaN                                  NaN                1.0   \n",
       "2020-w07         1.0                                  NaN                NaN   \n",
       "2020-w08         1.0                                  1.0                1.0   \n",
       "2020-w09         NaN                                  1.0                1.0   \n",
       "...              ...                                  ...                ...   \n",
       "2021-w51         1.0                                  NaN                1.0   \n",
       "2021-w52         1.0                                  NaN                2.0   \n",
       "2022-w01         NaN                                  NaN                NaN   \n",
       "2022-w02         1.0                                  NaN                1.0   \n",
       "2022-w03         1.0                                  NaN                2.0   \n",
       "\n",
       "city      Burnley  Croydon  East Hampshire  Hammersmith and Fulham  Haringey  \\\n",
       "yweek                                                                          \n",
       "2020-w05      1.0      NaN             NaN                     1.0       1.0   \n",
       "2020-w06      NaN      1.0             NaN                     NaN       NaN   \n",
       "2020-w07      1.0      NaN             NaN                     NaN       1.0   \n",
       "2020-w08      1.0      1.0             NaN                     2.0       1.0   \n",
       "2020-w09      1.0      1.0             NaN                     1.0       1.0   \n",
       "...           ...      ...             ...                     ...       ...   \n",
       "2021-w51      NaN      1.0             NaN                     1.0       1.0   \n",
       "2021-w52      2.0      2.0             NaN                     2.0       2.0   \n",
       "2022-w01      NaN      NaN             NaN                     NaN       NaN   \n",
       "2022-w02      NaN      1.0             NaN                     1.0       NaN   \n",
       "2022-w03      1.0      1.0             NaN                     2.0       2.0   \n",
       "\n",
       "city      Hounslow  Islington  ...  Liverpool  Manchester  \\\n",
       "yweek                          ...                          \n",
       "2020-w05       NaN        1.0  ...        1.0         1.0   \n",
       "2020-w06       NaN        NaN  ...        1.0         NaN   \n",
       "2020-w07       NaN        1.0  ...        1.0         NaN   \n",
       "2020-w08       NaN        1.0  ...        1.0         2.0   \n",
       "2020-w09       NaN        NaN  ...        3.0         NaN   \n",
       "...            ...        ...  ...        ...         ...   \n",
       "2021-w51       1.0        1.0  ...        NaN         1.0   \n",
       "2021-w52       2.0        1.0  ...        1.0         2.0   \n",
       "2022-w01       NaN        NaN  ...        NaN         NaN   \n",
       "2022-w02       2.0        NaN  ...        2.0         1.0   \n",
       "2022-w03       2.0        1.0  ...        2.0         1.0   \n",
       "\n",
       "city      Newcastle upon Tyne  Newham  Norwich  Sheffield  Southampton  \\\n",
       "yweek                                                                    \n",
       "2020-w05                  1.0     1.0      1.0        NaN          1.0   \n",
       "2020-w06                  NaN     NaN      NaN        NaN          NaN   \n",
       "2020-w07                  1.0     NaN      1.0        NaN          1.0   \n",
       "2020-w08                  1.0     1.0      1.0        1.0          1.0   \n",
       "2020-w09                  1.0     2.0      1.0        NaN          1.0   \n",
       "...                       ...     ...      ...        ...          ...   \n",
       "2021-w51                  NaN     1.0      1.0        NaN          1.0   \n",
       "2021-w52                  1.0     2.0      1.0        NaN          1.0   \n",
       "2022-w01                  NaN     NaN      NaN        NaN          NaN   \n",
       "2022-w02                  1.0     2.0      2.0        NaN          2.0   \n",
       "2022-w03                  1.0     1.0      1.0        NaN          1.0   \n",
       "\n",
       "city      Trafford  Watford  Wolverhampton  \n",
       "yweek                                       \n",
       "2020-w05       1.0      1.0            1.0  \n",
       "2020-w06       NaN      1.0            NaN  \n",
       "2020-w07       NaN      NaN            NaN  \n",
       "2020-w08       2.0      1.0            1.0  \n",
       "2020-w09       1.0      1.0            1.0  \n",
       "...            ...      ...            ...  \n",
       "2021-w51       NaN      NaN            NaN  \n",
       "2021-w52       2.0      2.0            NaN  \n",
       "2022-w01       1.0      NaN            1.0  \n",
       "2022-w02       1.0      1.0            1.0  \n",
       "2022-w03       2.0      1.0            1.0  \n",
       "\n",
       "[67 rows x 22 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "game_schedule = ltla_game_df_w.pivot_table(values='games_num', index=yweek_col, columns= 'city').sort_index()\n",
    "game_schedule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f8a8bfe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T05:06:23.005555Z",
     "start_time": "2022-05-14T05:06:23.000004Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4105750e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:50:54.959390Z",
     "start_time": "2022-05-14T16:50:53.594524Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2800x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(28, 8), dpi = 100)\n",
    "ax = sns.heatmap(game_schedule.T, cmap=\"rocket_r\", linewidths=0.4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "55ddcc2d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:50:56.682717Z",
     "start_time": "2022-05-14T16:50:56.654652Z"
    }
   },
   "outputs": [],
   "source": [
    "ltla_game_df_w.to_csv('data/week/all_games_d_{}.csv'.format(yweek_col), index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0eb9e7b",
   "metadata": {},
   "source": [
    "# Full Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f1e965c0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:50:58.600700Z",
     "start_time": "2022-05-14T16:50:58.566191Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>areaName</th>\n",
       "      <th>yweek</th>\n",
       "      <th>games_num</th>\n",
       "      <th>weekday1</th>\n",
       "      <th>weekday2</th>\n",
       "      <th>weekday3</th>\n",
       "      <th>weekday4</th>\n",
       "      <th>weekday5</th>\n",
       "      <th>weekday6</th>\n",
       "      <th>weekday7</th>\n",
       "      <th>score</th>\n",
       "      <th>scoreB</th>\n",
       "      <th>host</th>\n",
       "      <th>win</th>\n",
       "      <th>tie</th>\n",
       "      <th>loss</th>\n",
       "      <th>max</th>\n",
       "      <th>gap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Birmingham</td>\n",
       "      <td>2020-w05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Birmingham</td>\n",
       "      <td>2020-w07</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Birmingham</td>\n",
       "      <td>2020-w08</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Birmingham</td>\n",
       "      <td>2020-w25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Birmingham</td>\n",
       "      <td>2020-w26</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1011</th>\n",
       "      <td>Wolverhampton</td>\n",
       "      <td>2021-w49</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1012</th>\n",
       "      <td>Wolverhampton</td>\n",
       "      <td>2021-w50</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>Wolverhampton</td>\n",
       "      <td>2022-w01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1014</th>\n",
       "      <td>Wolverhampton</td>\n",
       "      <td>2022-w02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015</th>\n",
       "      <td>Wolverhampton</td>\n",
       "      <td>2022-w03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1016 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           areaName     yweek  games_num  weekday1  weekday2  weekday3  \\\n",
       "0        Birmingham  2020-w05          1         0         0         0   \n",
       "1        Birmingham  2020-w07          1         0         0         0   \n",
       "2        Birmingham  2020-w08          1         0         0         0   \n",
       "3        Birmingham  2020-w25          1         0         0         0   \n",
       "4        Birmingham  2020-w26          2         0         0         1   \n",
       "...             ...       ...        ...       ...       ...       ...   \n",
       "1011  Wolverhampton  2021-w49          1         0         0         0   \n",
       "1012  Wolverhampton  2021-w50          2         0         0         1   \n",
       "1013  Wolverhampton  2022-w01          1         1         0         0   \n",
       "1014  Wolverhampton  2022-w02          1         0         0         0   \n",
       "1015  Wolverhampton  2022-w03          1         0         0         0   \n",
       "\n",
       "      weekday4  weekday5  weekday6  weekday7  score  scoreB  host  win  tie  \\\n",
       "0            0         0         1         0    1.0     2.0   0.0  0.0  0.0   \n",
       "1            0         0         0         1    2.0     3.0   1.0  0.0  0.0   \n",
       "2            0         0         1         0    0.0     2.0   0.0  0.0  0.0   \n",
       "3            0         0         0         1    1.0     2.0   1.0  0.0  0.0   \n",
       "4            0         0         1         0    0.5     1.0   0.5  0.0  0.5   \n",
       "...        ...       ...       ...       ...    ...     ...   ...  ...  ...   \n",
       "1011         0         0         1         0    0.0     1.0   0.0  0.0  0.0   \n",
       "1012         0         0         0         1    0.5     0.0   0.5  0.5  0.5   \n",
       "1013         0         0         0         0    1.0     0.0   0.0  1.0  0.0   \n",
       "1014         0         0         1         0    3.0     1.0   1.0  1.0  0.0   \n",
       "1015         0         0         1         0    2.0     1.0   0.0  1.0  0.0   \n",
       "\n",
       "      loss  max  gap  \n",
       "0      1.0  2.0  1.0  \n",
       "1      1.0  3.0  1.0  \n",
       "2      1.0  2.0  2.0  \n",
       "3      1.0  2.0  1.0  \n",
       "4      0.5  1.0  1.0  \n",
       "...    ...  ...  ...  \n",
       "1011   1.0  1.0  1.0  \n",
       "1012   0.0  1.0  1.0  \n",
       "1013   0.0  1.0  1.0  \n",
       "1014   0.0  3.0  2.0  \n",
       "1015   0.0  2.0  1.0  \n",
       "\n",
       "[1016 rows x 18 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ltla_game_df_w = ltla_game_df_w.rename(columns = {\"city\": \"areaName\"})\n",
    "ltla_game_df_w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d3727c7a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:51:00.930885Z",
     "start_time": "2022-05-14T16:51:00.865880Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>areaidx</th>\n",
       "      <th>areaCode</th>\n",
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       "      <td>2020-w14</td>\n",
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       "      <th>3</th>\n",
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       "      <td>15</td>\n",
       "      <td>2020-w15</td>\n",
       "      <td>5.221876</td>\n",
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       "      <td>16</td>\n",
       "      <td>2020-w16</td>\n",
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       "      <td>109</td>\n",
       "      <td>2022-w04</td>\n",
       "      <td>71.595861</td>\n",
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       "      <th>30953</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>110</td>\n",
       "      <td>2022-w05</td>\n",
       "      <td>58.032670</td>\n",
       "      <td>56278</td>\n",
       "      <td>1566.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30954</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>111</td>\n",
       "      <td>2022-w06</td>\n",
       "      <td>48.249385</td>\n",
       "      <td>57580</td>\n",
       "      <td>1302.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30955</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>112</td>\n",
       "      <td>2022-w07</td>\n",
       "      <td>38.762563</td>\n",
       "      <td>58626</td>\n",
       "      <td>1046.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30956 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       areaidx   areaCode     areaName areaType                 NHSER21NM  \\\n",
