08_index.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ab642ffd",
   "metadata": {},
   "source": [
    "# Index scores\n",
    "\n",
    "This notebook generates index scores that compare either the proportion or rate within a cluster to the national average. The source of anciliarry data is listed in the manuscript. \n",
    "\n",
    "The `darribas/gds_py:6.1` Docker container was used to create a computational environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "becoming-synthetic",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "typical-adult",
   "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>cluster</th>\n",
       "      <th>PCDS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>AB10 1AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>AB10 1AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>AB10 1AG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>AB10 1AH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>AB10 1AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710710</th>\n",
       "      <td>4</td>\n",
       "      <td>ZE3 9JU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710711</th>\n",
       "      <td>3</td>\n",
       "      <td>ZE3 9JW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710712</th>\n",
       "      <td>3</td>\n",
       "      <td>ZE3 9JX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710713</th>\n",
       "      <td>4</td>\n",
       "      <td>ZE3 9JY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710714</th>\n",
       "      <td>4</td>\n",
       "      <td>ZE3 9JZ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1710715 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         cluster      PCDS\n",
       "0              0  AB10 1AB\n",
       "1              0  AB10 1AF\n",
       "2              2  AB10 1AG\n",
       "3              2  AB10 1AH\n",
       "4              0  AB10 1AL\n",
       "...          ...       ...\n",
       "1710710        4   ZE3 9JU\n",
       "1710711        3   ZE3 9JW\n",
       "1710712        3   ZE3 9JX\n",
       "1710713        4   ZE3 9JY\n",
       "1710714        4   ZE3 9JZ\n",
       "\n",
       "[1710715 rows x 2 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_labels = pd.read_csv(\"sentinel_gb_encoder_map_3_20210317_smoothed_10k_labels.csv\", index_col=0)\n",
    "cluster_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "speaking-wyoming",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ctax = pd.read_csv(\"sentinel_postcode_clusters_ctax.csv\").drop(columns=\"CLUSTER\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "direct-final",
   "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>PCDS</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "      <th>H</th>\n",
       "      <th>I</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL1 1AG</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015385</td>\n",
       "      <td>0.061538</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.292308</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.015385</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL1 1AJ</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015385</td>\n",
       "      <td>0.061538</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.292308</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.015385</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL1 1AR</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.056338</td>\n",
       "      <td>0.366197</td>\n",
       "      <td>0.140845</td>\n",
       "      <td>0.239437</td>\n",
       "      <td>0.154930</td>\n",
       "      <td>0.028169</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL1 1AS</td>\n",
       "      <td>0.009709</td>\n",
       "      <td>0.087379</td>\n",
       "      <td>0.135922</td>\n",
       "      <td>0.388350</td>\n",
       "      <td>0.271845</td>\n",
       "      <td>0.038835</td>\n",
       "      <td>0.058252</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL1 1AT</td>\n",
       "      <td>0.013158</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.144737</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.118421</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16140</th>\n",
       "      <td>B25 8EA</td>\n",
       "      <td>0.780822</td>\n",
       "      <td>0.191781</td>\n",
       "      <td>0.027397</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16141</th>\n",
       "      <td>B25 8EE</td>\n",
       "      <td>0.597015</td>\n",
       "      <td>0.253731</td>\n",
       "      <td>0.119403</td>\n",
       "      <td>0.014925</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16142</th>\n",
       "      <td>B25 8EF</td>\n",
       "      <td>0.597015</td>\n",
       "      <td>0.253731</td>\n",
       "      <td>0.119403</td>\n",
       "      <td>0.014925</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16143</th>\n",
       "      <td>B25 8EG</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.484375</td>\n",
       "      <td>0.078125</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16144</th>\n",
       "      <td>B25 8EH</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.484375</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16145 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          PCDS         A         B         C         D         E         F  \\\n",
       "0      AL1 1AG  0.000000  0.015385  0.061538  0.538462  0.292308  0.076923   \n",
       "1      AL1 1AJ  0.000000  0.015385  0.061538  0.538462  0.292308  0.076923   \n",
       "2      AL1 1AR  0.000000  0.000000  0.056338  0.366197  0.140845  0.239437   \n",
       "3      AL1 1AS  0.009709  0.087379  0.135922  0.388350  0.271845  0.038835   \n",
