Clean_Raw_Datasets.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "UK_POPN_SIZE_2020 = 66647112\n",
    "TIER_INTRO_DATE = \"2020-06-15\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ECDC_GBL_DAILY_CASE_DEATH_2020_CLEAN = 'data/ecdc/clean/ecdc_daily_worldwide_cases_2020_12_14.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ECDC_GBL_DAILY_CASE_DEATH_2020 = \"data/ecdc/ecdc_daily_worldwide_cases_2020_12_14.csv\"\n",
    "\n",
    "GOV_UK_DAILY_CASE_LTLA = \"data/gov_uk/gov_uk_ltla_newCasesBySpecimenDate_2021-05-09.csv\"\n",
    "\n",
    "ICL_NPIS = \"data/icl/npis_23Mar_02Dec.csv\"\n",
    "\n",
    "\n",
    "ICL_NPIS_CLEAN = \"data/icl/clean/npis_23Mar_02Dec.csv\"\n",
    "GOV_UK_DAILY_CASE_LTLA_TIER_CLEAN = \"data/gov_uk_icl/clean/gov_uk_ltla_tier_newCasesBySpecimenDate_2021-05-09.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\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>dateRep</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>countriesAndTerritories</th>\n",
       "      <th>geoId</th>\n",
       "      <th>countryterritoryCode</th>\n",
       "      <th>popData2019</th>\n",
       "      <th>continentExp</th>\n",
       "      <th>Cumulative_number_for_14_days_of_COVID-19_cases_per_100000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14/12/2020</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>746</td>\n",
       "      <td>6</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>9.013779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13/12/2020</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>298</td>\n",
       "      <td>9</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>7.052776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12/12/2020</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>113</td>\n",
       "      <td>11</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>6.868768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11/12/2020</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>63</td>\n",
       "      <td>10</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>7.134266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10/12/2020</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>202</td>\n",
       "      <td>16</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>6.968658</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61895</th>\n",
       "      <td>25/03/2020</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>14645473.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61896</th>\n",
       "      <td>24/03/2020</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>14645473.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61897</th>\n",
       "      <td>23/03/2020</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>14645473.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61898</th>\n",
       "      <td>22/03/2020</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>14645473.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61899</th>\n",
       "      <td>21/03/2020</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>14645473.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61900 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          dateRep  day  month  year  cases  deaths countriesAndTerritories  \\\n",
       "0      14/12/2020   14     12  2020    746       6             Afghanistan   \n",
       "1      13/12/2020   13     12  2020    298       9             Afghanistan   \n",
       "2      12/12/2020   12     12  2020    113      11             Afghanistan   \n",
       "3      11/12/2020   11     12  2020     63      10             Afghanistan   \n",
       "4      10/12/2020   10     12  2020    202      16             Afghanistan   \n",
       "...           ...  ...    ...   ...    ...     ...                     ...   \n",
       "61895  25/03/2020   25      3  2020      0       0                Zimbabwe   \n",
       "61896  24/03/2020   24      3  2020      0       1                Zimbabwe   \n",
       "61897  23/03/2020   23      3  2020      0       0                Zimbabwe   \n",
       "61898  22/03/2020   22      3  2020      1       0                Zimbabwe   \n",
       "61899  21/03/2020   21      3  2020      1       0                Zimbabwe   \n",
       "\n",
       "      geoId countryterritoryCode  popData2019 continentExp  \\\n",
       "0        AF                  AFG   38041757.0         Asia   \n",
       "1        AF                  AFG   38041757.0         Asia   \n",
       "2        AF                  AFG   38041757.0         Asia   \n",
       "3        AF                  AFG   38041757.0         Asia   \n",
       "4        AF                  AFG   38041757.0         Asia   \n",
       "...     ...                  ...          ...          ...   \n",
       "61895    ZW                  ZWE   14645473.0       Africa   \n",
       "61896    ZW                  ZWE   14645473.0       Africa   \n",
       "61897    ZW                  ZWE   14645473.0       Africa   \n",
       "61898    ZW                  ZWE   14645473.0       Africa   \n",
       "61899    ZW                  ZWE   14645473.0       Africa   \n",
       "\n",
       "       Cumulative_number_for_14_days_of_COVID-19_cases_per_100000  \n",
       "0                                               9.013779           \n",
       "1                                               7.052776           \n",
       "2                                               6.868768           \n",
       "3                                               7.134266           \n",
       "4                                               6.968658           \n",
       "...                                                  ...           \n",
       "61895                                                NaN           \n",
       "61896                                                NaN           \n",
       "61897                                                NaN           \n",
       "61898                                                NaN           \n",
       "61899                                                NaN           \n",
       "\n",
       "[61900 rows x 12 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecdc_gbl_case_death_2020_df = pd.read_csv(ECDC_GBL_DAILY_CASE_DEATH_2020)\n",
    "ecdc_gbl_case_death_2020_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What are the date ranges?**\n",
    "\n",
    "Dec 31 2019 - Dec 14 2020.  This is as expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dateRep                                                        object\n",
       "day                                                             int64\n",
       "month                                                           int64\n",
       "year                                                            int64\n",
       "cases                                                           int64\n",
       "deaths                                                          int64\n",
       "countriesAndTerritories                                        object\n",
       "geoId                                                          object\n",
       "countryterritoryCode                                           object\n",
       "popData2019                                                   float64\n",
       "continentExp                                                   object\n",
       "Cumulative_number_for_14_days_of_COVID-19_cases_per_100000    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecdc_gbl_case_death_2020_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['14/12/2020', '13/12/2020', '12/12/2020', '11/12/2020',\n",
       "       '10/12/2020', '09/12/2020', '08/12/2020', '07/12/2020',\n",
       "       '06/12/2020', '05/12/2020', '04/12/2020', '03/12/2020',\n",
       "       '02/12/2020', '01/12/2020', '30/11/2020', '29/11/2020',\n",
       "       '28/11/2020', '27/11/2020', '26/11/2020', '25/11/2020',\n",
       "       '24/11/2020', '23/11/2020', '22/11/2020', '21/11/2020',\n",
       "       '20/11/2020', '19/11/2020', '18/11/2020', '17/11/2020',\n",
       "       '16/11/2020', '15/11/2020', '14/11/2020', '13/11/2020',\n",
       "       '12/11/2020', '11/11/2020', '10/11/2020', '09/11/2020',\n",
       "       '08/11/2020', '07/11/2020', '06/11/2020', '05/11/2020',\n",
       "       '04/11/2020', '03/11/2020', '02/11/2020', '01/11/2020',\n",
       "       '31/10/2020', '30/10/2020', '29/10/2020', '28/10/2020',\n",
       "       '27/10/2020', '26/10/2020', '25/10/2020', '24/10/2020',\n",
       "       '23/10/2020', '22/10/2020', '21/10/2020', '20/10/2020',\n",
       "       '19/10/2020', '18/10/2020', '17/10/2020', '16/10/2020',\n",
       "       '15/10/2020', '14/10/2020', '13/10/2020', '12/10/2020',\n",
       "       '11/10/2020', '10/10/2020', '09/10/2020', '08/10/2020',\n",
       "       '07/10/2020', '06/10/2020', '05/10/2020', '04/10/2020',\n",
       "       '03/10/2020', '02/10/2020', '01/10/2020', '30/09/2020',\n",
       "       '29/09/2020', '28/09/2020', '27/09/2020', '26/09/2020',\n",
       "       '25/09/2020', '24/09/2020', '23/09/2020', '22/09/2020',\n",
       "       '21/09/2020', '20/09/2020', '19/09/2020', '18/09/2020',\n",
       "       '17/09/2020', '16/09/2020', '15/09/2020', '14/09/2020',\n",
       "       '13/09/2020', '12/09/2020', '11/09/2020', '10/09/2020',\n",
       "       '09/09/2020', '08/09/2020', '07/09/2020', '06/09/2020',\n",
       "       '05/09/2020', '04/09/2020', '03/09/2020', '02/09/2020',\n",
       "       '01/09/2020', '31/08/2020', '30/08/2020', '29/08/2020',\n",
       "       '28/08/2020', '27/08/2020', '26/08/2020', '25/08/2020',\n",
       "       '24/08/2020', '23/08/2020', '22/08/2020', '21/08/2020',\n",
       "       '20/08/2020', '19/08/2020', '18/08/2020', '17/08/2020',\n",
       "       '16/08/2020', '15/08/2020', '14/08/2020', '13/08/2020',\n",
       "       '12/08/2020', '11/08/2020', '10/08/2020', '09/08/2020',\n",
       "       '08/08/2020', '07/08/2020', '06/08/2020', '05/08/2020',\n",
       "       '04/08/2020', '03/08/2020', '02/08/2020', '01/08/2020',\n",
       "       '31/07/2020', '30/07/2020', '29/07/2020', '28/07/2020',\n",
       "       '27/07/2020', '26/07/2020', '25/07/2020', '24/07/2020',\n",
       "       '23/07/2020', '22/07/2020', '21/07/2020', '20/07/2020',\n",
       "       '19/07/2020', '18/07/2020', '17/07/2020', '16/07/2020',\n",
       "       '15/07/2020', '14/07/2020', '13/07/2020', '12/07/2020',\n",
       "       '11/07/2020', '10/07/2020', '09/07/2020', '08/07/2020',\n",
       "       '07/07/2020', '06/07/2020', '05/07/2020', '04/07/2020',\n",
       "       '03/07/2020', '02/07/2020', '01/07/2020', '30/06/2020',\n",
       "       '29/06/2020', '28/06/2020', '27/06/2020', '26/06/2020',\n",
       "       '25/06/2020', '24/06/2020', '23/06/2020', '22/06/2020',\n",
       "       '21/06/2020', '20/06/2020', '19/06/2020', '18/06/2020',\n",
       "       '17/06/2020', '16/06/2020', '15/06/2020', '14/06/2020',\n",
       "       '13/06/2020', '12/06/2020', '11/06/2020', '10/06/2020',\n",
       "       '09/06/2020', '08/06/2020', '07/06/2020', '06/06/2020',\n",
       "       '05/06/2020', '04/06/2020', '03/06/2020', '02/06/2020',\n",
       "       '01/06/2020', '31/05/2020', '30/05/2020', '29/05/2020',\n",
       "       '28/05/2020', '27/05/2020', '26/05/2020', '25/05/2020',\n",
       "       '24/05/2020', '23/05/2020', '22/05/2020', '21/05/2020',\n",
       "       '20/05/2020', '19/05/2020', '18/05/2020', '17/05/2020',\n",
       "       '16/05/2020', '15/05/2020', '14/05/2020', '13/05/2020',\n",
       "       '12/05/2020', '11/05/2020', '10/05/2020', '09/05/2020',\n",
       "       '08/05/2020', '07/05/2020', '06/05/2020', '05/05/2020',\n",
       "       '04/05/2020', '03/05/2020', '02/05/2020', '01/05/2020',\n",
       "       '30/04/2020', '29/04/2020', '28/04/2020', '27/04/2020',\n",
       "       '26/04/2020', '25/04/2020', '24/04/2020', '23/04/2020',\n",
       "       '22/04/2020', '21/04/2020', '20/04/2020', '19/04/2020',\n",
       "       '18/04/2020', '17/04/2020', '16/04/2020', '15/04/2020',\n",
       "       '14/04/2020', '13/04/2020', '12/04/2020', '11/04/2020',\n",
       "       '10/04/2020', '09/04/2020', '08/04/2020', '07/04/2020',\n",
       "       '06/04/2020', '05/04/2020', '04/04/2020', '03/04/2020',\n",
       "       '02/04/2020', '01/04/2020', '31/03/2020', '30/03/2020',\n",
       "       '29/03/2020', '28/03/2020', '27/03/2020', '26/03/2020',\n",
       "       '25/03/2020', '24/03/2020', '23/03/2020', '22/03/2020',\n",
       "       '21/03/2020', '20/03/2020', '19/03/2020', '18/03/2020',\n",
       "       '17/03/2020', '16/03/2020', '15/03/2020', '11/03/2020',\n",
       "       '08/03/2020', '02/03/2020', '01/03/2020', '29/02/2020',\n",
       "       '28/02/2020', '27/02/2020', '26/02/2020', '25/02/2020',\n",
       "       '24/02/2020', '23/02/2020', '22/02/2020', '21/02/2020',\n",
       "       '20/02/2020', '19/02/2020', '18/02/2020', '17/02/2020',\n",
       "       '16/02/2020', '15/02/2020', '14/02/2020', '13/02/2020',\n",
       "       '12/02/2020', '11/02/2020', '10/02/2020', '09/02/2020',\n",
       "       '08/02/2020', '07/02/2020', '06/02/2020', '05/02/2020',\n",
       "       '04/02/2020', '03/02/2020', '02/02/2020', '01/02/2020',\n",
