Census code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "import pymc as pm\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "import xml.etree.ElementTree as ET\n",
    "import urllib2\n",
    "import sqlite3 as lite\n",
    "import urllib\n",
    "import zipfile,os.path,os\n",
    "import sqlalchemy as sa\n",
    "import pandas as pd\n",
    "import csv\n",
    "from StringIO import StringIO\n",
    "from zipfile import ZipFile\n",
    "import shutil\n",
    "import time\n",
    "\n",
    "def adjustpostcode(postcode):\n",
    "    \"\"\"Formats postcode into 7 character format, so \"a1 2cd\" becomes \"A1  2CD\" or \"Gl54 1AB\" becomes \"GL541AB\".\"\"\"\n",
    "    postcode = postcode.upper()\n",
    "    res = re.search('([A-Z]{1,2}[0-9]{1,2}) *([0-9][A-Z]{2})',postcode);\n",
    "    if (res==None):\n",
    "        return postcode #TODO can't understand it, just send it back, need to do something better, throw an exception?\n",
    "    groups = res.groups()\n",
    "    if len(groups)==2:\n",
    "        first = groups[0]\n",
    "        last = groups[1]\n",
    "        middle = \" \"*(7-(len(first)+len(last)))\n",
    "        return first+middle+last\n",
    "    return postcode \n",
    "\n",
    "def go_get_data(postcodes,dataset,pathToData=''):\n",
    "    \"\"\"\n",
    "    Returns a list of dictionaries, one for each postcode, providing the latitude, longitude, output area and an array of datafor each\n",
    "    \"\"\"\n",
    "    results = []\n",
    "    geoAreas = []\n",
    "    for postcode in postcodes:\n",
    "        pc = adjustpostcode(postcode)\n",
    "        pathToData = ''\n",
    "        conn = lite.connect(pathToData+'geo.db')\n",
    "        geodb = conn.cursor()        \n",
    "        c_oa = geodb.execute(\"SELECT oa11, lat, long FROM geo WHERE pcd=?;\",(pc,));\n",
    "        oa = None;\n",
    "        for r in c_oa:\n",
    "            results.append({'oa':str(r[0]),'lat':r[1],'lon':r[2],'postcode':postcode})\n",
    "            geoAreas.append(str(r[0]))\n",
    "\n",
    "    geoAreaslist = ','.join(geoAreas)    \n",
    "    #QS414EW\n",
    "    #url = \"http://web.ons.gov.uk/ons/api/data/dataset/QS102EW.xml?context=Census&apikey=cHkIiioOQX&geog=2011STATH&diff=&totals=false&dm/2011STATH=%s\" % geoAreaslist\n",
    "    url = \"http://web.ons.gov.uk/ons/api/data/dataset/%s.xml?context=Census&apikey=cHkIiioOQX&geog=2011STATH&diff=&totals=false&dm/2011STATH=%s\" % (dataset,geoAreaslist)\n",
    "    response = urllib2.urlopen(url)\n",
    "    xmlstring = response.read();\n",
    "    xmlstring = re.sub('(xmlns:[^=]*)=\"[^\"]*\"', '\\\\1=\"_\"', xmlstring)\n",
    "    root = ET.fromstring(xmlstring);\n",
    "    \n",
    "    data_results = {}\n",
    "    for a in root.findall(\"{_}genericData/{_}DataSet/{_}Group/{_}Series\"):\n",
    "        loc = a.find(\"{_}SeriesKey/{_}Value[@concept='Location']\")\n",
    "        if loc is None:            \n",
    "            continue\n",
    "        location_string = loc.attrib['value']\n",
    "        if location_string not in data_results:\n",
    "            data_results[location_string] = []\n",
    "        for dp in a.findall(\"{_}Obs/{_}ObsValue\"):\n",
    "            data_string = dp.attrib['value']\n",
    "            data_results[location_string].append( float(data_string) )\n",
    "    \n",
    "    for res in results:\n",
    "        for i,d in enumerate(data_results[res['oa']]):\n",
    "            res[dataset+\"_%d\" % i] = d\n",
    "       #res[dataset] = data_results[res['oa']]\n",
    "    return results\n",
    "\n",
    "def get_data(postcodes,dataset,pathToData=''):\n",
    "    chunksize = 149\n",
    "    results = []\n",
    "    while len(postcodes)>0:\n",
    "        num = min(chunksize,len(postcodes))\n",