       "4      AL1 1AT  0.013158  0.026316  0.144737  0.473684  0.118421  0.052632   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "16140  B25 8EA  0.780822  0.191781  0.027397  0.000000  0.000000  0.000000   \n",
       "16141  B25 8EE  0.597015  0.253731  0.119403  0.014925  0.000000  0.000000   \n",
       "16142  B25 8EF  0.597015  0.253731  0.119403  0.014925  0.000000  0.000000   \n",
       "16143  B25 8EG  0.437500  0.484375  0.078125  0.000000  0.000000  0.000000   \n",
       "16144  B25 8EH  0.437500  0.484375  0.000000       NaN       NaN       NaN   \n",
       "\n",
       "              G         H    I  \n",
       "0      0.015385  0.000000  0.0  \n",
       "1      0.015385  0.000000  0.0  \n",
       "2      0.154930  0.028169  0.0  \n",
       "3      0.058252  0.000000  0.0  \n",
       "4      0.157895  0.026316  0.0  \n",
       "...         ...       ...  ...  \n",
       "16140  0.000000  0.000000  0.0  \n",
       "16141  0.000000  0.000000  0.0  \n",
       "16142  0.000000  0.000000  0.0  \n",
       "16143  0.000000  0.000000  0.0  \n",
       "16144       NaN       NaN  NaN  \n",
       "\n",
       "[16145 rows x 10 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bearing-glass",
   "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>PCDS</th>\n",
       "      <th>cluster label</th>\n",
       "      <th>proportion_3</th>\n",
       "      <th>proportion_4</th>\n",
       "      <th>proportion_0</th>\n",
       "      <th>workplace_population</th>\n",
       "      <th>covered</th>\n",
       "      <th>detached</th>\n",
       "      <th>semi_detached</th>\n",
       "      <th>terraced</th>\n",
       "      <th>flat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AB10 1AB</td>\n",
       "      <td>2</td>\n",
       "      <td>0.688002</td>\n",
       "      <td>0.154029</td>\n",
       "      <td>0.140871</td>\n",
       "      <td>2931.0</td>\n",
       "      <td>14566.224457</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AB10 1AF</td>\n",
       "      <td>2</td>\n",
       "      <td>0.723461</td>\n",
       "      <td>0.172465</td>\n",
       "      <td>0.086827</td>\n",
       "      <td>2931.0</td>\n",
       "      <td>17481.052254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AB10 1AG</td>\n",
       "      <td>2</td>\n",
       "      <td>0.691347</td>\n",
       "      <td>0.148875</td>\n",
       "      <td>0.142631</td>\n",
       "      <td>2931.0</td>\n",
       "      <td>15737.569746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AB10 1AH</td>\n",
       "      <td>2</td>\n",
       "      <td>0.794462</td>\n",
       "      <td>0.057871</td>\n",
       "      <td>0.132798</td>\n",
       "      <td>432.0</td>\n",
       "      <td>12242.946777</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AB10 1AL</td>\n",
       "      <td>2</td>\n",
       "      <td>0.765798</td>\n",
       "      <td>0.079204</td>\n",
       "      <td>0.154998</td>\n",
       "      <td>2931.0</td>\n",
       "      <td>10882.305136</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710710</th>\n",
       "      <td>ZE3 9JU</td>\n",
       "      <td>3</td>\n",
       "      <td>0.659383</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.340617</td>\n",
       "      <td>279.0</td>\n",
       "      <td>1158.156785</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710711</th>\n",
       "      <td>ZE3 9JW</td>\n",
       "      <td>4</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>279.0</td>\n",
       "      <td>854.548031</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710712</th>\n",
       "      <td>ZE3 9JX</td>\n",
       "      <td>4</td>\n",
       "      <td>0.707672</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.292328</td>\n",
       "      <td>279.0</td>\n",
       "      <td>2116.580516</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710713</th>\n",
       "      <td>ZE3 9JY</td>\n",
       "      <td>4</td>\n",
       "      <td>0.537500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.462500</td>\n",
       "      <td>279.0</td>\n",
       "      <td>1355.006560</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710714</th>\n",
       "      <td>ZE3 9JZ</td>\n",
       "      <td>3</td>\n",
       "      <td>0.579167</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.420833</td>\n",
       "      <td>279.0</td>\n",
       "      <td>3304.243419</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1710715 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             PCDS  cluster label  proportion_3  proportion_4  proportion_0  \\\n",
       "0        AB10 1AB              2      0.688002      0.154029      0.140871   \n",
       "1        AB10 1AF              2      0.723461      0.172465      0.086827   \n",
       "2        AB10 1AG              2      0.691347      0.148875      0.142631   \n",
       "3        AB10 1AH              2      0.794462      0.057871      0.132798   \n",
       "4        AB10 1AL              2      0.765798      0.079204      0.154998   \n",
       "...           ...            ...           ...           ...           ...   \n",
       "1710710   ZE3 9JU              3      0.659383      0.000000      0.340617   \n",
       "1710711   ZE3 9JW              4      0.611111      0.000000      0.388889   \n",
       "1710712   ZE3 9JX              4      0.707672      0.000000      0.292328   \n",
       "1710713   ZE3 9JY              4      0.537500      0.000000      0.462500   \n",
       "1710714   ZE3 9JZ              3      0.579167      0.000000      0.420833   \n",
       "\n",
       "         workplace_population       covered  detached  semi_detached  \\\n",
       "0                      2931.0  14566.224457       0.0            0.0   \n",
       "1                      2931.0  17481.052254       0.0            0.0   \n",