       "0            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "1            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "2            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "3            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "4            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "...        ...        ...          ...      ...                       ...   \n",
       "30951      305  E09000033  Westminster     ltla                    London   \n",
       "30952      305  E09000033  Westminster     ltla                    London   \n",
       "30953      305  E09000033  Westminster     ltla                    London   \n",
       "30954      305  E09000033  Westminster     ltla                    London   \n",
       "30955      305  E09000033  Westminster     ltla                    London   \n",
       "\n",
       "       yweek_idx     yweek  new_case_per_10k  cumCasesWeek  newCasesWeek  ...  \\\n",
       "0             12  2020-w12          0.319707             3           3.0  ...   \n",
       "1             13  2020-w13          0.959120            12           9.0  ...   \n",
       "2             14  2020-w14          2.877361            39          27.0  ...   \n",
       "3             15  2020-w15          5.221876            88          49.0  ...   \n",
       "4             16  2020-w16          7.779530           161          73.0  ...   \n",
       "...          ...       ...               ...           ...           ...  ...   \n",
       "30951        108  2022-w03         74.486378         52780        2010.0  ...   \n",
       "30952        109  2022-w04         71.595861         54712        1932.0  ...   \n",
       "30953        110  2022-w05         58.032670         56278        1566.0  ...   \n",
       "30954        111  2022-w06         48.249385         57580        1302.0  ...   \n",
       "30955        112  2022-w07         38.762563         58626        1046.0  ...   \n",
       "\n",
       "       weekday6  weekday7  score  scoreB  host  win  tie  loss  max  gap  \n",
       "0           NaN       NaN    NaN     NaN   NaN  NaN  NaN   NaN  NaN  NaN  \n",
       "1           NaN       NaN    NaN     NaN   NaN  NaN  NaN   NaN  NaN  NaN  \n",
       "2           NaN       NaN    NaN     NaN   NaN  NaN  NaN   NaN  NaN  NaN  \n",
       "3           NaN       NaN    NaN     NaN   NaN  NaN  NaN   NaN  NaN  NaN  \n",
       "4           NaN       NaN    NaN     NaN   NaN  NaN  NaN   NaN  NaN  NaN  \n",
       "...         ...       ...    ...     ...   ...  ...  ...   ...  ...  ...  \n",
       "30951       NaN       NaN    NaN     NaN   NaN  NaN  NaN   NaN  NaN  NaN  \n",
       "30952       NaN       NaN    NaN     NaN   NaN  NaN  NaN   NaN  NaN  NaN  \n",
       "30953       NaN       NaN    NaN     NaN   NaN  NaN  NaN   NaN  NaN  NaN  \n",
       "30954       NaN       NaN    NaN     NaN   NaN  NaN  NaN   NaN  NaN  NaN  \n",
       "30955       NaN       NaN    NaN     NaN   NaN  NaN  NaN   NaN  NaN  NaN  \n",
       "\n",
       "[30956 rows x 36 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = pd.merge(ltla_covid_covariate_df, ltla_game_df_w, on = ['areaName', yweek_col], how = 'left')\n",
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c461fb1a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:51:04.273487Z",
     "start_time": "2022-05-14T16:51:04.213541Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>areaidx</th>\n",
       "      <th>areaCode</th>\n",
       "      <th>areaName</th>\n",
       "      <th>areaType</th>\n",
       "      <th>NHSER21NM</th>\n",
       "      <th>yweek_idx</th>\n",
       "      <th>yweek</th>\n",
       "      <th>new_case_per_10k</th>\n",
       "      <th>cumCasesWeek</th>\n",
       "      <th>newCasesWeek</th>\n",
       "      <th>...</th>\n",
       "      <th>old_dependency_ratio</th>\n",
       "      <th>young_dependency_ratio</th>\n",
       "      <th>popu_all</th>\n",
       "      <th>Median age</th>\n",
       "      <th>People per sq. km</th>\n",
       "      <th>Average Score</th>\n",
       "      <th>Area (sq km)</th>\n",
       "      <th>male_avg_age</th>\n",
       "      <th>female_avg_age</th>\n",
       "      <th>female_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
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       "      <td>0.319707</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
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       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>13</td>\n",
       "      <td>2020-w13</td>\n",
       "      <td>0.959120</td>\n",
       "      <td>12</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>14</td>\n",
       "      <td>2020-w14</td>\n",
       "      <td>2.877361</td>\n",
       "      <td>39</td>\n",
       "      <td>27.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>15</td>\n",
       "      <td>2020-w15</td>\n",
       "      <td>5.221876</td>\n",
       "      <td>88</td>\n",
       "      <td>49.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
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       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>16</td>\n",
       "      <td>2020-w16</td>\n",
       "      <td>7.779530</td>\n",
       "      <td>161</td>\n",
       "      <td>73.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>30951</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>108</td>\n",
       "      <td>2022-w03</td>\n",
       "      <td>74.486378</td>\n",
       "      <td>52780</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30952</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>109</td>\n",
       "      <td>2022-w04</td>\n",
       "      <td>71.595861</td>\n",
       "      <td>54712</td>\n",
       "      <td>1932.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
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       "      <td>2022-w05</td>\n",
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       "      <td>56278</td>\n",
       "      <td>1566.0</td>\n",
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       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
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       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
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       "    <tr>\n",
       "      <th>30954</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>111</td>\n",
       "      <td>2022-w06</td>\n",
       "      <td>48.249385</td>\n",
       "      <td>57580</td>\n",
       "      <td>1302.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30955</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>112</td>\n",
       "      <td>2022-w07</td>\n",
       "      <td>38.762563</td>\n",
       "      <td>58626</td>\n",
       "      <td>1046.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30956 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       areaidx   areaCode     areaName areaType                 NHSER21NM  \\\n",
       "0            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "1            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "2            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "3            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "4            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "...        ...        ...          ...      ...                       ...   \n",
       "30951      305  E09000033  Westminster     ltla                    London   \n",
       "30952      305  E09000033  Westminster     ltla                    London   \n",
       "30953      305  E09000033  Westminster     ltla                    London   \n",
       "30954      305  E09000033  Westminster     ltla                    London   \n",
       "30955      305  E09000033  Westminster     ltla                    London   \n",
       "\n",
       "       yweek_idx     yweek  new_case_per_10k  cumCasesWeek  newCasesWeek  ...  \\\n",
       "0             12  2020-w12          0.319707             3           3.0  ...   \n",
       "1             13  2020-w13          0.959120            12           9.0  ...   \n",
       "2             14  2020-w14          2.877361            39          27.0  ...   \n",
       "3             15  2020-w15          5.221876            88          49.0  ...   \n",
       "4             16  2020-w16          7.779530           161          73.0  ...   \n",
       "...          ...       ...               ...           ...           ...  ...   \n",
       "30951        108  2022-w03         74.486378         52780        2010.0  ...   \n",
       "30952        109  2022-w04         71.595861         54712        1932.0  ...   \n",
       "30953        110  2022-w05         58.032670         56278        1566.0  ...   \n",
       "30954        111  2022-w06         48.249385         57580        1302.0  ...   \n",
       "30955        112  2022-w07         38.762563         58626        1046.0  ...   \n",
       "\n",
       "       old_dependency_ratio  young_dependency_ratio  popu_all  Median age  \\\n",
       "0                  0.313702                0.288634     93836        41.7   \n",
       "1                  0.313702                0.288634     93836        41.7   \n",
       "2                  0.313702                0.288634     93836        41.7   \n",
       "3                  0.313702                0.288634     93836        41.7   \n",
       "4                  0.313702                0.288634     93836        41.7   \n",
       "...                     ...                     ...       ...         ...   \n",
       "30951              0.175793                0.223543    269848        36.3   \n",
       "30952              0.175793                0.223543    269848        36.3   \n",
       "30953              0.175793                0.223543    269848        36.3   \n",
       "30954              0.175793                0.223543    269848        36.3   \n",
       "30955              0.175793                0.223543    269848        36.3   \n",
       "\n",
       "       People per sq. km  Average Score  Area (sq km)  male_avg_age  \\\n",
       "0                    999           35.0       93.7125     40.157418   \n",
       "1                    999           35.0       93.7125     40.157418   \n",
       "2                    999           35.0       93.7125     40.157418   \n",
       "3                    999           35.0       93.7125     40.157418   \n",
       "4                    999           35.0       93.7125     40.157418   \n",
       "...                  ...            ...           ...           ...   \n",
       "30951              12167           20.3       21.4826     36.918846   \n",
       "30952              12167           20.3       21.4826     36.918846   \n",
       "30953              12167           20.3       21.4826     36.918846   \n",
       "30954              12167           20.3       21.4826     36.918846   \n",
       "30955              12167           20.3       21.4826     36.918846   \n",
       "\n",
       "       female_avg_age  female_rate  \n",
       "0           42.190274     0.509730  \n",
       "1           42.190274     0.509730  \n",
       "2           42.190274     0.509730  \n",
       "3           42.190274     0.509730  \n",
       "4           42.190274     0.509730  \n",
       "...               ...          ...  \n",
       "30951       38.427939     0.472677  \n",
       "30952       38.427939     0.472677  \n",
       "30953       38.427939     0.472677  \n",
       "30954       38.427939     0.472677  \n",
       "30955       38.427939     0.472677  \n",
       "\n",
       "[30956 rows x 36 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['areaidx', 'areaCode', 'areaName', 'areaType', 'NHSER21NM', \n",
    "        'yweek_idx', yweek_col, \n",
    "        'new_case_per_10k', 'cumCasesWeek', 'newCasesWeek',\n",
    "        \n",
    "        'games_num', 'host', 'win', 'tie', 'loss', \n",
    "        'max', 'gap', 'score', 'scoreB', \n",
    "        'weekday1', 'weekday2', 'weekday3', 'weekday4', 'weekday5', 'weekday6', 'weekday7', \n",
    "        \n",
    "        'old_dependency_ratio', 'young_dependency_ratio', 'popu_all',\n",
    "        'Median age', 'People per sq. km', 'Average Score', 'Area (sq km)',\n",
    "        'male_avg_age', 'female_avg_age', 'female_rate', ]\n",
    "\n",
    "final_df = final_df[cols]\n",
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bd6096f0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:51:11.034561Z",
     "start_time": "2022-05-14T16:51:10.496534Z"
    }
   },
   "outputs": [],
   "source": [
    "final_df.to_csv('data/week/all_cities_ydz_{}.csv'.format(yweek_col), index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ff81f4a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-11T05:03:29.450792Z",
     "start_time": "2022-05-11T05:03:29.400854Z"
    }
   },
   "source": [