    "        results.extend(go_get_data(postcodes[0:num],dataset,pathToData))\n",
    "        del postcodes[0:num]\n",
    "        time.sleep(2) #just give their server some time\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "get_data(['s6 3af','gl54 4nq'],'QS411EW','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def setup_postcodes(pathToData):\n",
    "    \"\"\"Creates databases and files, downloads data, and populates the datafiles\"\"\"       \n",
    "    url = \"https://ago-item-storage.s3-external-1.amazonaws.com/a26683d2393743f4b87c89141cd1b2e8/NSPL_FEB_2017_UK.zip?X-Amz-Security-Token=FQoDYXdzEOz%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaDNnej9L6SZy5Qb3j8iKcA8DP4euIlUueTPtPplc0%2Ft2xEqK558PzosoBZG03VDr5kDJSTHYfvxXUTsaQM3KHYrAJjd7QMzPuzPRV6Vin%2FP6W5ZMa%2FKFmOQ7i33WJF4i9l17HSrq4PzMmfAENbBXVyBvBVSIgSdbZ61RLsunOz1Z%2Fz1%2FLtVFikM20J1ZUsyOeNCuDsgJMqH3KmIiwnfqSJdb%2FqyE2w3%2FBDlw8%2Fn1tGmP01bzL%2BPRk%2BXrNVbCi1Qzv%2F8QqJTjTrLGn3qWNXg48lt86RObkOtpfr9JY26D%2FpvrFZS6%2FAKKryFBBTvKcprjnE9EOpGbS8ouwaOdWg03sK0yoR%2Ffkns%2BoaEdgAmTnvtxGUfg7oxDu%2BczwP7s1ddvyTwUSdKsllN38Rpv%2Bhyb5i35iKdWHqM2pFiBGzIj29%2BCHTs%2BkDXAepj3a194nwxSceMlJUgsIhE3NtSkKkIyFPYR0FMzKapOf3zNXrv9jgS6YfKoVaigMWfFLLQM8RqyRkguT93Zoiz%2BPuJa3GC7f5JRf4EEvICNDPgmgbZY47Vj7AHRECO6S3F7G%2FEAo84r1xQU%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20170306T120558Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAI32ZWKV2CB37RBWQ%2F20170306%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=a038f0108ed73b2e42c7fa994065edb4e5cb5b4e91a2391adfd701aa349d497e\"\n",
    "    print \"Creating postcode database in %s\" % (pathToData+'geo.db')\n",
    "    if os.path.isfile(pathToData+'geo.db'):\n",
    "        print \"geo.db exists, skipping\"\n",
    "        return\n",
    "    print \"Downloading \"+url\n",
    "    if os.path.exists('/tmp/psych_postcodes'):\n",
    "        shutil.rmtree('/tmp/psych_postcodes')\n",
    "    os.makedirs('/tmp/psych_postcodes')\n",
    "    urllib.urlretrieve(url, \"/tmp/psych_postcodes/postcodes.zip\")\n",
    "    postcode_zipfile = \"/tmp/psych_postcodes/postcodes.zip\"\n",
    "\n",
    "    print \"Opening postcodes.zip\"\n",
    "    zf = zipfile.ZipFile(postcode_zipfile)\n",
    "    for f in zf.infolist():       \n",
    "        zf.extract(f.filename,\"/tmp/psych_postcodes\")\n",
    "\n",
    "    print \"Importing CSV file to sqlite\"  #note:Switched from using pandas as it ran out of memory.\n",
    "\n",
    "\n",
    "    csvfile = '/tmp/psych_postcodes/Data/NSPL_FEB_2017_UK.csv'\n",
    "    csvReader = csv.reader(open(csvfile), delimiter=',', quotechar='\"')\n",
    "    conn = lite.connect(pathToData+'geo.db')\n",
    "\n",
    "    conn.execute('CREATE TABLE IF NOT EXISTS geo (pcd TEXT, oa11 TEXT, lsoa11 TEXT, lat REAL, long REAL)')\n",
    "    firstRow = True\n",
    "    n = 0\n",
    "    for row in csvReader:\n",
    "        n+=1\n",
    "        if (n%500000==0):\n",
    "            print \"     %d rows imported\" % n                \n",
    "        if firstRow:\n",
    "            firstRow = False\n",
    "            continue\n",
    "        conn.execute('INSERT INTO geo (pcd, oa11, lsoa11, lat, long) values (?, ?, ?, ?, ?)', (row[0],row[9],row[24],row[32], row[33]))\n",
    "\n",
    "    print \"     Creating indices\"        \n",
    "    conn.execute('CREATE INDEX pcds ON geo(pcd)')\n",
    "    conn.execute('CREATE INDEX oa11s ON geo(oa11)')\n",
    "    print \"Complete\"\n",
    "    conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "def load_pricepaid():\n",
    "    \"\"\"\n",
    "    Download and load UK housing price data from the Land Registry, 2016\n",
    "    Returns panda dataframe with just the price and postcode\n",
    "    \"\"\"\n",
    "    #for the year's data, use: http://prod.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-2016.txt\n",