       "2                      2931.0  15737.569746       0.0            0.0   \n",
       "3                       432.0  12242.946777       0.0            0.0   \n",
       "4                      2931.0  10882.305136       0.0            0.0   \n",
       "...                       ...           ...       ...            ...   \n",
       "1710710                 279.0   1158.156785       0.0            0.0   \n",
       "1710711                 279.0    854.548031       0.0            0.0   \n",
       "1710712                 279.0   2116.580516       0.0            0.0   \n",
       "1710713                 279.0   1355.006560       0.0            0.0   \n",
       "1710714                 279.0   3304.243419       0.0            0.0   \n",
       "\n",
       "         terraced  flat  \n",
       "0             0.0   0.0  \n",
       "1             0.0   0.0  \n",
       "2             0.0   0.0  \n",
       "3             0.0   0.0  \n",
       "4             0.0   0.0  \n",
       "...           ...   ...  \n",
       "1710710       0.0   0.0  \n",
       "1710711       0.0   0.0  \n",
       "1710712       0.0   0.0  \n",
       "1710713       0.0   0.0  \n",
       "1710714       0.0   0.0  \n",
       "\n",
       "[1710715 rows x 11 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aux_data = pd.read_parquet('../urbangrammar_samba/postcode_classification/data/aux_data.pq')\n",
    "aux_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "baking-armstrong",
   "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>PCDS</th>\n",
       "      <th>BP_PRE_1900</th>\n",
       "      <th>BP_1900_1918</th>\n",
       "      <th>BP_1919_1929</th>\n",
       "      <th>BP_1930_1939</th>\n",
       "      <th>BP_1945_1954</th>\n",
       "      <th>BP_1955_1964</th>\n",
       "      <th>BP_1965_1972</th>\n",
       "      <th>BP_1973_1982</th>\n",
       "      <th>BP_1983_1992</th>\n",
       "      <th>BP_1993_1999</th>\n",
       "      <th>BP_2000_2008</th>\n",
       "      <th>BP_2009_2011</th>\n",
       "      <th>BP_2012_2014</th>\n",
       "      <th>BP_2015_2017</th>\n",
       "      <th>BP_2018_2020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL1 1AG</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.030769</td>\n",
       "      <td>0.061538</td>\n",
       "      <td>0.338462</td>\n",
       "      <td>0.138462</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030769</td>\n",
       "      <td>0.046154</td>\n",
       "      <td>0.046154</td>\n",
       "      <td>0.092308</td>\n",
       "      <td>0.015385</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015385</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL1 1AJ</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.030769</td>\n",
       "      <td>0.061538</td>\n",
       "      <td>0.338462</td>\n",
       "      <td>0.138462</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030769</td>\n",
       "      <td>0.046154</td>\n",
       "      <td>0.046154</td>\n",
       "      <td>0.092308</td>\n",
       "      <td>0.015385</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015385</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL1 1AR</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.056338</td>\n",
       "      <td>0.042254</td>\n",
       "      <td>0.070423</td>\n",
       "      <td>0.014085</td>\n",
       "      <td>0.239437</td>\n",
       "      <td>0.323944</td>\n",
       "      <td>0.056338</td>\n",
       "      <td>0.098592</td>\n",
       "      <td>0.028169</td>\n",
       "      <td>0.042254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.014085</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL1 1AS</td>\n",
       "      <td>0.514563</td>\n",
       "      <td>0.106796</td>\n",
       "      <td>0.019417</td>\n",
       "      <td>0.038835</td>\n",
       "      <td>0.009709</td>\n",
       "      <td>0.009709</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009709</td>\n",
       "      <td>0.067961</td>\n",
       "      <td>0.067961</td>\n",
       "      <td>0.048544</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009709</td>\n",
       "      <td>0.058252</td>\n",
       "      <td>0.038835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL1 1AT</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.092105</td>\n",
       "      <td>0.118421</td>\n",
       "      <td>0.276316</td>\n",
       "      <td>0.131579</td>\n",
       "      <td>0.065789</td>\n",
       "      <td>0.039474</td>\n",
       "      <td>0.013158</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.065789</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.026316</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>AL1 2DS</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.031579</td>\n",
       "      <td>0.010526</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.147368</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.084211</td>\n",
       "      <td>0.021053</td>\n",
       "      <td>0.042105</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.115789</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>AL1 2DT</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.031579</td>\n",
       "      <td>0.010526</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.147368</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.084211</td>\n",
       "      <td>0.021053</td>\n",
       "      <td>0.042105</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.115789</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>AL1 2DU</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.031579</td>\n",
       "      <td>0.010526</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.147368</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.084211</td>\n",
       "      <td>0.021053</td>\n",