    "# [JUMP] Data Prepare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c1315f03",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:51:13.470642Z",
     "start_time": "2022-05-14T16:51:13.349729Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>areaidx</th>\n",
       "      <th>areaCode</th>\n",
       "      <th>areaName</th>\n",
       "      <th>areaType</th>\n",
       "      <th>NHSER21NM</th>\n",
       "      <th>yweek_idx</th>\n",
       "      <th>yweek</th>\n",
       "      <th>new_case_per_10k</th>\n",
       "      <th>cumCasesWeek</th>\n",
       "      <th>newCasesWeek</th>\n",
       "      <th>games_num</th>\n",
       "      <th>host</th>\n",
       "      <th>win</th>\n",
       "      <th>tie</th>\n",
       "      <th>loss</th>\n",
       "      <th>max</th>\n",
       "      <th>gap</th>\n",
       "      <th>score</th>\n",
       "      <th>scoreB</th>\n",
       "      <th>weekday1</th>\n",
       "      <th>weekday2</th>\n",
       "      <th>weekday3</th>\n",
       "      <th>weekday4</th>\n",
       "      <th>weekday5</th>\n",
       "      <th>weekday6</th>\n",
       "      <th>weekday7</th>\n",
       "      <th>old_dependency_ratio</th>\n",
       "      <th>young_dependency_ratio</th>\n",
       "      <th>popu_all</th>\n",
       "      <th>Median age</th>\n",
       "      <th>People per sq. km</th>\n",
       "      <th>Average Score</th>\n",
       "      <th>Area (sq km)</th>\n",
       "      <th>male_avg_age</th>\n",
       "      <th>female_avg_age</th>\n",
       "      <th>female_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>12</td>\n",
       "      <td>2020-w12</td>\n",
       "      <td>0.319707</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>13</td>\n",
       "      <td>2020-w13</td>\n",
       "      <td>0.959120</td>\n",
       "      <td>12</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>14</td>\n",
       "      <td>2020-w14</td>\n",
       "      <td>2.877361</td>\n",
       "      <td>39</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>15</td>\n",
       "      <td>2020-w15</td>\n",
       "      <td>5.221876</td>\n",
       "      <td>88</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>16</td>\n",
       "      <td>2020-w16</td>\n",
       "      <td>7.779530</td>\n",
       "      <td>161</td>\n",
       "      <td>73.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30951</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>108</td>\n",
       "      <td>2022-w03</td>\n",
       "      <td>74.486378</td>\n",
       "      <td>52780</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30952</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>109</td>\n",
       "      <td>2022-w04</td>\n",
       "      <td>71.595861</td>\n",
       "      <td>54712</td>\n",
       "      <td>1932.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30953</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>110</td>\n",
       "      <td>2022-w05</td>\n",
       "      <td>58.032670</td>\n",
       "      <td>56278</td>\n",
       "      <td>1566.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
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       "    <tr>\n",
       "      <th>30954</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>111</td>\n",
       "      <td>2022-w06</td>\n",
       "      <td>48.249385</td>\n",
       "      <td>57580</td>\n",
       "      <td>1302.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30955</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>112</td>\n",
       "      <td>2022-w07</td>\n",
       "      <td>38.762563</td>\n",
       "      <td>58626</td>\n",
       "      <td>1046.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30956 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       areaidx   areaCode     areaName areaType                 NHSER21NM  \\\n",
       "0            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "1            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "2            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "3            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "4            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "...        ...        ...          ...      ...                       ...   \n",
       "30951      305  E09000033  Westminster     ltla                    London   \n",
       "30952      305  E09000033  Westminster     ltla                    London   \n",
       "30953      305  E09000033  Westminster     ltla                    London   \n",
       "30954      305  E09000033  Westminster     ltla                    London   \n",
       "30955      305  E09000033  Westminster     ltla                    London   \n",
       "\n",
       "       yweek_idx     yweek  new_case_per_10k  cumCasesWeek  newCasesWeek  \\\n",
       "0             12  2020-w12          0.319707             3           3.0   \n",
       "1             13  2020-w13          0.959120            12           9.0   \n",
       "2             14  2020-w14          2.877361            39          27.0   \n",
       "3             15  2020-w15          5.221876            88          49.0   \n",
       "4             16  2020-w16          7.779530           161          73.0   \n",
       "...          ...       ...               ...           ...           ...   \n",
       "30951        108  2022-w03         74.486378         52780        2010.0   \n",
       "30952        109  2022-w04         71.595861         54712        1932.0   \n",
       "30953        110  2022-w05         58.032670         56278        1566.0   \n",
       "30954        111  2022-w06         48.249385         57580        1302.0   \n",
       "30955        112  2022-w07         38.762563         58626        1046.0   \n",
       "\n",
       "       games_num  host  win  tie  loss  max  gap  score  scoreB  weekday1  \\\n",
       "0            NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "1            NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "2            NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "3            NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "4            NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "...          ...   ...  ...  ...   ...  ...  ...    ...     ...       ...   \n",
       "30951        NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "30952        NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "30953        NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "30954        NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "30955        NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "\n",
       "       weekday2  weekday3  weekday4  weekday5  weekday6  weekday7  \\\n",
       "0           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "...         ...       ...       ...       ...       ...       ...   \n",
       "30951       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "30952       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "30953       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "30954       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "30955       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "       old_dependency_ratio  young_dependency_ratio  popu_all  Median age  \\\n",
       "0                  0.313702                0.288634     93836        41.7   \n",
       "1                  0.313702                0.288634     93836        41.7   \n",
       "2                  0.313702                0.288634     93836        41.7   \n",
       "3                  0.313702                0.288634     93836        41.7   \n",
       "4                  0.313702                0.288634     93836        41.7   \n",
       "...                     ...                     ...       ...         ...   \n",
       "30951              0.175793                0.223543    269848        36.3   \n",
       "30952              0.175793                0.223543    269848        36.3   \n",
       "30953              0.175793                0.223543    269848        36.3   \n",
       "30954              0.175793                0.223543    269848        36.3   \n",
       "30955              0.175793                0.223543    269848        36.3   \n",
       "\n",
       "       People per sq. km  Average Score  Area (sq km)  male_avg_age  \\\n",
       "0                    999           35.0       93.7125     40.157418   \n",
       "1                    999           35.0       93.7125     40.157418   \n",
       "2                    999           35.0       93.7125     40.157418   \n",
       "3                    999           35.0       93.7125     40.157418   \n",
       "4                    999           35.0       93.7125     40.157418   \n",
       "...                  ...            ...           ...           ...   \n",
       "30951              12167           20.3       21.4826     36.918846   \n",
       "30952              12167           20.3       21.4826     36.918846   \n",
       "30953              12167           20.3       21.4826     36.918846   \n",
       "30954              12167           20.3       21.4826     36.918846   \n",
       "30955              12167           20.3       21.4826     36.918846   \n",
       "\n",
       "       female_avg_age  female_rate  \n",
       "0           42.190274     0.509730  \n",
       "1           42.190274     0.509730  \n",
       "2           42.190274     0.509730  \n",
       "3           42.190274     0.509730  \n",
       "4           42.190274     0.509730  \n",
       "...               ...          ...  \n",
       "30951       38.427939     0.472677  \n",
       "30952       38.427939     0.472677  \n",
       "30953       38.427939     0.472677  \n",
       "30954       38.427939     0.472677  \n",
       "30955       38.427939     0.472677  \n",
       "\n",
       "[30956 rows x 36 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "yweek_col = 'yweek'\n",
    "final_df = pd.read_csv('data/week/all_cities_ydz_{}.csv'.format(yweek_col))\n",
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1f55b7be",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:51:32.539777Z",
     "start_time": "2022-05-14T16:51:32.527933Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22\n"
     ]
    }
   ],
   "source": [
    "GameCityList = list(final_df[- final_df['games_num'].isna()]['areaName'].unique())\n",
    "print(len(GameCityList))\n",
    "# GameCityList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "64c33df7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:51:34.028332Z",
     "start_time": "2022-05-14T16:51:34.005370Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86\n"
     ]
    }
   ],
   "source": [
    "path = 'data/ltla_neighbours_df.csv'\n",
    "ltla_neighbours_df = pd.read_csv(path).rename(columns = {'LAD21NM': 'areaName',})\n",
    "ltla_neighbours_df = ltla_neighbours_df[ltla_neighbours_df['areaName'].isin(GameCityList)]\n",
    "NeighborCityList = list(set(sum(ltla_neighbours_df['neighboors'].apply(eval).to_list(), [])))\n",
    "NeighborCityList = [i for i in NeighborCityList if i not in GameCityList]\n",
    "print(len(NeighborCityList))\n",
    "# NeighborCityList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c663bc6c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:51:34.706804Z",
     "start_time": "2022-05-14T16:51:34.656215Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>areaidx</th>\n",
       "      <th>areaCode</th>\n",
       "      <th>areaName</th>\n",
       "      <th>areaType</th>\n",
       "      <th>NHSER21NM</th>\n",
       "      <th>yweek_idx</th>\n",
       "      <th>yweek</th>\n",
       "      <th>new_case_per_10k</th>\n",
       "      <th>cumCasesWeek</th>\n",
       "      <th>newCasesWeek</th>\n",
       "      <th>games_num</th>\n",
       "      <th>host</th>\n",
       "      <th>win</th>\n",
       "      <th>tie</th>\n",
       "      <th>loss</th>\n",
       "      <th>max</th>\n",
       "      <th>gap</th>\n",
       "      <th>score</th>\n",
       "      <th>scoreB</th>\n",
       "      <th>weekday1</th>\n",
       "      <th>weekday2</th>\n",
       "      <th>weekday3</th>\n",
       "      <th>weekday4</th>\n",
       "      <th>weekday5</th>\n",
       "      <th>weekday6</th>\n",
       "      <th>weekday7</th>\n",
       "      <th>old_dependency_ratio</th>\n",
       "      <th>young_dependency_ratio</th>\n",
       "      <th>popu_all</th>\n",
       "      <th>Median age</th>\n",
       "      <th>People per sq. km</th>\n",
       "      <th>Average Score</th>\n",
       "      <th>Area (sq km)</th>\n",
       "      <th>male_avg_age</th>\n",
       "      <th>female_avg_age</th>\n",
       "      <th>female_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>12</td>\n",
       "      <td>2020-w12</td>\n",
       "      <td>0.319707</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>13</td>\n",
