Resampling_SOM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outline\n",
    "## 1- Data Preprocessing and Cleaning\n",
    "###    1-1- Removing the repeated email_id subscriptions and using the latest one\n",
    "###    1-2- Finding Geo data related to Zip codes\n",
    "## 2- How to fill the empty tables?\n",
    "###    2-1 - Step wise Bootstraping and Resampling using SOM\n",
    "####       2-1-* it is good to decide how much of data we want to fill. There is an inverse relation between filling and accuracy\n",
    "## 3- Possible Products\n",
    "###   3-1-One dimensional histograms of distribution of (Specific and Vicinity) Demand based on Room, Size and Price ranges for a specific area\n",
    "###   3-2-Three dimensional histograms of distribution of (Specific  and Vicinity) Demand based on Room, Size and Price ranges all at once for a specific area\n",
    "###   3-3-Sensitivity analysis of Room,Size, Price on Demand for a specific area\n",
    "###   3-4-Sensitivity analysis of all the areas at once\n",
    "###   3-5- Next potential steps\n",
    "####     3-5-1- Region Clustering based ond the distribution of demands\n",
    "####     3-5-1- Time series analysis: Comparison of multi-dimensional demand for different months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import datetime\n",
    "import pandas as pd\n",
    "# import pandas.io.data\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import sys\n",
    "import sompy as SOM# from pandas import Series, DataFrame\n",
    "pd.__version__\n",
    "%matplotlib inline\n",
    "import  pysparse\n",
    "from pylab import matshow, savefig\n",
    "from scipy.linalg import norm\n",
    "import time\n",
    "# from IPython.html.widgets import *\n",
    "from ipywidgets import interact, HTML, FloatSlider\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>email_hash</th>\n",
       "      <th>room_min</th>\n",
       "      <th>room_max</th>\n",
       "      <th>price_min</th>\n",
       "      <th>price_max</th>\n",
       "      <th>size_min</th>\n",
       "      <th>size_max</th>\n",
       "      <th>zip</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>modified</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1ea4c109f194b7d5a85a4f95b8898c7543e2d42b</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3232</td>\n",
       "      <td>47.0015</td>\n",
       "      <td>7.1091</td>\n",
       "      <td>2015-03-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fd8357409b8c4829e7d17af7e5f54f7c75ef6715</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1260</td>\n",
       "      <td>46.3829</td>\n",
       "      <td>6.2269</td>\n",
       "      <td>2015-03-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>634ff84744a1cab0da0ffe86677c1464414a4bc4</td>\n",
       "      <td>40.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>8002</td>\n",
       "      <td>47.3606</td>\n",
       "      <td>8.5325</td>\n",
       "      <td>2015-03-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30379e6624190ee14e2acaa7320029737e65d733</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8134</td>\n",
       "      <td>47.3079</td>\n",
       "      <td>8.5218</td>\n",
       "      <td>2015-03-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c32c092483ce6037a19c02e56c150d331eb7fae3</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8000</td>\n",
       "      <td>47.3667</td>\n",
       "      <td>8.5500</td>\n",
       "      <td>2015-03-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 email_hash  room_min  room_max  price_min  \\\n",
       "0  1ea4c109f194b7d5a85a4f95b8898c7543e2d42b      30.0       NaN        NaN   \n",
       "1  fd8357409b8c4829e7d17af7e5f54f7c75ef6715      10.0      10.0      500.0   \n",
       "2  634ff84744a1cab0da0ffe86677c1464414a4bc4      40.0      60.0     2400.0   \n",
       "3  30379e6624190ee14e2acaa7320029737e65d733      30.0       NaN        NaN   \n",
       "4  c32c092483ce6037a19c02e56c150d331eb7fae3      40.0       NaN        NaN   \n",
       "\n",
       "   price_max  size_min  size_max   zip      lat     lon    modified  \n",
       "0        NaN       NaN       NaN  3232  47.0015  7.1091  2015-03-04  \n",
       "1      900.0       NaN       NaN  1260  46.3829  6.2269  2015-03-04  \n",
       "2     5000.0     100.0     200.0  8002  47.3606  8.5325  2015-03-04  \n",
       "3     2000.0       NaN       NaN  8134  47.3079  8.5218  2015-03-04  \n",
       "4     3500.0       NaN       NaN  8000  47.3667  8.5500  2015-03-04  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month = 3\n",
    "path= '/Users/SVM/Dropbox/Applications/realmatch360/Data/subscription/rent_unq_ids_2015_'+str(month)+'_01_ETH.csv'\n",
    "# subs = pd.read_csv(path,dtype={'price_min': np.float,'price_max': np.float,'size_min': np.float,'size_max': np.float})\n",
    "\n",
    "subs = pd.read_csv(path)\n",
    "# subs = subs.sort_values('zip')\n",
    "subs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "room_min 0.813728798186\n",
      "room_max 0.483533786848\n",
      "price_min 0.366968707483\n",
      "price_max 0.874427210884\n",
      "size_min 0.25452244898\n",
      "size_max 0.0848979591837\n"
     ]
    },
    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count_complete</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_max</th>\n",
       "      <td>93600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>size_min</th>\n",
       "      <td>280611</td>\n",
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       "    <tr>\n",
       "      <th>price_min</th>\n",
       "      <td>404583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>room_max</th>\n",
       "      <td>533096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>room_min</th>\n",
       "      <td>897136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_max</th>\n",
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       "    </tr>\n",
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      "text/plain": [
       "           count_complete\n",
       "size_max            93600\n",
       "size_min           280611\n",
       "price_min          404583\n",
       "room_max           533096\n",
       "room_min           897136\n",
       "price_max          964056"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valued_Data = subs[['room_min', 'room_max', 'price_min', 'price_max','size_min', 'size_max', 'lat', 'lon']].copy()\n",
    "\n",
    "#Taking out the extreme values as they have bad effects on SOM\n",
    "stat = valued_Data.describe(percentiles=[.001,.005,.02,.03,.04,.05,.1,.2,.3,.4,.5,.6,.7,.8,.9,.95,.99,.995,.999])\n",
    "\n",
    "\n",
    "for i in range(valued_Data.shape[1]-3):\n",
    "    mx = stat[valued_Data.columns[i]].ix['99.9%']\n",
    "    ind = valued_Data[valued_Data.columns[i]]>mx\n",
    "    valued_Data[valued_Data.columns[i]].ix[ind]=mx\n",
    "    mn = stat[valued_Data.columns[i]].ix['0.1%']\n",
    "    ind = valued_Data[valued_Data.columns[i]]<mn\n",
    "    valued_Data[valued_Data.columns[i]].ix[ind]=mn\n",
    "\n",
    "\n",
    "sz_complete = []\n",
    "ind_completes = {}\n",
    "ind_nons = {}\n",
    "for i in range(valued_Data.shape[1]-2):\n",
    "    Data = valued_Data.values[:,i]\n",
    "    \n",
    "    #empties\n",
    "    ind = valued_Data[valued_Data.columns[i]].isnull().values[:]\n",
    "    ind_nons[valued_Data.columns[i]]=set(np.arange(len(ind))[ind])\n",
    "    \n",
    "    #Completes\n",
    "    ind = np.bitwise_not(ind)\n",
    "    ind_completes[valued_Data.columns[i]]=set(np.arange(len(ind))[ind])\n",
    "    sz_complete.append(ind.sum())\n",
    "    print valued_Data.columns[i], ind.sum()/float(valued_Data.shape[0])\n",
    "sz_complete = pd.DataFrame(data=sz_complete,index=valued_Data.columns.values[:-2],columns=['count_complete'])\n",
    "sz_complete = sz_complete.sort_values('count_complete')\n",
    "sz_complete "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Procedure to fill in the gaps systematically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_max</th>\n",
       "      <th>size_min</th>\n",
       "      <th>price_min</th>\n",
       "      <th>room_max</th>\n",
       "      <th>room_min</th>\n",
       "      <th>price_max</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>30.0</td>\n",
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       "      <td>7.1091</td>\n",
       "      <td>47.0015</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>6.2269</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>8.5325</td>\n",
       "      <td>47.3606</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>8.5218</td>\n",
       "      <td>47.3079</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>8.5500</td>\n",
       "      <td>47.3667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   size_max  size_min  price_min  room_max  room_min  price_max     lon  \\\n",
       "0       NaN       NaN        NaN       NaN      30.0        NaN  7.1091   \n",
       "1       NaN       NaN      500.0      10.0      10.0      900.0  6.2269   \n",
       "2     200.0     100.0     2400.0      60.0      40.0     5000.0  8.5325   \n",
       "3       NaN       NaN        NaN       NaN      30.0     2000.0  8.5218   \n",
       "4       NaN       NaN        NaN       NaN      40.0     3500.0  8.5500   \n",
       "\n",
       "       lat  \n",
       "0  47.0015  \n",
       "1  46.3829  \n",
       "2  47.3606  \n",
       "3  47.3079  \n",
       "4  47.3667  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data_to_fill = valued_Data[list(sz_complete.index.values)+['lon','lat']].copy()\n",
    "Data_to_fill.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "(0,) 18213\n",
      "(1,) 1392\n",
      "(2,) 10391\n",
      "(3,) 3132\n",
      "(4,) 332\n",
      "(5,) 470\n",
      "(0, 1) 186359\n",
      "(0, 2) 22682\n",
      "(0, 3) 26503\n",
      "(0, 4) 852\n",
      "(0, 5) 502\n",
      "(1, 2) 2519\n",
      "(1, 3) 110\n",
      "(1, 4) 346\n",
      "(1, 5) 3\n",
      "(2, 3) 2101\n",
      "(2, 4) 825\n",
      "(2, 5) 4365\n",
      "(3, 4) 3064\n",
      "(3, 5) 255\n",
      "(4, 5) 1\n",
      "(0, 1, 2) 121163\n",
      "(0, 1, 3) 40325\n",
      "(0, 1, 4) 3661\n",
      "(0, 1, 5) 2039\n",
      "(0, 2, 3) 85561\n",
      "(0, 2, 4) 2553\n",
      "(0, 2, 5) 2681\n",
      "(0, 3, 4) 5479\n",
      "(0, 3, 5) 3915\n",
      "(0, 4, 5) 91\n",
      "(1, 2, 3) 1051\n",
      "(1, 2, 4) 3161\n",
      "(1, 2, 5) 315\n",
      "(1, 3, 4) 319\n",
      "(1, 3, 5) 126\n",
      "(1, 4, 5) 0\n",
      "(2, 3, 4) 1566\n",
      "(2, 3, 5) 432\n",
      "(2, 4, 5) 25\n",
      "(3, 4, 5) 34\n",
      "(0, 1, 2, 3) 191390\n",
      "(0, 1, 2, 4) 23444\n",
      "(0, 1, 2, 5) 62813\n",
      "(0, 1, 3, 4) 39812\n",
      "(0, 1, 3, 5) 6201\n",
      "(0, 1, 4, 5) 206\n",
      "(0, 2, 3, 4) 17778\n",
      "(0, 2, 3, 5) 9905\n",
      "(0, 2, 4, 5) 204\n",
      "(0, 3, 4, 5) 469\n",
      "(1, 2, 3, 4) 450\n",
      "(1, 2, 3, 5) 279\n",
      "(1, 2, 4, 5) 172\n",
      "(1, 3, 4, 5) 134\n",
      "(2, 3, 4, 5) 0\n",
      "(0, 1, 2, 3, 4) 91292\n",
      "(0, 1, 2, 3, 5) 33713\n",
      "(0, 1, 2, 4, 5) 5086\n",
      "(0, 1, 3, 4, 5) 4008\n",
      "(0, 2, 3, 4, 5) 0\n",
      "(1, 2, 3, 4, 5) 0\n",
      "62\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "\n",
    "# #This is a list of possible combination of columns if it is not empty (i.e some rows)\n",
    "comb_list = []\n",
    "#This is to say for each the indices (rows) belong to whic unique combs\n",
    "comb_rows = []\n",
    "k = 0\n",
    "for i in range(1,6):\n",
    "    for r in itertools.combinations(range(6),i):\n",
    "        k = k+1\n",
    "        #At least it has len(r) nulls including the columns in r\n",
    "        fltr0 = Data_to_fill.ix[:,r].isnull().sum(axis=1)>=len(r)\n",
    "        #Among them we chose only those with exactly len(r) columns of null, which are necessarily those columns\n",
    "        fltr1 = Data_to_fill.ix[fltr0].isnull().sum(axis=1)==len(r)\n",
    "        print r, Data_to_fill.ix[fltr1.index].ix[fltr1].shape[0]\n",
    "        \n",
    "#         print Data_to_fill.values[:3,r]\n",
    "print k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# for i in range(1,6):\n",
    "#     for r in itertools.combinations(range(6),i):\n",
    "#         ind = Data_to_fill.isnull().values[:]\n",
    "#         sum_ind = ind.sum(axis=1)\n",
    "#         Data_tr = Data_to_fill.ix[sum_ind==0].values[:]\n",
    "    \n",
    "#         msz = [50,50]\n",
    "#         #Train a SOM based on Data_tr\n",
    "#         somP = SOM.SOM('som', Data_tr.astype(float), mapsize = msz,norm_method = 'var',initmethod='pca')\n",
    "#         somP.train(n_job = 1, shared_memory = 'no',verbose='off')\n",
    "#         bmus = somP.project_data(somP.data_raw)\n",
    "#         print 'size of training data is ',Data_tr.shape\n",
    "    \n",
    "    \n",