    "\n",
    "    filename = \"pp-monthly-update-new-version.csv\"\n",
    "    if not os.path.isfile(filename):\n",
    "        #Property Type \tD = Detached, S = Semi-Detached, T = Terraced, F = Flats/Maisonettes, O = Other\n",
    "        os.system('wget http://prod.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/'+filename)\n",
    "    pp = pd.read_csv(filename,header=None,usecols=[1,3,4],names=[\"price\", \"postcode\",\"type\"])\n",
    "    return pp\n",
    "    \n",
    "def load_postcode():\n",
    "    \"\"\"\n",
    "    Download and load UK postcode locations, from 'freepostcodes.org.uk', where\n",
    "    they've converted the large ordnance survey datafiles in to smaller csv files.\n",
    "    \n",
    "    This takes a little while to run\n",
    "    \n",
    "    Returns a panda dataframe with the postcode, easting and northing\n",
    "    \"\"\"\n",
    "    \n",
    "    unzip_data_path = \"Code-Point Open/Data\"\n",
    "    if not os.path.isfile(unzip_data_path+\"/ab.csv\"):\n",
    "        os.system(\"wget http://www.freepostcodes.org.uk/static/code-point-open/codepo_gb.zip\")\n",
    "        os.system(\"unzip codepo_gb.zip\")\n",
    "\n",
    "    df = pd.DataFrame()\n",
    "    for filename in os.listdir(unzip_data_path): \n",
    "        df = df.append( pd.read_csv(unzip_data_path+\"/\"+filename,header=None,usecols=[0,10,11],names=[\"postcode\",\"easting\",\"northing\"]) )\n",
    "    return df\n",
    "\n",
    "def load_prices_and_postcode():\n",
    "    \"\"\"\n",
    "    Download and load both the prices and postcodes, and inner join the two tables\n",
    "    \n",
    "    Returns a dataframe with the postcode, price, easting and northing\n",
    "    \"\"\"\n",
    "    pp = load_pricepaid()\n",
    "    pc = load_postcode()\n",
    "    complete = pd.merge(pc,pp,on=\"postcode\",how=\"inner\")\n",
    "    return complete\n",
    "\n",
    "def add_ons_column(df,dataset):\n",
    "    \"\"\"\n",
    "    This adds a column from the ONS dataset. It makes API queries to the census API, and so it is recommended \n",
    "    that it only be used on a reduced dataset.\n",
    "    \"\"\"\n",
    "    x = df['postcode'].values.tolist()\n",
    "    ons_results = get_data(x,dataset)    \n",
    "    ons_df = pd.DataFrame(ons_results).drop_duplicates()\n",
    "    return pd.merge(df,ons_df,on=\"postcode\",how=\"inner\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import dp4gp_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating postcode database in geo.db\n",
      "geo.db exists, skipping\n"
     ]
    }
   ],
   "source": [
    "dp4gp_datasets.setup_postcodes('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "c = dp4gp_datasets.load_prices_and_postcode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "c = c[c['type']=='F']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "c = c[0:200]\n",
    "#d = dp4gp_datasets.add_popden_column(c)\n",
    "d = dp4gp_datasets.add_ons_column(c,'KS608EW')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1000000.0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZkAAAEACAYAAABhzAtFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBxkzM6vM/wfuzvAtxk2WZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9223f52290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#prop of people over 60\n",
    "#plt.plot((d['KS102EW_12']+d['KS102EW_13']+d['KS102EW_14']+d['KS102EW_15']+d['KS102EW_16'])/d['KS102EW_0'],d['price'],'.',alpha=0.2)\n",
    "\n",
    "plt.plot((d['KS608EW_1'])/d['KS608EW_0'],d['price'],'.',alpha=0.2)\n",
    "plt.ylim([0,1e6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "e = dp4gp_datasets.add_ons_column(d,'QS501EW')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1000000.0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZUAAAEACAYAAAB78OvLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f922590a9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot((e['QS501EW_6'])/e['QS501EW_0'],e['price'],'.',alpha=0.2)\n",