       "      <td>2020-w13</td>\n",
       "      <td>0.959120</td>\n",
       "      <td>12</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>14</td>\n",
       "      <td>2020-w14</td>\n",
       "      <td>2.877361</td>\n",
       "      <td>39</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>15</td>\n",
       "      <td>2020-w15</td>\n",
       "      <td>5.221876</td>\n",
       "      <td>88</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>16</td>\n",
       "      <td>2020-w16</td>\n",
       "      <td>7.779530</td>\n",
       "      <td>161</td>\n",
       "      <td>73.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30951</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>108</td>\n",
       "      <td>2022-w03</td>\n",
       "      <td>74.486378</td>\n",
       "      <td>52780</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30952</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>109</td>\n",
       "      <td>2022-w04</td>\n",
       "      <td>71.595861</td>\n",
       "      <td>54712</td>\n",
       "      <td>1932.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30953</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>110</td>\n",
       "      <td>2022-w05</td>\n",
       "      <td>58.032670</td>\n",
       "      <td>56278</td>\n",
       "      <td>1566.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30954</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>111</td>\n",
       "      <td>2022-w06</td>\n",
       "      <td>48.249385</td>\n",
       "      <td>57580</td>\n",
       "      <td>1302.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30955</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>112</td>\n",
       "      <td>2022-w07</td>\n",
       "      <td>38.762563</td>\n",
       "      <td>58626</td>\n",
       "      <td>1046.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22386 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       areaidx   areaCode     areaName areaType                 NHSER21NM  \\\n",
       "0            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "1            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "2            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "3            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "4            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "...        ...        ...          ...      ...                       ...   \n",
       "30951      305  E09000033  Westminster     ltla                    London   \n",
       "30952      305  E09000033  Westminster     ltla                    London   \n",
       "30953      305  E09000033  Westminster     ltla                    London   \n",
       "30954      305  E09000033  Westminster     ltla                    London   \n",
       "30955      305  E09000033  Westminster     ltla                    London   \n",
       "\n",
       "       yweek_idx     yweek  new_case_per_10k  cumCasesWeek  newCasesWeek  \\\n",
       "0             12  2020-w12          0.319707             3           3.0   \n",
       "1             13  2020-w13          0.959120            12           9.0   \n",
       "2             14  2020-w14          2.877361            39          27.0   \n",
       "3             15  2020-w15          5.221876            88          49.0   \n",
       "4             16  2020-w16          7.779530           161          73.0   \n",
       "...          ...       ...               ...           ...           ...   \n",
       "30951        108  2022-w03         74.486378         52780        2010.0   \n",
       "30952        109  2022-w04         71.595861         54712        1932.0   \n",
       "30953        110  2022-w05         58.032670         56278        1566.0   \n",
       "30954        111  2022-w06         48.249385         57580        1302.0   \n",
       "30955        112  2022-w07         38.762563         58626        1046.0   \n",
       "\n",
       "       games_num  host  win  tie  loss  max  gap  score  scoreB  weekday1  \\\n",
       "0            NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "1            NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "2            NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "3            NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "4            NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "...          ...   ...  ...  ...   ...  ...  ...    ...     ...       ...   \n",
       "30951        NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "30952        NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "30953        NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "30954        NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "30955        NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "\n",
       "       weekday2  weekday3  weekday4  weekday5  weekday6  weekday7  \\\n",
       "0           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "...         ...       ...       ...       ...       ...       ...   \n",
       "30951       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "30952       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "30953       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "30954       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "30955       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "       old_dependency_ratio  young_dependency_ratio  popu_all  Median age  \\\n",
       "0                  0.313702                0.288634     93836        41.7   \n",
       "1                  0.313702                0.288634     93836        41.7   \n",
       "2                  0.313702                0.288634     93836        41.7   \n",
       "3                  0.313702                0.288634     93836        41.7   \n",
       "4                  0.313702                0.288634     93836        41.7   \n",
       "...                     ...                     ...       ...         ...   \n",
       "30951              0.175793                0.223543    269848        36.3   \n",
       "30952              0.175793                0.223543    269848        36.3   \n",
       "30953              0.175793                0.223543    269848        36.3   \n",
       "30954              0.175793                0.223543    269848        36.3   \n",
       "30955              0.175793                0.223543    269848        36.3   \n",
       "\n",
       "       People per sq. km  Average Score  Area (sq km)  male_avg_age  \\\n",
       "0                    999           35.0       93.7125     40.157418   \n",
       "1                    999           35.0       93.7125     40.157418   \n",
       "2                    999           35.0       93.7125     40.157418   \n",
       "3                    999           35.0       93.7125     40.157418   \n",
       "4                    999           35.0       93.7125     40.157418   \n",
       "...                  ...            ...           ...           ...   \n",
       "30951              12167           20.3       21.4826     36.918846   \n",
       "30952              12167           20.3       21.4826     36.918846   \n",
       "30953              12167           20.3       21.4826     36.918846   \n",
       "30954              12167           20.3       21.4826     36.918846   \n",
       "30955              12167           20.3       21.4826     36.918846   \n",
       "\n",
       "       female_avg_age  female_rate  \n",
       "0           42.190274     0.509730  \n",
       "1           42.190274     0.509730  \n",
       "2           42.190274     0.509730  \n",
       "3           42.190274     0.509730  \n",
       "4           42.190274     0.509730  \n",
       "...               ...          ...  \n",
       "30951       38.427939     0.472677  \n",
       "30952       38.427939     0.472677  \n",
       "30953       38.427939     0.472677  \n",
       "30954       38.427939     0.472677  \n",
       "30955       38.427939     0.472677  \n",
       "\n",
       "[22386 rows x 36 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PAY ATTENTION HERE\n",
    "# DROP NeighborCityList or Not\n",
    "final_df = final_df[-final_df['areaName'].isin(NeighborCityList)]\n",
    "final_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7164384a",
   "metadata": {},
   "source": [
    "## Game Seasons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c3e8493e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:51:36.216907Z",
     "start_time": "2022-05-14T16:51:36.185498Z"
    }
   },
   "outputs": [],
   "source": [
    "start_time = '2020-w19'\n",
    "treat_time = '2020-w25'\n",
    "end_time   = '2020-w30'\n",
    "season_name = 'S1A1'\n",
    "\n",
    "### Your Operation\n",
    "\n",
    "df = final_df[(final_df[yweek_col] >= start_time) & (final_df[yweek_col] <= end_time)].reset_index(drop = True)\n",
    "df['season'] = season_name\n",
    "df['areaNameS'] = df['areaName'] + df['season']\n",
    "tmp = df.groupby('areaName')['games_num'].sum()\n",
    "tmp = tmp[tmp > 0]\n",
    "treat = list(tmp.index)\n",
    "df['treat'] = df['areaName'].isin(treat).astype(int)\n",
    "df['post'] = df[yweek_col].apply(lambda x: int(x >= treat_time))\n",
    "# df[df['areaName'] == 'Liverpool']\n",
    "final_df_s1 = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8f0a1b78",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:51:36.756544Z",
     "start_time": "2022-05-14T16:51:36.723573Z"
    }
   },
   "outputs": [],
   "source": [
    "start_time = '2020-w31'\n",
    "treat_time = '2020-w37'\n",
    "end_time   = '2020-w43'\n",
    "season_name = 'S2A2'\n",
    "\n",
    "### Your Operation\n",
    "\n",
    "df = final_df[(final_df[yweek_col] >= start_time) & (final_df[yweek_col] <= end_time)].reset_index(drop = True)\n",
    "df['season'] = season_name\n",
    "df['areaNameS'] = df['areaName'] + df['season']\n",
    "tmp = df.groupby('areaName')['games_num'].sum()\n",
    "tmp = tmp[tmp > 0]\n",
    "treat = list(tmp.index)\n",
    "df['treat'] = df['areaName'].isin(treat).astype(int)\n",
    "df['post'] = df[yweek_col].apply(lambda x: int(x >= treat_time))\n",
    "\n",
    "final_df_s2 = df\n",
    "# df[df['areaName'] == 'Liverpool']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dcd71ef6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:51:37.251013Z",
     "start_time": "2022-05-14T16:51:37.222253Z"
    }
   },
   "outputs": [],
   "source": [
    "start_time = '2020-w44'\n",
    "treat_time = '2020-w49'\n",
    "end_time   = '2020-w53'\n",
    "season_name = 'S3B1'\n",
    "\n",
    "### Your Operation\n",
    "\n",
    "df = final_df[(final_df[yweek_col] >= start_time) & (final_df[yweek_col] <= end_time)].reset_index(drop = True)\n",
    "df['season'] = season_name\n",
    "df['areaNameS'] = df['areaName'] + df['season']\n",
    "tmp = df.groupby('areaName')['games_num'].sum()\n",
    "tmp = tmp[tmp > 0]\n",
    "treat = list(tmp.index)\n",
    "df['treat'] = df['areaName'].isin(treat).astype(int)\n",
    "df['post'] = df[yweek_col].apply(lambda x: int(x >= treat_time))\n",
    "# df[df['areaName'] == 'Liverpool']\n",
    "final_df_a1 = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "17aebef2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:51:37.727720Z",
     "start_time": "2022-05-14T16:51:37.697074Z"
    }
   },
   "outputs": [],
   "source": [
    "start_time = '2021-w11'\n",
    "treat_time = '2021-w17'\n",
    "end_time   = '2021-w22'\n",
    "season_name = 'S4B2'\n",
    "\n",
    "### Your Operation\n",
    "\n",
    "df = final_df[(final_df[yweek_col] >= start_time) & (final_df[yweek_col] <= end_time)].reset_index(drop = True)\n",
    "df['season'] = season_name\n",
    "df['areaNameS'] = df['areaName'] + df['season']\n",
    "tmp = df.groupby('areaName')['games_num'].sum()\n",
    "tmp = tmp[tmp > 0]\n",
    "treat = list(tmp.index)\n",
    "df['treat'] = df['areaName'].isin(treat).astype(int)\n",
    "df['post'] = df[yweek_col].apply(lambda x: int(x >= treat_time))\n",
    "# df[df['areaName'] == 'Liverpool']\n",
    "final_df_a2 = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6e517d5a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:51:38.284403Z",
     "start_time": "2022-05-14T16:51:38.212432Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>areaidx</th>\n",
       "      <th>areaCode</th>\n",
       "      <th>areaName</th>\n",
       "      <th>areaType</th>\n",
       "      <th>NHSER21NM</th>\n",
       "      <th>yweek_idx</th>\n",
       "      <th>yweek</th>\n",
       "      <th>new_case_per_10k</th>\n",
       "      <th>cumCasesWeek</th>\n",
       "      <th>newCasesWeek</th>\n",
       "      <th>games_num</th>\n",
       "      <th>host</th>\n",
       "      <th>win</th>\n",
       "      <th>tie</th>\n",
       "      <th>loss</th>\n",
       "      <th>max</th>\n",