       "       '31/01/2020', '30/01/2020', '29/01/2020', '28/01/2020',\n",
       "       '27/01/2020', '26/01/2020', '25/01/2020', '24/01/2020',\n",
       "       '23/01/2020', '22/01/2020', '21/01/2020', '20/01/2020',\n",
       "       '19/01/2020', '18/01/2020', '17/01/2020', '16/01/2020',\n",
       "       '15/01/2020', '14/01/2020', '13/01/2020', '12/01/2020',\n",
       "       '11/01/2020', '10/01/2020', '09/01/2020', '08/01/2020',\n",
       "       '07/01/2020', '06/01/2020', '05/01/2020', '04/01/2020',\n",
       "       '03/01/2020', '02/01/2020', '01/01/2020', '31/12/2019',\n",
       "       '14/03/2020', '13/03/2020', '12/03/2020', '10/03/2020',\n",
       "       '09/03/2020', '06/03/2020', '05/03/2020', '04/03/2020',\n",
       "       '03/03/2020', '07/03/2020'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We want to format string to datetime.  Check if there are any zero padding.\n",
    "ecdc_gbl_case_death_2020_df[\"dateRep\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ecdc_gbl_case_death_2020_df[\"dateRep\"] = pd.to_datetime(ecdc_gbl_case_death_2020_df[\"dateRep\"], format=\"%d/%m/%Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2019-12-31 00:00:00')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecdc_gbl_case_death_2020_df[\"dateRep\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-12-14 00:00:00')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecdc_gbl_case_death_2020_df[\"dateRep\"].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What are the date ranges for the UK?**\n",
    "\n",
    "Dec 31 2019 - Dec 14 2021\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola',\n",
       "       'Anguilla', 'Antigua_and_Barbuda', 'Argentina', 'Armenia', 'Aruba',\n",
       "       'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain',\n",
       "       'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin',\n",
       "       'Bermuda', 'Bhutan', 'Bolivia',\n",
       "       'Bonaire, Saint Eustatius and Saba', 'Bosnia_and_Herzegovina',\n",
       "       'Botswana', 'Brazil', 'British_Virgin_Islands',\n",
       "       'Brunei_Darussalam', 'Bulgaria', 'Burkina_Faso', 'Burundi',\n",
       "       'Cambodia', 'Cameroon', 'Canada', 'Cape_Verde',\n",
       "       'Cases_on_an_international_conveyance_Japan', 'Cayman_Islands',\n",
       "       'Central_African_Republic', 'Chad', 'Chile', 'China', 'Colombia',\n",
       "       'Comoros', 'Congo', 'Costa_Rica', 'Cote_dIvoire', 'Croatia',\n",
       "       'Cuba', 'Curaçao', 'Cyprus', 'Czechia',\n",
       "       'Democratic_Republic_of_the_Congo', 'Denmark', 'Djibouti',\n",
       "       'Dominica', 'Dominican_Republic', 'Ecuador', 'Egypt',\n",
       "       'El_Salvador', 'Equatorial_Guinea', 'Eritrea', 'Estonia',\n",
       "       'Eswatini', 'Ethiopia', 'Falkland_Islands_(Malvinas)',\n",
       "       'Faroe_Islands', 'Fiji', 'Finland', 'France', 'French_Polynesia',\n",
       "       'Gabon', 'Gambia', 'Georgia', 'Germany', 'Ghana', 'Gibraltar',\n",
       "       'Greece', 'Greenland', 'Grenada', 'Guam', 'Guatemala', 'Guernsey',\n",
       "       'Guinea', 'Guinea_Bissau', 'Guyana', 'Haiti', 'Holy_See',\n",
       "       'Honduras', 'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran',\n",
       "       'Iraq', 'Ireland', 'Isle_of_Man', 'Israel', 'Italy', 'Jamaica',\n",
       "       'Japan', 'Jersey', 'Jordan', 'Kazakhstan', 'Kenya', 'Kosovo',\n",
       "       'Kuwait', 'Kyrgyzstan', 'Laos', 'Latvia', 'Lebanon', 'Lesotho',\n",
       "       'Liberia', 'Libya', 'Liechtenstein', 'Lithuania', 'Luxembourg',\n",
       "       'Madagascar', 'Malawi', 'Malaysia', 'Maldives', 'Mali', 'Malta',\n",
       "       'Marshall_Islands', 'Mauritania', 'Mauritius', 'Mexico', 'Moldova',\n",
       "       'Monaco', 'Mongolia', 'Montenegro', 'Montserrat', 'Morocco',\n",
       "       'Mozambique', 'Myanmar', 'Namibia', 'Nepal', 'Netherlands',\n",
       "       'New_Caledonia', 'New_Zealand', 'Nicaragua', 'Niger', 'Nigeria',\n",
       "       'North_Macedonia', 'Northern_Mariana_Islands', 'Norway', 'Oman',\n",
       "       'Pakistan', 'Palestine', 'Panama', 'Papua_New_Guinea', 'Paraguay',\n",
       "       'Peru', 'Philippines', 'Poland', 'Portugal', 'Puerto_Rico',\n",
       "       'Qatar', 'Romania', 'Russia', 'Rwanda', 'Saint_Kitts_and_Nevis',\n",
       "       'Saint_Lucia', 'Saint_Vincent_and_the_Grenadines', 'San_Marino',\n",
       "       'Sao_Tome_and_Principe', 'Saudi_Arabia', 'Senegal', 'Serbia',\n",
       "       'Seychelles', 'Sierra_Leone', 'Singapore', 'Sint_Maarten',\n",
       "       'Slovakia', 'Slovenia', 'Solomon_Islands', 'Somalia',\n",
       "       'South_Africa', 'South_Korea', 'South_Sudan', 'Spain', 'Sri_Lanka',\n",
       "       'Sudan', 'Suriname', 'Sweden', 'Switzerland', 'Syria', 'Taiwan',\n",
       "       'Tajikistan', 'Thailand', 'Timor_Leste', 'Togo',\n",
       "       'Trinidad_and_Tobago', 'Tunisia', 'Turkey',\n",
       "       'Turks_and_Caicos_islands', 'Uganda', 'Ukraine',\n",
       "       'United_Arab_Emirates', 'United_Kingdom',\n",
       "       'United_Republic_of_Tanzania', 'United_States_of_America',\n",
       "       'United_States_Virgin_Islands', 'Uruguay', 'Uzbekistan', 'Vanuatu',\n",
       "       'Venezuela', 'Vietnam', 'Wallis_and_Futuna', 'Western_Sahara',\n",
       "       'Yemen', 'Zambia', 'Zimbabwe'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecdc_gbl_case_death_2020_df[\"countriesAndTerritories\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dateRep</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
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       "      <th>deaths</th>\n",
       "      <th>countriesAndTerritories</th>\n",
       "      <th>geoId</th>\n",
       "      <th>countryterritoryCode</th>\n",
       "      <th>popData2019</th>\n",
       "      <th>continentExp</th>\n",
       "      <th>Cumulative_number_for_14_days_of_COVID-19_cases_per_100000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>58378</th>\n",
       "      <td>2020-12-14</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>18447</td>\n",
       "      <td>144</td>\n",
       "      <td>United_Kingdom</td>\n",
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       "      <td>GBR</td>\n",
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       "      <td>Europe</td>\n",
       "      <td>348.216139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58379</th>\n",
       "      <td>2020-12-13</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>21501</td>\n",
       "      <td>520</td>\n",
       "      <td>United_Kingdom</td>\n",
       "      <td>UK</td>\n",
       "      <td>GBR</td>\n",
       "      <td>66647112.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>338.775370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58380</th>\n",
       "      <td>2020-12-12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>21672</td>\n",
       "      <td>424</td>\n",
       "      <td>United_Kingdom</td>\n",
       "      <td>UK</td>\n",
       "      <td>GBR</td>\n",
       "      <td>66647112.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>330.327892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58381</th>\n",
       "      <td>2020-12-11</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>20964</td>\n",
       "      <td>516</td>\n",
       "      <td>United_Kingdom</td>\n",
       "      <td>UK</td>\n",
       "      <td>GBR</td>\n",
       "      <td>66647112.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>319.925341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58382</th>\n",
       "      <td>2020-12-10</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>16578</td>\n",
       "      <td>533</td>\n",
       "      <td>United_Kingdom</td>\n",
       "      <td>UK</td>\n",
       "      <td>GBR</td>\n",
       "      <td>66647112.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>314.810340</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58723</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>United_Kingdom</td>\n",
       "      <td>UK</td>\n",
       "      <td>GBR</td>\n",
       "      <td>66647112.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58724</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>United_Kingdom</td>\n",
       "      <td>UK</td>\n",
       "      <td>GBR</td>\n",
       "      <td>66647112.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58725</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>United_Kingdom</td>\n",
       "      <td>UK</td>\n",
       "      <td>GBR</td>\n",
       "      <td>66647112.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58726</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>United_Kingdom</td>\n",
       "      <td>UK</td>\n",
       "      <td>GBR</td>\n",
       "      <td>66647112.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58727</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>United_Kingdom</td>\n",
       "      <td>UK</td>\n",
       "      <td>GBR</td>\n",
       "      <td>66647112.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>350 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         dateRep  day  month  year  cases  deaths countriesAndTerritories  \\\n",
       "58378 2020-12-14   14     12  2020  18447     144          United_Kingdom   \n",
       "58379 2020-12-13   13     12  2020  21501     520          United_Kingdom   \n",
       "58380 2020-12-12   12     12  2020  21672     424          United_Kingdom   \n",
       "58381 2020-12-11   11     12  2020  20964     516          United_Kingdom   \n",
       "58382 2020-12-10   10     12  2020  16578     533          United_Kingdom   \n",
       "...          ...  ...    ...   ...    ...     ...                     ...   \n",
       "58723 2020-01-04    4      1  2020      0       0          United_Kingdom   \n",
       "58724 2020-01-03    3      1  2020      0       0          United_Kingdom   \n",
       "58725 2020-01-02    2      1  2020      0       0          United_Kingdom   \n",
       "58726 2020-01-01    1      1  2020      0       0          United_Kingdom   \n",
       "58727 2019-12-31   31     12  2019      0       0          United_Kingdom   \n",
       "\n",
       "      geoId countryterritoryCode  popData2019 continentExp  \\\n",
       "58378    UK                  GBR   66647112.0       Europe   \n",
       "58379    UK                  GBR   66647112.0       Europe   \n",
       "58380    UK                  GBR   66647112.0       Europe   \n",
       "58381    UK                  GBR   66647112.0       Europe   \n",
       "58382    UK                  GBR   66647112.0       Europe   \n",
       "...     ...                  ...          ...          ...   \n",
       "58723    UK                  GBR   66647112.0       Europe   \n",
       "58724    UK                  GBR   66647112.0       Europe   \n",
       "58725    UK                  GBR   66647112.0       Europe   \n",
       "58726    UK                  GBR   66647112.0       Europe   \n",
       "58727    UK                  GBR   66647112.0       Europe   \n",
       "\n",
       "       Cumulative_number_for_14_days_of_COVID-19_cases_per_100000  \n",
       "58378                                         348.216139           \n",
       "58379                                         338.775370           \n",
       "58380                                         330.327892           \n",
       "58381                                         319.925341           \n",
       "58382                                         314.810340           \n",
       "...                                                  ...           \n",
       "58723                                                NaN           \n",
       "58724                                                NaN           \n",
       "58725                                                NaN           \n",
       "58726                                                NaN           \n",
       "58727                                                NaN           \n",
       "\n",
       "[350 rows x 12 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecdc_gbl_case_death_2020_df[ecdc_gbl_case_death_2020_df[\"countriesAndTerritories\"] == \"United_Kingdom\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2019-12-31 00:00:00')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecdc_gbl_case_death_2020_df[ecdc_gbl_case_death_2020_df[\"countriesAndTerritories\"] == \"United_Kingdom\"][\"dateRep\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-12-14 00:00:00')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecdc_gbl_case_death_2020_df[ecdc_gbl_case_death_2020_df[\"countriesAndTerritories\"] == \"United_Kingdom\"][\"dateRep\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>dateRep</th>\n",
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       "      <th>Cumulative_number_for_14_days_of_COVID-19_cases_per_100000</th>\n",
       "      <th>iso_week</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-12-14</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>746</td>\n",
       "      <td>6</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
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       "      <td>Asia</td>\n",
       "      <td>9.013779</td>\n",
       "      <td>51</td>\n",
