mjpieters/Postcode scores.ipynb

## Postcode scores.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import string\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "_nums = re.compile(\"\\d+\")\n",
    "_lscore = {l: s for s, l in enumerate(string.ascii_uppercase, 1)}\n",
    "\n",
    "\n",
    "def pcscore(postcode, _nums=_nums.findall, _ls_get=_lscore.get):\n",
    "    return (\n",
    "        sum(_ls_get(c, 0) for c in postcode) +\n",
    "        sum(map(int, _nums(postcode)))\n",
    "    )\n",
    "\n",
    "\n",
    "URL = \"https://www.doogal.co.uk/files/postcodes.zip\"\n",
    "string_cols = dict.fromkeys(\n",
    "    (\n",
    "        \"Postcode\", \"Grid Ref\", \"County\", \"District\", \"Ward\", \"District Code\",\n",
    "        \"Ward Code\", \"Country\", \"County Code\", \"Constituency\", \"Parish\",\n",
    "        \"National Park\", \"Built up area\", \"Built up sub-division\",\n",
    "        \"Lower layer super output area\", \"Rural/urban\", \"Region\", \"LSOA Code\",\n",
    "        \"Local authority\", \"MSOA Code\", \"Middle layer super output area\",\n",
    "        \"Parish Code\", \"Census output area\", \"Constituency Code\",\n",
    "        \"Nearest station\", \"Postcode area\", \"Postcode district\",\n",
    "        \"Police force\", \"Water company\", \"Plus Code\"\n",
    "    ), pd.StringDtype()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "postcodes = pd.read_csv(\n",
    "    URL,\n",
    "    dtype={\n",
    "        \"In Use?\": np.bool,\n",
    "        \"Easting\": \"UInt32\",\n",
    "        \"Northing\": \"UInt32\",\n",
    "        \"Population\": \"UInt16\",\n",
    "        \"Households\": \"UInt16\",\n",
    "        \"Index of Multiple Deprivation\": \"UInt16\",\n",
    "        \"London zone\": \"UInt8\",\n",
    "        \"Quality\": \"UInt8\",\n",
    "        \"User Type\": \"UInt8\",\n",
    "        \"Altitude\": \"Int16\",\n",
    "        **string_cols,\n",
    "    },\n",
    "    true_values=[\"Yes\"],\n",
    "    false_values=[\"No\"],\n",
    "    parse_dates=[\"Introduced\", \"Terminated\", \"Last updated\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "postcodes[\"Score\"] = postcodes.Postcode.apply(pcscore).astype(np.uint8)\n",
    "in_use = postcodes[postcodes[\"In Use?\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x113fe87f0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "in_use.Score.plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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></th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Lowest (any)</th>\n",
       "      <th>0</th>\n",
       "      <td>AB1 0AA</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50092</th>\n",
       "      <td>B1 1AA</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111228</th>\n",
       "      <td>BA1 0AA</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Lowest (in use)</th>\n",
       "      <th>50100</th>\n",
       "      <td>B1 1BA</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50358</th>\n",
       "      <td>B1 2AA</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111592</th>\n",
       "      <td>BA1 1AA</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120328</th>\n",
       "      <td>BA2 0AA</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Highest</th>\n",
       "      <th>2592524</th>\n",
       "      <td>WV99 1ZZ</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Postcode  Score\n",
       "Lowest (any)    0         AB1 0AA      6\n",
       "                50092      B1 1AA      6\n",
       "                111228    BA1 0AA      6\n",
       "Lowest (in use) 50100      B1 1BA      7\n",
       "                50358      B1 2AA      7\n",
       "                111592    BA1 1AA      7\n",
       "                120328    BA2 0AA      7\n",
       "Highest         2592524  WV99 1ZZ    197"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat(\n",
    "    (\n",
    "        postcodes[postcodes.Score == postcodes.Score.min()][[\"Postcode\", \"Score\"]],\n",
    "        in_use[in_use.Score == in_use.Score.min()][[\"Postcode\", \"Score\"]],\n",
    "        in_use[in_use.Score == in_use.Score.max()][[\"Postcode\", \"Score\"]]\n",
    "    ), keys=[\"Lowest (any)\", \"Lowest (in use)\", \"Highest\"]\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {},
	"outputs": [],
	"source": [
	"import re\n",
	"import string\n",
	"import pandas as pd\n",
	"import numpy as np\n",
	"\n",
	"_nums = re.compile(\"\\d+\")\n",
	"_lscore = {l: s for s, l in enumerate(string.ascii_uppercase, 1)}\n",