       "      <th>gap</th>\n",
       "      <th>score</th>\n",
       "      <th>scoreB</th>\n",
       "      <th>weekday1</th>\n",
       "      <th>weekday2</th>\n",
       "      <th>weekday3</th>\n",
       "      <th>weekday4</th>\n",
       "      <th>weekday5</th>\n",
       "      <th>weekday6</th>\n",
       "      <th>weekday7</th>\n",
       "      <th>old_dependency_ratio</th>\n",
       "      <th>young_dependency_ratio</th>\n",
       "      <th>popu_all</th>\n",
       "      <th>Median age</th>\n",
       "      <th>People per sq. km</th>\n",
       "      <th>Average Score</th>\n",
       "      <th>Area (sq km)</th>\n",
       "      <th>male_avg_age</th>\n",
       "      <th>female_avg_age</th>\n",
       "      <th>female_rate</th>\n",
       "      <th>season</th>\n",
       "      <th>areaNameS</th>\n",
       "      <th>treat</th>\n",
       "      <th>post</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>249</td>\n",
       "      <td>E08000012</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North West</td>\n",
       "      <td>79</td>\n",
       "      <td>2021-w26</td>\n",
       "      <td>54.448383</td>\n",
       "      <td>57018</td>\n",
       "      <td>2725.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.214742</td>\n",
       "      <td>0.241551</td>\n",
       "      <td>500474</td>\n",
       "      <td>34.8</td>\n",
       "      <td>4453</td>\n",
       "      <td>42.4</td>\n",
       "      <td>111.8357</td>\n",
       "      <td>36.823739</td>\n",
       "      <td>38.438335</td>\n",
       "      <td>0.499682</td>\n",
       "      <td>S5A3</td>\n",
       "      <td>LiverpoolS5A3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>249</td>\n",
       "      <td>E08000012</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North West</td>\n",
       "      <td>80</td>\n",
       "      <td>2021-w27</td>\n",
       "      <td>49.992607</td>\n",
       "      <td>59520</td>\n",
       "      <td>2502.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.214742</td>\n",
       "      <td>0.241551</td>\n",
       "      <td>500474</td>\n",
       "      <td>34.8</td>\n",
       "      <td>4453</td>\n",
       "      <td>42.4</td>\n",
       "      <td>111.8357</td>\n",
       "      <td>36.823739</td>\n",
       "      <td>38.438335</td>\n",
       "      <td>0.499682</td>\n",
       "      <td>S5A3</td>\n",
       "      <td>LiverpoolS5A3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>249</td>\n",
       "      <td>E08000012</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North West</td>\n",
       "      <td>81</td>\n",
       "      <td>2021-w28</td>\n",
       "      <td>51.930770</td>\n",
       "      <td>62119</td>\n",
       "      <td>2599.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.214742</td>\n",
       "      <td>0.241551</td>\n",
       "      <td>500474</td>\n",
       "      <td>34.8</td>\n",
       "      <td>4453</td>\n",
       "      <td>42.4</td>\n",
       "      <td>111.8357</td>\n",
       "      <td>36.823739</td>\n",
       "      <td>38.438335</td>\n",
       "      <td>0.499682</td>\n",
       "      <td>S5A3</td>\n",
       "      <td>LiverpoolS5A3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2355</th>\n",
       "      <td>249</td>\n",
       "      <td>E08000012</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North West</td>\n",
       "      <td>82</td>\n",
       "      <td>2021-w29</td>\n",
       "      <td>38.543461</td>\n",
       "      <td>64048</td>\n",
       "      <td>1929.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.214742</td>\n",
       "      <td>0.241551</td>\n",
       "      <td>500474</td>\n",
       "      <td>34.8</td>\n",
       "      <td>4453</td>\n",
       "      <td>42.4</td>\n",
       "      <td>111.8357</td>\n",
       "      <td>36.823739</td>\n",
       "      <td>38.438335</td>\n",
       "      <td>0.499682</td>\n",
       "      <td>S5A3</td>\n",
       "      <td>LiverpoolS5A3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2356</th>\n",
       "      <td>249</td>\n",
       "      <td>E08000012</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North West</td>\n",
       "      <td>83</td>\n",
       "      <td>2021-w30</td>\n",
       "      <td>36.305582</td>\n",
       "      <td>65865</td>\n",
       "      <td>1817.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.214742</td>\n",
       "      <td>0.241551</td>\n",
       "      <td>500474</td>\n",
       "      <td>34.8</td>\n",
       "      <td>4453</td>\n",
       "      <td>42.4</td>\n",
       "      <td>111.8357</td>\n",
       "      <td>36.823739</td>\n",
       "      <td>38.438335</td>\n",
       "      <td>0.499682</td>\n",
       "      <td>S5A3</td>\n",
       "      <td>LiverpoolS5A3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2357</th>\n",
       "      <td>249</td>\n",
       "      <td>E08000012</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North West</td>\n",
       "      <td>84</td>\n",
       "      <td>2021-w31</td>\n",
       "      <td>36.665241</td>\n",
       "      <td>67700</td>\n",
       "      <td>1835.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.214742</td>\n",
       "      <td>0.241551</td>\n",
       "      <td>500474</td>\n",
       "      <td>34.8</td>\n",
       "      <td>4453</td>\n",
       "      <td>42.4</td>\n",
       "      <td>111.8357</td>\n",
       "      <td>36.823739</td>\n",
       "      <td>38.438335</td>\n",
       "      <td>0.499682</td>\n",
       "      <td>S5A3</td>\n",
       "      <td>LiverpoolS5A3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2358</th>\n",
       "      <td>249</td>\n",
       "      <td>E08000012</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North West</td>\n",
       "      <td>85</td>\n",
       "      <td>2021-w32</td>\n",
       "      <td>37.704256</td>\n",
       "      <td>69587</td>\n",
       "      <td>1887.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.214742</td>\n",
       "      <td>0.241551</td>\n",
       "      <td>500474</td>\n",
       "      <td>34.8</td>\n",
       "      <td>4453</td>\n",
       "      <td>42.4</td>\n",
       "      <td>111.8357</td>\n",
       "      <td>36.823739</td>\n",
       "      <td>38.438335</td>\n",
       "      <td>0.499682</td>\n",
       "      <td>S5A3</td>\n",
       "      <td>LiverpoolS5A3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2359</th>\n",
       "      <td>249</td>\n",
       "      <td>E08000012</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North West</td>\n",
       "      <td>86</td>\n",
       "      <td>2021-w33</td>\n",
       "      <td>35.046776</td>\n",
       "      <td>71341</td>\n",
       "      <td>1754.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.214742</td>\n",
       "      <td>0.241551</td>\n",
       "      <td>500474</td>\n",
       "      <td>34.8</td>\n",
       "      <td>4453</td>\n",
       "      <td>42.4</td>\n",
       "      <td>111.8357</td>\n",
       "      <td>36.823739</td>\n",
       "      <td>38.438335</td>\n",
       "      <td>0.499682</td>\n",
       "      <td>S5A3</td>\n",
       "      <td>LiverpoolS5A3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2360</th>\n",
       "      <td>249</td>\n",
       "      <td>E08000012</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North West</td>\n",
       "      <td>87</td>\n",
       "      <td>2021-w34</td>\n",
       "      <td>34.527268</td>\n",
       "      <td>73069</td>\n",
       "      <td>1728.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.214742</td>\n",
       "      <td>0.241551</td>\n",
       "      <td>500474</td>\n",
       "      <td>34.8</td>\n",
       "      <td>4453</td>\n",
       "      <td>42.4</td>\n",
       "      <td>111.8357</td>\n",
       "      <td>36.823739</td>\n",
       "      <td>38.438335</td>\n",
       "      <td>0.499682</td>\n",
       "      <td>S5A3</td>\n",
       "      <td>LiverpoolS5A3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2361</th>\n",
       "      <td>249</td>\n",
       "      <td>E08000012</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North West</td>\n",
       "      <td>88</td>\n",
       "      <td>2021-w35</td>\n",
       "      <td>41.680487</td>\n",
       "      <td>75155</td>\n",
       "      <td>2086.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.214742</td>\n",
       "      <td>0.241551</td>\n",
       "      <td>500474</td>\n",
       "      <td>34.8</td>\n",
       "      <td>4453</td>\n",
       "      <td>42.4</td>\n",
       "      <td>111.8357</td>\n",
       "      <td>36.823739</td>\n",
       "      <td>38.438335</td>\n",
       "      <td>0.499682</td>\n",
       "      <td>S5A3</td>\n",
       "      <td>LiverpoolS5A3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2362</th>\n",
       "      <td>249</td>\n",
       "      <td>E08000012</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North West</td>\n",
       "      <td>89</td>\n",
       "      <td>2021-w36</td>\n",
       "      <td>34.407382</td>\n",
       "      <td>76877</td>\n",
       "      <td>1722.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.214742</td>\n",
       "      <td>0.241551</td>\n",
       "      <td>500474</td>\n",
       "      <td>34.8</td>\n",
       "      <td>4453</td>\n",
       "      <td>42.4</td>\n",
       "      <td>111.8357</td>\n",
       "      <td>36.823739</td>\n",
       "      <td>38.438335</td>\n",
       "      <td>0.499682</td>\n",
       "      <td>S5A3</td>\n",
       "      <td>LiverpoolS5A3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2363</th>\n",
       "      <td>249</td>\n",
       "      <td>E08000012</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North West</td>\n",
       "      <td>90</td>\n",
       "      <td>2021-w37</td>\n",
       "      <td>32.209465</td>\n",
       "      <td>78489</td>\n",
       "      <td>1612.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.214742</td>\n",
       "      <td>0.241551</td>\n",
       "      <td>500474</td>\n",
       "      <td>34.8</td>\n",
       "      <td>4453</td>\n",
       "      <td>42.4</td>\n",
       "      <td>111.8357</td>\n",
       "      <td>36.823739</td>\n",
       "      <td>38.438335</td>\n",
       "      <td>0.499682</td>\n",
       "      <td>S5A3</td>\n",
       "      <td>LiverpoolS5A3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      areaidx   areaCode   areaName areaType   NHSER21NM  yweek_idx     yweek  \\\n",
       "2352      249  E08000012  Liverpool     ltla  North West         79  2021-w26   \n",
       "2353      249  E08000012  Liverpool     ltla  North West         80  2021-w27   \n",
       "2354      249  E08000012  Liverpool     ltla  North West         81  2021-w28   \n",
       "2355      249  E08000012  Liverpool     ltla  North West         82  2021-w29   \n",
       "2356      249  E08000012  Liverpool     ltla  North West         83  2021-w30   \n",
       "2357      249  E08000012  Liverpool     ltla  North West         84  2021-w31   \n",
       "2358      249  E08000012  Liverpool     ltla  North West         85  2021-w32   \n",
       "2359      249  E08000012  Liverpool     ltla  North West         86  2021-w33   \n",
       "2360      249  E08000012  Liverpool     ltla  North West         87  2021-w34   \n",
       "2361      249  E08000012  Liverpool     ltla  North West         88  2021-w35   \n",
       "2362      249  E08000012  Liverpool     ltla  North West         89  2021-w36   \n",
       "2363      249  E08000012  Liverpool     ltla  North West         90  2021-w37   \n",
       "\n",
       "      new_case_per_10k  cumCasesWeek  newCasesWeek  games_num  host   win  \\\n",
       "2352         54.448383         57018        2725.0        NaN   NaN   NaN   \n",
       "2353         49.992607         59520        2502.0        NaN   NaN   NaN   \n",
       "2354         51.930770         62119        2599.0        NaN   NaN   NaN   \n",
       "2355         38.543461         64048        1929.0        NaN   NaN   NaN   \n",
       "2356         36.305582         65865        1817.0        NaN   NaN   NaN   \n",
       "2357         36.665241         67700        1835.0        NaN   NaN   NaN   \n",
       "2358         37.704256         69587        1887.0        1.0  0.50  1.00   \n",
       "2359         35.046776         71341        1754.0        1.0  0.50  0.50   \n",
       "2360         34.527268         73069        1728.0        1.0  0.50  0.50   \n",
       "2361         41.680487         75155        2086.0        NaN   NaN   NaN   \n",
       "2362         34.407382         76877        1722.0        1.0  0.00  1.00   \n",
       "2363         32.209465         78489        1612.0        2.0  0.75  0.75   \n",
       "\n",
       "      tie  loss  max  gap  score  scoreB  weekday1  weekday2  weekday3  \\\n",