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       "      <th>1</th>\n",
       "      <td>2020-12-13</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>298</td>\n",
       "      <td>9</td>\n",
       "      <td>Afghanistan</td>\n",
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       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>7.052776</td>\n",
       "      <td>50</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-12-12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>113</td>\n",
       "      <td>11</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>6.868768</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-12-11</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>63</td>\n",
       "      <td>10</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>7.134266</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-12-10</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>202</td>\n",
       "      <td>16</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>6.968658</td>\n",
       "      <td>50</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61895</th>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>14645473.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61896</th>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>14645473.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61897</th>\n",
       "      <td>2020-03-23</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>14645473.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61898</th>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>14645473.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61899</th>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>14645473.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61900 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         dateRep  day  month  year  cases  deaths countriesAndTerritories  \\\n",
       "0     2020-12-14   14     12  2020    746       6             Afghanistan   \n",
       "1     2020-12-13   13     12  2020    298       9             Afghanistan   \n",
       "2     2020-12-12   12     12  2020    113      11             Afghanistan   \n",
       "3     2020-12-11   11     12  2020     63      10             Afghanistan   \n",
       "4     2020-12-10   10     12  2020    202      16             Afghanistan   \n",
       "...          ...  ...    ...   ...    ...     ...                     ...   \n",
       "61895 2020-03-25   25      3  2020      0       0                Zimbabwe   \n",
       "61896 2020-03-24   24      3  2020      0       1                Zimbabwe   \n",
       "61897 2020-03-23   23      3  2020      0       0                Zimbabwe   \n",
       "61898 2020-03-22   22      3  2020      1       0                Zimbabwe   \n",
       "61899 2020-03-21   21      3  2020      1       0                Zimbabwe   \n",
       "\n",
       "      geoId countryterritoryCode  popData2019 continentExp  \\\n",
       "0        AF                  AFG   38041757.0         Asia   \n",
       "1        AF                  AFG   38041757.0         Asia   \n",
       "2        AF                  AFG   38041757.0         Asia   \n",
       "3        AF                  AFG   38041757.0         Asia   \n",
       "4        AF                  AFG   38041757.0         Asia   \n",
       "...     ...                  ...          ...          ...   \n",
       "61895    ZW                  ZWE   14645473.0       Africa   \n",
       "61896    ZW                  ZWE   14645473.0       Africa   \n",
       "61897    ZW                  ZWE   14645473.0       Africa   \n",
       "61898    ZW                  ZWE   14645473.0       Africa   \n",
       "61899    ZW                  ZWE   14645473.0       Africa   \n",
       "\n",
       "       Cumulative_number_for_14_days_of_COVID-19_cases_per_100000  iso_week  \n",
       "0                                               9.013779                 51  \n",
       "1                                               7.052776                 50  \n",
       "2                                               6.868768                 50  \n",
       "3                                               7.134266                 50  \n",
       "4                                               6.968658                 50  \n",
       "...                                                  ...                ...  \n",
       "61895                                                NaN                 13  \n",
       "61896                                                NaN                 13  \n",
       "61897                                                NaN                 13  \n",
       "61898                                                NaN                 12  \n",
       "61899                                                NaN                 12  \n",
       "\n",
       "[61900 rows x 13 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecdc_gbl_case_death_2020_df[\"iso_week\"] = ecdc_gbl_case_death_2020_df[\"dateRep\"].dt.isocalendar()[\"week\"]\n",
    "ecdc_gbl_case_death_2020_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>dateRep</th>\n",
       "      <th>cases_per_100k</th>\n",
       "      <th>deaths_per_1m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>58727</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58726</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>58725</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58724</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58723</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58382</th>\n",
       "      <td>107563.0</td>\n",
       "      <td>2867.0</td>\n",
       "      <td>2020-12-10</td>\n",
       "      <td>161.391839</td>\n",
       "      <td>43.017618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58381</th>\n",
       "      <td>113649.0</td>\n",
       "      <td>2969.0</td>\n",
       "      <td>2020-12-11</td>\n",
       "      <td>170.523518</td>\n",
       "      <td>44.548067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58380</th>\n",
       "      <td>119023.0</td>\n",
       "      <td>2889.0</td>\n",
       "      <td>2020-12-12</td>\n",
       "      <td>178.586883</td>\n",
       "      <td>43.347715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58379</th>\n",
       "      <td>124985.0</td>\n",
       "      <td>3012.0</td>\n",
       "      <td>2020-12-13</td>\n",
       "      <td>187.532507</td>\n",
       "      <td>45.193256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58378</th>\n",
       "      <td>126161.0</td>\n",
       "      <td>2925.0</td>\n",
       "      <td>2020-12-14</td>\n",
       "      <td>189.297025</td>\n",
       "      <td>43.887873</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>350 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          cases  deaths    dateRep  cases_per_100k  deaths_per_1m\n",
       "58727       NaN     NaN 2019-12-31             NaN            NaN\n",
       "58726       NaN     NaN 2020-01-01             NaN            NaN\n",
       "58725       NaN     NaN 2020-01-02             NaN            NaN\n",
       "58724       NaN     NaN 2020-01-03             NaN            NaN\n",
       "58723       NaN     NaN 2020-01-04             NaN            NaN\n",
       "...         ...     ...        ...             ...            ...\n",
       "58382  107563.0  2867.0 2020-12-10      161.391839      43.017618\n",
       "58381  113649.0  2969.0 2020-12-11      170.523518      44.548067\n",
       "58380  119023.0  2889.0 2020-12-12      178.586883      43.347715\n",
       "58379  124985.0  3012.0 2020-12-13      187.532507      45.193256\n",
       "58378  126161.0  2925.0 2020-12-14      189.297025      43.887873\n",
       "\n",
       "[350 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecdc_gbl_case_death_2020_uk_df = ecdc_gbl_case_death_2020_df[ecdc_gbl_case_death_2020_df[\"countriesAndTerritories\"] == \"United_Kingdom\"].copy()\n",
    "ecdc_gbl_case_death_2020_uk_df = ecdc_gbl_case_death_2020_uk_df.sort_values(\"dateRep\", ascending=True)\n",
    "ecdc_gbl_case_death_2020_uk_df = ecdc_gbl_case_death_2020_uk_df.rolling(window='7D', \n",
    "                                                                        on=\"dateRep\",\n",
    "                                                                        min_periods=7)[\"cases\", \"deaths\"].sum()\n",
    "ecdc_gbl_case_death_2020_uk_df[\"cases_per_100k\"] = ecdc_gbl_case_death_2020_uk_df[\"cases\"] * 1e5 / UK_POPN_SIZE_2020\n",
    "ecdc_gbl_case_death_2020_uk_df[\"deaths_per_1m\"] = ecdc_gbl_case_death_2020_uk_df[\"deaths\"] * 1e6 / UK_POPN_SIZE_2020\n",
    "ecdc_gbl_case_death_2020_uk_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>dateRep</th>\n",
       "      <th>cases_per_100k</th>\n",
       "      <th>deaths_per_1m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>58695</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>0.003001</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58694</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>0.003001</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58693</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>0.003001</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58692</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020-02-04</td>\n",
       "      <td>0.003001</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58691</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>0.004501</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>58382</th>\n",
       "      <td>107563.0</td>\n",
       "      <td>2867.0</td>\n",
       "      <td>2020-12-10</td>\n",
       "      <td>161.391839</td>\n",
       "      <td>43.017618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58381</th>\n",
       "      <td>113649.0</td>\n",
       "      <td>2969.0</td>\n",
       "      <td>2020-12-11</td>\n",
       "      <td>170.523518</td>\n",
       "      <td>44.548067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58380</th>\n",
       "      <td>119023.0</td>\n",
       "      <td>2889.0</td>\n",
       "      <td>2020-12-12</td>\n",
       "      <td>178.586883</td>\n",
       "      <td>43.347715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58379</th>\n",
       "      <td>124985.0</td>\n",
       "      <td>3012.0</td>\n",
       "      <td>2020-12-13</td>\n",
       "      <td>187.532507</td>\n",
       "      <td>45.193256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58378</th>\n",
       "      <td>126161.0</td>\n",
       "      <td>2925.0</td>\n",
       "      <td>2020-12-14</td>\n",
       "      <td>189.297025</td>\n",
       "      <td>43.887873</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>315 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          cases  deaths    dateRep  cases_per_100k  deaths_per_1m\n",
       "58695       2.0     0.0 2020-02-01        0.003001       0.000000\n",
       "58694       2.0     0.0 2020-02-02        0.003001       0.000000\n",
       "58693       2.0     0.0 2020-02-03        0.003001       0.000000\n",
       "58692       2.0     0.0 2020-02-04        0.003001       0.000000\n",
       "58691       3.0     0.0 2020-02-05        0.004501       0.000000\n",
       "...         ...     ...        ...             ...            ...\n",
       "58382  107563.0  2867.0 2020-12-10      161.391839      43.017618\n",
       "58381  113649.0  2969.0 2020-12-11      170.523518      44.548067\n",
       "58380  119023.0  2889.0 2020-12-12      178.586883      43.347715\n",
       "58379  124985.0  3012.0 2020-12-13      187.532507      45.193256\n",
       "58378  126161.0  2925.0 2020-12-14      189.297025      43.887873\n",
       "\n",
       "[315 rows x 5 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecdc_gbl_case_death_2020_uk_df[~ecdc_gbl_case_death_2020_uk_df[\"cases\"].isna() &\n",
    "                               (ecdc_gbl_case_death_2020_uk_df[\"cases\"] != 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>cases_per_100k</th>\n",
       "      <th>deaths_per_1m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>344.000000</td>\n",
       "      <td>344.000000</td>\n",
       "      <td>344.000000</td>\n",
       "      <td>344.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36432.026163</td>\n",
       "      <td>1281.444767</td>\n",
       "      <td>54.664073</td>\n",
       "      <td>19.227311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>51399.596139</td>\n",
       "      <td>1726.010102</td>\n",
       "      <td>77.122016</td>\n",
       "      <td>25.897748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4141.750000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>6.214448</td>\n",
       "      <td>1.020299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10147.000000</td>\n",
       "      <td>353.000000</td>\n",
       "      <td>15.224966</td>\n",
       "      <td>5.296554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>33246.500000</td>\n",
       "      <td>2252.750000</td>\n",
       "      <td>49.884382</td>\n",
       "      <td>33.801165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>177318.000000</td>\n",
       "      <td>6597.000000</td>\n",
       "      <td>266.055039</td>\n",