       "      <td>0.042105</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.115789</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>AL1 2DX</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.031579</td>\n",
       "      <td>0.010526</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.147368</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.084211</td>\n",
       "      <td>0.021053</td>\n",
       "      <td>0.042105</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.115789</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>AL1 2EA</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.031579</td>\n",
       "      <td>0.010526</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.147368</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>239 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        PCDS  BP_PRE_1900  BP_1900_1918  BP_1919_1929  BP_1930_1939  \\\n",
       "0    AL1 1AG     0.200000      0.030769      0.061538      0.338462   \n",
       "1    AL1 1AJ     0.200000      0.030769      0.061538      0.338462   \n",
       "2    AL1 1AR     0.000000      0.056338      0.042254      0.070423   \n",
       "3    AL1 1AS     0.514563      0.106796      0.019417      0.038835   \n",
       "4    AL1 1AT     0.000000      0.026316      0.092105      0.118421   \n",
       "..       ...          ...           ...           ...           ...   \n",
       "234  AL1 2DS     0.315789      0.031579      0.010526      0.052632   \n",
       "235  AL1 2DT     0.315789      0.031579      0.010526      0.052632   \n",
       "236  AL1 2DU     0.315789      0.031579      0.010526      0.052632   \n",
       "237  AL1 2DX     0.315789      0.031579      0.010526      0.052632   \n",
       "238  AL1 2EA     0.315789      0.031579      0.010526      0.052632   \n",
       "\n",
       "     BP_1945_1954  BP_1955_1964  BP_1965_1972  BP_1973_1982  BP_1983_1992  \\\n",
       "0        0.138462      0.000000      0.030769      0.046154      0.046154   \n",
       "1        0.138462      0.000000      0.030769      0.046154      0.046154   \n",
       "2        0.014085      0.239437      0.323944      0.056338      0.098592   \n",
       "3        0.009709      0.009709      0.000000      0.009709      0.067961   \n",
       "4        0.276316      0.131579      0.065789      0.039474      0.013158   \n",
       "..            ...           ...           ...           ...           ...   \n",
       "234      0.052632      0.147368      0.052632      0.052632      0.084211   \n",
       "235      0.052632      0.147368      0.052632      0.052632      0.084211   \n",
       "236      0.052632      0.147368      0.052632      0.052632      0.084211   \n",
       "237      0.052632      0.147368      0.052632      0.052632      0.084211   \n",
       "238      0.052632      0.147368           NaN           NaN           NaN   \n",
       "\n",
       "     BP_1993_1999  BP_2000_2008  BP_2009_2011  BP_2012_2014  BP_2015_2017  \\\n",
       "0        0.092308      0.015385           0.0      0.015385      0.000000   \n",
       "1        0.092308      0.015385           0.0      0.015385      0.000000   \n",
       "2        0.028169      0.042254           0.0      0.014085      0.000000   \n",
       "3        0.067961      0.048544           0.0      0.009709      0.058252   \n",
       "4        0.000000      0.065789           0.0      0.000000      0.105263   \n",
       "..            ...           ...           ...           ...           ...   \n",
       "234      0.021053      0.042105           0.0      0.115789      0.000000   \n",
       "235      0.021053      0.042105           0.0      0.115789      0.000000   \n",
       "236      0.021053      0.042105           0.0      0.115789      0.000000   \n",
       "237      0.021053      0.042105           0.0      0.115789      0.000000   \n",
       "238           NaN           NaN           NaN           NaN           NaN   \n",
       "\n",
       "     BP_2018_2020  \n",
       "0        0.000000  \n",
       "1        0.000000  \n",
       "2        0.014085  \n",
       "3        0.038835  \n",
       "4        0.026316  \n",
       "..            ...  \n",
       "234      0.000000  \n",
       "235      0.000000  \n",
       "236      0.000000  \n",
       "237      0.000000  \n",
       "238           NaN  \n",
       "\n",
       "[239 rows x 16 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age = pd.read_csv(\"../urbangrammar_samba/postcode_classification/data/sentinel_postcode_clusters_age.csv\").drop(columns=\"CLUSTER\")\n",
    "age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "seventh-principle",
   "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>pcds</th>\n",
       "      <th>usertype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AB10 1AB</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AB10 1AF</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AB10 1AG</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AB10 1AH</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AB10 1AL</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6086</th>\n",
       "      <td>AB24 4JF</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6087</th>\n",
       "      <td>AB24 4JG</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6088</th>\n",
       "      <td>AB24 4JH</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6089</th>\n",
       "      <td>AB24 4JJ</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6090</th>\n",
       "      <td>AB24 4JL</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6091 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          pcds  usertype\n",
       "0     AB10 1AB         1\n",
       "1     AB10 1AF         1\n",
       "2     AB10 1AG         1\n",
       "3     AB10 1AH         1\n",
       "4     AB10 1AL         0\n",