    "#         print \"combination is: \", r\n",
    "#         #At least it has len(r) nulls including the columns in r\n",
    "#         fltr0 = Data_to_fill.ix[:,r].isnull().sum(axis=1)>=len(r)\n",
    "#         #Among them we chose only those with exactly len(r) columns of null, which are necessarily those columns\n",
    "#         #Other wise, we are not sure if in addition to these r columns there is no null values\n",
    "#         fltr1 = Data_to_fill.ix[fltr0].isnull().sum(axis=1)==len(r)\n",
    "#         X_to_fill = Data_to_fill.ix[fltr1.index].ix[fltr1]\n",
    "#         if X_to_fill.shape[0]>1:\n",
    "#             ind_non_missing_dims = list(set(range(Data_to_fill.shape[1])).difference(r))\n",
    "#             for j, Target in enumerate(r[::-1]):\n",
    "#                 y_train = Data_tr[:,Target]\n",
    "            \n",
    "#                 #project hist consider the size of projection file\n",
    "#                 print 'Target is {} and ind_non_missing is {} and size of data to fill is {} '.format(Target,ind_non_missing_dims,X_to_fill.shape) \n",
    "#                 preds = fill_with_SOM(somP,bmus,X_to_fill.values[:],ind_non_missing_dims,Target,y_train,knn=1,which='SOM_hist',msz=msz)\n",
    "#                 X_to_fill.values[:,Target]= preds[:,0]\n",
    "#                 #now we use the estimated dimension for the projection of the next missing cols\n",
    "#                 ind_non_missing_dims.append(Target)\n",
    "            \n",
    "#         else:\n",
    "#             print 'Size of data to fill is {} '.format(X_to_fill.shape) \n",
    "            \n",
    "            \n",
    "            \n",
    "# #             preds = np.ones(ind_dim.sum())\n",
    "            \n",
    "#         Data_to_fill.values[X_to_fill.index]=X_to_fill.values[:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Data_to_fill.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# sz_complete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# path= '/Users/SVM/Dropbox/Applications/realmatch360/Data/subscription/Filled_rent_unq_ids_2015_'+str(month)+'_01_ETH.csv'\n",
    "# Data_to_fill.to_csv(path_or_buf=path,index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_max</th>\n",
       "      <th>size_min</th>\n",
       "      <th>price_min</th>\n",
       "      <th>room_max</th>\n",
       "      <th>room_min</th>\n",
       "      <th>price_max</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>7.1091</td>\n",
       "      <td>47.0015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>6.2269</td>\n",
       "      <td>46.3829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>8.5325</td>\n",
       "      <td>47.3606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>8.5218</td>\n",
       "      <td>47.3079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>150.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>8.5500</td>\n",
       "      <td>47.3667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   size_max  size_min  price_min  room_max  room_min  price_max     lon  \\\n",
       "0     140.0      80.0     1200.0      50.0      30.0     2000.0  7.1091   \n",
       "1      35.0      30.0      500.0      10.0      10.0      900.0  6.2269   \n",
       "2     200.0     100.0     2400.0      60.0      40.0     5000.0  8.5325   \n",
       "3     100.0      75.0     1400.0      40.0      30.0     2000.0  8.5218   \n",
       "4     150.0     100.0     2500.0      50.0      40.0     3500.0  8.5500   \n",
       "\n",
       "       lat  \n",
       "0  47.0015  \n",
       "1  46.3829  \n",
       "2  47.3606  \n",
       "3  47.3079  \n",
       "4  47.3667  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path= '/Users/SVM/Dropbox/Applications/realmatch360/Data/subscription/Test_Filled_rent_unq_ids_2015_'+str(month)+'_01_ETH.csv'\n",
    "Filled_Data = pd.read_csv(path)\n",
    "Filled_Data = Filled_Data.dropna()\n",
    "Filled_Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1102500, 8)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_max</th>\n",
       "      <th>size_min</th>\n",
       "      <th>price_min</th>\n",
       "      <th>room_max</th>\n",
       "      <th>room_min</th>\n",
       "      <th>price_max</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>7.1091</td>\n",
       "      <td>47.0015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>6.2269</td>\n",
       "      <td>46.3829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>8.5325</td>\n",
       "      <td>47.3606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>8.5218</td>\n",
       "      <td>47.3079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>150.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>8.5500</td>\n",
       "      <td>47.3667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   size_max  size_min  price_min  room_max  room_min  price_max     lon  \\\n",
       "0     140.0      80.0     1200.0      50.0      30.0     2000.0  7.1091   \n",
       "1      35.0      30.0      500.0      10.0      10.0      900.0  6.2269   \n",
       "2     200.0     100.0     2400.0      60.0      40.0     5000.0  8.5325   \n",
       "3     100.0      75.0     1400.0      40.0      30.0     2000.0  8.5218   \n",
       "4     150.0     100.0     2500.0      50.0      40.0     3500.0  8.5500   \n",
       "\n",
       "       lat  \n",
       "0  47.0015  \n",
       "1  46.3829  \n",
       "2  47.3606  \n",
       "3  47.3079  \n",
       "4  47.3667  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remove outliers based on a global statistics, calculated previously\n",
    "# \n",
    "\n",
    "for i in range(Filled_Data.shape[1]-2):\n",
    "    mx = stat[Filled_Data.columns[i]].ix['99.9%']\n",
    "    ind = Filled_Data[Filled_Data.columns[i]]>mx\n",
    "    Filled_Data[Filled_Data.columns[i]].ix[ind]=mx\n",
    "    mn = stat[Filled_Data.columns[i]].ix['0.1%']\n",
    "    ind = Filled_Data[Filled_Data.columns[i]]<mn\n",
    "    Filled_Data[Filled_Data.columns[i]].ix[ind]=mn\n",
    "\n",
    "Filled_Data = Filled_Data.dropna()\n",
    "print Data_to_fill.shape\n",
    "Filled_Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Procedure to check the distribution of the filled data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1102500, 8)\n",
      "(56230, 8)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_max</th>\n",
       "      <th>size_min</th>\n",
       "      <th>price_min</th>\n",
       "      <th>room_max</th>\n",
       "      <th>room_min</th>\n",
       "      <th>price_max</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>8.5325</td>\n",
       "      <td>47.3606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>200.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>9.0525</td>\n",
       "      <td>47.4519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>70.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>6.6987</td>\n",
       "      <td>46.5737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>70.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>6.6303</td>\n",
       "      <td>46.5224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>70.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>6.6322</td>\n",
       "      <td>46.5195</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     size_max  size_min  price_min  room_max  room_min  price_max     lon  \\\n",
       "2       200.0     100.0     2400.0      60.0      40.0     5000.0  8.5325   \n",
       "91      200.0      20.0      400.0      40.0      10.0     1800.0  9.0525   \n",
       "679      70.0      40.0     1000.0      30.0      20.0     1300.0  6.6987   \n",
       "680      70.0      40.0     1000.0      30.0      20.0     1300.0  6.6303   \n",
       "681      70.0      40.0     1000.0      30.0      20.0     1300.0  6.6322   \n",
       "\n",
       "         lat  \n",
       "2    47.3606  \n",
       "91   47.4519  \n",
       "679  46.5737  \n",
       "680  46.5224  \n",
       "681  46.5195  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_Data_only_complete = subs[Filled_Data.columns.values].copy()\n",
    "# valued_Data = subs[['price_min','size_min','room_min','price_max','room_max', 'lat', 'lon']].copy()\n",
    "print original_Data_only_complete.shape\n",
    "\n",
    "#Taking out the extreme values as they have bad effects on SOM\n",
    "# stat = original_Data_only_complete.describe(percentiles=[.001,.005,.02,.03,.04,.05,.1,.2,.3,.4,.5,.6,.7,.8,.9,.95,.99,.995,.999])\n",
    "for i in range(original_Data_only_complete.shape[1]-2):\n",
    "    mx = stat[original_Data_only_complete.columns[i]].ix['99.9%']\n",
    "    ind = original_Data_only_complete[original_Data_only_complete.columns[i]]>mx\n",
    "    original_Data_only_complete[original_Data_only_complete.columns[i]].ix[ind]=mx\n",
    "    mn = stat[original_Data_only_complete.columns[i]].ix['0.1%']\n",
    "    ind = original_Data_only_complete[original_Data_only_complete.columns[i]]<mn\n",
    "    original_Data_only_complete[original_Data_only_complete.columns[i]].ix[ind]=mn\n",
    "\n",
    "original_Data_only_complete = original_Data_only_complete.dropna()\n",
    "print original_Data_only_complete.shape\n",
    "original_Data_only_complete.head()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x123a8de90>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vvrqyc5arQ7H9rXjRrGHOUkv5UwI01/6EHtPPALvS/4NZRETgRuAeYDbwapPr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\n/05Yl+C1S3qdokjS9vXX3edXXy3JRP2NBa+TYTSbe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ofD/634tuO8HoNF6x+Pp5gcVzHL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uxcAOqENUHsXAUdahLU9cpy3A64DbCF08HwX+vN1lakKdZgD3Ee6Lfzp6kZ2Q2H4CYZrw54G7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\nkiRJKu3/AlcmeK1ydRAGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123a8d850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18,18))\n",
    "k = 1\n",
    "for i, dim1 in enumerate(Filled_Data.columns.values[:-2]):\n",
    "    for dim2 in Filled_Data.columns.values[i+1:-2]:\n",
    "        plt.subplot(5,4,k)\n",
    "        k = k+1\n",
    "#         plt.title(dim1 +\" vs \" +dim2)\n",
    "        plt.xlabel(dim1)\n",
    "        plt.ylabel(dim2)\n",
    "# codebook_denorm.price_min.values[:] = np.around(codebook_denorm.price_min/100,decimals=0)*100\n",
    "# codebook_denorm.price_max.values[:] = np.around(codebook_denorm.price_max/100,decimals=0)*100\n",
    "        plt.plot(Filled_Data[dim1],Filled_Data[dim2],'r.',markersize=3.2,label='Filled Data')\n",
    "        plt.plot(original_Data_only_complete[dim1],original_Data_only_complete[dim2],'g.',markersize=.8,label='Original Sample')\n",
    "\n",
    "plt.tight_layout()\n",
    "font = {'size'   : 12}\n",
    "plt.rc('font', **font)\n",
    "plt.legend(loc='best',bbox_to_anchor = (1.51, 1.015),fontsize = 'medium')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random\n",
    "Tr_data_size = int(.2*Filled_Data.shape[0])\n",
    "ind_row_train = random.sample(range(Filled_Data.shape[0]),Tr_data_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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g70IaQHDffaBdmkRE5ABSyCCz0WrRyjuqyYYSFS9UwyoRETmc+oODIOh9vlPI8OX7Kd95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\nmS0zs2O629bMhpjZK2a2xsxeNrOCsGW3+Pv6yMxOj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TDXp84453b6/+42s2fxyrM7+yxI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YAyq1AQ+XUMggAuprZFDc8HzD1EgHsbqEdQtw/v7fB2BqqczCxrx/7YiIiAxIkKtLNHEm3/o5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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x131c70d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig = plt.figure(figsize=(12,12))\n",
    "for i in range(Filled_Data.shape[1]-2):\n",
    "    ax = plt.subplot(3,2,i+1)\n",
    "    try:\n",
    "        Filled_Data.ix[ind_row_train,i].plot(kind='kde',linewidth=2,color='red',label='Filled Data')\n",
    "        original_Data_only_complete.ix[:,i].plot(kind='kde',linewidth=.8,color='green',label='Original Sample')\n",
    "        plt.title(Filled_Data.columns.values[i])\n",
    "    except:\n",
    "        continue\n",
    "    \n",
    "\n",
    "font = {'size'   : 10}\n",
    "plt.rc('font', **font)\n",
    "plt.tight_layout()\n",
    "plt.legend(loc='best',bbox_to_anchor = (1.61, 1.015),fontsize = 'medium')\n",
    "plt.show()\n",
    "\n",
    "# path = '/Files/Research from 2014/Data_Python/Resamping/HITS_Pics/'+which+'_kde'+'_som_sz_'+str(msz0)+'_'+str(msz1)+'.png'\n",
    "\n",
    "# fig.savefig(path,transparent=False, dpi=200)\n",
    "# for i in range(codebook.shape[1]):\n",
    "#     ax1 = plt.subplot(3, 3, i)\n",
    "#     data.ix[:,i].plot('r-',kind='kde',linewidth=2,color='green')\n",
    "#     plt.title(sm.compname[0][i])\n",
    "#     font = {'size'   : 10}\n",
    "#     plt.rc('font', **font)\n",
    "#     ax1.set_ylabel(\"\")\n",