       "      <td>98.984034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               cases       deaths  cases_per_100k  deaths_per_1m\n",
       "count     344.000000   344.000000      344.000000     344.000000\n",
       "mean    36432.026163  1281.444767       54.664073      19.227311\n",
       "std     51399.596139  1726.010102       77.122016      25.897748\n",
       "min         0.000000     0.000000        0.000000       0.000000\n",
       "25%      4141.750000    68.000000        6.214448       1.020299\n",
       "50%     10147.000000   353.000000       15.224966       5.296554\n",
       "75%     33246.500000  2252.750000       49.884382      33.801165\n",
       "max    177318.000000  6597.000000      266.055039      98.984034"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecdc_gbl_case_death_2020_uk_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(20, 5))\n",
    "ax.plot(ecdc_gbl_case_death_2020_uk_df[ecdc_gbl_case_death_2020_uk_df[\"dateRep\"] > TIER_INTRO_DATE][\"dateRep\"], \n",
    "        ecdc_gbl_case_death_2020_uk_df[ecdc_gbl_case_death_2020_uk_df[\"dateRep\"] > TIER_INTRO_DATE][\"cases_per_100k\"],\n",
    "       label=\"UK Daily New Cases\")\n",
    "ax.axvline(\"2020-10-15\", color = \"green\", alpha=0.5, label=\"Tier Introduction\")\n",
    "ax.axvline(\"2020-11-05\", color = \"red\", alpha=0.5, label=\"National Lockodnw\")\n",
    "ax.set_ylabel(\"Case per 100k, Rolling 7 Day Average\")\n",
    "ax.set_xlabel(\"Date\")\n",
    "ax.set_title(\"UK Daily New Cases\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "ecdc_gbl_case_death_2020_uk_df.to_csv(ECDC_GBL_DAILY_CASE_DEATH_2020_CLEAN, index=False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r--  1 tn6  937    19K 10 May 23:41 data/ecdc/clean/ecdc_daily_worldwide_cases_2020_12_14.csv\r\n"
     ]
    }
   ],
   "source": [
    "! ls -lht {ECDC_GBL_DAILY_CASE_DEATH_2020_CLEAN}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     351 data/ecdc/clean/ecdc_daily_worldwide_cases_2020_12_14.csv\r\n"
     ]
    }
   ],
   "source": [
    "! wc -l {ECDC_GBL_DAILY_CASE_DEATH_2020_CLEAN}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cases,deaths,dateRep,cases_per_100k,deaths_per_1m\r\n",
      ",,2019-12-31,,\r\n",
      ",,2020-01-01,,\r\n",
      ",,2020-01-02,,\r\n",
      ",,2020-01-03,,\r\n",
      ",,2020-01-04,,\r\n",
      ",,2020-01-05,,\r\n",
      "0.0,0.0,2020-01-06,0.0,0.0\r\n",
      "0.0,0.0,2020-01-07,0.0,0.0\r\n",
      "0.0,0.0,2020-01-08,0.0,0.0\r\n"
     ]
    }
   ],
   "source": [
    "! head {ECDC_GBL_DAILY_CASE_DEATH_2020_CLEAN}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clean Imperial Collage Curated Dataset of Tiered Interventions in UK Local Authorities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>ltla</th>\n",
       "      <th>limited_to_groups_of_6_indoors</th>\n",
       "      <th>limited_to_groups_of_6_outdoors</th>\n",
       "      <th>curfew_of_10pm_for_hospitality_venues</th>\n",
       "      <th>instruction_to_work_from_home_where_possible</th>\n",
       "      <th>travel_discouraged</th>\n",
       "      <th>no_indoor_mixing</th>\n",
       "      <th>overnight_stays_discouraged</th>\n",
       "      <th>residents_cannot_leave_the_local_area</th>\n",
       "      <th>...</th>\n",
       "      <th>tier_2_nested</th>\n",
       "      <th>tier_1_nested</th>\n",
       "      <th>Tier</th>\n",
       "      <th>tier_1_BD_nested</th>\n",
       "      <th>tier_2_BD_nested</th>\n",
       "      <th>tier_3_BD_nested</th>\n",
       "      <th>Tier_BD</th>\n",
       "      <th>tier_1_BD</th>\n",
       "      <th>tier_2_BD</th>\n",
       "      <th>tier_3_BD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23/03/2020</td>\n",
       "      <td>Adur</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24/03/2020</td>\n",
       "      <td>Adur</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25/03/2020</td>\n",
       "      <td>Adur</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26/03/2020</td>\n",
       "      <td>Adur</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27/03/2020</td>\n",
       "      <td>Adur</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>80320</th>\n",
       "      <td>28/11/2020</td>\n",
       "      <td>York</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80321</th>\n",
       "      <td>29/11/2020</td>\n",
       "      <td>York</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80322</th>\n",
       "      <td>30/11/2020</td>\n",
       "      <td>York</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80323</th>\n",
       "      <td>01/12/2020</td>\n",
       "      <td>York</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80324</th>\n",
       "      <td>02/12/2020</td>\n",
       "      <td>York</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80325 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date  ltla  limited_to_groups_of_6_indoors  \\\n",
       "0      23/03/2020  Adur                               1   \n",
       "1      24/03/2020  Adur                               1   \n",
       "2      25/03/2020  Adur                               1   \n",
       "3      26/03/2020  Adur                               1   \n",
       "4      27/03/2020  Adur                               1   \n",
       "...           ...   ...                             ...   \n",
       "80320  28/11/2020  York                               1   \n",
       "80321  29/11/2020  York                               1   \n",
       "80322  30/11/2020  York                               1   \n",
       "80323  01/12/2020  York                               1   \n",
       "80324  02/12/2020  York                               1   \n",
       "\n",
       "       limited_to_groups_of_6_outdoors  curfew_of_10pm_for_hospitality_venues  \\\n",
       "0                                    1                                      1   \n",
       "1                                    1                                      1   \n",
       "2                                    1                                      1   \n",
       "3                                    1                                      1   \n",
       "4                                    1                                      1   \n",
       "...                                ...                                    ...   \n",
       "80320                                1                                      1   \n",
       "80321                                1                                      1   \n",
       "80322                                1                                      1   \n",
       "80323                                1                                      1   \n",
       "80324                                1                                      1   \n",
       "\n",
       "       instruction_to_work_from_home_where_possible  travel_discouraged  \\\n",
       "0                                                 1                   1   \n",
       "1                                                 1                   1   \n",
       "2                                                 1                   1   \n",
       "3                                                 1                   1   \n",
       "4                                                 1                   1   \n",
       "...                                             ...                 ...   \n",
       "80320                                             1                   1   \n",
       "80321                                             1                   1   \n",
       "80322                                             1                   1   \n",
       "80323                                             1                   1   \n",
       "80324                                             1                   1   \n",
       "\n",
       "       no_indoor_mixing  overnight_stays_discouraged  \\\n",
       "0                     1                            1   \n",
       "1                     1                            1   \n",
       "2                     1                            1   \n",
       "3                     1                            1   \n",
       "4                     1                            1   \n",
       "...                 ...                          ...   \n",
       "80320                 1                            1   \n",
       "80321                 1                            1   \n",
       "80322                 1                            1   \n",
       "80323                 1                            1   \n",
       "80324                 1                            1   \n",
       "\n",
       "       residents_cannot_leave_the_local_area  ...  tier_2_nested  \\\n",
       "0                                          1  ...              0   \n",
       "1                                          1  ...              0   \n",
       "2                                          1  ...              0   \n",
       "3                                          1  ...              0   \n",
       "4                                          1  ...              0   \n",
       "...                                      ...  ...            ...   \n",
       "80320                                      1  ...              0   \n",
       "80321                                      1  ...              0   \n",
       "80322                                      1  ...              0   \n",
       "80323                                      1  ...              0   \n",
       "80324                                      1  ...              0   \n",
       "\n",
       "       tier_1_nested  Tier  tier_1_BD_nested  tier_2_BD_nested  \\\n",
       "0                  0     0                 1                 1   \n",
       "1                  0     0                 1                 1   \n",
       "2                  0     0                 1                 1   \n",
       "3                  0     0                 1                 1   \n",
       "4                  0     0                 1                 1   \n",
       "...              ...   ...               ...               ...   \n",
       "80320              0     0                 1                 1   \n",
       "80321              0     0                 1                 1   \n",
       "80322              0     0                 1                 1   \n",
       "80323              0     0                 1                 1   \n",
       "80324              0     0                 1                 1   \n",
       "\n",
       "       tier_3_BD_nested  Tier_BD  tier_1_BD  tier_2_BD  tier_3_BD  \n",
       "0                     1        3          0          0          1  \n",
       "1                     1        3          0          0          1  \n",
       "2                     1        3          0          0          1  \n",
       "3                     1        3          0          0          1  \n",
       "4                     1        3          0          0          1  \n",
       "...                 ...      ...        ...        ...        ...  \n",
       "80320                 1        3          0          0          1  \n",
       "80321                 1        3          0          0          1  \n",
       "80322                 1        3          0          0          1  \n",
       "80323                 1        3          0          0          1  \n",
       "80324                 1        3          0          0          1  \n",
       "\n",
       "[80325 rows x 45 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "icl_npis_df = pd.read_csv(ICL_NPIS)\n",
    "icl_npis_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'ltla', 'limited_to_groups_of_6_indoors',\n",
       "       'limited_to_groups_of_6_outdoors',\n",
       "       'curfew_of_10pm_for_hospitality_venues',\n",
       "       'instruction_to_work_from_home_where_possible', 'travel_discouraged',\n",
       "       'no_indoor_mixing', 'overnight_stays_discouraged',\n",
       "       'residents_cannot_leave_the_local_area',\n",
       "       'non.essential_retail_closures', 'schools_closed',\n",
       "       'places_of_worship_closed', 'weddings_not_allowed',\n",
       "       'organised_sport_not_allowed', 'tourist_attractions_closed',\n",
       "       'gyms_closed', 'public_buildings_closed',\n",
       "       'personal_care_contact_services_closed', 'arts_venues_closed',\n",
       "       'sit.down_hospitality_closed_takeaway_only',\n",
       "       'pubs_and_bars_closed_table_service_only', 'essential_travel_only',\n",
       "       'tier_1', 'tier_2', 'tier_3', 'national_lockdown', 'source_record',\n",
       "       'ltla20cd', 'ltla20nm', 'utla20cd', 'utla', 'rgn19cd', 'region',\n",
       "       'tier_3_nested', 'tier_2_nested', 'tier_1_nested', 'Tier',\n",
       "       'tier_1_BD_nested', 'tier_2_BD_nested', 'tier_3_BD_nested', 'Tier_BD',\n",
       "       'tier_1_BD', 'tier_2_BD', 'tier_3_BD'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "icl_npis_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Adur', 'Allerdale', 'Amber Valley', 'Arun', 'Ashfield', 'Ashford',\n",
       "       'Babergh', 'Barking and Dagenham', 'Barnet', 'Barnsley',\n",
       "       'Barrow-in-Furness', 'Basildon', 'Basingstoke and Deane',\n",