       "...        ...       ...\n",
       "6086  AB24 4JF         0\n",
       "6087  AB24 4JG         0\n",
       "6088  AB24 4JH         0\n",
       "6089  AB24 4JJ         0\n",
       "6090  AB24 4JL         0\n",
       "\n",
       "[6091 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usertype = pd.read_csv(\"../urbangrammar_samba/postcode_classification/data/postcode_usertype_202102.csv\")\n",
    "usertype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "correct-football",
   "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>pcds</th>\n",
       "      <th>Companies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AB10 1AB</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AB10 1AF</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AB10 1AG</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AB10 1AH</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AB10 1AL</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20256</th>\n",
       "      <td>AL3 5TY</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20257</th>\n",
       "      <td>AL3 5TZ</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20258</th>\n",
       "      <td>AL3 5UA</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20259</th>\n",
       "      <td>AL3 5UB</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20260</th>\n",
       "      <td>AL3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20261 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           pcds  Companies\n",
       "0      AB10 1AB        1.0\n",
       "1      AB10 1AF        0.0\n",
       "2      AB10 1AG        0.0\n",
       "3      AB10 1AH        0.0\n",
       "4      AB10 1AL        0.0\n",
       "...         ...        ...\n",
       "20256   AL3 5TY        1.0\n",
       "20257   AL3 5TZ        1.0\n",
       "20258   AL3 5UA        0.0\n",
       "20259   AL3 5UB        1.0\n",
       "20260      AL3         NaN\n",
       "\n",
       "[20261 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "company = pd.read_csv(\"../urbangrammar_samba/postcode_classification/data/current_company_count.csv\")\n",
    "company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "incorporated-voltage",
   "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>POSTCODE</th>\n",
       "      <th>TOTAL</th>\n",
       "      <th>FLATS</th>\n",
       "      <th>HOUSES</th>\n",
       "      <th>BUNGALOWS</th>\n",
       "      <th>MOBILE_HOME</th>\n",
       "      <th>DETACHED</th>\n",
       "      <th>SEMI_DETACHED</th>\n",
       "      <th>TERRACED</th>\n",
       "      <th>FLATS_PC</th>\n",
       "      <th>HOUSES_PC</th>\n",
       "      <th>BUNGALOWS_PC</th>\n",
       "      <th>MOBILE_HOME_PC</th>\n",
       "      <th>DETACHED_PC</th>\n",
       "      <th>SEMI_DETACHED_PC</th>\n",
       "      <th>TERRACED_PC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL1 1AG</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL1 1AJ</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>43.3</td>\n",
       "      <td>53.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL1 1AR</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL1 1AS</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL1 1AT</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151987</th>\n",
       "      <td>CA11 9BS</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.8</td>\n",
       "      <td>18.8</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151988</th>\n",
       "      <td>CA11 9BT</td>\n",
       "      <td>40</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>45.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.5</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151989</th>\n",
       "      <td>CA11 9BU</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.7</td>\n",
       "      <td>73.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151990</th>\n",
       "      <td>CA11 9BX</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151991</th>\n",
       "      <td>CA11 9BY</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>90.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>151992 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        POSTCODE  TOTAL  FLATS  HOUSES  BUNGALOWS  MOBILE_HOME  DETACHED  \\\n",
       "0        AL1 1AG      3      0       3          0            0         0   \n",
       "1        AL1 1AJ     30      0      30          0            0         1   \n",
       "2        AL1 1AR     19     19       0          0            0         0   \n",
       "3        AL1 1AS     10      0       9          1            0         0   \n",
       "4        AL1 1AT      8      0       8          0            0         4   \n",
       "...          ...    ...    ...     ...        ...          ...       ...   \n",
       "151987  CA11 9BS     16      0      16          0            0        11   \n",
       "151988  CA11 9BT     40     18      22          0            0         0   \n",
       "151989  CA11 9BU     15      0      15          0            0         4   \n",
       "151990  CA11 9BX     15     15       0          0            0         0   \n",
       "151991  CA11 9BY     11      0      11          0            0         0   \n",
       "\n",
       "        SEMI_DETACHED  TERRACED  FLATS_PC  HOUSES_PC  BUNGALOWS_PC  \\\n",
       "0                   0         3       0.0      100.0           0.0   \n",
       "1                  13        16       0.0      100.0           0.0   \n",
       "2                   0         0     100.0        0.0           0.0   \n",
       "3                   0        10       0.0       90.0          10.0   \n",
       "4                   4         0       0.0      100.0           0.0   \n",
       "...               ...       ...       ...        ...           ...   \n",