    "# plt.show()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Demand distributions \n",
    "##### Here we divide the searches to \"Specific\" and \"Vicinity\" search, based on a threshold for the number of areas per unique email id."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1102499, 10)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_max</th>\n",
       "      <th>size_min</th>\n",
       "      <th>price_min</th>\n",
       "      <th>room_max</th>\n",
       "      <th>room_min</th>\n",
       "      <th>price_max</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>zip</th>\n",
       "      <th>email_hash</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>7.1091</td>\n",
       "      <td>47.0015</td>\n",
       "      <td>3232</td>\n",
       "      <td>1ea4c109f194b7d5a85a4f95b8898c7543e2d42b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>6.2269</td>\n",
       "      <td>46.3829</td>\n",
       "      <td>1260</td>\n",
       "      <td>fd8357409b8c4829e7d17af7e5f54f7c75ef6715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>8.5325</td>\n",
       "      <td>47.3606</td>\n",
       "      <td>8002</td>\n",
       "      <td>634ff84744a1cab0da0ffe86677c1464414a4bc4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>8.5218</td>\n",
       "      <td>47.3079</td>\n",
       "      <td>8134</td>\n",
       "      <td>30379e6624190ee14e2acaa7320029737e65d733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>150.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>8.5500</td>\n",
       "      <td>47.3667</td>\n",
       "      <td>8000</td>\n",
       "      <td>c32c092483ce6037a19c02e56c150d331eb7fae3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   size_max  size_min  price_min  room_max  room_min  price_max     lon  \\\n",
       "0     140.0      80.0     1200.0      50.0      30.0     2000.0  7.1091   \n",
       "1      35.0      30.0      500.0      10.0      10.0      900.0  6.2269   \n",
       "2     200.0     100.0     2400.0      60.0      40.0     5000.0  8.5325   \n",
       "3     100.0      75.0     1400.0      40.0      30.0     2000.0  8.5218   \n",
       "4     150.0     100.0     2500.0      50.0      40.0     3500.0  8.5500   \n",
       "\n",
       "       lat   zip                                email_hash  \n",
       "0  47.0015  3232  1ea4c109f194b7d5a85a4f95b8898c7543e2d42b  \n",
       "1  46.3829  1260  fd8357409b8c4829e7d17af7e5f54f7c75ef6715  \n",
       "2  47.3606  8002  634ff84744a1cab0da0ffe86677c1464414a4bc4  \n",
       "3  47.3079  8134  30379e6624190ee14e2acaa7320029737e65d733  \n",
       "4  47.3667  8000  c32c092483ce6037a19c02e56c150d331eb7fae3  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Complete_data = pd.concat((Filled_Data,subs[['zip','email_hash']]),join='inner',axis=1)\n",
    "print Complete_data.shape\n",
    "Complete_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#to add emails to the files.\n",
    "# path= '/Users/SVM/Dropbox/Applications/realmatch360/Data/subscription/Test_Filled_rent_unq_ids_with_emails_2015_'+str(month)+'_01_ETH.csv'\n",
    "# Complete_data.to_csv(path,index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    82026.000000\n",
      "mean        13.440848\n",
      "std         22.126170\n",
      "min          1.000000\n",
      "10%          1.000000\n",
      "50%          3.000000\n",
      "60%          7.000000\n",
      "65%         10.000000\n",
      "70%         13.000000\n",
      "75%         16.000000\n",
      "80%         22.000000\n",
      "90%         38.000000\n",
      "max        146.000000\n",
      "dtype: float64\n",
      "threshold_for_area_search:  16.0\n",
      "(1102499, 11)\n"
     ]
    }
   ],
   "source": [
    "#To find those specific searches to those of area search\n",
    "gb_count =  Complete_data.groupby(by='email_hash').size()\n",
    "stat_emails = gb_count.describe(percentiles=[.1,.5,.6,.65,.7,.75,.8,.9])\n",
    "threshold_for_area_search = stat_emails.ix[\"75%\"]\n",
    "print stat_emails\n",
    "print 'threshold_for_area_search: ',threshold_for_area_search\n",
    "# gb_count['specific_area'] = np.ones(gb_count.shape)\n",
    "specific_search = gb_count[Complete_data['email_hash']].values\n",
    "specific_search[specific_search<=threshold_for_area_search]=1\n",
    "specific_search[specific_search>threshold_for_area_search]=0\n",
    "\n",
    "Complete_data['specific_search'] = specific_search\n",
    "print Complete_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_max</th>\n",
       "      <th>size_min</th>\n",
       "      <th>price_min</th>\n",
       "      <th>room_max</th>\n",
       "      <th>room_min</th>\n",
       "      <th>price_max</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>zip</th>\n",
       "      <th>email_hash</th>\n",
       "      <th>specific_search</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1102495</th>\n",
       "      <td>70.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>8.2454</td>\n",
       "      <td>47.2591</td>\n",
       "      <td>6287</td>\n",
       "      <td>92096d76b2ab3157cb425a221b8f142e10680eee</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102496</th>\n",
       "      <td>50.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>8.2715</td>\n",
       "      <td>47.2671</td>\n",
       "      <td>6288</td>\n",
       "      <td>92096d76b2ab3157cb425a221b8f142e10680eee</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102497</th>\n",
       "      <td>70.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>8.2985</td>\n",
       "      <td>47.2401</td>\n",
       "      <td>6289</td>\n",
       "      <td>92096d76b2ab3157cb425a221b8f142e10680eee</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102498</th>\n",
       "      <td>70.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>8.2367</td>\n",
       "      <td>47.2248</td>\n",
       "      <td>6294</td>\n",
       "      <td>92096d76b2ab3157cb425a221b8f142e10680eee</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102499</th>\n",
       "      <td>50.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>8.2280</td>\n",
       "      <td>47.2416</td>\n",
       "      <td>6295</td>\n",
       "      <td>92096d76b2ab3157cb425a221b8f142e10680eee</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         size_max  size_min  price_min  room_max  room_min  price_max     lon  \\\n",
       "1102495      70.0      50.0      800.0      40.0      20.0      900.0  8.2454   \n",
       "1102496      50.0      30.0      800.0      30.0      20.0      900.0  8.2715   \n",
       "1102497      70.0      50.0      800.0      40.0      20.0      900.0  8.2985   \n",
       "1102498      70.0      40.0      800.0      30.0      20.0      900.0  8.2367   \n",
       "1102499      50.0      30.0      800.0      30.0      20.0      900.0  8.2280   \n",
       "\n",
       "             lat   zip                                email_hash  \\\n",
       "1102495  47.2591  6287  92096d76b2ab3157cb425a221b8f142e10680eee   \n",
       "1102496  47.2671  6288  92096d76b2ab3157cb425a221b8f142e10680eee   \n",
       "1102497  47.2401  6289  92096d76b2ab3157cb425a221b8f142e10680eee   \n",
       "1102498  47.2248  6294  92096d76b2ab3157cb425a221b8f142e10680eee   \n",
       "1102499  47.2416  6295  92096d76b2ab3157cb425a221b8f142e10680eee   \n",
       "\n",
       "         specific_search  \n",
       "1102495                0  \n",
       "1102496                0  \n",
       "1102497                0  \n",
       "1102498                0  \n",
       "1102499                0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Complete_data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **** A very important note regarding histograms\n",
    "#### unique number of emails for each zip codes is smaller than what we will see in the histogram. Because, we assume when a person says for example, room_min=1 and room max=5, then he creates demands for all the possible rooms in between (i.e. 1,2,3,4,5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def hist1d(Area):\n",
    "    q = Area\n",
    "#     q = 8134\n",
    "    # ind_q_specific = (Complete_data['zip']==q) & (Complete_data['specific_search']==1)\n",
    "    ind_q_vicinity = (Complete_data['zip']==q)\n",
    "    q_data_vicinity = Complete_data.ix[ind_q_vicinity,:]\n",
    "\n",
    "    fig = plt.figure(figsize=(12,3))\n",
    "    font = {'size'   : 8}\n",
    "    plt.rc('font', **font)\n",
    "    if q_data_vicinity.shape[0]>=1:\n",
    "    \n",
    "        rooms = []\n",
    "        sizes =[]\n",
    "        prices = []\n",
    "        print \"**************************************************************\"\n",
    "        print 'Number of unique vicinity search: {}.'.format(q_data_vicinity.shape[0])\n",
    "        for i in range(len(q_data_vicinity)):\n",
    "            rooms = rooms + list(np.arange(q_data_vicinity['room_min'].values[i]/10, q_data_vicinity['room_max'].values[i]/10+1))\n",
    "            sizes = sizes + list(np.around(np.linspace(q_data_vicinity['size_min'].values[i], q_data_vicinity['size_max'].values[i],num=10)))\n",
    "            prices= prices + list(np.around(np.linspace(q_data_vicinity['price_min'].values[i], q_data_vicinity['price_max'].values[i],num=10)))\n",
    "            \n",
    "            #An alternative not to use max values for rooms and min values for Pric\n",
    "#             rooms = rooms + list(np.arange(q_data_vicinity['room_min'].values[i]/10, q_data_vicinity['room_min'].values[i]/10+3))\n",
    "#             sizes = sizes + list(np.around(np.linspace(q_data_vicinity['size_min'].values[i], q_data_vicinity['size_min'].values[i]+100,num=10)))\n",
    "#             prices= prices + list(np.around(np.linspace(q_data_vicinity['price_max'].values[i]-500, q_data_vicinity['price_max'].values[i],num=10)))\n",
    "\n",
    "            \n",
    "            \n",
    "        ax = plt.subplot(1,3,1)\n",
    "        plt.title('Room')\n",
    "        \n",
    "\n",
    "        room_bin = range(int(stat['room_min'].ix['2%']/10),int(stat['room_max'].ix['99.5%']/10+1))\n",
    "        a = plt.hist(rooms,bins=room_bin,alpha=1,color='white',linewidth=.5,edgecolor='black',rwidth=1)\n",
    "        ax.yaxis.grid(True)\n",
    "    \n",
    "        ax = plt.subplot(1,3,2)\n",
    "        plt.title('Size')\n",
    "    \n",
    "        mn = int(stat['size_min'].ix['2%'])\n",
    "        mx = int(stat['size_max'].ix['99%'])\n",
    "        R = mx-mn\n",
    "        # stp = int(R/30)\n",
    "        stp = 20\n",
    "        size_bin = range(mn,mx+stp,stp)\n",
    "        a = plt.hist(sizes,bins=size_bin,alpha=1,color='white',linewidth=.5,edgecolor='black')\n",
    "        ax.yaxis.grid(True)\n",
    "    \n",
    "        ax = plt.subplot(1,3,3)\n",
    "        plt.title('Price')\n",
    "    \n",
    "        mn = int(stat['price_min'].ix['2%'])\n",
    "        mx = int(stat['price_max'].ix['99.5%'])\n",
    "        R = mx-mn\n",
    "        stp = 200\n",
    "        price_bin = range(mn,mx+stp,stp)\n",
    "    \n",
    "        a = plt.hist(prices,bins=price_bin,alpha=1,color='white',linewidth=.5,edgecolor='black')\n",
    "        ax.yaxis.grid(True)\n",
    "        plt.tight_layout()\n",
    "        \n",
    "        \n",
    "    else:\n",
    "        print \"\\n**************************************************************\"\n",
    "        print 'Not enough vicinity this area with the zip code {}.'.format(Area)\n",
    "        return\n",
    "    \n",
    "    ind_q_specific = (Complete_data['zip']==q) & (Complete_data['specific_search']==1)\n",
    "    q_data_specific = Complete_data.ix[ind_q_specific,:]\n",
    "    q_data_specific = Complete_data.ix[ind_q_specific,:]\n",
    "    rooms = []\n",
    "    sizes =[]\n",
    "    prices = []\n",
    "    if q_data_specific.shape[0]>=1:\n",
    "        print 'Number of unique specific search: {}.'.format(q_data_specific.shape[0])\n",
    "        for i in range(len(q_data_specific)):\n",
    "            rooms = rooms + list(np.arange(q_data_specific['room_min'].values[i]/10, q_data_specific['room_max'].values[i]/10+1))\n",
    "            sizes = sizes + list(np.around(np.linspace(q_data_specific['size_min'].values[i], q_data_specific['size_max'].values[i],num=10)))\n",
    "            prices= prices + list(np.around(np.linspace(q_data_specific['price_min'].values[i], q_data_specific['price_max'].values[i],num=10)))\n",
    "            \n",
    "            #An alternative not to use max values for rooms and min values for Pric\n",
    "#             rooms = rooms + list(np.arange(q_data_specific['room_min'].values[i]/10, q_data_specific['room_min'].values[i]/10+3))\n",
    "#             sizes = sizes + list(np.around(np.linspace(q_data_specific['size_min'].values[i], q_data_specific['size_min'].values[i]+100,num=10)))\n",
    "#             prices= prices + list(np.around(np.linspace(q_data_specific['price_max'].values[i]-500, q_data_specific['price_max'].values[i],num=10)))\n",