       "2352  NaN   NaN  NaN  NaN    NaN     NaN       NaN       NaN       NaN   \n",
       "2353  NaN   NaN  NaN  NaN    NaN     NaN       NaN       NaN       NaN   \n",
       "2354  NaN   NaN  NaN  NaN    NaN     NaN       NaN       NaN       NaN   \n",
       "2355  NaN   NaN  NaN  NaN    NaN     NaN       NaN       NaN       NaN   \n",
       "2356  NaN   NaN  NaN  NaN    NaN     NaN       NaN       NaN       NaN   \n",
       "2357  NaN   NaN  NaN  NaN    NaN     NaN       NaN       NaN       NaN   \n",
       "2358  0.0  0.00  3.0  2.5   3.00    0.50       0.0       0.0       0.0   \n",
       "2359  0.5  0.00  2.0  1.0   2.00    1.00       0.0       0.0       0.0   \n",
       "2360  0.5  0.00  1.5  1.0   1.50    0.50       0.0       0.0       0.0   \n",
       "2361  NaN   NaN  NaN  NaN    NaN     NaN       NaN       NaN       NaN   \n",
       "2362  0.0  0.00  3.0  3.0   3.00    0.00       0.0       0.0       0.0   \n",
       "2363  0.0  0.25  3.0  2.0   2.25    1.25       1.0       0.0       0.0   \n",
       "\n",
       "      weekday4  weekday5  weekday6  weekday7  old_dependency_ratio  \\\n",
       "2352       NaN       NaN       NaN       NaN              0.214742   \n",
       "2353       NaN       NaN       NaN       NaN              0.214742   \n",
       "2354       NaN       NaN       NaN       NaN              0.214742   \n",
       "2355       NaN       NaN       NaN       NaN              0.214742   \n",
       "2356       NaN       NaN       NaN       NaN              0.214742   \n",
       "2357       NaN       NaN       NaN       NaN              0.214742   \n",
       "2358       0.0       0.0       1.0       0.0              0.214742   \n",
       "2359       0.0       0.0       1.0       0.0              0.214742   \n",
       "2360       0.0       0.0       1.0       0.0              0.214742   \n",
       "2361       NaN       NaN       NaN       NaN              0.214742   \n",
       "2362       0.0       0.0       0.0       1.0              0.214742   \n",
       "2363       0.0       0.0       1.0       0.0              0.214742   \n",
       "\n",
       "      young_dependency_ratio  popu_all  Median age  People per sq. km  \\\n",
       "2352                0.241551    500474        34.8               4453   \n",
       "2353                0.241551    500474        34.8               4453   \n",
       "2354                0.241551    500474        34.8               4453   \n",
       "2355                0.241551    500474        34.8               4453   \n",
       "2356                0.241551    500474        34.8               4453   \n",
       "2357                0.241551    500474        34.8               4453   \n",
       "2358                0.241551    500474        34.8               4453   \n",
       "2359                0.241551    500474        34.8               4453   \n",
       "2360                0.241551    500474        34.8               4453   \n",
       "2361                0.241551    500474        34.8               4453   \n",
       "2362                0.241551    500474        34.8               4453   \n",
       "2363                0.241551    500474        34.8               4453   \n",
       "\n",
       "      Average Score  Area (sq km)  male_avg_age  female_avg_age  female_rate  \\\n",
       "2352           42.4      111.8357     36.823739       38.438335     0.499682   \n",
       "2353           42.4      111.8357     36.823739       38.438335     0.499682   \n",
       "2354           42.4      111.8357     36.823739       38.438335     0.499682   \n",
       "2355           42.4      111.8357     36.823739       38.438335     0.499682   \n",
       "2356           42.4      111.8357     36.823739       38.438335     0.499682   \n",
       "2357           42.4      111.8357     36.823739       38.438335     0.499682   \n",
       "2358           42.4      111.8357     36.823739       38.438335     0.499682   \n",
       "2359           42.4      111.8357     36.823739       38.438335     0.499682   \n",
       "2360           42.4      111.8357     36.823739       38.438335     0.499682   \n",
       "2361           42.4      111.8357     36.823739       38.438335     0.499682   \n",
       "2362           42.4      111.8357     36.823739       38.438335     0.499682   \n",
       "2363           42.4      111.8357     36.823739       38.438335     0.499682   \n",
       "\n",
       "     season      areaNameS  treat  post  \n",
       "2352   S5A3  LiverpoolS5A3      1     0  \n",
       "2353   S5A3  LiverpoolS5A3      1     0  \n",
       "2354   S5A3  LiverpoolS5A3      1     0  \n",
       "2355   S5A3  LiverpoolS5A3      1     0  \n",
       "2356   S5A3  LiverpoolS5A3      1     0  \n",
       "2357   S5A3  LiverpoolS5A3      1     0  \n",
       "2358   S5A3  LiverpoolS5A3      1     1  \n",
       "2359   S5A3  LiverpoolS5A3      1     1  \n",
       "2360   S5A3  LiverpoolS5A3      1     1  \n",
       "2361   S5A3  LiverpoolS5A3      1     1  \n",
       "2362   S5A3  LiverpoolS5A3      1     1  \n",
       "2363   S5A3  LiverpoolS5A3      1     1  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df_s3 = final_df[(final_df[yweek_col] >= '2021-w25') & (final_df[yweek_col] <= '2021-w37' )].reset_index(drop = True)\n",
    "final_df_s3\n",
    "\n",
    "\n",
    "start_time = '2021-w26'\n",
    "treat_time = '2021-w32'\n",
    "end_time   = '2021-w37'\n",
    "season_name = 'S5A3'\n",
    "\n",
    "### Your Operation\n",
    "\n",
    "df = final_df[(final_df[yweek_col] >= start_time) & (final_df[yweek_col] <= end_time)].reset_index(drop = True)\n",
    "df['season'] = season_name\n",
    "df['areaNameS'] = df['areaName'] + df['season']\n",
    "tmp = df.groupby('areaName')['games_num'].sum()\n",
    "tmp = tmp[tmp > 0]\n",
    "treat = list(tmp.index)\n",
    "df['treat'] = df['areaName'].isin(treat).astype(int)\n",
    "df['post'] = df[yweek_col].apply(lambda x: int(x >= treat_time))\n",
    "\n",
    "final_df_s3 = df\n",
    "df[df['areaName'] == 'Liverpool']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4f5adbf2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:51:38.759898Z",
     "start_time": "2022-05-14T16:51:38.730183Z"
    }
   },
   "outputs": [],
   "source": [
    "start_time = '2021-w43'\n",
    "treat_time = '2021-w48'\n",
    "end_time   = '2021-w52'\n",
    "season_name = 'S6B3'\n",
    "\n",
    "### Your Operation\n",
    "\n",
    "df = final_df[(final_df[yweek_col] >= start_time) & (final_df[yweek_col] <= end_time)].reset_index(drop = True)\n",
    "df['season'] = season_name\n",
    "df['areaNameS'] = df['areaName'] + df['season']\n",
    "tmp = df.groupby('areaName')['games_num'].sum()\n",
    "tmp = tmp[tmp > 0]\n",
    "treat = list(tmp.index)\n",
    "df['treat'] = df['areaName'].isin(treat).astype(int)\n",
    "df['post'] = df[yweek_col].apply(lambda x: int(x >= treat_time))\n",
    "# df[df['areaName'] == 'Liverpool']\n",
    "final_df_a3 = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5ea56260",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:51:39.240747Z",
     "start_time": "2022-05-14T16:51:39.180528Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>areaidx</th>\n",
       "      <th>areaCode</th>\n",
       "      <th>areaName</th>\n",
       "      <th>areaType</th>\n",
       "      <th>NHSER21NM</th>\n",
       "      <th>yweek_idx</th>\n",
       "      <th>yweek</th>\n",
       "      <th>new_case_per_10k</th>\n",
       "      <th>cumCasesWeek</th>\n",
       "      <th>newCasesWeek</th>\n",
       "      <th>games_num</th>\n",
       "      <th>host</th>\n",
       "      <th>win</th>\n",
       "      <th>tie</th>\n",
       "      <th>loss</th>\n",
       "      <th>max</th>\n",
       "      <th>gap</th>\n",
       "      <th>score</th>\n",
       "      <th>scoreB</th>\n",
       "      <th>weekday1</th>\n",
       "      <th>weekday2</th>\n",
       "      <th>weekday3</th>\n",
       "      <th>weekday4</th>\n",
       "      <th>weekday5</th>\n",
       "      <th>weekday6</th>\n",
       "      <th>weekday7</th>\n",
       "      <th>old_dependency_ratio</th>\n",
       "      <th>young_dependency_ratio</th>\n",
       "      <th>popu_all</th>\n",
       "      <th>Median age</th>\n",
       "      <th>People per sq. km</th>\n",
       "      <th>Average Score</th>\n",
       "      <th>Area (sq km)</th>\n",
       "      <th>male_avg_age</th>\n",
       "      <th>female_avg_age</th>\n",
       "      <th>female_rate</th>\n",
       "      <th>season</th>\n",
       "      <th>areaNameS</th>\n",
       "      <th>treat</th>\n",
       "      <th>post</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>19</td>\n",
       "      <td>2020-w19</td>\n",
       "      <td>8.205806</td>\n",
       "      <td>415</td>\n",
       "      <td>77.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
       "      <td>S1A1</td>\n",
       "      <td>HartlepoolS1A1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>20</td>\n",
       "      <td>2020-w20</td>\n",
       "      <td>7.459824</td>\n",
       "      <td>485</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
       "      <td>S1A1</td>\n",
       "      <td>HartlepoolS1A1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>21</td>\n",
       "      <td>2020-w21</td>\n",
       "      <td>3.303636</td>\n",
       "      <td>516</td>\n",
       "      <td>31.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
       "      <td>S1A1</td>\n",
       "      <td>HartlepoolS1A1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>22</td>\n",
       "      <td>2020-w22</td>\n",
       "      <td>2.237947</td>\n",
       "      <td>537</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
       "      <td>S1A1</td>\n",
       "      <td>HartlepoolS1A1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>23</td>\n",
       "      <td>2020-w23</td>\n",
       "      <td>2.557654</td>\n",
       "      <td>561</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
       "      <td>S1A1</td>\n",
       "      <td>HartlepoolS1A1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>15175</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>101</td>\n",
       "      <td>2021-w48</td>\n",
       "      <td>36.872610</td>\n",
       "      <td>29584</td>\n",
       "      <td>995.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "      <td>S6B3</td>\n",
       "      <td>WestminsterS6B3</td>\n",
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       "    <tr>\n",
       "      <th>15176</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>102</td>\n",
       "      <td>2021-w49</td>\n",
       "      <td>67.408319</td>\n",
       "      <td>31403</td>\n",
       "      <td>1819.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "      <td>S6B3</td>\n",
       "      <td>WestminsterS6B3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>15177</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>103</td>\n",
       "      <td>2021-w50</td>\n",
       "      <td>194.813376</td>\n",
       "      <td>36660</td>\n",
       "      <td>5257.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "      <td>S6B3</td>\n",
       "      <td>WestminsterS6B3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>15178</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>104</td>\n",
       "      <td>2021-w51</td>\n",
       "      <td>160.423646</td>\n",
       "      <td>40989</td>\n",
       "      <td>4329.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "      <td>S6B3</td>\n",
       "      <td>WestminsterS6B3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>15179</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>105</td>\n",
       "      <td>2021-w52</td>\n",
       "      <td>146.119297</td>\n",
       "      <td>44932</td>\n",
       "      <td>3943.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "      <td>S6B3</td>\n",
       "      <td>WestminsterS6B3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15180 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       areaidx   areaCode     areaName areaType                 NHSER21NM  \\\n",
       "0            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "1            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "2            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "3            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "4            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "...        ...        ...          ...      ...                       ...   \n",
       "15175      305  E09000033  Westminster     ltla                    London   \n",
       "15176      305  E09000033  Westminster     ltla                    London   \n",
       "15177      305  E09000033  Westminster     ltla                    London   \n",
       "15178      305  E09000033  Westminster     ltla                    London   \n",