       "151987              3         2       0.0      100.0           0.0   \n",
       "151988             13         9      45.0       55.0           0.0   \n",
       "151989             11         0       0.0      100.0           0.0   \n",
       "151990              0         0     100.0        0.0           0.0   \n",
       "151991              1        10       0.0      100.0           0.0   \n",
       "\n",
       "        MOBILE_HOME_PC  DETACHED_PC  SEMI_DETACHED_PC  TERRACED_PC  \n",
       "0                  0.0          0.0               0.0        100.0  \n",
       "1                  0.0          3.3              43.3         53.3  \n",
       "2                  0.0          0.0               0.0          0.0  \n",
       "3                  0.0          0.0               0.0        100.0  \n",
       "4                  0.0         50.0              50.0          0.0  \n",
       "...                ...          ...               ...          ...  \n",
       "151987             0.0         68.8              18.8         12.5  \n",
       "151988             0.0          0.0              32.5         22.5  \n",
       "151989             0.0         26.7              73.3          0.0  \n",
       "151990             0.0          0.0               0.0          0.0  \n",
       "151991             0.0          0.0               9.1         90.9  \n",
       "\n",
       "[151992 rows x 16 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type_form = pd.read_csv(\"../urbangrammar_samba/postcode_classification/data/postcode_type_form.csv\")\n",
    "type_form"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "modular-format",
   "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>PCDS</th>\n",
       "      <th>NDVI_MEAN</th>\n",
       "      <th>NDVI_MEDIAN</th>\n",
       "      <th>NDVI_STD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AB10 1AB</td>\n",
       "      <td>0.149381</td>\n",
       "      <td>0.127472</td>\n",
       "      <td>0.092737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AB10 1AF</td>\n",
       "      <td>0.112939</td>\n",
       "      <td>0.096465</td>\n",
       "      <td>0.077419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AB10 1AG</td>\n",
       "      <td>0.122832</td>\n",
       "      <td>0.101210</td>\n",
       "      <td>0.093663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AB10 1AH</td>\n",
       "      <td>0.141073</td>\n",
       "      <td>0.112266</td>\n",
       "      <td>0.090351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AB10 1AL</td>\n",
       "      <td>0.187529</td>\n",
       "      <td>0.169233</td>\n",
       "      <td>0.118746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1266127</th>\n",
       "      <td>S32 4TF</td>\n",
       "      <td>0.589442</td>\n",
       "      <td>0.634462</td>\n",
       "      <td>0.115294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1266128</th>\n",
       "      <td>S32 4TG</td>\n",
       "      <td>0.600543</td>\n",
       "      <td>0.656322</td>\n",
       "      <td>0.205402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1266129</th>\n",
       "      <td>S32 4TH</td>\n",
       "      <td>0.411093</td>\n",
       "      <td>0.392318</td>\n",
       "      <td>0.241424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1266130</th>\n",
       "      <td>S32 4TJ</td>\n",
       "      <td>0.510979</td>\n",
       "      <td>0.513437</td>\n",
       "      <td>0.165641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1266131</th>\n",
       "      <td>S32 4TL</td>\n",
       "      <td>0.683651</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1266132 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             PCDS  NDVI_MEAN  NDVI_MEDIAN  NDVI_STD\n",
       "0        AB10 1AB   0.149381     0.127472  0.092737\n",
       "1        AB10 1AF   0.112939     0.096465  0.077419\n",
       "2        AB10 1AG   0.122832     0.101210  0.093663\n",
       "3        AB10 1AH   0.141073     0.112266  0.090351\n",
       "4        AB10 1AL   0.187529     0.169233  0.118746\n",
       "...           ...        ...          ...       ...\n",
       "1266127   S32 4TF   0.589442     0.634462  0.115294\n",
       "1266128   S32 4TG   0.600543     0.656322  0.205402\n",
       "1266129   S32 4TH   0.411093     0.392318  0.241424\n",
       "1266130   S32 4TJ   0.510979     0.513437  0.165641\n",
       "1266131   S32 4TL   0.683651          NaN       NaN\n",
       "\n",
       "[1266132 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndvi = pd.read_csv(\"../urbangrammar_samba/postcode_classification/data/sentinel_postcode_ndvi_20210419.csv\")\n",
    "ndvi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "australian-wheel",
   "metadata": {},
   "outputs": [],
   "source": [
    "master = cluster_labels.merge(\n",
    "    ctax, how=\"left\", on=\"PCDS\").merge(\n",
    "    aux_data, how=\"left\", on=\"PCDS\").merge(\n",
    "    age, how=\"left\", on=\"PCDS\").merge(\n",
    "    usertype, how=\"left\", left_on=\"PCDS\", right_on=\"pcds\").merge(\n",
    "    company, how=\"left\", left_on=\"PCDS\", right_on=\"pcds\").merge(\n",
    "    type_form, how=\"left\", left_on=\"PCDS\", right_on=\"POSTCODE\").merge(\n",
    "    ndvi[[\"PCDS\", \"NDVI_MEAN\"]], how=\"left\", on=\"PCDS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a5bb4c7e-8163-464f-a034-41660c172927",
   "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>cluster</th>\n",
       "      <th>PCDS</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "      <th>H</th>\n",
       "      <th>...</th>\n",
       "      <th>SEMI_DETACHED</th>\n",
       "      <th>TERRACED</th>\n",
       "      <th>FLATS_PC</th>\n",
       "      <th>HOUSES_PC</th>\n",
       "      <th>BUNGALOWS_PC</th>\n",
       "      <th>MOBILE_HOME_PC</th>\n",