    "            \n",
    "            \n",
    "        ax =plt.subplot(1,3,1)\n",
    "        a = plt.hist(rooms,bins=room_bin,alpha=1,color='red',linewidth=0.5,edgecolor='black',rwidth=1, align='mid')\n",
    "        ax.yaxis.grid(True)\n",
    "        plt.ylabel('Number')\n",
    "        ax = plt.subplot(1,3,2)\n",
    "        a = plt.hist(sizes,bins=size_bin,alpha=1,color='red',linewidth=0.5,edgecolor='black' )\n",
    "        ax.yaxis.grid(True)\n",
    "        plt.ylabel('Number')\n",
    "        ax = plt.subplot(1,3,3)\n",
    "        a = plt.hist(prices,bins=price_bin,alpha=1,color='red',linewidth=0.5,edgecolor='black' )\n",
    "        ax.yaxis.grid(True)\n",
    "        plt.ylabel('Number')\n",
    "        plt.tight_layout()\n",
    "        \n",
    "    else:\n",
    "        print \"\\n**************************************************************\"\n",
    "        print 'Not enough specific search for this area with the zip code {}.'.format(Area)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Histogram 3d "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def hist3d(Area):\n",
    "    import numpy as np\n",
    "    q = Area\n",
    "    import itertools\n",
    "    room_bin = range(int(stat['room_min'].ix['2%']/10),int(stat['room_max'].ix['99.5%']/10+1))\n",
    "    \n",
    "    mn = int(stat['size_min'].ix['2%'])\n",
    "    mx = int(stat['size_max'].ix['99%'])\n",
    "    R = mx-mn\n",
    "    stp = 20\n",
    "    size_bin = range(mn,mx+stp,stp)\n",
    "\n",
    "    mn = int(stat['price_min'].ix['2%'])\n",
    "    mx = int(stat['price_max'].ix['99.5%'])\n",
    "    R = mx-mn\n",
    "    stp = 200\n",
    "    price_bin = range(mn,mx+stp,stp)\n",
    "    random_demand = np.zeros((0,6))\n",
    "    geo_info = []\n",
    "    H_all = []\n",
    "\n",
    "    ind_q_specific = (Complete_data['zip']==q) & (Complete_data['specific_search']==1)\n",
    "    q_data_specific = Complete_data.ix[ind_q_specific,:]\n",
    "    q_data_specific = Complete_data.ix[ind_q_specific,:]\n",
    "    rooms = []\n",
    "    sizes =[]\n",
    "    prices = []\n",
    "    H_q = np.zeros((len(room_bin)-1, len(size_bin)-1, len(price_bin)-1))\n",
    "    \n",
    "    \n",
    "    if len(q_data_specific) >= 1:\n",
    "        print 'Number of specific search: {} for this area with the zip code {}.'.format(q_data_specific.shape[0],Area)\n",
    "        for i in range(len(q_data_specific)):\n",
    "            room_rng = list(np.arange(q_data_specific['room_min'].values[i]/10, q_data_specific['room_max'].values[i]/10+1))\n",
    "            size_rng = list(np.around(np.linspace(q_data_specific['size_min'].values[i], q_data_specific['size_max'].values[i],num=10)))\n",
    "            price_rng = list(np.around(np.linspace(q_data_specific['price_min'].values[i], q_data_specific['price_max'].values[i],num=10)))\n",
    "            iterables = [ room_rng, size_rng, price_rng ]\n",
    "            all_combs = np.zeros((len(room_rng)*len(size_rng)*len(price_rng),3))\n",
    "            for k,t in enumerate(itertools.product(*iterables)):\n",
    "                all_combs[k] = t \n",
    "        \n",
    "            H, edges = np.histogramdd(all_combs, bins = (room_bin, size_bin, price_bin))    \n",
    "            H_q = H_q + H\n",
    "        \n",
    "        lon = q_data_specific['lon'].values[i]\n",
    "        lat = q_data_specific['lat'].values[i]\n",
    "        geo_info.append([q,lon,lat])\n",
    "        H_all.append(H_q)\n",
    "\n",
    "\n",
    "        cmapname=\"RdYlBu_r\"\n",
    "        d = []\n",
    "        for i,room in enumerate(room_bin[:-1]):\n",
    "            for j, size in enumerate(size_bin[:-1]):\n",
    "                for k, price in enumerate(price_bin[:-1]):\n",
    "            \n",
    "                    if H_q[i,j,k]>0:\n",
    "                        d.append([room,size,price,H_q[i,j,k]])\n",
    "        d = np.asarray(d)\n",
    "            \n",
    "        from mpl_toolkits.mplot3d import Axes3D\n",
    "        from matplotlib import cm\n",
    "        import matplotlib.pyplot as plt\n",
    "\n",
    "        fig = plt.figure(figsize=(12,10))\n",
    "        ax = fig.gca(projection='3d',animated= False )\n",
    "\n",
    "        sc =ax.scatter3D(d[:,0], d[:,1], d[:,2],c=100*d[:,3]/float(H_q.sum()),s=d[:,3]/7,marker='o',edgecolor='gray',linewidth=.1,alpha=1,cmap=cmapname)\n",
    "        \n",
    "        \n",
    "        ax.set_xlim3d(left=room_bin[0],right=room_bin[-2])\n",
    "        ax.set_ylim3d(bottom=size_bin[0],top=size_bin[-2])\n",
    "        ax.set_zlim3d(bottom=price_bin[0],top=price_bin[-2])\n",
    "        \n",
    "        ax.view_init(elev=10., azim=127,)\n",
    "    \n",
    "        ax.set_xlabel('room')\n",
    "        ax.set_ylabel('size')\n",
    "        ax.set_zlabel('price',labelpad=.01)\n",
    "        mn = 100*np.min(d[:,3])/float(H_q.sum())\n",
    "        mx = 100*np.max(d[:,3])/float(H_q.sum())\n",
    "        plt.colorbar(sc,ticks=np.round(np.linspace(mn,mx,5),decimals=5),shrink=0.3,label='Percent')\n",
    "        font = {'size'   : 12}\n",
    "        plt.rc('font', **font)\n",
    "        plt.show()\n",
    "    else:\n",
    "        print \"\\n**************************************************************\"\n",
    "        print 'Not enough specific search for this area with the zip code {}.'.format(Area)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def hist1d_Supply_Demand(Area):\n",
    "    q = Area\n",
    "#     q = 8134\n",
    "    # ind_q_specific = (Complete_data['zip']==q) & (Complete_data['specific_search']==1)\n",
    "    ind_q_vicinity = (Complete_data['zip']==q)\n",
    "    q_data_vicinity = Complete_data.ix[ind_q_vicinity,:]\n",
    "\n",
    "    fig = plt.figure(figsize=(12,9))\n",
    "    font = {'size'   : 8}\n",
    "    plt.rc('font', **font)\n",
    "    room_bin = range(int(stat['room_min'].ix['2%']/10),int(stat['room_max'].ix['99.5%']/10+1))\n",
    "    mn = int(stat['size_min'].ix['2%'])\n",
    "    mx = int(stat['size_max'].ix['99%'])\n",
    "    R = mx-mn\n",
    "        # stp = int(R/30)\n",
    "    stp = 20\n",
    "    size_bin = range(mn,mx+stp,stp)\n",
    "    \n",
    "    mn = int(stat['price_min'].ix['2%'])\n",
    "    mx = int(stat['price_max'].ix['99.5%'])\n",
    "    R = mx-mn\n",
    "    stp = 200\n",
    "    price_bin = range(mn,mx+stp,stp)\n",
    "    \n",
    "#     if q_data_vicinity.shape[0]>=1:\n",
    "    \n",
    "#         rooms = []\n",
    "#         sizes =[]\n",
    "#         prices = []\n",
    "#         print \"**************************************************************\"\n",
    "#         print 'Number of unique vicinity search: {}.'.format(q_data_vicinity.shape[0])\n",
    "#         for i in range(len(q_data_vicinity)):\n",
    "#             rooms = rooms + list(np.arange(q_data_vicinity['room_min'].values[i]/10, q_data_vicinity['room_max'].values[i]/10+1))\n",
    "#             sizes = sizes + list(np.around(np.linspace(q_data_vicinity['size_min'].values[i], q_data_vicinity['size_max'].values[i],num=10)))\n",
    "#             prices= prices + list(np.around(np.linspace(q_data_vicinity['price_min'].values[i], q_data_vicinity['price_max'].values[i],num=10)))\n",
    "  \n",
    "            \n",
    "#         ax = plt.subplot(2,3,1)\n",
    "#         plt.title('Room')\n",
    "        \n",
    "\n",
    "        \n",
    "#         a = plt.hist(rooms,bins=room_bin,alpha=1,color='white',linewidth=.5,edgecolor='black',rwidth=1)\n",
    "#         ax.yaxis.grid(True)\n",
    "    \n",
    "#         ax = plt.subplot(2,3,2)\n",
    "#         plt.title('Size')\n",
    "    \n",
    "        \n",
    "#         a = plt.hist(sizes,bins=size_bin,alpha=1,color='white',linewidth=.5,edgecolor='black')\n",
    "#         ax.yaxis.grid(True)\n",
    "    \n",
    "#         ax = plt.subplot(2,3,3)\n",
    "#         plt.title('Price')\n",
    "    \n",
    "        \n",
    "    \n",
    "#         a = plt.hist(prices,bins=price_bin,alpha=1,color='white',linewidth=.5,edgecolor='black')\n",
    "#         ax.yaxis.grid(True)\n",
    "#         plt.tight_layout()\n",
    "        \n",
    "        \n",
    "#     else:\n",
    "#         print \"\\n**************************************************************\"\n",
    "#         print 'Not enough vicinity this area with the zip code {}.'.format(Area)\n",
    "#         return\n",
    "    \n",
    "    ind_q_specific = (Complete_data['zip']==q) & (Complete_data['specific_search']==1)\n",
    "    q_data_specific = Complete_data.ix[ind_q_specific,:]\n",
    "    q_data_specific = Complete_data.ix[ind_q_specific,:]\n",
    "    rooms = []\n",
    "    sizes =[]\n",
    "    prices = []\n",
    "    if q_data_specific.shape[0]>=1:\n",
    "        print 'Number of unique specific search: {}.'.format(q_data_specific.shape[0])\n",
    "        for i in range(len(q_data_specific)):\n",
    "            rooms = rooms + list(np.arange(q_data_specific['room_min'].values[i]/10, q_data_specific['room_max'].values[i]/10+1))\n",
    "            sizes = sizes + list(np.around(np.linspace(q_data_specific['size_min'].values[i], q_data_specific['size_max'].values[i],num=10)))\n",
    "            prices= prices + list(np.around(np.linspace(q_data_specific['price_min'].values[i], q_data_specific['price_max'].values[i],num=10)))\n",
    "            \n",
    "            #An alternative not to use max values for rooms and min values for Pric\n",
    "#             rooms = rooms + list(np.arange(q_data_specific['room_min'].values[i]/10, q_data_specific['room_min'].values[i]/10+3))\n",
    "#             sizes = sizes + list(np.around(np.linspace(q_data_specific['size_min'].values[i], q_data_specific['size_min'].values[i]+100,num=10)))\n",
    "#             prices= prices + list(np.around(np.linspace(q_data_specific['price_max'].values[i]-500, q_data_specific['price_max'].values[i],num=10)))\n",
    "            \n",
    "            \n",
    "        ax =plt.subplot(3,3,1)\n",
    "        a = plt.hist(rooms,bins=room_bin,alpha=1,color='red',linewidth=0.5,edgecolor='black',rwidth=1, align='mid')\n",
    "        ax.yaxis.grid(True)\n",
    "        plt.ylabel('Number')\n",
    "        plt.title('Demand: Room')\n",
    "        ax = plt.subplot(3,3,2)\n",
    "        a = plt.hist(sizes,bins=size_bin,alpha=1,color='red',linewidth=0.5,edgecolor='black' )\n",
    "        ax.yaxis.grid(True)\n",
    "        plt.ylabel('Number')\n",
    "        plt.title('Demand: Size')\n",
    "        ax = plt.subplot(3,3,3)\n",
    "        a = plt.hist(prices,bins=price_bin,alpha=1,color='red',linewidth=0.5,edgecolor='black' )\n",
    "        ax.yaxis.grid(True)\n",
    "        plt.ylabel('Number')\n",
    "        plt.title('Demand: Price')\n",
    "        plt.tight_layout()\n",
    "        \n",
    "        \n",
    "        \n",
    "        ax = plt.subplot(3,3,7)\n",
    "#         a = plt.hist(rooms,bins=room_bin,alpha=.4,color='red',linewidth=.5,edgecolor='black',normed='Yes')\n",
    "        DF = pd.DataFrame(data=rooms,columns=['room'])\n",
    "        DF['room'].plot(kind='kde',linewidth=2,color='red',label='Filled Data')\n",
    "        \n",
    "        ax = plt.subplot(3,3,8)\n",
    "        DF = pd.DataFrame(data=sizes,columns=['size'])\n",
    "#         a = plt.hist(sizes,bins=size_bin,alpha=.4,color='red',linewidth=.5,edgecolor='black',normed='Yes')\n",
    "        DF['size'].plot(kind='kde',linewidth=2,color='red',label='Filled Data')\n",
    "        \n",
    "        \n",
    "        ax = plt.subplot(3,3,9)\n",
    "        DF = pd.DataFrame(data=prices,columns=['price'])\n",
    "#         a = plt.hist(prices,bins=price_bin,alpha=.4,color='red',linewidth=.5,edgecolor='black',normed='Yes')\n",
    "        DF['price'].plot(kind='kde',linewidth=2,color='red',label='Filled Data')\n",
    "        \n",
    "    else:\n",
    "        print \"\\n**************************************************************\"\n",
    "        print 'Not enough specific search for this area with the zip code {}.'.format(Area)\n",
    "        \n",
    "    \n",
    "    \n",
    "    \n",
    "    ind_q_Supply = (listing['ZIP']==q)\n",
    "    q_data_Supply = listing.ix[ind_q_Supply]\n",
    "\n",
    "\n",
    "    if q_data_Supply.shape[0]>=1:\n",
    "    \n",
    "        rooms = []\n",
    "        sizes =[]\n",
    "        prices = []\n",
    "        print \"**************************************************************\"\n",
    "        print 'Number of unique Supply search: {}.'.format(q_data_Supply.shape[0])\n",
    "        \n",
    "            \n",
    "        ax = plt.subplot(3,3,4)\n",
    "        plt.title('Supply: Room')\n",
    "        \n",
    "        rooms = q_data_Supply['Rooms'].dropna().values[:]\n",
    "        room_bin = range(int(stat['room_min'].ix['2%']/10),int(stat['room_max'].ix['99.5%']/10+1))\n",