       "       'Bassetlaw', 'Bath and North East Somerset', 'Bedford', 'Bexley',\n",
       "       'Birmingham', 'Blaby', 'Blackburn with Darwen', 'Blackpool',\n",
       "       'Bolsover', 'Bolton', 'Boston',\n",
       "       'Bournemouth, Christchurch and Poole', 'Bracknell Forest',\n",
       "       'Bradford', 'Braintree', 'Breckland', 'Brent', 'Brentwood',\n",
       "       'Brighton and Hove', 'Bristol, City of', 'Broadland', 'Bromley',\n",
       "       'Bromsgrove', 'Broxbourne', 'Broxtowe', 'Buckinghamshire',\n",
       "       'Burnley', 'Bury', 'Calderdale', 'Cambridge', 'Camden',\n",
       "       'Cannock Chase', 'Canterbury', 'Carlisle', 'Castle Point',\n",
       "       'Central Bedfordshire', 'Charnwood', 'Chelmsford', 'Cheltenham',\n",
       "       'Cherwell', 'Cheshire East', 'Cheshire West and Chester',\n",
       "       'Chesterfield', 'Chichester', 'Chorley', 'City of London',\n",
       "       'Colchester', 'Copeland', 'Corby', 'Cornwall', 'Cotswold',\n",
       "       'County Durham', 'Coventry', 'Craven', 'Crawley', 'Croydon',\n",
       "       'Dacorum', 'Darlington', 'Dartford', 'Daventry', 'Derby',\n",
       "       'Derbyshire Dales', 'Doncaster', 'Dorset', 'Dover', 'Dudley',\n",
       "       'Ealing', 'East Cambridgeshire', 'East Devon', 'East Hampshire',\n",
       "       'East Hertfordshire', 'East Lindsey', 'East Northamptonshire',\n",
       "       'East Riding of Yorkshire', 'East Staffordshire', 'East Suffolk',\n",
       "       'Eastbourne', 'Eastleigh', 'Eden', 'Elmbridge', 'Enfield',\n",
       "       'Epping Forest', 'Epsom and Ewell', 'Erewash', 'Exeter', 'Fareham',\n",
       "       'Fenland', 'Folkestone and Hythe', 'Forest of Dean', 'Fylde',\n",
       "       'Gateshead', 'Gedling', 'Gloucester', 'Gosport', 'Gravesham',\n",
       "       'Great Yarmouth', 'Greenwich', 'Guildford', 'Hackney', 'Halton',\n",
       "       'Hambleton', 'Hammersmith and Fulham', 'Harborough', 'Haringey',\n",
       "       'Harlow', 'Harrogate', 'Harrow', 'Hart', 'Hartlepool', 'Hastings',\n",
       "       'Havant', 'Havering', 'Herefordshire, County of', 'Hertsmere',\n",
       "       'High Peak', 'Hillingdon', 'Hinckley and Bosworth', 'Horsham',\n",
       "       'Hounslow', 'Huntingdonshire', 'Hyndburn', 'Ipswich',\n",
       "       'Isle of Wight', 'Isles of Scilly', 'Islington',\n",
       "       'Kensington and Chelsea', 'Kettering',\n",
       "       \"King's Lynn and West Norfolk\", 'Kingston upon Hull, City of',\n",
       "       'Kingston upon Thames', 'Kirklees', 'Knowsley', 'Lambeth',\n",
       "       'Lancaster', 'Leeds', 'Leicester', 'Lewes', 'Lewisham',\n",
       "       'Lichfield', 'Lincoln', 'Liverpool', 'Luton', 'Maidstone',\n",
       "       'Maldon', 'Malvern Hills', 'Manchester', 'Mansfield', 'Medway',\n",
       "       'Melton', 'Mendip', 'Merton', 'Mid Devon', 'Mid Suffolk',\n",
       "       'Mid Sussex', 'Middlesbrough', 'Milton Keynes', 'Mole Valley',\n",
       "       'New Forest', 'Newark and Sherwood', 'Newcastle upon Tyne',\n",
       "       'Newcastle-under-Lyme', 'Newham', 'None', 'North Devon',\n",
       "       'North East Derbyshire', 'North East Lincolnshire',\n",
       "       'North Hertfordshire', 'North Kesteven', 'North Lincolnshire',\n",
       "       'North Norfolk', 'North Somerset', 'North Tyneside',\n",
       "       'North Warwickshire', 'North West Leicestershire', 'Northampton',\n",
       "       'Northumberland', 'Norwich', 'Nottingham', 'Nuneaton and Bedworth',\n",
       "       'Oadby and Wigston', 'Oldham', 'Oxford', 'Pendle', 'Peterborough',\n",
       "       'Plymouth', 'Portsmouth', 'Preston', 'Reading', 'Redbridge',\n",
       "       'Redcar and Cleveland', 'Redditch', 'Reigate and Banstead',\n",
       "       'Ribble Valley', 'Richmond upon Thames', 'Richmondshire',\n",
       "       'Rochdale', 'Rochford', 'Rossendale', 'Rother', 'Rotherham',\n",
       "       'Rugby', 'Runnymede', 'Rushcliffe', 'Rushmoor', 'Rutland',\n",
       "       'Ryedale', 'Salford', 'Sandwell', 'Scarborough', 'Sedgemoor',\n",
       "       'Sefton', 'Selby', 'Sevenoaks', 'Sheffield', 'Shropshire',\n",
       "       'Slough', 'Solihull', 'Somerset West and Taunton',\n",
       "       'South Cambridgeshire', 'South Derbyshire',\n",
       "       'South Gloucestershire', 'South Hams', 'South Holland',\n",
       "       'South Kesteven', 'South Lakeland', 'South Norfolk',\n",
       "       'South Northamptonshire', 'South Oxfordshire', 'South Ribble',\n",
       "       'South Somerset', 'South Staffordshire', 'South Tyneside',\n",
       "       'Southampton', 'Southend-on-Sea', 'Southwark', 'Spelthorne',\n",
       "       'St Albans', 'St. Helens', 'Stafford', 'Staffordshire Moorlands',\n",
       "       'Stevenage', 'Stockport', 'Stockton-on-Tees', 'Stoke-on-Trent',\n",
       "       'Stratford-on-Avon', 'Stroud', 'Sunderland', 'Surrey Heath',\n",
       "       'Sutton', 'Swale', 'Swindon', 'Tameside', 'Tamworth', 'Tandridge',\n",
       "       'Teignbridge', 'Telford and Wrekin', 'Tendring', 'Test Valley',\n",
       "       'Tewkesbury', 'Thanet', 'Three Rivers', 'Thurrock',\n",
       "       'Tonbridge and Malling', 'Torbay', 'Torridge', 'Tower Hamlets',\n",
       "       'Trafford', 'Tunbridge Wells', 'Uttlesford', 'Vale of White Horse',\n",
       "       'Wakefield', 'Walsall', 'Waltham Forest', 'Wandsworth',\n",
       "       'Warrington', 'Warwick', 'Watford', 'Waverley', 'Wealden',\n",
       "       'Wellingborough', 'Welwyn Hatfield', 'West Berkshire',\n",
       "       'West Devon', 'West Lancashire', 'West Lindsey',\n",
       "       'West Oxfordshire', 'West Suffolk', 'Westminster', 'Wigan',\n",
       "       'Wiltshire', 'Winchester', 'Windsor and Maidenhead', 'Wirral',\n",
       "       'Woking', 'Wokingham', 'Wolverhampton', 'Worcester', 'Worthing',\n",
       "       'Wychavon', 'Wyre', 'Wyre Forest', 'York'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "icl_npis_df[\"ltla\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "icl_npis_df[\"date\"] = pd.to_datetime(icl_npis_df[\"date\"], format=\"%d/%m/%Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-03-23 00:00:00')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-12-02 00:00:00')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(icl_npis_df[\"date\"].min())\n",
    "display(icl_npis_df[\"date\"].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "246"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "icl_npis_df[icl_npis_df[\"tier_1\"] == 1][\"ltla\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "141"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "icl_npis_df[icl_npis_df[\"tier_2\"] == 1][\"ltla\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "icl_npis_df[icl_npis_df[\"tier_3\"] == 1][\"ltla\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "icl_npis_df[\"tier\"] = \"1\"\n",
    "icl_npis_df.loc[icl_npis_df[\"tier_2\"] == 1, \"tier\"] = \"2\"\n",
    "icl_npis_df.loc[icl_npis_df[\"tier_3\"] == 1, \"tier\"] = \"3\"\n",
    "icl_npis_df.loc[icl_npis_df[\"national_lockdown\"] == 1, \"tier\"] = \"national_lockdown\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "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>tier_1</th>\n",
       "      <th>tier_2</th>\n",
       "      <th>tier_3</th>\n",
       "      <th>national_lockdown</th>\n",
       "      <th>tier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4537</th>\n",
       "      <td>2020-10-11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4538</th>\n",
       "      <td>2020-10-12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4539</th>\n",
       "      <td>2020-10-13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4540</th>\n",
       "      <td>2020-10-14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4541</th>\n",
       "      <td>2020-10-15</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4542</th>\n",
       "      <td>2020-10-16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4543</th>\n",
       "      <td>2020-10-17</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4544</th>\n",
       "      <td>2020-10-18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4545</th>\n",
       "      <td>2020-10-19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4546</th>\n",
       "      <td>2020-10-20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4547</th>\n",
       "      <td>2020-10-21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4548</th>\n",
       "      <td>2020-10-22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4549</th>\n",
       "      <td>2020-10-23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4550</th>\n",
       "      <td>2020-10-24</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4551</th>\n",
       "      <td>2020-10-25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4552</th>\n",
       "      <td>2020-10-26</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4553</th>\n",
       "      <td>2020-10-27</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4554</th>\n",
       "      <td>2020-10-28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4555</th>\n",
       "      <td>2020-10-29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4556</th>\n",
       "      <td>2020-10-30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4557</th>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4558</th>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4559</th>\n",
       "      <td>2020-11-02</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4560</th>\n",
       "      <td>2020-11-03</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4561</th>\n",
       "      <td>2020-11-04</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4562</th>\n",
       "      <td>2020-11-05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4563</th>\n",
       "      <td>2020-11-06</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4564</th>\n",
       "      <td>2020-11-07</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4565</th>\n",
       "      <td>2020-11-08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4566</th>\n",
       "      <td>2020-11-09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4567</th>\n",
       "      <td>2020-11-10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4568</th>\n",
       "      <td>2020-11-11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4569</th>\n",
       "      <td>2020-11-12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4570</th>\n",
       "      <td>2020-11-13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4571</th>\n",
       "      <td>2020-11-14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4572</th>\n",
       "      <td>2020-11-15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4573</th>\n",
       "      <td>2020-11-16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4574</th>\n",
       "      <td>2020-11-17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4575</th>\n",
       "      <td>2020-11-18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4576</th>\n",
       "      <td>2020-11-19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4577</th>\n",
       "      <td>2020-11-20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4578</th>\n",
       "      <td>2020-11-21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4579</th>\n",
       "      <td>2020-11-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4580</th>\n",
       "      <td>2020-11-23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4581</th>\n",
       "      <td>2020-11-24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4582</th>\n",
       "      <td>2020-11-25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4583</th>\n",
       "      <td>2020-11-26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4584</th>\n",
       "      <td>2020-11-27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4585</th>\n",
       "      <td>2020-11-28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4586</th>\n",
       "      <td>2020-11-29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4587</th>\n",
       "      <td>2020-11-30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4588</th>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4589</th>\n",
       "      <td>2020-12-02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  tier_1  tier_2  tier_3  national_lockdown               tier\n",
       "4537 2020-10-11       0       0       0                  0                  1\n",
       "4538 2020-10-12       0       0       0                  0                  1\n",
       "4539 2020-10-13       0       0       0                  0                  1\n",
       "4540 2020-10-14       0       1       0                  0                  2\n",
       "4541 2020-10-15       0       1       0                  0                  2\n",
       "4542 2020-10-16       0       1       0                  0                  2\n",
       "4543 2020-10-17       0       1       0                  0                  2\n",
       "4544 2020-10-18       0       1       0                  0                  2\n",
       "4545 2020-10-19       0       1       0                  0                  2\n",
       "4546 2020-10-20       0       1       0                  0                  2\n",