    "plt.ylim([0,1e6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(e,open( \"house_dataset.p\", \"wb\" ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.39572193,  0.39572193,  0.39204545,  0.23699422,  0.23699422,\n",
       "        0.23699422,  0.23699422,  0.23699422,  0.23699422,  0.23699422,\n",
       "        0.23699422,  0.23699422,  0.23699422,  0.23699422,  0.23699422,\n",
       "        0.23699422,  0.23699422,  0.23699422,  0.23699422,  0.23699422,\n",
       "        0.23699422,  0.23699422,  0.23699422,  0.23699422,  0.23699422,\n",
       "        0.23699422,  0.23699422,  0.23699422,  0.23699422,  0.23699422,\n",
       "        0.23699422,  0.23699422,  0.23699422,  0.23699422,  0.23699422,\n",
       "        0.23699422,  0.23699422,  0.23699422,  0.23699422,  0.23699422,\n",
       "        0.23699422,  0.23699422,  0.39664804,  0.39664804,  0.39664804,\n",
       "        0.39664804,  0.39664804,  0.39664804,  0.39664804,  0.37222222,\n",
       "        0.37222222,  0.36206897,  0.36206897,  0.39106145,  0.39106145,\n",
       "        0.39106145,  0.1460177 ,  0.32916667,  0.32916667,  0.32916667,\n",
       "        0.26007326,  0.1042654 ,  0.40569395,  0.38461538,  0.35955056,\n",
       "        0.36363636,  0.52777778,  0.43884892,  0.48430493,  0.4372093 ,\n",
       "        0.20093458,  0.15163934,  0.30222222,  0.54407295,  0.35185185,\n",
       "        0.28699552,  0.32954545,  0.28699552,  0.27251732,  0.10810811,\n",
       "        0.43579767,  0.22834646,  0.26086957,  0.17154812,  0.3452381 ,\n",
       "        0.3452381 ,  0.36073059,  0.50362319,  0.47692308,  0.32022472,\n",
       "        0.13872832,  0.13872832,  0.08633094,  0.43157895,  0.49112426,\n",
       "        0.54358974,  0.54358974,  0.29924242,  0.29924242,  0.22277228,\n",
       "        0.44444444,  0.18072289,  0.29310345,  0.59230769,  0.59230769,\n",
       "        0.59230769,  0.59230769,  0.59230769,  0.35384615,  0.41877256,\n",
       "        0.48076923,  0.39873418,  0.35390947,  0.46587537,  0.46587537,\n",
       "        0.40182648,  0.51190476,  0.51190476,  0.51190476,  0.51190476,\n",
       "        0.51190476,  0.49528302,  0.51465798,  0.54245283,  0.42222222,\n",
       "        0.38935574,  0.38935574,  0.38935574,  0.44518272,  0.22222222,\n",
       "        0.31683168,  0.32850242,  0.30285714,  0.30285714,  0.08878505,\n",
       "        0.38983051,  0.31736527,  0.31578947,  0.31578947,  0.07731959,\n",
       "        0.32733813,  0.3647343 ,  0.39649123,  0.39649123,  0.39649123,\n",
       "        0.39910314,  0.39649123,  0.39649123,  0.35338346,  0.4141791 ,\n",
       "        0.44363636,  0.40591398,  0.32425068,  0.37373737,  0.32425068,\n",
       "        0.32425068,  0.27272727,  0.25236593,  0.17010309,  0.30501931,\n",
       "        0.23636364,  0.23636364,  0.23636364,  0.2       ,  0.18534483,\n",
       "        0.18534483,  0.2211838 ,  0.39252336,  0.3754386 ,  0.48292683,\n",
       "        0.48292683,  0.37535817,  0.3961039 ,  0.34065934,  0.40314136,\n",
       "        0.34597156,  0.34597156,  0.3003663 ,  0.3003663 ,  0.24203822,\n",
       "        0.3003663 ,  0.38604651,  0.35636364,  0.43820225,  0.43820225,\n",
       "        0.33193277,  0.41530055,  0.41530055,  0.38461538,  0.37313433,\n",
       "        0.40659341,  0.325     ,  0.29527559,  0.2893617 ,  0.28368794,\n",
       "        0.47712418,  0.29736211,  0.29347826,  0.29019608,  0.51408451])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(e['QS501EW_6']/e['QS501EW_0']).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}