    "        a = plt.hist(rooms,bins=room_bin,alpha=1,color='blue',linewidth=.5,edgecolor='black',rwidth=1)\n",
    "        ax.yaxis.grid(True)\n",
    "    \n",
    "        ax = plt.subplot(3,3,5)\n",
    "        plt.title('Supply: Size')\n",
    "    \n",
    "        mn = int(stat['size_min'].ix['2%'])\n",
    "        mx = int(stat['size_max'].ix['99%'])\n",
    "        R = mx-mn\n",
    "        # stp = int(R/30)\n",
    "        stp = 20\n",
    "        size_bin = range(mn,mx+stp,stp)\n",
    "        \n",
    "        \n",
    "        sizes = q_data_Supply['Living space'].dropna().values[:]\n",
    "        \n",
    "        a = plt.hist(sizes,bins=size_bin,alpha=1,color='blue',linewidth=.5,edgecolor='black')\n",
    "        ax.yaxis.grid(True)\n",
    "    \n",
    "        ax = plt.subplot(3,3,6)\n",
    "        plt.title('Supply: Price')\n",
    "        \n",
    "        mn = int(stat['price_min'].ix['2%'])\n",
    "        mx = int(stat['price_max'].ix['99.5%'])\n",
    "        R = mx-mn\n",
    "        stp = 200\n",
    "        price_bin = range(mn,mx+stp,stp)\n",
    "        \n",
    "        prices = q_data_Supply['Rent'].dropna().values[:]\n",
    "        a = plt.hist(prices,bins=price_bin,alpha=1,color='blue',linewidth=.5,edgecolor='black')\n",
    "        \n",
    "        ax.yaxis.grid(True)\n",
    "        plt.tight_layout()\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "#         a = plt.hist(rooms,bins=room_bin,alpha=.4,color='blue',linewidth=.5,edgecolor='black',normed='Yes')\n",
    "#         q_data_Supply['Rooms'].plot(kind='kde',linewidth=2,color='blue',label='Filled Data')\n",
    "        \n",
    "    \n",
    "        ax = plt.subplot(3,3,7)\n",
    "        DF = pd.DataFrame(data=rooms,columns=['room'])\n",
    "        DF['room'].plot(kind='kde',linewidth=2,color='blue',label='Filled Data')\n",
    "        mn = int(stat['room_min'].ix['2%']/10)\n",
    "        mx = int(stat['room_max'].ix['99%']/10+1)\n",
    "        plt.xlim(mn,mx)\n",
    "        plt.title('Relative Supply/Demand Distributions: Room')\n",
    "        \n",
    "        \n",
    "        ax = plt.subplot(3,3,8)\n",
    "        DF = pd.DataFrame(data=sizes,columns=['size'])\n",
    "        DF['size'].plot(kind='kde',linewidth=2,color='blue',label='Filled Data')\n",
    "#         a = plt.hist(sizes,bins=size_bin,alpha=.4,color='blue',linewidth=.5,edgecolor='black',normed='Yes')\n",
    "#         q_data_Supply['Living space'].plot(kind='kde',linewidth=2,color='blue',label='Filled Data')\n",
    "        \n",
    "        mn = int(stat['size_min'].ix['2%'])\n",
    "        mx = int(stat['size_max'].ix['99%'])\n",
    "        plt.xlim(mn,mx)\n",
    "        plt.title('Relative Supply/Demand Distributions: Size')\n",
    "        \n",
    "        ax = plt.subplot(3,3,9)\n",
    "        DF = pd.DataFrame(data=prices,columns=['price'])\n",
    "        DF['price'].plot(kind='kde',linewidth=2,color='blue',label='Filled Data')\n",
    "#         a = plt.hist(prices,bins=price_bin,alpha=.4,color='blue',linewidth=.5,edgecolor='black',normed='Yes')\n",
    "#         q_data_Supply['Rent'].plot(kind='kde',linewidth=2,color='blue',label='Filled Data')\n",
    "        \n",
    "        mn = int(stat['price_min'].ix['2%'])\n",
    "        mx = int(stat['price_max'].ix['99.5%'])\n",
    "        plt.xlim(mn,mx)\n",
    "        plt.title('Relative Supply/Demand Distributions: Price')\n",
    "        \n",
    "        \n",
    "    else:\n",
    "        print \"\\n**************************************************************\"\n",
    "        print 'Not enough Supply this area with the zip code {}.'.format(Area)\n",
    "        return\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def histDemand(Area,generator):\n",
    "    generator(Area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique specific search: 1354.\n",
      "**************************************************************\n",
      "Number of unique Supply search: 140.\n"
     ]
    },
    {
     "data": {
      "image/png": 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OPWZWhpxzz61pWwALgIeBd5B1shYBt5NdpnispH2q6sysi0x2Svw/yJLDtWTf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\n6uRqZicQBj4od4hPEWlAyotEpJKU50g9Sa1my8LQjOPN7Pc5+8+NSt9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Tufvp1691X9++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\nooWSAS4Bx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Jbqz9df62Joz55J9eN76SVYvVqtWqeckrTbJo8+feDEE+GPP+Af//Bbmqj5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19ca02510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(histDemand, Area=(8001,8009,1),\n",
    "        generator={'Supply':hist1d_Supply,'One dimensional': hist1d, 'Three dimensional': hist3d,'Suppply_Demand_normed':hist1d_Supply_Demand\n",
    "                   });\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sensitivity Analysis\n",
    "### Willingness to pay <-> (Area, Rooms, Size, Price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def sensitivit_analysis(Area,Rooms,Size,Rent):\n",
    "    import itertools\n",
    "    import numpy as np\n",
    "\n",
    "    q = Area\n",
    "    house = [Rooms,Size,Rent]\n",
    "    howmanydemand= 0\n",
    "\n",
    "\n",
    "    ind_q_specific = (Complete_data['zip']==q) & (Complete_data['specific_search']==1)\n",
    "    q_data_specific = Complete_data.ix[ind_q_specific,:]\n",
    "    q_data_specific = Complete_data.ix[ind_q_specific,:]\n",
    "    rooms = []\n",
    "    sizes =[]\n",
    "    prices = []\n",
    "    if len(q_data_specific) >= 1:\n",
    "        for i in range(len(q_data_specific)):\n",
    "            room_rng = (q_data_specific['room_min'].values[i]/10, q_data_specific['room_max'].values[i]/10)\n",
    "            size_rng = (q_data_specific['size_min'].values[i], q_data_specific['size_max'].values[i])\n",
    "            price_rng = (q_data_specific['price_min'].values[i], q_data_specific['price_max'].values[i])\n",
    "            \n",
    "            if (house[0]>=room_rng[0]):\n",
    "                if (house[1]>=size_rng[0]):\n",
    "                    if (house[2]<=price_rng[-1]):\n",
    "                        howmanydemand+=1\n",
    "    \n",
    "        print \"\\n**************************************************************\"\n",
    "        print 'Total demand for this house is {} out of {} specific demands, which is {}% of the demand in this region.\\\n",
    "        '.format(howmanydemand,len(q_data_specific),np.around(100*howmanydemand/float(len(q_data_specific)),decimals=2))\n",
    "        print \"**************************************************************\"\n",
    "    else:\n",
    "        print 'Not enough data for this area with the zip code {}.'.format(Area)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "**************************************************************\n",
      "Total demand for this house is 115 out of 1354 specific demands, which is 8.49% of the demand in this region.        \n",
      "**************************************************************\n"
     ]
    }
   ],
   "source": [
    "interact(sensitivit_analysis, Area=(8001,8012,1),Rooms=(1,10,1),Size=(20,400,20),Rent=(200,6000,100));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Complete_data_zip = Complete_data.copy()\n",
    "# Complete_data_zip.index = Complete_data_zip['zip']\n",
    "# print Complete_data_zip.shape\n",
    "# Complete_data_zip_specific = Complete_data_zip.ix[Complete_data_zip.specific_search==1]\n",
    "# print Complete_data_zip_specific.shape\n",
    "# zip_GB = Complete_data_zip_specific.groupby(by='zip')\n",
    "# long_lat_zip = zip_GB['zip','lon','lat'].first()\n",
    "\n",
    "# ind_zip = zip_GB.specific_search.sum()>20\n",
    "# total_specific_demand=zip_GB.specific_search.sum()[ind_zip]\n",
    "# # Complete_data_zip.ix[ind_zip].groupby(by='zip').filter(lambda x: x.specific_search.sum()>100).groupby(by='zip').specific_search.sum().shape\n",
    "# total_interest_in_property = Complete_data_zip_specific.ix[ind_zip].groupby(by='zip').apply(check_search)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def sensitivit_analysis_all_f(Rooms,New_Rooms,Size,New_Size,Rent,New_Rent,Demand_Threshold):\n",
    "    cmapname=\"RdYlBu_r\"\n",
    "#     cmapname=\"B\"\n",
    "    import itertools\n",
    "    import numpy as np    \n",
    "    \n",
    "    def check_search(df):\n",
    "        a = df['room_min'].values[:]<=house[0]*10\n",
    "        b = df['size_min'].values[:]<=house[1]\n",
    "        c = df['price_max'].values[:]>=house[2]\n",
    "        return (a*b*c).sum()\n",
    "    \n",
    "    house = [Rooms,Size,Rent]\n",
    "    house1 = [New_Rooms,New_Size,New_Rent]\n",
    "    \n",
    "    geo_info = []\n",
    "    total_demand = []\n",
    "    percentofchange = []\n",
    "    \n",
    "    Complete_data_zip = Complete_data.copy()\n",
    "    Complete_data_zip.index = Complete_data_zip['zip']\n",
    "#     print Complete_data_zip.shape\n",
    "    Complete_data_zip_specific = Complete_data_zip.ix[Complete_data_zip.specific_search==1]\n",
    "    \n",
    "    long_lat_zip_all = Complete_data_zip.groupby(by='zip')['zip','lon','lat'].first()\n",
    "    \n",
    "#     print Complete_data_zip_specific.shape\n",
    "    zip_GB = Complete_data_zip_specific.groupby(by='zip')\n",
    "    long_lat_zip_specific = zip_GB['zip','lon','lat'].first()\n",
    "    \n",
    "    ind_zip = zip_GB.specific_search.sum()>Demand_Threshold\n",
    "    \n",
    "    \n",
    "    long_lat_zip_sel = long_lat_zip_specific.ix[ind_zip]\n",
    "    \n",
    "    total_specific_demand=zip_GB.specific_search.sum()[ind_zip]\n",
    "    total_demand = total_specific_demand.values[:]\n",
    "    \n",
    "    total_interest_in_property0 = Complete_data_zip_specific.ix[ind_zip].groupby(by='zip').apply(check_search)\n",
    "    \n",
    "    \n",
    "    house = house1\n",
    "    total_interest_in_property1 = Complete_data_zip_specific.ix[ind_zip].groupby(by='zip').apply(check_search)\n",
    "#     print total_interest_in_property1/(total_specific_demand.values[:]).astype(float)\n",
    "\n",
    "    percentofchange = 100*(total_interest_in_property1.values[:]-total_interest_in_property0.values[:])/(total_specific_demand.values[:]).astype(float)\n",
    "    percentofcoverage = 100*total_interest_in_property1.values[:]/(total_specific_demand.values[:]).astype(float)\n",
    "    \n",
    "    \n",
    "    #To Plot\n",
    "    fig = plt.figure(figsize=(20,12))\n",
    "    ax = fig.add_subplot(2,2,1)\n",
    "    sc = plt.scatter(long_lat_zip_all.lon,long_lat_zip_all.lat,c='None',s=20,marker='o',edgecolor='gray',linewidth=.3, cmap=cmapname ,alpha=.4)\n",
    "    \n",
    "    mn = np.min(percentofchange)  \n",
    "    mx = np.max(percentofchange)\n",
    "    mn = -50\n",
    "    mx = 50\n",
    "    sc = plt.scatter(long_lat_zip_sel.lon,long_lat_zip_sel.lat,c=percentofchange,s=20,vmin=mn,vmax=mx,marker='o',edgecolor='None', cmap=cmapname ,alpha=1)\n",
    "    \n",
    "    eps = .004\n",
    "    X_mn= valued_Data.lon.min()*(1-eps)\n",
    "    Y_mn= valued_Data.lat.min()*(1-eps)\n",
    "    X_mx= valued_Data.lon.max()*(1+eps)\n",
    "    Y_mx= valued_Data.lat.max()*(1+eps)\n",
    "    plt.xlim(X_mn,X_mx)\n",
    "    plt.ylim(Y_mn,Y_mx)\n",
    "    if mn == mx:\n",
    "        mx = mn + 6\n",
    "    plt.colorbar(sc,ticks=np.round(np.linspace(mn,mx,5),decimals=3).astype(int),shrink=0.6)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.title(\"Percent of Change in Demand Coverage\")\n",
    "    plt.axis('off')\n",
    "    \n",
    "    \n",
    "    ax = fig.add_subplot(2,2,2)\n",
    "    sc = plt.scatter(long_lat_zip_all.lon,long_lat_zip_all.lat,c='None',s=20,marker='o',edgecolor='gray',linewidth=.3, cmap=cmapname ,alpha=.4)\n",
    "    \n",
    "    mn = np.min(percentofcoverage)  \n",
    "    mx = np.max(percentofcoverage)\n",
    "    mn = 0\n",
    "    mx =100\n",
    "    sc = plt.scatter(long_lat_zip_sel.lon,long_lat_zip_sel.lat,c=percentofcoverage,s=20,vmin=mn,vmax=mx,marker='o',edgecolor='None', cmap=cmapname ,alpha=1)\n",
    "    \n",
    "    eps = .004\n",
    "    X_mn= valued_Data.lon.min()*(1-eps)\n",
    "    Y_mn= valued_Data.lat.min()*(1-eps)\n",
    "    X_mx= valued_Data.lon.max()*(1+eps)\n",
    "    Y_mx= valued_Data.lat.max()*(1+eps)\n",
    "    plt.xlim(X_mn,X_mx)\n",
    "    plt.ylim(Y_mn,Y_mx)\n",
    "    if mn == mx:\n",
    "        mx = mn + 6\n",
    "    plt.colorbar(sc,ticks=np.round(np.linspace(mn,mx,5),decimals=3).astype(int),shrink=0.6)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.title(\"New Percent of Demand Coverage\")\n",
    "    plt.axis('off')\n",
    "    \n",
    "    \n",
    "    \n",
    "    ax = fig.add_subplot(2,2,3)\n",