       "4547 2020-10-21       0       1       0                  0                  2\n",
       "4548 2020-10-22       0       1       0                  0                  2\n",
       "4549 2020-10-23       0       1       0                  0                  2\n",
       "4550 2020-10-24       0       1       0                  0                  2\n",
       "4551 2020-10-25       0       1       0                  0                  2\n",
       "4552 2020-10-26       0       1       0                  0                  2\n",
       "4553 2020-10-27       0       1       0                  0                  2\n",
       "4554 2020-10-28       0       1       0                  0                  2\n",
       "4555 2020-10-29       0       1       0                  0                  2\n",
       "4556 2020-10-30       0       1       0                  0                  2\n",
       "4557 2020-10-31       0       1       0                  0                  2\n",
       "4558 2020-11-01       0       1       0                  0                  2\n",
       "4559 2020-11-02       0       1       0                  0                  2\n",
       "4560 2020-11-03       0       1       0                  0                  2\n",
       "4561 2020-11-04       0       1       0                  0                  2\n",
       "4562 2020-11-05       0       0       0                  1  national_lockdown\n",
       "4563 2020-11-06       0       0       0                  1  national_lockdown\n",
       "4564 2020-11-07       0       0       0                  1  national_lockdown\n",
       "4565 2020-11-08       0       0       0                  1  national_lockdown\n",
       "4566 2020-11-09       0       0       0                  1  national_lockdown\n",
       "4567 2020-11-10       0       0       0                  1  national_lockdown\n",
       "4568 2020-11-11       0       0       0                  1  national_lockdown\n",
       "4569 2020-11-12       0       0       0                  1  national_lockdown\n",
       "4570 2020-11-13       0       0       0                  1  national_lockdown\n",
       "4571 2020-11-14       0       0       0                  1  national_lockdown\n",
       "4572 2020-11-15       0       0       0                  1  national_lockdown\n",
       "4573 2020-11-16       0       0       0                  1  national_lockdown\n",
       "4574 2020-11-17       0       0       0                  1  national_lockdown\n",
       "4575 2020-11-18       0       0       0                  1  national_lockdown\n",
       "4576 2020-11-19       0       0       0                  1  national_lockdown\n",
       "4577 2020-11-20       0       0       0                  1  national_lockdown\n",
       "4578 2020-11-21       0       0       0                  1  national_lockdown\n",
       "4579 2020-11-22       0       0       0                  1  national_lockdown\n",
       "4580 2020-11-23       0       0       0                  1  national_lockdown\n",
       "4581 2020-11-24       0       0       0                  1  national_lockdown\n",
       "4582 2020-11-25       0       0       0                  1  national_lockdown\n",
       "4583 2020-11-26       0       0       0                  1  national_lockdown\n",
       "4584 2020-11-27       0       0       0                  1  national_lockdown\n",
       "4585 2020-11-28       0       0       0                  1  national_lockdown\n",
       "4586 2020-11-29       0       0       0                  1  national_lockdown\n",
       "4587 2020-11-30       0       0       0                  1  national_lockdown\n",
       "4588 2020-12-01       0       0       0                  1  national_lockdown\n",
       "4589 2020-12-02       0       0       0                  1  national_lockdown"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visual sanity check\n",
    "icl_npis_df[(icl_npis_df[\"ltla\"] == \"Birmingham\") &\n",
    "           (icl_npis_df[\"date\"] > \"2020-10-10\") ][[\"date\", \"tier_1\", \"tier_2\", \"tier_3\", \"national_lockdown\", \"tier\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clean coronavirus.data.gov.uk Dataset for Cases by LTLA\n",
    "\n",
    "from https://coronavirus.data.gov.uk/details/download"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "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>newCasesBySpecimenDate</th>\n",
       "      <th>newCasesBySpecimenDateSmooth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S12000033</td>\n",
       "      <td>Aberdeen City</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S12000033</td>\n",
       "      <td>Aberdeen City</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S12000033</td>\n",
       "      <td>Aberdeen City</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S12000033</td>\n",
       "      <td>Aberdeen City</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2020-03-07</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S12000033</td>\n",
       "      <td>Aberdeen City</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2020-03-08</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162352</th>\n",
       "      <td>E06000014</td>\n",
       "      <td>York</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2021-05-04</td>\n",
       "      <td>5</td>\n",
       "      <td>4.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162353</th>\n",
       "      <td>E06000014</td>\n",
       "      <td>York</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2021-05-05</td>\n",
       "      <td>5</td>\n",
       "      <td>4.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162354</th>\n",
       "      <td>E06000014</td>\n",
       "      <td>York</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2021-05-06</td>\n",
       "      <td>6</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162355</th>\n",
       "      <td>E06000014</td>\n",
       "      <td>York</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2021-05-07</td>\n",
       "      <td>7</td>\n",
       "      <td>4.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162356</th>\n",
       "      <td>E06000014</td>\n",
       "      <td>York</td>\n",
       "      <td>ltla</td>\n",
       "      <td>2021-05-08</td>\n",
       "      <td>1</td>\n",
       "      <td>4.428571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>162357 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         areaCode       areaName areaType       date  newCasesBySpecimenDate  \\\n",
       "0       S12000033  Aberdeen City     ltla 2020-03-04                       1   \n",
       "1       S12000033  Aberdeen City     ltla 2020-03-05                       0   \n",
       "2       S12000033  Aberdeen City     ltla 2020-03-06                       0   \n",
       "3       S12000033  Aberdeen City     ltla 2020-03-07                       0   \n",
       "4       S12000033  Aberdeen City     ltla 2020-03-08                       0   \n",
       "...           ...            ...      ...        ...                     ...   \n",
       "162352  E06000014           York     ltla 2021-05-04                       5   \n",
       "162353  E06000014           York     ltla 2021-05-05                       5   \n",
       "162354  E06000014           York     ltla 2021-05-06                       6   \n",
       "162355  E06000014           York     ltla 2021-05-07                       7   \n",
       "162356  E06000014           York     ltla 2021-05-08                       1   \n",
       "\n",
       "        newCasesBySpecimenDateSmooth  \n",
       "0                                NaN  \n",
       "1                                NaN  \n",
       "2                                NaN  \n",
       "3                                NaN  \n",
       "4                                NaN  \n",
       "...                              ...  \n",
       "162352                      4.714286  \n",
       "162353                      4.142857  \n",
       "162354                      4.000000  \n",
       "162355                      4.571429  \n",
       "162356                      4.428571  \n",
       "\n",
       "[162357 rows x 6 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gov_uk_case_ltla_df = pd.read_csv(GOV_UK_DAILY_CASE_LTLA)\n",
    "gov_uk_case_ltla_df[\"date\"] = pd.to_datetime(gov_uk_case_ltla_df[\"date\"], format=\"%Y-%m-%d\")\n",
    "gov_uk_case_ltla_df = gov_uk_case_ltla_df.sort_values([\"areaName\", \"date\"], ascending=True).reset_index(drop=True)\n",
    "# rolling 7 day average cases ending on the row date\n",
    "gov_uk_case_ltla_df[\"newCasesBySpecimenDateSmooth\"] = (gov_uk_case_ltla_df\n",
    "                                                       .groupby(\"areaName\")\n",
    "                                                       .rolling(window='7D', \n",
    "                                                                on=\"date\",\n",
    "                                                                min_periods=7)\n",
    "                                                       [\"newCasesBySpecimenDate\"]\n",
    "                                                       .mean()\n",
    "                                                       .reset_index(drop=True))\n",
    "gov_uk_case_ltla_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Which LTLAs in the Imperial College tier by ltla dataset are not found in the Gov UK daily cases by ltla dataset?\n",
    "\n",
    "There are 5.  We choose just to ignore all of them except \"City of London\" for simplicity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Buckinghamshire', 'City of London', 'Cornwall', 'Hackney',\n",
       "       'Isles of Scilly', 'None'], dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "icl_npis_df[~icl_npis_df[\"ltla\"].isin(gov_uk_case_ltla_df[\"areaName\"])][\"ltla\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Which LTLAs in the Gov UK daily cases by ltla dataset are not found in the Imperial College tier by ltla dataset?\n",
    "\n",
    "There are 71.  We choose just to ignore all of them except \"Hackney and City of London\" for simplicity.  Manually hack the dataset to change \"Hackney and City of London\" to \"City of London\" to reconcile data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Aberdeen City', 'Aberdeenshire', 'Angus',\n",
       "       'Antrim and Newtownabbey', 'Ards and North Down',\n",
       "       'Argyll and Bute', 'Armagh City, Banbridge and Craigavon',\n",
       "       'Aylesbury Vale', 'Belfast', 'Blaenau Gwent', 'Bridgend',\n",
       "       'Caerphilly', 'Cardiff', 'Carmarthenshire',\n",
       "       'Causeway Coast and Glens', 'Ceredigion', 'Chiltern',\n",
       "       'City of Edinburgh', 'Clackmannanshire',\n",
       "       'Comhairle nan Eilean Siar', 'Conwy',\n",
       "       'Cornwall and Isles of Scilly', 'Denbighshire',\n",
       "       'Derry City and Strabane', 'Dumfries and Galloway', 'Dundee City',\n",
       "       'East Ayrshire', 'East Dunbartonshire', 'East Lothian',\n",
       "       'East Renfrewshire', 'Falkirk', 'Fermanagh and Omagh', 'Fife',\n",
       "       'Flintshire', 'Glasgow City', 'Gwynedd',\n",
       "       'Hackney and City of London', 'Highland', 'Inverclyde',\n",
       "       'Isle of Anglesey', 'Lisburn and Castlereagh', 'Merthyr Tydfil',\n",
       "       'Mid Ulster', 'Mid and East Antrim', 'Midlothian', 'Monmouthshire',\n",
       "       'Moray', 'Neath Port Talbot', 'Newport', 'Newry, Mourne and Down',\n",
       "       'North Ayrshire', 'North Lanarkshire', 'Orkney Islands',\n",
       "       'Pembrokeshire', 'Perth and Kinross', 'Powys', 'Renfrewshire',\n",
       "       'Rhondda Cynon Taf', 'Scottish Borders', 'Shetland Islands',\n",
       "       'South Ayrshire', 'South Bucks', 'South Lanarkshire', 'Stirling',\n",
       "       'Swansea', 'Torfaen', 'Vale of Glamorgan', 'West Dunbartonshire',\n",
       "       'West Lothian', 'Wrexham', 'Wycombe'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "71"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(gov_uk_case_ltla_df[~gov_uk_case_ltla_df[\"areaName\"].isin(icl_npis_df[\"ltla\"])][\"areaName\"].unique())\n",
    "display(gov_uk_case_ltla_df[~gov_uk_case_ltla_df[\"areaName\"].isin(icl_npis_df[\"ltla\"])][\"areaName\"].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "gov_uk_case_ltla_df.loc[gov_uk_case_ltla_df[\"areaName\"] == \"Hackney and City of London\", \"areaName\"] = \"City of London\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>date</th>\n",
       "      <th>newCasesBySpecimenDateSmooth</th>\n",
       "      <th>ltla</th>\n",
       "      <th>tier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adur</td>\n",
       "      <td>2020-03-23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Adur</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adur</td>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Adur</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adur</td>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>Adur</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adur</td>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>Adur</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adur</td>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Adur</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79037</th>\n",