       "15179      305  E09000033  Westminster     ltla                    London   \n",
       "\n",
       "       yweek_idx     yweek  new_case_per_10k  cumCasesWeek  newCasesWeek  \\\n",
       "0             19  2020-w19          8.205806           415          77.0   \n",
       "1             20  2020-w20          7.459824           485          70.0   \n",
       "2             21  2020-w21          3.303636           516          31.0   \n",
       "3             22  2020-w22          2.237947           537          21.0   \n",
       "4             23  2020-w23          2.557654           561          24.0   \n",
       "...          ...       ...               ...           ...           ...   \n",
       "15175        101  2021-w48         36.872610         29584         995.0   \n",
       "15176        102  2021-w49         67.408319         31403        1819.0   \n",
       "15177        103  2021-w50        194.813376         36660        5257.0   \n",
       "15178        104  2021-w51        160.423646         40989        4329.0   \n",
       "15179        105  2021-w52        146.119297         44932        3943.0   \n",
       "\n",
       "       games_num  host  win  tie  loss  max  gap  score  scoreB  weekday1  \\\n",
       "0            NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "1            NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "2            NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "3            NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "4            NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "...          ...   ...  ...  ...   ...  ...  ...    ...     ...       ...   \n",
       "15175        NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "15176        NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "15177        NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "15178        NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "15179        NaN   NaN  NaN  NaN   NaN  NaN  NaN    NaN     NaN       NaN   \n",
       "\n",
       "       weekday2  weekday3  weekday4  weekday5  weekday6  weekday7  \\\n",
       "0           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "...         ...       ...       ...       ...       ...       ...   \n",
       "15175       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "15176       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "15177       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "15178       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "15179       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "       old_dependency_ratio  young_dependency_ratio  popu_all  Median age  \\\n",
       "0                  0.313702                0.288634     93836        41.7   \n",
       "1                  0.313702                0.288634     93836        41.7   \n",
       "2                  0.313702                0.288634     93836        41.7   \n",
       "3                  0.313702                0.288634     93836        41.7   \n",
       "4                  0.313702                0.288634     93836        41.7   \n",
       "...                     ...                     ...       ...         ...   \n",
       "15175              0.175793                0.223543    269848        36.3   \n",
       "15176              0.175793                0.223543    269848        36.3   \n",
       "15177              0.175793                0.223543    269848        36.3   \n",
       "15178              0.175793                0.223543    269848        36.3   \n",
       "15179              0.175793                0.223543    269848        36.3   \n",
       "\n",
       "       People per sq. km  Average Score  Area (sq km)  male_avg_age  \\\n",
       "0                    999           35.0       93.7125     40.157418   \n",
       "1                    999           35.0       93.7125     40.157418   \n",
       "2                    999           35.0       93.7125     40.157418   \n",
       "3                    999           35.0       93.7125     40.157418   \n",
       "4                    999           35.0       93.7125     40.157418   \n",
       "...                  ...            ...           ...           ...   \n",
       "15175              12167           20.3       21.4826     36.918846   \n",
       "15176              12167           20.3       21.4826     36.918846   \n",
       "15177              12167           20.3       21.4826     36.918846   \n",
       "15178              12167           20.3       21.4826     36.918846   \n",
       "15179              12167           20.3       21.4826     36.918846   \n",
       "\n",
       "       female_avg_age  female_rate season        areaNameS  treat  post  \n",
       "0           42.190274     0.509730   S1A1   HartlepoolS1A1      0     0  \n",
       "1           42.190274     0.509730   S1A1   HartlepoolS1A1      0     0  \n",
       "2           42.190274     0.509730   S1A1   HartlepoolS1A1      0     0  \n",
       "3           42.190274     0.509730   S1A1   HartlepoolS1A1      0     0  \n",
       "4           42.190274     0.509730   S1A1   HartlepoolS1A1      0     0  \n",
       "...               ...          ...    ...              ...    ...   ...  \n",
       "15175       38.427939     0.472677   S6B3  WestminsterS6B3      0     1  \n",
       "15176       38.427939     0.472677   S6B3  WestminsterS6B3      0     1  \n",
       "15177       38.427939     0.472677   S6B3  WestminsterS6B3      0     1  \n",
       "15178       38.427939     0.472677   S6B3  WestminsterS6B3      0     1  \n",
       "15179       38.427939     0.472677   S6B3  WestminsterS6B3      0     1  \n",
       "\n",
       "[15180 rows x 40 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soccer_covid_data = pd.concat([final_df_s1, final_df_s2, final_df_s3,\n",
    "                               final_df_a1, final_df_a2, final_df_a3,]).reset_index(drop = True)\n",
    "soccer_covid_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6fcd318a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:51:39.704891Z",
     "start_time": "2022-05-14T16:51:39.672306Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "# soccer_covid_data['itidx'] = soccer_covid_data['areaidx'].astype(str) + '-' + soccer_covid_data['yweek_idx'].astype(str)\n",
    "# soccer_covid_data['itidx'] = soccer_covid_data['itidx'].apply(lambda x: int(x.replace('-', '999' * (8 - len(x)))))\n",
    "soccer_covid_data['treat_post'] = soccer_covid_data['treat'] * soccer_covid_data['post']\n",
    "soccer_covid_data['win_rate'] = soccer_covid_data['win'] / soccer_covid_data['games_num'] \n",
    "soccer_covid_data['host_rate'] = soccer_covid_data['host'] / soccer_covid_data['games_num'] \n",
    "# soccer_covid_data['log_new_case_per_10k'] = np.log(soccer_covid_data['new_case_per_10k'])\n",
    "\n",
    "# soccer_covid_data['win_rate'] = soccer_covid_data['win_rate'].fillna(0)\n",
    "# soccer_covid_data['host_rate'] = soccer_covid_data['host_rate'].fillna(0)\n",
    "\n",
    "soccer_covid_data = soccer_covid_data.fillna(0)\n",
    "\n",
    "# soccer_covid_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e0929eff",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:53:15.808664Z",
     "start_time": "2022-05-14T16:53:15.737906Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/floydluo/opt/miniconda3/envs/on/lib/python3.9/site-packages/pandas/core/arraylike.py:364: RuntimeWarning: divide by zero encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>areaidx</th>\n",
       "      <th>areaCode</th>\n",
       "      <th>areaName</th>\n",
       "      <th>areaType</th>\n",
       "      <th>NHSER21NM</th>\n",
       "      <th>yweek_idx</th>\n",
       "      <th>yweek</th>\n",
       "      <th>new_case_per_10k</th>\n",
       "      <th>cumCasesWeek</th>\n",
       "      <th>newCasesWeek</th>\n",
       "      <th>games_num</th>\n",
       "      <th>host</th>\n",
       "      <th>win</th>\n",
       "      <th>tie</th>\n",
       "      <th>loss</th>\n",
       "      <th>max</th>\n",
       "      <th>gap</th>\n",
       "      <th>score</th>\n",
       "      <th>scoreB</th>\n",
       "      <th>weekday1</th>\n",
       "      <th>weekday2</th>\n",
       "      <th>weekday3</th>\n",
       "      <th>weekday4</th>\n",
       "      <th>weekday5</th>\n",
       "      <th>weekday6</th>\n",
       "      <th>weekday7</th>\n",
       "      <th>old_dependency_ratio</th>\n",
       "      <th>young_dependency_ratio</th>\n",
       "      <th>popu_all</th>\n",
       "      <th>Median age</th>\n",
       "      <th>People per sq. km</th>\n",
       "      <th>Average Score</th>\n",
       "      <th>Area (sq km)</th>\n",
       "      <th>male_avg_age</th>\n",
       "      <th>female_avg_age</th>\n",
       "      <th>female_rate</th>\n",
       "      <th>season</th>\n",
       "      <th>areaNameS</th>\n",
       "      <th>treat</th>\n",
       "      <th>post</th>\n",
       "      <th>treat_post</th>\n",
       "      <th>win_rate</th>\n",
       "      <th>host_rate</th>\n",
       "      <th>log_new_case_per_10k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>19</td>\n",
       "      <td>2020-w19</td>\n",
       "      <td>8.205806</td>\n",
       "      <td>415</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
       "      <td>S1A1</td>\n",
       "      <td>HartlepoolS1A1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.104842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>20</td>\n",
       "      <td>2020-w20</td>\n",
       "      <td>7.459824</td>\n",
       "      <td>485</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
       "      <td>S1A1</td>\n",
       "      <td>HartlepoolS1A1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.009532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>21</td>\n",
       "      <td>2020-w21</td>\n",
       "      <td>3.303636</td>\n",
       "      <td>516</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
       "      <td>S1A1</td>\n",
       "      <td>HartlepoolS1A1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.195024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>22</td>\n",
       "      <td>2020-w22</td>\n",
       "      <td>2.237947</td>\n",
       "      <td>537</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
       "      <td>S1A1</td>\n",
       "      <td>HartlepoolS1A1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.805559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>E06000001</td>\n",
       "      <td>Hartlepool</td>\n",
       "      <td>ltla</td>\n",
       "      <td>North East and Yorkshire</td>\n",
       "      <td>23</td>\n",
       "      <td>2020-w23</td>\n",
       "      <td>2.557654</td>\n",
       "      <td>561</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.313702</td>\n",
       "      <td>0.288634</td>\n",
       "      <td>93836</td>\n",
       "      <td>41.7</td>\n",
       "      <td>999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.7125</td>\n",
       "      <td>40.157418</td>\n",
       "      <td>42.190274</td>\n",
       "      <td>0.509730</td>\n",
       "      <td>S1A1</td>\n",
       "      <td>HartlepoolS1A1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.939090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15175</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>101</td>\n",
       "      <td>2021-w48</td>\n",
       "      <td>36.872610</td>\n",
       "      <td>29584</td>\n",
       "      <td>995.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "      <td>S6B3</td>\n",
       "      <td>WestminsterS6B3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.607469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15176</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>102</td>\n",
       "      <td>2021-w49</td>\n",
       "      <td>67.408319</td>\n",
       "      <td>31403</td>\n",
       "      <td>1819.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "      <td>S6B3</td>\n",
       "      <td>WestminsterS6B3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.210768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15177</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>103</td>\n",
       "      <td>2021-w50</td>\n",
       "      <td>194.813376</td>\n",
       "      <td>36660</td>\n",
       "      <td>5257.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "      <td>S6B3</td>\n",
       "      <td>WestminsterS6B3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.272042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15178</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>104</td>\n",
       "      <td>2021-w51</td>\n",
       "      <td>160.423646</td>\n",
       "      <td>40989</td>\n",
       "      <td>4329.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "      <td>S6B3</td>\n",