       "      <th>DETACHED_PC</th>\n",
       "      <th>SEMI_DETACHED_PC</th>\n",
       "      <th>TERRACED_PC</th>\n",
       "      <th>NDVI_MEAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>AB10 1AB</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.149381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>AB10 1AF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.112939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>AB10 1AG</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.122832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>AB10 1AH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.141073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>AB10 1AL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.187529</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>1710710</th>\n",
       "      <td>4</td>\n",
       "      <td>ZE3 9JU</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710711</th>\n",
       "      <td>3</td>\n",
       "      <td>ZE3 9JW</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710712</th>\n",
       "      <td>3</td>\n",
       "      <td>ZE3 9JX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710713</th>\n",
       "      <td>4</td>\n",
       "      <td>ZE3 9JY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710714</th>\n",
       "      <td>4</td>\n",
       "      <td>ZE3 9JZ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1710715 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         cluster      PCDS   A   B   C   D   E   F   G   H  ...  \\\n",
       "0              0  AB10 1AB NaN NaN NaN NaN NaN NaN NaN NaN  ...   \n",
       "1              0  AB10 1AF NaN NaN NaN NaN NaN NaN NaN NaN  ...   \n",
       "2              2  AB10 1AG NaN NaN NaN NaN NaN NaN NaN NaN  ...   \n",
       "3              2  AB10 1AH NaN NaN NaN NaN NaN NaN NaN NaN  ...   \n",
       "4              0  AB10 1AL NaN NaN NaN NaN NaN NaN NaN NaN  ...   \n",
       "...          ...       ...  ..  ..  ..  ..  ..  ..  ..  ..  ...   \n",
       "1710710        4   ZE3 9JU NaN NaN NaN NaN NaN NaN NaN NaN  ...   \n",
       "1710711        3   ZE3 9JW NaN NaN NaN NaN NaN NaN NaN NaN  ...   \n",
       "1710712        3   ZE3 9JX NaN NaN NaN NaN NaN NaN NaN NaN  ...   \n",
       "1710713        4   ZE3 9JY NaN NaN NaN NaN NaN NaN NaN NaN  ...   \n",
       "1710714        4   ZE3 9JZ NaN NaN NaN NaN NaN NaN NaN NaN  ...   \n",
       "\n",
       "         SEMI_DETACHED  TERRACED  FLATS_PC  HOUSES_PC  BUNGALOWS_PC  \\\n",
       "0                  NaN       NaN       NaN        NaN           NaN   \n",
       "1                  NaN       NaN       NaN        NaN           NaN   \n",
       "2                  NaN       NaN       NaN        NaN           NaN   \n",
       "3                  NaN       NaN       NaN        NaN           NaN   \n",
       "4                  NaN       NaN       NaN        NaN           NaN   \n",
       "...                ...       ...       ...        ...           ...   \n",
       "1710710            NaN       NaN       NaN        NaN           NaN   \n",
       "1710711            NaN       NaN       NaN        NaN           NaN   \n",
       "1710712            NaN       NaN       NaN        NaN           NaN   \n",
       "1710713            NaN       NaN       NaN        NaN           NaN   \n",
       "1710714            NaN       NaN       NaN        NaN           NaN   \n",
       "\n",
       "         MOBILE_HOME_PC  DETACHED_PC  SEMI_DETACHED_PC  TERRACED_PC  NDVI_MEAN  \n",
       "0                   NaN          NaN               NaN          NaN   0.149381  \n",
       "1                   NaN          NaN               NaN          NaN   0.112939  \n",
       "2                   NaN          NaN               NaN          NaN   0.122832  \n",
       "3                   NaN          NaN               NaN          NaN   0.141073  \n",
       "4                   NaN          NaN               NaN          NaN   0.187529  \n",
       "...                 ...          ...               ...          ...        ...  \n",
       "1710710             NaN          NaN               NaN          NaN        NaN  \n",
       "1710711             NaN          NaN               NaN          NaN        NaN  \n",
       "1710712             NaN          NaN               NaN          NaN        NaN  \n",
       "1710713             NaN          NaN               NaN          NaN        NaN  \n",
       "1710714             NaN          NaN               NaN          NaN        NaN  \n",
       "\n",
       "[1710715 rows x 57 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "blessed-repeat",
   "metadata": {},
   "outputs": [],
   "source": [
    "master = master.drop(columns=[\"cluster label\", \"pcds_x\", \"pcds_y\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "metropolitan-mineral",
   "metadata": {},
   "outputs": [],
   "source": [
    "master = master.drop(columns='POSTCODE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ideal-camping",
   "metadata": {},
   "outputs": [],
   "source": [
    "master = master.rename(columns={\n",
    "    'ward_smoothed': \"cluster\",\n",
    "    'A': \"ctax_band_A\", 'B': \"ctax_band_B\", 'C': \"ctax_band_C\", 'D': \"ctax_band_D\", 'E': \"ctax_band_E\",\n",
    "    'F': \"ctax_band_F\", 'G': \"ctax_band_G\", 'H': \"ctax_band_H\", 'I': \"ctax_band_I\",\n",
    "    'proportion_3': '3-way junctions', 'proportion_4': '4-way junctions', 'proportion_0': 'cul-de-sacs',\n",
    "    'covered': \"covered_area\", 'detached': 'detached_census', \n",
    "    'semi_detached': 'semi_detached_census', 'terraced': 'terraced_census', 'flat': 'flat_census',\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "advance-swaziland",
   "metadata": {},
   "outputs": [],
   "source": [