       "      <td>York</td>\n",
       "      <td>2020-11-28</td>\n",
       "      <td>23.285714</td>\n",
       "      <td>York</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79038</th>\n",
       "      <td>York</td>\n",
       "      <td>2020-11-29</td>\n",
       "      <td>20.857143</td>\n",
       "      <td>York</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79039</th>\n",
       "      <td>York</td>\n",
       "      <td>2020-11-30</td>\n",
       "      <td>19.428571</td>\n",
       "      <td>York</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79040</th>\n",
       "      <td>York</td>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>York</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79041</th>\n",
       "      <td>York</td>\n",
       "      <td>2020-12-02</td>\n",
       "      <td>23.142857</td>\n",
       "      <td>York</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79042 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      areaName       date  newCasesBySpecimenDateSmooth  ltla  \\\n",
       "0         Adur 2020-03-23                           NaN  Adur   \n",
       "1         Adur 2020-03-24                           NaN  Adur   \n",
       "2         Adur 2020-03-25                      0.714286  Adur   \n",
       "3         Adur 2020-03-26                      0.857143  Adur   \n",
       "4         Adur 2020-03-27                      1.000000  Adur   \n",
       "...        ...        ...                           ...   ...   \n",
       "79037     York 2020-11-28                     23.285714  York   \n",
       "79038     York 2020-11-29                     20.857143  York   \n",
       "79039     York 2020-11-30                     19.428571  York   \n",
       "79040     York 2020-12-01                     23.000000  York   \n",
       "79041     York 2020-12-02                     23.142857  York   \n",
       "\n",
       "                    tier  \n",
       "0      national_lockdown  \n",
       "1      national_lockdown  \n",
       "2      national_lockdown  \n",
       "3      national_lockdown  \n",
       "4      national_lockdown  \n",
       "...                  ...  \n",
       "79037  national_lockdown  \n",
       "79038  national_lockdown  \n",
       "79039  national_lockdown  \n",
       "79040  national_lockdown  \n",
       "79041  national_lockdown  \n",
       "\n",
       "[79042 rows x 5 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gov_uk_case_ltla_tier_df = (gov_uk_case_ltla_df[[\"areaName\", \"date\", \"newCasesBySpecimenDateSmooth\"]]\n",
    "                            .merge(icl_npis_df[[\"date\", \"ltla\", \"tier\"]], \n",
    "                                   how=\"inner\",\n",
    "                                    left_on=[\"areaName\", \"date\"],\n",
    "                                    right_on=[\"ltla\", \"date\"]))\n",
    "gov_uk_case_ltla_tier_df = gov_uk_case_ltla_tier_df.sort_values([\"ltla\", \"date\"], ascending=True).reset_index(drop=True)\n",
    "gov_uk_case_ltla_tier_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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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>areaName</th>\n",
       "      <th>date</th>\n",
       "      <th>newCasesBySpecimenDateSmooth</th>\n",
       "      <th>ltla</th>\n",
       "      <th>tier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14535</th>\n",
       "      <td>City of London</td>\n",
       "      <td>2020-03-23</td>\n",
       "      <td>10.428571</td>\n",
       "      <td>City of London</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14536</th>\n",
       "      <td>City of London</td>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>12.428571</td>\n",
       "      <td>City of London</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14537</th>\n",
       "      <td>City of London</td>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>13.714286</td>\n",
       "      <td>City of London</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14538</th>\n",
       "      <td>City of London</td>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>16.285714</td>\n",
       "      <td>City of London</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14539</th>\n",
       "      <td>City of London</td>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>16.714286</td>\n",
       "      <td>City of London</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14785</th>\n",
       "      <td>City of London</td>\n",
       "      <td>2020-11-28</td>\n",
       "      <td>59.285714</td>\n",
       "      <td>City of London</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14786</th>\n",
       "      <td>City of London</td>\n",
       "      <td>2020-11-29</td>\n",
       "      <td>55.142857</td>\n",
       "      <td>City of London</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14787</th>\n",
       "      <td>City of London</td>\n",
       "      <td>2020-11-30</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>City of London</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14788</th>\n",
       "      <td>City of London</td>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>64.142857</td>\n",
       "      <td>City of London</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14789</th>\n",
       "      <td>City of London</td>\n",
       "      <td>2020-12-02</td>\n",
       "      <td>71.142857</td>\n",
       "      <td>City of London</td>\n",
       "      <td>national_lockdown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>255 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             areaName       date  newCasesBySpecimenDateSmooth  \\\n",
       "14535  City of London 2020-03-23                     10.428571   \n",
       "14536  City of London 2020-03-24                     12.428571   \n",
       "14537  City of London 2020-03-25                     13.714286   \n",
       "14538  City of London 2020-03-26                     16.285714   \n",
       "14539  City of London 2020-03-27                     16.714286   \n",
       "...               ...        ...                           ...   \n",
       "14785  City of London 2020-11-28                     59.285714   \n",
       "14786  City of London 2020-11-29                     55.142857   \n",
       "14787  City of London 2020-11-30                     60.000000   \n",
       "14788  City of London 2020-12-01                     64.142857   \n",
       "14789  City of London 2020-12-02                     71.142857   \n",
       "\n",
       "                 ltla               tier  \n",
       "14535  City of London  national_lockdown  \n",
       "14536  City of London  national_lockdown  \n",
       "14537  City of London  national_lockdown  \n",
       "14538  City of London  national_lockdown  \n",
       "14539  City of London  national_lockdown  \n",
       "...               ...                ...  \n",
       "14785  City of London  national_lockdown  \n",
       "14786  City of London  national_lockdown  \n",
       "14787  City of London  national_lockdown  \n",
       "14788  City of London  national_lockdown  \n",
       "14789  City of London  national_lockdown  \n",
       "\n",
       "[255 rows x 5 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# double check city of london manual corrections was modified correctly\n",
    "gov_uk_case_ltla_tier_df.loc[gov_uk_case_ltla_tier_df[\"areaName\"] == \"City of London\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "gov_uk_case_ltla_tier_df[\"is_tier_change\"] = (\n",
    "    (gov_uk_case_ltla_tier_df[\"tier\"] != gov_uk_case_ltla_tier_df[\"tier\"].shift()) &\n",
    "    (gov_uk_case_ltla_tier_df[\"ltla\"] == gov_uk_case_ltla_tier_df[\"ltla\"].shift())\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "gov_uk_case_ltla_tier_df = gov_uk_case_ltla_tier_df[gov_uk_case_ltla_tier_df[\"date\"] >= TIER_INTRO_DATE]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "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>date</th>\n",
       "      <th>newCasesBySpecimenDateSmooth</th>\n",
       "      <th>ltla</th>\n",
       "      <th>tier</th>\n",
       "      <th>is_tier_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Adur</td>\n",
       "      <td>2020-06-15</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>Adur</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>Allerdale</td>\n",
       "      <td>2020-06-15</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>Allerdale</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>Amber Valley</td>\n",
       "      <td>2020-06-15</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>Amber Valley</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>849</th>\n",
       "      <td>Arun</td>\n",
       "      <td>2020-06-15</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>Arun</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>Ashfield</td>\n",
       "      <td>2020-06-15</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>Ashfield</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77851</th>\n",
       "      <td>Worthing</td>\n",
       "      <td>2020-06-15</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>Worthing</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78106</th>\n",
       "      <td>Wychavon</td>\n",
       "      <td>2020-06-15</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>Wychavon</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78361</th>\n",
       "      <td>Wyre</td>\n",
       "      <td>2020-06-15</td>\n",
       "      <td>2.428571</td>\n",
       "      <td>Wyre</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78616</th>\n",
       "      <td>Wyre Forest</td>\n",
       "      <td>2020-06-15</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>Wyre Forest</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78871</th>\n",
       "      <td>York</td>\n",
       "      <td>2020-06-15</td>\n",
       "      <td>1.857143</td>\n",
       "      <td>York</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>310 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           areaName       date  newCasesBySpecimenDateSmooth          ltla  \\\n",
       "84             Adur 2020-06-15                      0.428571          Adur   \n",
       "339       Allerdale 2020-06-15                      0.857143     Allerdale   \n",
       "594    Amber Valley 2020-06-15                      1.428571  Amber Valley   \n",
       "849            Arun 2020-06-15                      0.571429          Arun   \n",
       "1104       Ashfield 2020-06-15                      0.714286      Ashfield   \n",
       "...             ...        ...                           ...           ...   \n",
       "77851      Worthing 2020-06-15                      0.142857      Worthing   \n",
       "78106      Wychavon 2020-06-15                      0.714286      Wychavon   \n",
       "78361          Wyre 2020-06-15                      2.428571          Wyre   \n",
       "78616   Wyre Forest 2020-06-15                      0.571429   Wyre Forest   \n",
       "78871          York 2020-06-15                      1.857143          York   \n",
       "\n",
       "      tier  is_tier_change  \n",
       "84       1            True  \n",
       "339      1            True  \n",
       "594      1            True  \n",
       "849      1            True  \n",
       "1104     1            True  \n",
       "...    ...             ...  \n",
       "77851    1            True  \n",
       "78106    1            True  \n",
       "78361    1            True  \n",
       "78616    1            True  \n",
       "78871    1            True  \n",
       "\n",
       "[310 rows x 6 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Double check that the tier introduction date when Jun 15 2020 in the dataset\n",
    "gov_uk_case_ltla_tier_df[gov_uk_case_ltla_tier_df[\"is_tier_change\"] & \n",
    "                                                  (gov_uk_case_ltla_tier_df[\"tier\"] == \"1\") &\n",
    "                                                  (gov_uk_case_ltla_tier_df[\"date\"] == \"2020-06-15\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "310"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gov_uk_case_ltla_tier_df[\"ltla\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "areaName                                object\n",
       "date                            datetime64[ns]\n",
       "newCasesBySpecimenDateSmooth           float64\n",
       "ltla                                    object\n",
       "tier                                    object\n",
       "is_tier_change                            bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gov_uk_case_ltla_tier_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Days since tier change\n",
    "gov_uk_case_ltla_tier_df = gov_uk_case_ltla_tier_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "gov_uk_case_ltla_tier_df[\"time_delta\"] = ((gov_uk_case_ltla_tier_df[\"date\"] - \n",
    "                                           gov_uk_case_ltla_tier_df[\"date\"].shift()\n",
    "                                          ).fillna(pd.Timedelta(seconds=0))\n",
    "                                         )\n",