    "    sc = plt.scatter(long_lat_zip_all.lon,long_lat_zip_all.lat,c='None',s=20,marker='o',edgecolor='gray',linewidth=.3, cmap=cmapname ,alpha=.4)\n",
    "    \n",
    "    mn = np.min(total_demand)  \n",
    "    mx = np.max(total_demand)\n",
    "    sc = plt.scatter(long_lat_zip_sel.lon,long_lat_zip_sel.lat,c=total_demand,s=20,vmin=mn,vmax=mx,marker='o',edgecolor='None', cmap=cmapname ,alpha=1)\n",
    "    \n",
    "    eps = .004\n",
    "    X_mn= valued_Data.lon.min()*(1-eps)\n",
    "    Y_mn= valued_Data.lat.min()*(1-eps)\n",
    "    X_mx= valued_Data.lon.max()*(1+eps)\n",
    "    Y_mx= valued_Data.lat.max()*(1+eps)\n",
    "    plt.xlim(X_mn,X_mx)\n",
    "    plt.ylim(Y_mn,Y_mx)\n",
    "    if mn == mx:\n",
    "        mx = mn + 6\n",
    "    plt.colorbar(sc,ticks=np.round(np.linspace(mn,mx,5),decimals=3).astype(int),shrink=0.6)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.title(\"Total Specific Demand\")\n",
    "    plt.axis('off')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    font = {'size'   : 12}\n",
    "    plt.rc('font', **font)\n",
    "    plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "global name 'Complete_data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-ab264e5b1f78>\u001b[0m in \u001b[0;36msensitivit_analysis_all_f\u001b[0;34m(Rooms, New_Rooms, Size, New_Size, Rent, New_Rent, Demand_Threshold)\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0mpercentofchange\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m     \u001b[0mComplete_data_zip\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mComplete_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m     \u001b[0mComplete_data_zip\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mComplete_data_zip\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'zip'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;31m#     print Complete_data_zip.shape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: global name 'Complete_data' is not defined"
     ]
    }
   ],
   "source": [
    "interact(sensitivit_analysis_all_f,Rooms=(1,6,1),New_Rooms=(1,6,1),Size=(50,250,20),New_Size=(50,250,20),Rent=(1000,5000,200),New_Rent=(1000,5000,200),Demand_Threshold=(10,100,40));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Similar Analysis for Homegate Supply Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(55361, 8)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ZIP</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Living space</th>\n",
       "      <th>Year built</th>\n",
       "      <th>Last renovation</th>\n",
       "      <th>Rent</th>\n",
       "      <th>lng</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1954.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>645.0</td>\n",
       "      <td>8.041672</td>\n",
       "      <td>47.397999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>8.041403</td>\n",
       "      <td>47.396472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>58.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1116.0</td>\n",
       "      <td>8.050583</td>\n",
       "      <td>47.387093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>8.037885</td>\n",
       "      <td>47.387531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5000</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1290.0</td>\n",
       "      <td>8.031479</td>\n",
       "      <td>47.384245</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ZIP  Rooms  Living space  Year built  Last renovation    Rent       lng  \\\n",
       "0  5000    1.0          28.0      1954.0              NaN   645.0  8.041672   \n",
       "1  5000    2.0           NaN         NaN           2016.0  1400.0  8.041403   \n",
       "2  5000    2.5          58.0         NaN              NaN  1116.0  8.050583   \n",
       "3  5000    3.0           NaN         NaN           2013.0  1250.0  8.037885   \n",
       "4  5000    3.5           NaN         NaN              NaN  1290.0  8.031479   \n",
       "\n",
       "         lat  \n",
       "0  47.397999  \n",
       "1  47.396472  \n",
       "2  47.387093  \n",
       "3  47.387531  \n",
       "4  47.384245  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = '/Users/SVM/Dropbox/Applications/Crawlers/homegate/DB/rental_DB/homegatedb__'+'all_latlng_complete'+'rent.csv'\n",
    "listing_with_latlong = pd.read_csv(path)\n",
    "listing_with_latlong.head()\n",
    "\n",
    "#Kinds of features we are interested in\n",
    "cols = ['ID', 'ZIP', 'Date','Type','Rooms','Floor',\n",
    "       'Living space','Floor space','Room height','Volume',\n",
    "       'Year built','Last renovation','Net rent','Additional expenses',\n",
    "       'Rent','Available','lng','lat']\n",
    "listing = listing_with_latlong[cols]\n",
    "\n",
    "#Types of listing we are interested in\n",
    "Types = ['Apartment','Attic compartment','Attic flat','Bachelor flat','Bifamiliar house','Cellar compartment','Chalet',\n",
    "       'Duplex','Farm house','Granny flat','Home','Roof flat','Row house','Rustic house','Single house','Studio','Terrace flat',\n",
    "       'Terrace house','Villa']\n",
    "id_type = []\n",
    "for i in range(listing.shape[0]):\n",
    "    if listing['Type'].values[i] in Types:\n",
    "        id_type.append(i)\n",
    "listing = listing.ix[id_type]\n",
    "\n",
    "cols = ['ZIP','Rooms',\n",
    "       'Living space',\n",
    "       'Year built','Last renovation',\n",
    "       'Rent','lng','lat']\n",
    "listing = listing[cols]\n",
    "listing['ZIP'].ix[:] = (listing['ZIP'].values[:]).astype(int) \n",
    "ind_by = listing['Rent']=='by request' \n",
    "listing['Rent'].ix[ind_by] =np.nan\n",
    "ind_by = listing['Rent']=='EUR 2635' \n",
    "listing['Rent'].ix[ind_by] =np.nan\n",
    "\n",
    "ind_by = listing['Rent']=='BGN 1630' \n",
    "listing['Rent'].ix[ind_by] =np.nan\n",
    "\n",
    "listing['Rent'] = listing['Rent'].values[:].astype(float) \n",
    "\n",
    "#Remove outliers based on a global statistics, calculated previously\n",
    "# \n",
    "Supply_stat = listing.describe(percentiles=[.001,.01,.2,.5,.99,.999])\n",
    "for f in ['Rooms','Living space','Rent']:\n",
    "    mx = Supply_stat[f].ix['99.9%']\n",
    "    ind = listing[f]>mx\n",
    "    listing[f].ix[ind]=mx\n",
    "    mn = Supply_stat[f].ix['0.1%']\n",
    "    ind = listing[f]<mn\n",
    "    listing[f].ix[ind]=mn\n",
    "\n",
    "\n",
    "print listing.shape\n",
    "listing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Address</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>Date</th>\n",
       "      <th>Type</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Floor</th>\n",
       "      <th>Living space</th>\n",
       "      <th>Floor space</th>\n",
       "      <th>Room height</th>\n",
       "      <th>...</th>\n",
       "      <th>Available</th>\n",
       "      <th>Public transport</th>\n",
       "      <th>Shopping</th>\n",
       "      <th>Kindergarten</th>\n",
       "      <th>Primary school</th>\n",
       "      <th>Secondary school</th>\n",
       "      <th>Motorway</th>\n",
       "      <th>URL</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>105742696</td>\n",
       "      <td>Sonnmattweg 17 5000 Aarau</td>\n",
       "      <td>5000</td>\n",
       "      <td>04/07/16</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>by agreement</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://www.homegate.ch/rent/105742696</td>\n",
       "      <td>47.397999</td>\n",
       "      <td>8.041672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>105785811</td>\n",
       "      <td>K�_ttigerstrasse 6 5000 Aarau</td>\n",
       "      <td>5000</td>\n",
       "      <td>04/07/16</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>01.08.2016</td>\n",
       "      <td>10.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://www.homegate.ch/rent/105785811</td>\n",
       "      <td>47.396472</td>\n",
       "      <td>8.041403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>105842788</td>\n",
       "      <td>G̦nhardweg 8 5000 Aarau</td>\n",
       "      <td>5000</td>\n",
       "      <td>04/07/16</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>2.5</td>\n",
       "      <td>7</td>\n",
       "      <td>58.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>01.10.2016</td>\n",
       "      <td>200.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://www.homegate.ch/rent/105842788</td>\n",
       "      <td>47.387093</td>\n",
       "      <td>8.050583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>105849196</td>\n",
       "      <td>DAMMWEG 8a 5000 Aarau</td>\n",
       "      <td>5000</td>\n",
       "      <td>04/07/16</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>01.10.2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://www.homegate.ch/rent/105849196</td>\n",
       "      <td>47.387531</td>\n",
       "      <td>8.037885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>105502493</td>\n",
       "      <td>W̦schnauring 23 5000 Aarau</td>\n",
       "      <td>5000</td>\n",
       "      <td>04/07/16</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>immediately</td>\n",
       "      <td>400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://www.homegate.ch/rent/105502493</td>\n",
       "      <td>47.384245</td>\n",
       "      <td>8.031479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID                        Address   ZIP      Date       Type  Rooms  \\\n",
       "0  105742696      Sonnmattweg 17 5000 Aarau  5000  04/07/16  Apartment    1.0   \n",
       "1  105785811  K�_ttigerstrasse 6 5000 Aarau  5000  04/07/16  Apartment    2.0   \n",
       "2  105842788        G̦nhardweg 8 5000 Aarau  5000  04/07/16  Apartment    2.5   \n",
       "3  105849196          DAMMWEG 8a 5000 Aarau  5000  04/07/16  Apartment    3.0   \n",
       "4  105502493     W̦schnauring 23 5000 Aarau  5000  04/07/16  Apartment    3.5   \n",
       "\n",
       "  Floor  Living space  Floor space Room height    ...        Available  \\\n",
       "0     2          28.0          NaN         NaN    ...     by agreement   \n",
       "1     2           NaN          NaN         NaN    ...       01.08.2016   \n",
       "2     7          58.0          NaN         NaN    ...       01.10.2016   \n",
       "3     1           NaN          NaN         NaN    ...       01.10.2016   \n",
       "4     1           NaN          NaN         NaN    ...      immediately   \n",
       "\n",
       "   Public transport  Shopping Kindergarten Primary school Secondary school  \\\n",
       "0             700.0    1000.0        700.0          700.0           1300.0   \n",
       "1              10.0     150.0          NaN            NaN              NaN   \n",
       "2             200.0     800.0        500.0          400.0            400.0   \n",
       "3               NaN       NaN          NaN            NaN              NaN   \n",
       "4             400.0       NaN       1200.0         1200.0           1200.0   \n",
       "\n",
       "  Motorway                                    URL        lat       lng  \n",
       "0      NaN  http://www.homegate.ch/rent/105742696  47.397999  8.041672  \n",
       "1      NaN  http://www.homegate.ch/rent/105785811  47.396472  8.041403  \n",
       "2      NaN  http://www.homegate.ch/rent/105842788  47.387093  8.050583  \n",
       "3      NaN  http://www.homegate.ch/rent/105849196  47.387531  8.037885  \n",
       "4      NaN  http://www.homegate.ch/rent/105502493  47.384245  8.031479  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listing_with_latlong.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ZIP</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Living space</th>\n",
       "      <th>Year built</th>\n",
       "      <th>Last renovation</th>\n",
       "      <th>Rent</th>\n",
       "      <th>lng</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>55361.000000</td>\n",
       "      <td>54295.000000</td>\n",
       "      <td>47373.000000</td>\n",
       "      <td>26240.000000</td>\n",
       "      <td>10042.000000</td>\n",
       "      <td>5.412800e+04</td>\n",
       "      <td>55361.000000</td>\n",
       "      <td>55361.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5623.628475</td>\n",
       "      <td>3.653740</td>\n",
       "      <td>93.991366</td>\n",
       "      <td>1984.204688</td>\n",
       "      <td>2011.182035</td>\n",
       "      <td>2.235992e+03</td>\n",
       "      <td>8.026778</td>\n",
       "      <td>47.095559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2822.045670</td>\n",
       "      <td>1.325539</td>\n",
       "      <td>47.324266</td>\n",
       "      <td>71.349375</td>\n",
       "      <td>12.734647</td>\n",
       "      <td>1.318378e+04</td>\n",
       "      <td>0.913221</td>\n",
       "      <td>0.463472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1014.000000</td>\n",
       "      <td>7.000000e+01</td>\n",
       "      <td>5.968461</td>\n",