       "      <td>WestminsterS6B3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.077818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15179</th>\n",
       "      <td>305</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>ltla</td>\n",
       "      <td>London</td>\n",
       "      <td>105</td>\n",
       "      <td>2021-w52</td>\n",
       "      <td>146.119297</td>\n",
       "      <td>44932</td>\n",
       "      <td>3943.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.175793</td>\n",
       "      <td>0.223543</td>\n",
       "      <td>269848</td>\n",
       "      <td>36.3</td>\n",
       "      <td>12167</td>\n",
       "      <td>20.3</td>\n",
       "      <td>21.4826</td>\n",
       "      <td>36.918846</td>\n",
       "      <td>38.427939</td>\n",
       "      <td>0.472677</td>\n",
       "      <td>S6B3</td>\n",
       "      <td>WestminsterS6B3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.984423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15047 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       areaidx   areaCode     areaName areaType                 NHSER21NM  \\\n",
       "0            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "1            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "2            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "3            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "4            1  E06000001   Hartlepool     ltla  North East and Yorkshire   \n",
       "...        ...        ...          ...      ...                       ...   \n",
       "15175      305  E09000033  Westminster     ltla                    London   \n",
       "15176      305  E09000033  Westminster     ltla                    London   \n",
       "15177      305  E09000033  Westminster     ltla                    London   \n",
       "15178      305  E09000033  Westminster     ltla                    London   \n",
       "15179      305  E09000033  Westminster     ltla                    London   \n",
       "\n",
       "       yweek_idx     yweek  new_case_per_10k  cumCasesWeek  newCasesWeek  \\\n",
       "0             19  2020-w19          8.205806           415          77.0   \n",
       "1             20  2020-w20          7.459824           485          70.0   \n",
       "2             21  2020-w21          3.303636           516          31.0   \n",
       "3             22  2020-w22          2.237947           537          21.0   \n",
       "4             23  2020-w23          2.557654           561          24.0   \n",
       "...          ...       ...               ...           ...           ...   \n",
       "15175        101  2021-w48         36.872610         29584         995.0   \n",
       "15176        102  2021-w49         67.408319         31403        1819.0   \n",
       "15177        103  2021-w50        194.813376         36660        5257.0   \n",
       "15178        104  2021-w51        160.423646         40989        4329.0   \n",
       "15179        105  2021-w52        146.119297         44932        3943.0   \n",
       "\n",
       "       games_num  host  win  tie  loss  max  gap  score  scoreB  weekday1  \\\n",
       "0            0.0   0.0  0.0  0.0   0.0  0.0  0.0    0.0     0.0       0.0   \n",
       "1            0.0   0.0  0.0  0.0   0.0  0.0  0.0    0.0     0.0       0.0   \n",
       "2            0.0   0.0  0.0  0.0   0.0  0.0  0.0    0.0     0.0       0.0   \n",
       "3            0.0   0.0  0.0  0.0   0.0  0.0  0.0    0.0     0.0       0.0   \n",
       "4            0.0   0.0  0.0  0.0   0.0  0.0  0.0    0.0     0.0       0.0   \n",
       "...          ...   ...  ...  ...   ...  ...  ...    ...     ...       ...   \n",
       "15175        0.0   0.0  0.0  0.0   0.0  0.0  0.0    0.0     0.0       0.0   \n",
       "15176        0.0   0.0  0.0  0.0   0.0  0.0  0.0    0.0     0.0       0.0   \n",
       "15177        0.0   0.0  0.0  0.0   0.0  0.0  0.0    0.0     0.0       0.0   \n",
       "15178        0.0   0.0  0.0  0.0   0.0  0.0  0.0    0.0     0.0       0.0   \n",
       "15179        0.0   0.0  0.0  0.0   0.0  0.0  0.0    0.0     0.0       0.0   \n",
       "\n",
       "       weekday2  weekday3  weekday4  weekday5  weekday6  weekday7  \\\n",
       "0           0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "1           0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "2           0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "3           0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "4           0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "...         ...       ...       ...       ...       ...       ...   \n",
       "15175       0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "15176       0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "15177       0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "15178       0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "15179       0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "       old_dependency_ratio  young_dependency_ratio  popu_all  Median age  \\\n",
       "0                  0.313702                0.288634     93836        41.7   \n",
       "1                  0.313702                0.288634     93836        41.7   \n",
       "2                  0.313702                0.288634     93836        41.7   \n",
       "3                  0.313702                0.288634     93836        41.7   \n",
       "4                  0.313702                0.288634     93836        41.7   \n",
       "...                     ...                     ...       ...         ...   \n",
       "15175              0.175793                0.223543    269848        36.3   \n",
       "15176              0.175793                0.223543    269848        36.3   \n",
       "15177              0.175793                0.223543    269848        36.3   \n",
       "15178              0.175793                0.223543    269848        36.3   \n",
       "15179              0.175793                0.223543    269848        36.3   \n",
       "\n",
       "       People per sq. km  Average Score  Area (sq km)  male_avg_age  \\\n",
       "0                    999           35.0       93.7125     40.157418   \n",
       "1                    999           35.0       93.7125     40.157418   \n",
       "2                    999           35.0       93.7125     40.157418   \n",
       "3                    999           35.0       93.7125     40.157418   \n",
       "4                    999           35.0       93.7125     40.157418   \n",
       "...                  ...            ...           ...           ...   \n",
       "15175              12167           20.3       21.4826     36.918846   \n",
       "15176              12167           20.3       21.4826     36.918846   \n",
       "15177              12167           20.3       21.4826     36.918846   \n",
       "15178              12167           20.3       21.4826     36.918846   \n",
       "15179              12167           20.3       21.4826     36.918846   \n",
       "\n",
       "       female_avg_age  female_rate season        areaNameS  treat  post  \\\n",
       "0           42.190274     0.509730   S1A1   HartlepoolS1A1      0     0   \n",
       "1           42.190274     0.509730   S1A1   HartlepoolS1A1      0     0   \n",
       "2           42.190274     0.509730   S1A1   HartlepoolS1A1      0     0   \n",
       "3           42.190274     0.509730   S1A1   HartlepoolS1A1      0     0   \n",
       "4           42.190274     0.509730   S1A1   HartlepoolS1A1      0     0   \n",
       "...               ...          ...    ...              ...    ...   ...   \n",
       "15175       38.427939     0.472677   S6B3  WestminsterS6B3      0     1   \n",
       "15176       38.427939     0.472677   S6B3  WestminsterS6B3      0     1   \n",
       "15177       38.427939     0.472677   S6B3  WestminsterS6B3      0     1   \n",
       "15178       38.427939     0.472677   S6B3  WestminsterS6B3      0     1   \n",
       "15179       38.427939     0.472677   S6B3  WestminsterS6B3      0     1   \n",
       "\n",
       "       treat_post  win_rate  host_rate  log_new_case_per_10k  \n",
       "0               0       0.0        0.0              2.104842  \n",
       "1               0       0.0        0.0              2.009532  \n",
       "2               0       0.0        0.0              1.195024  \n",
       "3               0       0.0        0.0              0.805559  \n",
       "4               0       0.0        0.0              0.939090  \n",
       "...           ...       ...        ...                   ...  \n",
       "15175           0       0.0        0.0              3.607469  \n",
       "15176           0       0.0        0.0              4.210768  \n",
       "15177           0       0.0        0.0              5.272042  \n",
       "15178           0       0.0        0.0              5.077818  \n",
       "15179           0       0.0        0.0              4.984423  \n",
       "\n",
       "[15047 rows x 44 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soccer_covid_data['log_new_case_per_10k'] = np.log(soccer_covid_data['new_case_per_10k'])\n",
    "soccer_covid_data = soccer_covid_data[soccer_covid_data['log_new_case_per_10k'] != - np.inf]\n",
    "soccer_covid_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9c429e23",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:53:19.029952Z",
     "start_time": "2022-05-14T16:53:19.027034Z"
    }
   },
   "outputs": [],
   "source": [
    "# soccer_covid_data['log_new_case_per_10k'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b1cee486",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:53:19.737442Z",
     "start_time": "2022-05-14T16:53:19.400677Z"
    }
   },
   "outputs": [],
   "source": [
    "soccer_covid_data.to_csv('data/week/data_season_did_ydz_{}.csv'.format(yweek_col), index = False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "4111c3e9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:32:22.433009Z",
     "start_time": "2022-05-14T16:32:22.429462Z"
    }
   },
   "outputs": [],
   "source": [
    "# DateTable[DateTable[yweek_col].isin(['2020-w19', '2020-w25', '2020-w30'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "c9d92a98",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:32:18.820595Z",
     "start_time": "2022-05-14T16:32:18.817492Z"
    }
   },
   "outputs": [],
   "source": [
    "# DateTable[DateTable[yweek_col].isin(['2021-w42', '2021-w52'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "1f291906",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T16:32:16.080440Z",
     "start_time": "2022-05-14T16:32:16.071648Z"
    }
   },
   "outputs": [],
   "source": [
    "# DateTable[DateTable[yweek_col].isin(['2020-w44', '2020-w53'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99bc4953",
   "metadata": {},
   "source": [
    "## Game City"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "73a8c936",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-14T05:41:49.440083Z",
     "start_time": "2022-05-14T05:41:49.426375Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "donor_cities_df = soccer_covid_data[soccer_covid_data['treat'] == 0]# ['areaName'].unique()\n",
    "# donor_cities_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "f6fa9206",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-11T16:25:52.553766Z",
     "start_time": "2022-05-11T16:25:49.203619Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Birmingham (8295, 43)\n",
      "Bournemouth, Christchurch and Poole (8295, 43)\n",
      "Brighton and Hove (8295, 43)\n",
      "Burnley (8295, 43)\n",
      "Croydon (8295, 43)\n",
      "Hammersmith and Fulham (8295, 43)\n",
      "Haringey (8295, 43)\n",
      "Islington (8295, 43)\n",
      "Leicester (8295, 43)\n",
      "Liverpool (8295, 43)\n",
      "Manchester (8295, 43)\n",
      "Newcastle upon Tyne (8295, 43)\n",
      "Newham (8295, 43)\n",
      "Norwich (8295, 43)\n",
      "Sheffield (8295, 43)\n",
      "Southampton (8295, 43)\n",
      "Trafford (8295, 43)\n",
      "Watford (8295, 43)\n",
      "Wolverhampton (8295, 43)\n",
      "East Hampshire (8295, 43)\n",
      "Leeds (8295, 43)\n",
      "Hounslow (8295, 43)\n"
     ]
    }
   ],
   "source": [
    "game_cities = soccer_covid_data[soccer_covid_data['treat'] == 1]['areaName'].unique()\n",
    "\n",
    "for game_city in game_cities:\n",
    "    soccer_covid_data_game = pd.concat([soccer_covid_data[soccer_covid_data['areaName'] == game_city], donor_cities_df])\n",
    "    print(game_city, soccer_covid_data_game.shape)\n",
    "    soccer_covid_data_game.to_csv('data/gamecity/{}.csv'.format(game_city), index = False)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "081638bf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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