    "gov_uk_case_ltla_tier_df.loc[gov_uk_case_ltla_tier_df[\"is_tier_change\"], \"time_delta\"] = pd.Timedelta(seconds=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                        53010\n",
       "mean     0 days 23:38:07.945670628\n",
       "std      0 days 02:56:06.083169846\n",
       "min                0 days 00:00:00\n",
       "25%                1 days 00:00:00\n",
       "50%                1 days 00:00:00\n",
       "75%                1 days 00:00:00\n",
       "max                1 days 00:00:00\n",
       "Name: time_delta, dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gov_uk_case_ltla_tier_df[\"time_delta\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "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>areaName</th>\n",
       "      <th>date</th>\n",
       "      <th>newCasesBySpecimenDateSmooth</th>\n",
       "      <th>ltla</th>\n",
       "      <th>tier</th>\n",
       "      <th>is_tier_change</th>\n",
       "      <th>time_delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adur</td>\n",
       "      <td>2020-06-15</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>Adur</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adur</td>\n",
       "      <td>2020-06-16</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>Adur</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>1 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adur</td>\n",
       "      <td>2020-06-17</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>Adur</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>1 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adur</td>\n",
       "      <td>2020-06-18</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>Adur</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>1 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adur</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>Adur</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>1 days</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53005</th>\n",
       "      <td>York</td>\n",
       "      <td>2020-11-28</td>\n",
       "      <td>23.285714</td>\n",
       "      <td>York</td>\n",
       "      <td>national_lockdown</td>\n",
       "      <td>False</td>\n",
       "      <td>1 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53006</th>\n",
       "      <td>York</td>\n",
       "      <td>2020-11-29</td>\n",
       "      <td>20.857143</td>\n",
       "      <td>York</td>\n",
       "      <td>national_lockdown</td>\n",
       "      <td>False</td>\n",
       "      <td>1 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53007</th>\n",
       "      <td>York</td>\n",
       "      <td>2020-11-30</td>\n",
       "      <td>19.428571</td>\n",
       "      <td>York</td>\n",
       "      <td>national_lockdown</td>\n",
       "      <td>False</td>\n",
       "      <td>1 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53008</th>\n",
       "      <td>York</td>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>York</td>\n",
       "      <td>national_lockdown</td>\n",
       "      <td>False</td>\n",
       "      <td>1 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53009</th>\n",
       "      <td>York</td>\n",
       "      <td>2020-12-02</td>\n",
       "      <td>23.142857</td>\n",
       "      <td>York</td>\n",
       "      <td>national_lockdown</td>\n",
       "      <td>False</td>\n",
       "      <td>1 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53010 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      areaName       date  newCasesBySpecimenDateSmooth  ltla  \\\n",
       "0         Adur 2020-06-15                      0.428571  Adur   \n",
       "1         Adur 2020-06-16                      0.285714  Adur   \n",
       "2         Adur 2020-06-17                      0.285714  Adur   \n",
       "3         Adur 2020-06-18                      0.428571  Adur   \n",
       "4         Adur 2020-06-19                      0.571429  Adur   \n",
       "...        ...        ...                           ...   ...   \n",
       "53005     York 2020-11-28                     23.285714  York   \n",
       "53006     York 2020-11-29                     20.857143  York   \n",
       "53007     York 2020-11-30                     19.428571  York   \n",
       "53008     York 2020-12-01                     23.000000  York   \n",
       "53009     York 2020-12-02                     23.142857  York   \n",
       "\n",
       "                    tier  is_tier_change time_delta  \n",
       "0                      1            True     0 days  \n",
       "1                      1           False     1 days  \n",
       "2                      1           False     1 days  \n",
       "3                      1           False     1 days  \n",
       "4                      1           False     1 days  \n",
       "...                  ...             ...        ...  \n",
       "53005  national_lockdown           False     1 days  \n",
       "53006  national_lockdown           False     1 days  \n",
       "53007  national_lockdown           False     1 days  \n",
       "53008  national_lockdown           False     1 days  \n",
       "53009  national_lockdown           False     1 days  \n",
       "\n",
       "[53010 rows x 7 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gov_uk_case_ltla_tier_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cumsumtime(df):\n",
    "    df[\"d_since_tier_change\"] = df[\"time_delta\"].cumsum()\n",
    "    return df\n",
    "gov_uk_case_ltla_tier_df = gov_uk_case_ltla_tier_df.groupby([\"ltla\", \"tier\"]).apply(cumsumtime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    53010.000000\n",
       "mean        55.532541\n",
       "std         41.778650\n",
       "min          0.000000\n",
       "25%         17.000000\n",
       "50%         49.000000\n",
       "75%         91.000000\n",
       "max        142.000000\n",
       "Name: d_since_tier_change, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gov_uk_case_ltla_tier_df[\"d_since_tier_change\"] = gov_uk_case_ltla_tier_df[\"d_since_tier_change\"].astype(str).str.split(\" \", expand=True)[0].astype(int)\n",
    "gov_uk_case_ltla_tier_df['d_since_tier_change'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "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>date</th>\n",
       "      <th>newCasesBySpecimenDateSmooth</th>\n",
       "      <th>ltla</th>\n",
       "      <th>tier</th>\n",
       "      <th>is_tier_change</th>\n",
       "      <th>time_delta</th>\n",
       "      <th>d_since_tier_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adur</td>\n",
       "      <td>2020-06-15</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>Adur</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adur</td>\n",
       "      <td>2020-06-16</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>Adur</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>1 days</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adur</td>\n",
       "      <td>2020-06-17</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>Adur</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>1 days</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adur</td>\n",
       "      <td>2020-06-18</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>Adur</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>1 days</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adur</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>Adur</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>1 days</td>\n",
       "      <td>4</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>53005</th>\n",
       "      <td>York</td>\n",
       "      <td>2020-11-28</td>\n",
       "      <td>23.285714</td>\n",
       "      <td>York</td>\n",
       "      <td>national_lockdown</td>\n",
       "      <td>False</td>\n",
       "      <td>1 days</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53006</th>\n",
       "      <td>York</td>\n",
       "      <td>2020-11-29</td>\n",
       "      <td>20.857143</td>\n",
       "      <td>York</td>\n",
       "      <td>national_lockdown</td>\n",
       "      <td>False</td>\n",
       "      <td>1 days</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53007</th>\n",
       "      <td>York</td>\n",
       "      <td>2020-11-30</td>\n",
       "      <td>19.428571</td>\n",
       "      <td>York</td>\n",
       "      <td>national_lockdown</td>\n",
       "      <td>False</td>\n",
       "      <td>1 days</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53008</th>\n",
       "      <td>York</td>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>York</td>\n",
       "      <td>national_lockdown</td>\n",
       "      <td>False</td>\n",
       "      <td>1 days</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53009</th>\n",
       "      <td>York</td>\n",
       "      <td>2020-12-02</td>\n",
       "      <td>23.142857</td>\n",
       "      <td>York</td>\n",
       "      <td>national_lockdown</td>\n",
       "      <td>False</td>\n",
       "      <td>1 days</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53010 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      areaName       date  newCasesBySpecimenDateSmooth  ltla  \\\n",
       "0         Adur 2020-06-15                      0.428571  Adur   \n",
       "1         Adur 2020-06-16                      0.285714  Adur   \n",
       "2         Adur 2020-06-17                      0.285714  Adur   \n",
       "3         Adur 2020-06-18                      0.428571  Adur   \n",
       "4         Adur 2020-06-19                      0.571429  Adur   \n",
       "...        ...        ...                           ...   ...   \n",
       "53005     York 2020-11-28                     23.285714  York   \n",
       "53006     York 2020-11-29                     20.857143  York   \n",
       "53007     York 2020-11-30                     19.428571  York   \n",
       "53008     York 2020-12-01                     23.000000  York   \n",
       "53009     York 2020-12-02                     23.142857  York   \n",
       "\n",
       "                    tier  is_tier_change time_delta  d_since_tier_change  \n",
       "0                      1            True     0 days                    0  \n",
       "1                      1           False     1 days                    1  \n",
       "2                      1           False     1 days                    2  \n",
       "3                      1           False     1 days                    3  \n",
       "4                      1           False     1 days                    4  \n",
       "...                  ...             ...        ...                  ...  \n",
       "53005  national_lockdown           False     1 days                   23  \n",
       "53006  national_lockdown           False     1 days                   24  \n",
       "53007  national_lockdown           False     1 days                   25  \n",
       "53008  national_lockdown           False     1 days                   26  \n",
       "53009  national_lockdown           False     1 days                   27  \n",
       "\n",
       "[53010 rows x 8 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gov_uk_case_ltla_tier_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "gov_uk_case_ltla_tier_df[[\"ltla\", \"date\", \"tier\", \n",
    "                          \"is_tier_change\", \"d_since_tier_change\", \"newCasesBySpecimenDateSmooth\"]].to_csv(GOV_UK_DAILY_CASE_LTLA_TIER_CLEAN, index=False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r--  1 tn6  937   2.7M 10 May 23:41 data/gov_uk_icl/clean/gov_uk_ltla_tier_newCasesBySpecimenDate_2021-05-09.csv\r\n"
     ]
    }
   ],
   "source": [
    "! ls -lht {GOV_UK_DAILY_CASE_LTLA_TIER_CLEAN}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   53011 data/gov_uk_icl/clean/gov_uk_ltla_tier_newCasesBySpecimenDate_2021-05-09.csv\r\n"
     ]
    }
   ],
   "source": [
    "! wc -l {GOV_UK_DAILY_CASE_LTLA_TIER_CLEAN}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ltla,date,tier,is_tier_change,d_since_tier_change,newCasesBySpecimenDateSmooth\r\n",
      "Adur,2020-06-15,1,True,0,0.42857142857142855\r\n",
      "Adur,2020-06-16,1,False,1,0.2857142857142857\r\n",
      "Adur,2020-06-17,1,False,2,0.2857142857142857\r\n",
      "Adur,2020-06-18,1,False,3,0.42857142857142855\r\n",
      "Adur,2020-06-19,1,False,4,0.5714285714285714\r\n",
      "Adur,2020-06-20,1,False,5,0.2857142857142857\r\n",
      "Adur,2020-06-21,1,False,6,0.2857142857142857\r\n",
      "Adur,2020-06-22,1,False,7,0.2857142857142857\r\n",
      "Adur,2020-06-23,1,False,8,0.2857142857142857\r\n"
     ]
    }
   ],
   "source": [
    "! head {GOV_UK_DAILY_CASE_LTLA_TIER_CLEAN}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(20, 5))\n",
    "ax.plot(ecdc_gbl_case_death_2020_uk_df[ecdc_gbl_case_death_2020_uk_df[\"dateRep\"] > TIER_INTRO_DATE][\"dateRep\"], \n",
    "        ecdc_gbl_case_death_2020_uk_df[ecdc_gbl_case_death_2020_uk_df[\"dateRep\"] > TIER_INTRO_DATE][\"cases_per_100k\"])\n",
    "ax.set_ylabel(\"cases per 100k, rolling 7day average\")\n",
    "ax.set_xlabel(\"Date\")\n",
    "ax.set_title(\"UK Overall Cases\")\n",
    "plt.show()"
   ]
  }
 ],
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