	"\n",
	"\n",
	"def pcscore(postcode, _nums=_nums.findall, _ls_get=_lscore.get):\n",
	" return (\n",
	" sum(_ls_get(c, 0) for c in postcode) +\n",
	" sum(map(int, _nums(postcode)))\n",
	" )\n",
	"\n",
	"\n",
	"URL = \"https://www.doogal.co.uk/files/postcodes.zip\"\n",
	"string_cols = dict.fromkeys(\n",
	" (\n",
	" \"Postcode\", \"Grid Ref\", \"County\", \"District\", \"Ward\", \"District Code\",\n",
	" \"Ward Code\", \"Country\", \"County Code\", \"Constituency\", \"Parish\",\n",
	" \"National Park\", \"Built up area\", \"Built up sub-division\",\n",
	" \"Lower layer super output area\", \"Rural/urban\", \"Region\", \"LSOA Code\",\n",
	" \"Local authority\", \"MSOA Code\", \"Middle layer super output area\",\n",
	" \"Parish Code\", \"Census output area\", \"Constituency Code\",\n",
	" \"Nearest station\", \"Postcode area\", \"Postcode district\",\n",
	" \"Police force\", \"Water company\", \"Plus Code\"\n",
	" ), pd.StringDtype()\n",
	")"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {},
	"outputs": [],
	"source": [
	"postcodes = pd.read_csv(\n",
	" URL,\n",
	" dtype={\n",
	" \"In Use?\": np.bool,\n",
	" \"Easting\": \"UInt32\",\n",
	" \"Northing\": \"UInt32\",\n",
	" \"Population\": \"UInt16\",\n",
	" \"Households\": \"UInt16\",\n",
	" \"Index of Multiple Deprivation\": \"UInt16\",\n",
	" \"London zone\": \"UInt8\",\n",
	" \"Quality\": \"UInt8\",\n",
	" \"User Type\": \"UInt8\",\n",
	" \"Altitude\": \"Int16\",\n",
	" **string_cols,\n",
	" },\n",
	" true_values=[\"Yes\"],\n",
	" false_values=[\"No\"],\n",
	" parse_dates=[\"Introduced\", \"Terminated\", \"Last updated\"],\n",
	")"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 14,
	"metadata": {},
	"outputs": [],
	"source": [
	"postcodes[\"Score\"] = postcodes.Postcode.apply(pcscore).astype(np.uint8)\n",
	"in_use = postcodes[postcodes[\"In Use?\"]]"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 15,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"<matplotlib.axes._subplots.AxesSubplot at 0x113fe87f0>"
	]
	},
	"execution_count": 15,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
	"image/png": 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\n",
	"text/plain": [
	"<Figure size 432x288 with 1 Axes>"
	]
	},
	"metadata": {
	"needs_background": "light"
	},
	"output_type": "display_data"
	}
	],
	"source": [
	"in_use.Score.plot.hist()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 22,
	"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></th>\n",
	" <th>Postcode</th>\n",
	" <th>Score</th>\n",
	" </tr>\n",
	" </thead>\n",
	" <tbody>\n",
	" <tr>\n",
	" <th rowspan=\"3\" valign=\"top\">Lowest (any)</th>\n",
	" <th>0</th>\n",
	" <td>AB1 0AA</td>\n",
	" <td>6</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>50092</th>\n",
	" <td>B1 1AA</td>\n",
	" <td>6</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>111228</th>\n",
	" <td>BA1 0AA</td>\n",
	" <td>6</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th rowspan=\"4\" valign=\"top\">Lowest (in use)</th>\n",
	" <th>50100</th>\n",
	" <td>B1 1BA</td>\n",
	" <td>7</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>50358</th>\n",
	" <td>B1 2AA</td>\n",
	" <td>7</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>111592</th>\n",
	" <td>BA1 1AA</td>\n",
	" <td>7</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>120328</th>\n",
	" <td>BA2 0AA</td>\n",
	" <td>7</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>Highest</th>\n",
	" <th>2592524</th>\n",
	" <td>WV99 1ZZ</td>\n",
	" <td>197</td>\n",
	" </tr>\n",
	" </tbody>\n",
	"</table>\n",
	"</div>"
	],
	"text/plain": [
	" Postcode Score\n",
	"Lowest (any) 0 AB1 0AA 6\n",
	" 50092 B1 1AA 6\n",
	" 111228 BA1 0AA 6\n",
	"Lowest (in use) 50100 B1 1BA 7\n",
	" 50358 B1 2AA 7\n",
	" 111592 BA1 1AA 7\n",
	" 120328 BA2 0AA 7\n",
	"Highest 2592524 WV99 1ZZ 197"
	]
	},
	"execution_count": 22,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"pd.concat(\n",
	" (\n",
	" postcodes[postcodes.Score == postcodes.Score.min()][[\"Postcode\", \"Score\"]],\n",
	" in_use[in_use.Score == in_use.Score.min()][[\"Postcode\", \"Score\"]],\n",
	" in_use[in_use.Score == in_use.Score.max()][[\"Postcode\", \"Score\"]]\n",
	" ), keys=[\"Lowest (any)\", \"Lowest (in use)\", \"Highest\"]\n",
	")"
	]
	}
	],
	"metadata": {
	"kernelspec": {
	"display_name": "Python 3",
	"language": "python",
	"name": "python3"
	},
	"language_info": {
	"codemirror_mode": {
	"name": "ipython",
	"version": 3
	},
	"file_extension": ".py",
	"mimetype": "text/x-python",
	"name": "python",
	"nbconvert_exporter": "python",
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