       "      <td>45.821860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1%</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1432.717000</td>\n",
       "      <td>1968.041000</td>\n",
       "      <td>3.625400e+02</td>\n",
       "      <td>6.017421</td>\n",
       "      <td>45.836494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1%</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>1805.390000</td>\n",
       "      <td>1987.000000</td>\n",
       "      <td>6.800000e+02</td>\n",
       "      <td>6.121491</td>\n",
       "      <td>45.990131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>1950.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>1964.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>1.356000e+03</td>\n",
       "      <td>7.136893</td>\n",
       "      <td>46.544679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6003.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>1996.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>1.800000e+03</td>\n",
       "      <td>8.307027</td>\n",
       "      <td>47.293978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99%</th>\n",
       "      <td>9500.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>245.280000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>6.900000e+03</td>\n",
       "      <td>9.603502</td>\n",
       "      <td>47.696250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99.9%</th>\n",
       "      <td>9642.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>450.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>1.450000e+04</td>\n",
       "      <td>9.904930</td>\n",
       "      <td>47.747664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9658.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>3500.000000</td>\n",
       "      <td>9999.000000</td>\n",
       "      <td>2022.000000</td>\n",
       "      <td>1.380000e+06</td>\n",
       "      <td>10.370742</td>\n",
       "      <td>47.783855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ZIP         Rooms  Living space    Year built  \\\n",
       "count  55361.000000  54295.000000  47373.000000  26240.000000   \n",
       "mean    5623.628475      3.653740     93.991366   1984.204688   \n",
       "std     2822.045670      1.325539     47.324266     71.349375   \n",
       "min     1000.000000      1.000000      1.000000      1.000000   \n",
       "0.1%    1000.000000      1.000000     12.000000   1432.717000   \n",
       "1%      1000.000000      1.000000     24.000000   1805.390000   \n",
       "20%     1950.000000      2.500000     63.000000   1964.000000   \n",
       "50%     6003.000000      3.500000     88.000000   1996.000000   \n",
       "99%     9500.000000      7.500000    245.280000   2016.000000   \n",
       "99.9%   9642.000000     10.000000    450.000000   2017.000000   \n",
       "max     9658.000000     75.000000   3500.000000   9999.000000   \n",
       "\n",
       "       Last renovation          Rent           lng           lat  \n",
       "count     10042.000000  5.412800e+04  55361.000000  55361.000000  \n",
       "mean       2011.182035  2.235992e+03      8.026778     47.095559  \n",
       "std          12.734647  1.318378e+04      0.913221      0.463472  \n",
       "min        1014.000000  7.000000e+01      5.968461     45.821860  \n",
       "0.1%       1968.041000  3.625400e+02      6.017421     45.836494  \n",
       "1%         1987.000000  6.800000e+02      6.121491     45.990131  \n",
       "20%        2008.000000  1.356000e+03      7.136893     46.544679  \n",
       "50%        2013.000000  1.800000e+03      8.307027     47.293978  \n",
       "99%        2016.000000  6.900000e+03      9.603502     47.696250  \n",
       "99.9%      2017.000000  1.450000e+04      9.904930     47.747664  \n",
       "max        2022.000000  1.380000e+06     10.370742     47.783855  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Supply_stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def hist1d_Supply(Area):\n",
    "    q = Area\n",
    "# #     q = 8134\n",
    "#     # ind_q_specific = (Complete_data['zip']==q) & (Complete_data['specific_search']==1)\n",
    "#     ind_q_vicinity = (Complete_data['zip']==q)\n",
    "#     q_data_vicinity = Complete_data.ix[ind_q_vicinity,:]\n",
    "\n",
    "    fig = plt.figure(figsize=(12,3))\n",
    "    font = {'size'   : 8}\n",
    "    plt.rc('font', **font)\n",
    "    ind_q_Supply = (listing['ZIP']==q)\n",
    "    q_data_Supply = listing.ix[ind_q_Supply]\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    if q_data_Supply.shape[0]>=1:\n",
    "    \n",
    "        rooms = []\n",
    "        sizes =[]\n",
    "        prices = []\n",
    "        print \"**************************************************************\"\n",
    "        print 'Number of unique Supply Ads: {}.'.format(q_data_Supply.shape[0])\n",
    "        \n",
    "            \n",
    "        ax = plt.subplot(1,3,1)\n",
    "        plt.title('Supply: Room')\n",
    "        \n",
    "        rooms = q_data_Supply['Rooms'].dropna().values[:]\n",
    "        room_bin = range(int(stat['room_min'].ix['2%']/10),int(stat['room_max'].ix['99.5%']/10+1))\n",
    "        a = plt.hist(rooms,bins=room_bin,alpha=1,color='blue',linewidth=.5,edgecolor='black',rwidth=1,normed=True)\n",
    "        ax.yaxis.grid(True)\n",
    "        \n",
    "        ax = plt.subplot(1,3,2)\n",
    "        plt.title('Supply: Size')\n",
    "    \n",
    "        mn = int(stat['size_min'].ix['2%'])\n",
    "        mx = int(stat['size_max'].ix['99%'])\n",
    "        R = mx-mn\n",
    "        # stp = int(R/30)\n",
    "        stp = 20\n",
    "        size_bin = range(mn,mx+stp,stp)\n",
    "        \n",
    "        \n",
    "        sizes = q_data_Supply['Living space'].dropna().values[:]\n",
    "        \n",
    "        a = plt.hist(sizes,bins=size_bin,alpha=1,color='blue',linewidth=.5,edgecolor='black',normed=True)\n",
    "        ax.yaxis.grid(True)\n",
    "    \n",
    "        ax = plt.subplot(1,3,3)\n",
    "        plt.title('Supply: Price')\n",
    "        \n",
    "        mn = int(stat['price_min'].ix['2%'])\n",
    "        mx = int(stat['price_max'].ix['99.5%'])\n",
    "        R = mx-mn\n",
    "        stp = 200\n",
    "        price_bin = range(mn,mx+stp,stp)\n",
    "        \n",
    "        prices = q_data_Supply['Rent'].dropna().values[:]\n",
    "        a = plt.hist(prices,bins=price_bin,alpha=1,color='blue',linewidth=.5,edgecolor='black',normed=True)\n",
    "        \n",
    "        ax.yaxis.grid(True)\n",
    "        plt.tight_layout()\n",
    "        \n",
    "        \n",
    "#         Data = rooms.astype(float)[:,np.newaxis]\n",
    "#         print Data.shape\n",
    "#         msz0 = 30\n",
    "#         msz1 = 30\n",
    "#         sm1 = SOM.SOM('hhhh', Data, mapsize = [msz0, msz1],norm_method = 'var',initmethod='pca')\n",
    "#         sm1.train(n_job = 1, shared_memory = 'no',verbose='final')\n",
    "#         cd = SOM.denormalize_by(sm1.data_raw,sm1.codebook)\n",
    "\n",
    "        \n",
    "#         a = plt.hist(rooms,bins=room_bin,alpha=.4,color='blue',linewidth=.5,edgecolor='black',normed='Yes')\n",
    "#         q_data_Supply['Rooms'].plot(kind='kde',linewidth=2,color='blue',label='Filled Data')\n",
    "        \n",
    "    \n",
    "        ax = plt.subplot(1,3,1)\n",
    "        DF = pd.DataFrame(data=rooms,columns=['room'])\n",
    "        DF['room'].plot(kind='kde',linewidth=2,color='red',label='Filled Data')\n",
    "        \n",
    "#         ax = plt.subplot(1,3,1)\n",
    "#         DF = pd.DataFrame(data=cd[:,0],columns=['room'])\n",
    "#         DF['room'].plot(kind='kde',linewidth=2,color='green',label='Filled Data')\n",
    "        \n",
    "        mn = int(stat['room_min'].ix['2%']/10)\n",
    "        mx = int(stat['room_max'].ix['99%']/10+1)\n",
    "        plt.xlim(mn,mx)\n",
    "        plt.title('Relative SupplyDistributions: Room')\n",
    "        \n",
    "        \n",
    "        ax = plt.subplot(1,3,2)\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        DF = pd.DataFrame(data=sizes,columns=['size'])\n",
    "        DF['size'].plot(kind='kde',linewidth=2,color='red',label='Filled Data')\n",
    "#         a = plt.hist(sizes,bins=size_bin,alpha=.4,color='blue',linewidth=.5,edgecolor='black',normed='Yes')\n",
    "#         q_data_Supply['Living space'].plot(kind='kde',linewidth=2,color='blue',label='Filled Data')\n",
    "        \n",
    "        mn = int(stat['size_min'].ix['2%'])\n",
    "        mx = int(stat['size_max'].ix['99%'])\n",
    "        plt.xlim(mn,mx)\n",
    "        plt.title('Relative Supply Distributions: Size')\n",
    "        \n",
    "        ax = plt.subplot(1,3,3)\n",
    "        \n",
    "        \n",
    "#         Data = prices.astype(float)[:,np.newaxis]\n",
    "# #         Data = np.concatenate((Data,Data,Data),axis=0)\n",
    "#         print Data.shape\n",
    "#         msz0 = 10\n",
    "#         msz1 = 10\n",
    "#         sm1 = SOM.SOM('hhhh', Data, mapsize = [msz0, msz1],norm_method = 'var',initmethod='pca')\n",
    "#         sm1.train(n_job = 1, shared_memory = 'no',verbose='final')\n",
    "#         cd = SOM.denormalize_by(sm1.data_raw,sm1.codebook)\n",
    "        \n",
    "#         DF = pd.DataFrame(data=cd,columns=['price'])\n",
    "#         DF['price'].plot(kind='kde',linewidth=2,color='green',label='Filled Data')\n",
    "        \n",
    "        \n",
    "        DF = pd.DataFrame(data=prices,columns=['price'])\n",
    "        DF['price'].plot(kind='kde',linewidth=2,color='red',label='Filled Data')\n",
    "#         a = plt.hist(prices,bins=price_bin,alpha=.4,color='blue',linewidth=.5,edgecolor='black',normed='Yes')\n",
    "#         q_data_Supply['Rent'].plot(kind='kde',linewidth=2,color='blue',label='Filled Data')\n",
    "        \n",
    "        mn = int(stat['price_min'].ix['2%'])\n",
    "        mx = int(stat['price_max'].ix['99.5%'])\n",
    "        plt.xlim(mn,mx)\n",
    "        plt.title('Relative Supply Distributions: Price')\n",
    "        \n",
    "        \n",
    "    else:\n",
    "        print \"\\n**************************************************************\"\n",
    "        print 'Not enough Supply this area with the zip code {}.'.format(Area)\n",
    "        return\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def hist1d_Supply_query(Area,Min_Rooms,Max_Rooms,Min_Size,Max_Size,Min_Rent,Max_Rent,Supply_Threshold):\n",
    "    q = Area\n",
    "# #     q = 8134\n",
    "#     # ind_q_specific = (Complete_data['zip']==q) & (Complete_data['specific_search']==1)\n",
    "#     ind_q_vicinity = (Complete_data['zip']==q)\n",
    "#     q_data_vicinity = Complete_data.ix[ind_q_vicinity,:]\n",
    "\n",
    "    fig = plt.figure(figsize=(12,3))\n",
    "    font = {'size'   : 8}\n",
    "    plt.rc('font', **font)\n",
    "    ind_q_Supply = (listing['ZIP']==q)\n",
    "    q_data_Supply = listing.ix[ind_q_Supply]\n",
    "    \n",
    "    \n",
    "    import itertools\n",
    "    import numpy as np    \n",
    "    \n",
    "    if Min_Rooms >= Max_Rooms:\n",
    "            Max_Rooms = Min_Rooms\n",
    "    if Min_Size>=Max_Size:\n",
    "            Max_Size= Min_Size\n",
    "    if Min_Rent>=Max_Rent:\n",
    "            Max_Rent= Min_Rent\n",
    "            \n",
    "    def check_search(df):\n",
    "        \n",
    "        amn = df['Rooms']>=Min_Rooms\n",
    "        amx = df['Rooms']<=Max_Rooms\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        bmn = df['Living space']>=Min_Size\n",
    "        bmx = df['Living space']<=Max_Size\n",
    "        \n",
    "        \n",
    "        cmn = df['Rent']>=Min_Rent\n",
    "        cmx = df['Rent']<=Max_Rent\n",
    "        \n",
    "        return amn&bmn&cmn&amx&bmx&cmx\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "        \n",
    "    if q_data_Supply.shape[0]>=1:\n",
    "    \n",
    "        rooms = []\n",
    "        sizes =[]\n",
    "        prices = []\n",
    "        print \"**************************************************************\"\n",
    "        print 'Number of unique Supply Ads: {}.'.format(q_data_Supply.shape[0])\n",
    "        \n",
    "            \n",
    "        ax = plt.subplot(1,3,1)\n",
    "        plt.title('Supply: Room')\n",
    "        \n",
    "        rooms = q_data_Supply['Rooms'].dropna().values[:]\n",
    "        room_bin = range(int(stat['room_min'].ix['2%']/10),int(stat['room_max'].ix['99.5%']/10+1))\n",
    "        a = plt.hist(rooms,bins=room_bin,alpha=1,color='white',linewidth=.5,edgecolor='black',rwidth=1,normed=False)\n",
    "        ax.yaxis.grid(True)\n",
    "        \n",
    "        ax = plt.subplot(1,3,2)\n",
    "        plt.title('Supply: Size')\n",
    "    \n",
    "        mn = int(stat['size_min'].ix['2%'])\n",
    "        mx = int(stat['size_max'].ix['99%'])\n",
    "        R = mx-mn\n",
    "            # stp = int(R/30)\n",