    "master.to_parquet(\"../urbangrammar_samba/postcode_classification/data/master_table.pq\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "applied-contact",
   "metadata": {},
   "source": [
    "((Number of Band A Properties in Cluster Y / Total Properties in Cluster Y) / Total Properties in Cluster Y / Total Properties ) ) * 100\n",
    "\n",
    "Measure; Cluster 1, Cluster 2, Cluster 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "descending-pakistan",
   "metadata": {},
   "outputs": [],
   "source": [
    "index_tab = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "environmental-humanitarian",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['cluster', 'PCDS', 'ctax_band_A', 'ctax_band_B', 'ctax_band_C',\n",
       "       'ctax_band_D', 'ctax_band_E', 'ctax_band_F', 'ctax_band_G',\n",
       "       'ctax_band_H', 'ctax_band_I', '3-way junctions', '4-way junctions',\n",
       "       'cul-de-sacs', 'workplace_population', 'covered_area',\n",
       "       'detached_census', 'semi_detached_census', 'terraced_census',\n",
       "       'flat_census', 'BP_PRE_1900', 'BP_1900_1918', 'BP_1919_1929',\n",
       "       'BP_1930_1939', 'BP_1945_1954', 'BP_1955_1964', 'BP_1965_1972',\n",
       "       'BP_1973_1982', 'BP_1983_1992', 'BP_1993_1999', 'BP_2000_2008',\n",
       "       'BP_2009_2011', 'BP_2012_2014', 'BP_2015_2017', 'BP_2018_2020',\n",
       "       'usertype', 'Companies', 'TOTAL', 'FLATS', 'HOUSES', 'BUNGALOWS',\n",
       "       'MOBILE_HOME', 'DETACHED', 'SEMI_DETACHED', 'TERRACED', 'FLATS_PC',\n",
       "       'HOUSES_PC', 'BUNGALOWS_PC', 'MOBILE_HOME_PC', 'DETACHED_PC',\n",
       "       'SEMI_DETACHED_PC', 'TERRACED_PC', 'NDVI_MEAN'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "exact-disability",
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped = master.groupby('cluster')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "casual-picnic",
   "metadata": {},
   "outputs": [],
   "source": [
    "continuous = ['ctax_band_A', 'ctax_band_B', 'ctax_band_C', 'ctax_band_D',\n",
    "              'ctax_band_E', 'ctax_band_F', 'ctax_band_G','ctax_band_H', 'ctax_band_I',\n",
    "              '3-way junctions', '4-way junctions', 'cul-de-sacs', 'workplace_population',\n",
    "              'covered_area',\n",
    "              'BP_PRE_1900', 'BP_1900_1918', 'BP_1919_1929',\n",
    "              'BP_1930_1939', 'BP_1945_1954', 'BP_1955_1964', 'BP_1965_1972',\n",
    "              'BP_1973_1982', 'BP_1983_1992', 'BP_1993_1999', 'BP_2000_2008',\n",
    "              'BP_2009_2011', 'BP_2012_2014', 'BP_2015_2017', 'BP_2018_2020',\n",
    "              'Companies', 'FLATS_PC', 'HOUSES_PC','BUNGALOWS_PC', 'MOBILE_HOME_PC',\n",
    "              'DETACHED_PC', 'SEMI_DETACHED_PC', 'TERRACED_PC', 'NDVI_MEAN'\n",
    "             ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "textile-crack",
   "metadata": {},
   "outputs": [],
   "source": [
    "for c in continuous:\n",
    "    index = (grouped[c].mean() / master[c].mean()) * 100\n",
    "    index_tab[c] = index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "compound-fossil",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_7063/1305350093.py:1: RuntimeWarning: invalid value encountered in long_scalars\n",
      "  index_tab[\"usertype\"] = [((master[master.cluster == i].usertype == 0).sum() / (master.cluster == i).sum() /\n"
     ]
    }
   ],
   "source": [
    "index_tab[\"usertype\"] = [((master[master.cluster == i].usertype == 0).sum() / (master.cluster == i).sum() / \n",
    "         ((master.usertype == 0).sum() / len(master)) * 100) for i in range(1, 8)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "boring-protein",
   "metadata": {},
   "outputs": [],
   "source": [
    "index_tab.T.to_csv(\"index_heatmap.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "respected-retro",
   "metadata": {},
   "outputs": [],
   "source": [
    "index_tab = pd.read_csv(\"index_heatmap_10k.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f4c218b8-caa0-4f6c-b793-a592b1468e67",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_labels = [\n",
    "    'Council Tax A', 'Council Tax B', 'Council Tax C',\n",
    "    'Council Tax D', 'Council Tax E', 'Council Tax F', 'Council Tax G',\n",
    "    'Council Tax H', 'Council Tax I', '3-way Junctions', '4-way Junctions',\n",
    "    'Cul de sacs', 'Workplace Population', 'Covered Area',\n",
    "    'Built Pre 1900', 'Built 1900-18', 'Built 1919-29',\n",
    "    'Built 1930-39', 'Built 1945-54', 'Built 1955-64', 'Built 1965-72',\n",
    "    'Built 1973-82', 'Built 1983-92', 'Built 1993-99', 'Built 2000-08',\n",
    "    'Built 2009-11', 'Built 2012-14', 'Built 2015-17', 'Built 2018-20',\n",
    "    'usertype', 'Household Size', 'Number of Companies', 'Flats', 'Houses',\n",
    "    'Bungalows', 'Mobile Homes', 'Detached House', 'Semi Detached House', 'Terraced Houses','Mean NDVI'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e30b4448-59bb-4f69-b9a2-5d89e50ca28c",
   "metadata": {},
   "outputs": [],
   "source": [
    "index_tab.index = column_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "051fd6c3-49a9-46af-b39f-21330524351e",
   "metadata": {},
   "outputs": [],
   "source": [
    "index_tab = index_tab.drop([\"3-way Junctions\", \"Household Size\", \"usertype\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a64ee1cc-281e-4b21-b881-9f68c54ca97f",
   "metadata": {},
   "outputs": [],
   "source": [
    "index_tab.columns = [\"A\",\"B\",\"C\",\"D\",\"E\",\"F\",\"G\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "44d4ad86-4675-41d8-8863-95c153fa8a18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 16))\n",
    "sns.heatmap(index_tab, cmap=\"vlag\", annot=True, fmt='.0f', center=100,)\n",
    "plt.savefig(\"figs/index_heatmap_10k.pdf\", bbox_inches=\"tight\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}