    "        stp = 20\n",
    "        size_bin = range(mn,mx+stp,stp)\n",
    "        \n",
    "        \n",
    "        sizes = q_data_Supply['Living space'].dropna().values[:]\n",
    "        \n",
    "        a = plt.hist(sizes,bins=size_bin,alpha=1,color='white',linewidth=.5,edgecolor='black',normed=False)\n",
    "        ax.yaxis.grid(True)\n",
    "    \n",
    "        ax = plt.subplot(1,3,3)\n",
    "        plt.title('Supply: Price')\n",
    "        \n",
    "        mn = int(stat['price_min'].ix['2%'])\n",
    "        mx = int(stat['price_max'].ix['99.5%'])\n",
    "        R = mx-mn\n",
    "        stp = 200\n",
    "        price_bin = range(mn,mx+stp,stp)\n",
    "        \n",
    "        prices = q_data_Supply['Rent'].dropna().values[:]\n",
    "        a = plt.hist(prices,bins=price_bin,alpha=1,color='white',linewidth=.5,edgecolor='black',normed=False)\n",
    "    \n",
    "        ax.yaxis.grid(True)\n",
    "        plt.tight_layout()\n",
    "\n",
    "#         ax = plt.subplot(1,3,1)\n",
    "#         DF = pd.DataFrame(data=rooms,columns=['room'])\n",
    "#         DF['room'].plot(kind='kde',linewidth=2,color='red',label='Filled Data')\n",
    "        \n",
    "#         mn = int(stat['room_min'].ix['2%']/10)\n",
    "#         mx = int(stat['room_max'].ix['99%']/10+1)\n",
    "#         plt.xlim(mn,mx)\n",
    "#         plt.title('Relative SupplyDistributions: Room')\n",
    "        \n",
    "#         ax = plt.subplot(1,3,2)\n",
    "    \n",
    "#         DF = pd.DataFrame(data=sizes,columns=['size'])\n",
    "#         DF['size'].plot(kind='kde',linewidth=2,color='red',label='Filled Data')\n",
    "#         mn = int(stat['size_min'].ix['2%'])\n",
    "#         mx = int(stat['size_max'].ix['99%'])\n",
    "#         plt.xlim(mn,mx)\n",
    "#         plt.title('Relative Supply Distributions: Size')\n",
    "        \n",
    "#         ax = plt.subplot(1,3,3)\n",
    "        \n",
    "        \n",
    "        \n",
    "#         DF = pd.DataFrame(data=prices,columns=['price'])\n",
    "#         DF['price'].plot(kind='kde',linewidth=2,color='red',label='Filled Data')\n",
    "        \n",
    "        mn = int(stat['price_min'].ix['2%'])\n",
    "        mx = int(stat['price_max'].ix['99.5%'])\n",
    "        plt.xlim(mn,mx)\n",
    "        plt.title('Relative Supply Distributions: Price')\n",
    "        \n",
    "        \n",
    "    else:\n",
    "        print \"\\n**************************************************************\"\n",
    "        print 'Not enough Supply this area with the zip code {}.'.format(Area)\n",
    "        return\n",
    "    \n",
    "    \n",
    "    q_data_Supply_bounded = q_data_Supply.ix[check_search(q_data_Supply)]\n",
    "    \n",
    "    if q_data_Supply_bounded.shape[0]>=1:\n",
    "    \n",
    "        rooms = []\n",
    "        sizes =[]\n",
    "        prices = []\n",
    "        print \"**************************************************************\"\n",
    "        print 'Number of unique Supply Ads based on your query: {}.'.format(q_data_Supply_bounded.shape[0])\n",
    "        \n",
    "            \n",
    "        ax = plt.subplot(1,3,1)\n",
    "        plt.title('No. of rooms')\n",
    "        \n",
    "        rooms = q_data_Supply_bounded['Rooms'].dropna().values[:]\n",
    "        room_bin = range(int(stat['room_min'].ix['2%']/10),int(stat['room_max'].ix['99.5%']/10+1))\n",
    "        a = plt.hist(rooms,bins=room_bin,alpha=1,color='red',linewidth=.5,edgecolor='black',rwidth=1,normed=False)\n",
    "        ax.yaxis.grid(True)\n",
    "        \n",
    "        ax = plt.subplot(1,3,2)\n",
    "        plt.title('Living space (m^2)')\n",
    "    \n",
    "        mn = int(stat['size_min'].ix['2%'])\n",
    "        mx = int(stat['size_max'].ix['99%'])\n",
    "        R = mx-mn\n",
    "            # stp = int(R/30)\n",
    "        stp = 20\n",
    "        size_bin = range(mn,mx+stp,stp)\n",
    "        \n",
    "        \n",
    "        sizes = q_data_Supply_bounded['Living space'].dropna().values[:]\n",
    "        \n",
    "        a = plt.hist(sizes,bins=size_bin,alpha=1,color='red',linewidth=.5,edgecolor='black',normed=False)\n",
    "        ax.yaxis.grid(True)\n",
    "    \n",
    "        ax = plt.subplot(1,3,3)\n",
    "        plt.title('Monthly rent')\n",
    "        \n",
    "        mn = int(stat['price_min'].ix['2%'])\n",
    "        mx = int(stat['price_max'].ix['99.5%'])\n",
    "        R = mx-mn\n",
    "        stp = 200\n",
    "        price_bin = range(mn,mx+stp,stp)\n",
    "        \n",
    "        prices = q_data_Supply_bounded['Rent'].dropna().values[:]\n",
    "        a = plt.hist(prices,bins=price_bin,alpha=1,color='red',linewidth=.5,edgecolor='black',normed=False)\n",
    "    \n",
    "        ax.yaxis.grid(True)\n",
    "        plt.tight_layout()\n",
    "\n",
    "#         ax = plt.subplot(1,3,1)\n",
    "#         DF = pd.DataFrame(data=rooms,columns=['room'])\n",
    "#         DF['room'].plot(kind='kde',linewidth=2,color='green',label='Filled Data')\n",
    "        \n",
    "#         mn = int(stat['room_min'].ix['2%']/10)\n",
    "#         mx = int(stat['room_max'].ix['99%']/10+1)\n",
    "#         plt.xlim(mn,mx)\n",
    "#         plt.title('Relative SupplyDistributions: Room')\n",
    "        \n",
    "#         ax = plt.subplot(1,3,2)\n",
    "    \n",
    "#         DF = pd.DataFrame(data=sizes,columns=['size'])\n",
    "#         DF['size'].plot(kind='kde',linewidth=2,color='green',label='Filled Data')\n",
    "#         mn = int(stat['size_min'].ix['2%'])\n",
    "#         mx = int(stat['size_max'].ix['99%'])\n",
    "#         plt.xlim(mn,mx)\n",
    "#         plt.title('Relative Supply Distributions: Size')\n",
    "        \n",
    "#         ax = plt.subplot(1,3,3)\n",
    "        \n",
    "        \n",
    "        \n",
    "#         DF = pd.DataFrame(data=prices,columns=['price'])\n",
    "#         DF['price'].plot(kind='kde',linewidth=2,color='green',label='Filled Data')\n",
    "        \n",
    "#         mn = int(stat['price_min'].ix['2%'])\n",
    "#         mx = int(stat['price_max'].ix['99.5%'])\n",
    "#         plt.xlim(mn,mx)\n",
    "#         plt.title('Relative Supply Distributions: Price')\n",
    "        \n",
    "        path = '/Users/SVM/Dropbox/Applications/Crawlers/images/'\n",
    "        filename = path + 'ZIP_sensitivity_{}_Min_Rooms_{}_Max_Rooms_{}_Min_Size_{}_Max_Size_{}_Min_Rent_{}_Max_Rent_{}_.png'.format(Area,Min_Rooms,Max_Rooms,Min_Size,Max_Size,Min_Rent,Max_Rent)\n",
    "        fig.savefig(filename, dpi=200)\n",
    "        \n",
    "        \n",
    "    else:\n",
    "        print \"\\n**************************************************************\"\n",
    "        print 'Based on your query there are not enough Supply for this area with the zip code {}.'.format(Area)\n",
    "        return\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**************************************************************\n",
      "Number of unique Supply Ads: 188.\n",
      "**************************************************************\n",
      "Number of unique Supply Ads based on your query: 50.\n"
     ]
    },
    {
     "data": {
      "image/png": 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1ZMmSWhtdXpKXJKl6mflIU/EA8BTw6uJcaGtm/k5/IpM0rMo0uh4GXg48CdwIHAtMJ6O9\nQMtL7HUPTtqPwUYXen2WLc+lPD3t4KSSFoqIOA14fmZ+LSJelpmTEfH+iLgwM29q9Z5BH5y9U7nT\n+VTVyy1bHqbynAZnz8zSL+DXgF8GrizKPwb8SYv1sm7r16+vvc41a9bUWl8/9lHqVpEH5pRjevHq\nR/6ZVvYY9ViWqtOr3AOcQOPOnhNnzH89sG6W99S1m5XplJ+qXi4Ns3b5p0xHGsc2Fc8G/jewqihf\nAHyx0zYkSZKGRdFJ2EdpfMn8vYg4JiKmz5nOBr7Rv+gkDaOOjS7gnIi4OyLuBB7KzO3AlojYApwO\nfLrSCCVJkup1KbASuCoiNgGnAdsjYhw4Gbihj7FJGkIdn+nKzFuAW2bMuwq4qqqgJEmS+iUzrweu\nnzH7jH7EImlhKHOlS5IkSZLUJRtdkgZSRJxSDE66OSI+VMxzcFJJkjR0yo7TJUl1uz8zzwaIiA9F\nxEocnFSSJA0hr3RJGkiZ+VRT8UlgN8XgpBHx7j6FJUmSNGc2uiQNrIi4KCLuA5YCjwIvy8xVwEhE\nXNjf6CRJksrx9kJJAyszNwIbI+Ia4MLM/Eyx6DPACuCmVu8bHR1l+fLlAIyMjLBixYqBGsG+edT6\nQYjHsuVhLu/cuZPJyUmAZx1bkjRQZhs1eT4v+jAiez9GOF+zZk2t9TmKu4YJbUZlL/MCjm6afhfw\nBuCIovxO4NJZ3lffTs5Q9hj1WJaqM9/cM59XP/NPr3TKT1Uvl4ZZu/zjlS5Jg+r1EfHvgQQeAL5D\nY3DSx4AHgT/oZ3CSJEll2eiSNJAy80bgxhmzHZxUkiQNHTvSkCRJkqQK2eiSJEmSpArZ6JIkSZKk\nCtnokiRJkqQK2ZGGJHWwYcMGpqamOq63e/fuGqKRJEnDxkaXJHUwNTXF2NhYx/VGR0crj0WSJA2f\nyhpdV199dVWbbunJJ5+stT5JkiRJKqOyRtdb3/rWqjZ9iLvvvptPfvKTtdUnSZIkSWVV1uh67nOf\nW9WmD7FkyZLa6pIkSZKkubD3QkmSJEmqkI0uSZIkSaqQjS5JkiRJqlDpRldEvD0ithTTayNiS0Rc\nGxGLqgtP0uEqIk6JiLsiYnNEfKiYZ+6RVLmIOLPIP3dExNXFPPOPpK6VanRFxNHA6UBGxInAqsw8\nB7gXuLjC+CQdvu7PzLMzcxU0ToIw90iqxwRwXmaeC7wgIs7F/CNpHspe6XoL8JFieiUwXkzfBpzV\n25AkCTLzqabifuCHMfdIqkFmPpKZ+4viAeDVmH8kzUPHRldEHEnj251xIIDjgX3F4r3ASGXRSTqs\nRcRFEXEf8AIaQ1yYeyTVJiJOA54PTGL+kTQPZcbpejNwXVN5L/DiYvo4GonoEKOjoyxfvhyAkZER\nVqxYwerVqwEYHx8H6Fl5x44dPPTQQwfr7vX2Zysv9PosW55LeXp6YmKCXsnMjcDGiLgGeIpGzoE2\nuQd6n3+a96nd+nu2bmV0xQoAlo80zskmJicPKT/4xBOMjY11HY9ly5afKe/cuZPJ4jjrZf6JiBOA\na4BLgdcAJxeLas0/dZc75buql1u2PEzlOeWfzGz7AjYAtxSvR4HfBzYWy9YCl7R4T9Zp27ZtecUV\nV9RaZ2bmmjVraq1v/fr1tdYnzUeRBzrmmNlewNFN0+8C3tQp92RF+afssXfZ0qWZ0PG1Ztmynsco\nqWG+uaexCRYBNwMri/KJ/co/deuU76peLg2zdvnniPZNMsjMdZn5hsx8A7ArM98JbCl6Mjwd+HSn\nbUhSF14fEeMRcTvwgsz8KOYeSfW4lMYz7FdFxCbgpcAd5h9J3Spze+FB2ejFh8y8CriqkogkCcjM\nG4EbZ8zrWe5529vexvOe97yO633rW9/ixS9+ccf1JC0cmXk9cP2M2duA9/QhHEkLwJwaXZK0UDzv\nec87+FxVO2XWkSRJaqfj7YWSJEmSpO7Z6JIkSZKkCtnokiRJkqQK2eiSJEmSpArZ6JIkSZKkCtno\nkiRJkqQK2WW8JEmShsaGDRuYmpqadfnixYtZt25djRFJndnokiRJ0tCYmppqO4ai4ytqEHl7oSRJ\nkiRVyCtd87Bn61bGVq+urb5djz0GfnsjSZIkDRUbXfMwsm8fYw88UFt9o8uW1VaX1G8RcSbwPuAp\nYHtmviMiJoEvF6v8QmZO9i1ASZKkkmx0SRpUE8B5mbk/Iq6NiFOBezPz/D7HJUmSNCc2uiQNpMx8\npKl4gMYVr1dHxGZga2b+Tn8ik6TD165du9p2VLF79+76gpGGiI0uSQMtIk4Dnp+ZX4uIl2XmZES8\nPyIuzMyb+h2fJB1OlixZ0rbRNTo6Wlss0jCx0SVpYEXECcA1wKUATc9wfQZYAbRsdI2OjrJ8+XIA\nRkZGWLFiBauLTm/Gx8efte50eeby6fLExMSc1p/e+urp9VuU9zSNL9Nxe5YtW25b3rlzJ5OTjdQw\n83iVpIGRmT1/NTZbn23btuUVV1xRa52ZmZctXZoJtb3WLFtW+z5K3SrywHzyyCLgZmBlUT4GOKKY\nfidw6SzvKxXf+vXrS69Xdt2yOcFjWarOfHPPfF51n/9UoVO+W7NmzbyWl82n89lGL+qQutEu/zhO\nl6RBdSmwErgqIjYBpwHbI2IcOBm4oY+xSZIklebthZIGUmZeD1w/Y/YZ/YhFkiRpPmx0SZIkaSBs\n2LCBqabnXluxh0QNIxtdkiRJGghTU1Nte0cEe0jUcOr4TFdEnBIRd0XE5oj4UDFvbURsKQYsXVR9\nmJIkSfWIiBdGxI6IeCIijijmTUbEpuI10u8YJQ2XMh1p3J+ZZ2fmKoCIOBNYlZnnAPcCF1cZoCRJ\nUs0eBc4Hvtg0777MPL94Tc7yPklqqWOjKzOfairuB36YZ4aeuQ04q/dhSZIk9Udm7s/MvUA0zX5V\ncdfPu/sVl6ThVarL+Ii4KCLuA15A4zmwfcWivYCX2CVJ0kKUTdMvK+76GYmIC/sVkKThVKojjczc\nCGyMiGuAp4DjikXHAS0vsY+OjrJ8+XIARkZGWLFiRWUj0u/YsYOHHnroYN1VjHjfqnywvuLn6orL\nB+uraf8sW55LeXp6YmICSVpomm4p/AywArip1Xp1nv9UUW7O4a2W79mzZ17LO22/0/Jm3S63bLlX\n5Z07dzI52UgNHc9/Zhs1OZ8ZXf3opul3AW8CNhbltcAlLd5T7XDPM2zbti2vuOKKWuvMzLxs6dJM\nqO21Ztmy2vdR6hZtRmWv8lU2/6xfv770emXXLZsTPJal6vQy9wC3A4uAY4AjinnvBC6dZf3a9rMq\nnfLdmjVr5rW80/bL5Nv51iFVpV3+KXN74esjYjwibgdekJkfBbZExBbgdODTJbYhSZI0FCLiyIj4\nHHAacCtwKrA9IsaBk4Eb+hiepCHU8fbCzLwRuHHGvKuAq6oKSpIkqV8y8wDwuhmzz+hHLJIWhlId\naUiSJEmSulOqIw1JUu88/vjjjI2NlVp38eLFrFu3rtqAJElSpWx0SRpIxUDs76PRY+r2zHxHRKwF\nfg6YAEbz2eMIDo1FixaVbnSVXU+SJA0uby+UNKgmgPMy81zgBRFxLrAqM88B7gUu7mdwkiRJZdno\nkjSQMvORzNxfFA8Ar+aZYetuA87qR1ySJElz5e2FkgZaRJwGPJ/GQOxPF7P3AiOzvafM4KTTOg1+\nOHOww46DJRbrrZ5ev0X5kf3Tbcly9Y+Pjw/UYJCWLQ9SeU6Dk0pSv8w2gNd8Xjg4soMj67BHDwYo\nBU6g0U45EfgZ4Mpi/o8BfzLLe0rF18/BkS9burTU9uYSp6SGXuSebl91n/9UwcGRpe61yz9e6ZI0\nkCJiEfBRGg2t70XEduDXgT8BLgC+2M/4JEmDadeuXW07IbJXWPWDjS5Jg+pSYCVwVUQA/A5wR0Rs\nAb5Jo2dDSZKeZcmSJW0bXfYKq36w0SVpIGXm9cD1M2ZvA97Th3AkSZK6Zu+FkiRJklQhG12SJEmS\nVCEbXZIkSZJUIRtdkiRJklQhG12SJEmSVCEbXZIkSZJUIRtdkiRJklQhG12SJEmSVCEbXZIkSZJU\nIRtdkiRJklQhG12SJEmSVKGOja6IODMi7oqIOyLi6mLe2ojYEhHXRsSi6sOUdDiKiBdGxI6IeCIi\njijmTUbEpuI10u8YJS08s+SeKz33kdStI0usMwGcl5n7i0RzLrAqM8+JiLXAxcAnqgxSDY8//jhj\nY2O11rl48WLWrVtXa51Sk0eB84FPNc27LzPP71M8kg4Pz8o9EXEisNpzH0nd6tjoysxHmooHgFcD\n40X5NuCNmHhqsWjRotobXXXXJzXLzP3A/oiIptmviojNwNbM/J0+hSZpAWvKPdOzVuK5j6R5KP1M\nV0ScBjwfmAT2FbP3At7eI6lq2TT9ssxcBYxExIX9CkjSYWUEz30kzUOZ2wuJiBOAa4BLgdcAJxeL\njqPRCDvE6Ogoy5cvB2BkZIQVK1awevVqAMbHxwF6Vt6xYwcPPfTQwbp7vf3ZygfrK36urrh8sL6a\n9s+y5bmUp6cnJiaoUmZO55zPACuAm2auUyb/TOu0fzP3p+PnUay3enr9FuVH9u+fU/3j4+N9//1a\ntjyo5Z07dzI52UgLFeafvcCLiulZz32g3vOfbsrXXXcdJ510EvDM5zUd78TEBN/97ncP7kur9+/Z\ns2dey5t/R90sb1bVcsuWy5bnlH8ys+0LWATcDKwsyicCG4vptcAlLd6Tddq2bVteccUVtdaZmXnZ\n0qWZUNvrsqVLa9/H9evX116nFoYiD3TMMWVewO1FLjoGOKKY907g0hbrloqv7N/2+vXrS69bNifM\n5Vj2GJTmpoLcc0SZc5/sw/lPNzrllDVr1lS6vFP9ZXJe1TFI3WqXf45o3yQDGle3VgJXRcQm4KXA\nHRGxBTgd+HSJbUjSnEXEkRHxOeA04FbgVGB7RIzTuOJ+Qx/Dk7RAzcg9nwWW47mPpHko05HG9cD1\nM2ZvA95TSUSSVMjMA8DrZsw+ox+xSDp8zJJ7tuO5j6QulbnSJUmSJEnqUqmONCRJkqRBsGfrVsaK\nzgxaefCeexhr06nBrsceA4fEUc1sdEmSJGlojOzbx9gDD8y6/G+POoqxzZtnXT66bFkVYUlt2eiS\npJo9um9f229pm/mNrCRJw89GlyTV7IQDB9p+C9vMb2QlSRp+dqQhSZIkSRWy0SVJkiRJFfL2QkmS\npAVgw4YNTE1NtV1n9+7dNUXT2q5duxhr85zqto99jLHx8bbb2L93b2+Dkmpgo0uSJGkBmJqaatug\nARgdHa0lltksWbKkbYyXf+ADjP3d37Xdxi8ddVSPo5Kq5+2FkiRJklQhG12SJEmSVCEbXZIGVkS8\nMCJ2RMQTEXFEMe/KiNgSEddGxKJ+xyhJktSJjS5Jg+xR4HzgiwARcSKwOjPPAe4FLu5jbJIkSaXY\nkcYQeXTfPsZWr661zl2PPQYdHsqVqpKZ+4H9ETE9ayUwXkzfBrwR+ET9kUmSJJVno2uInHDgAGOb\nN9da5+iyZbXWJ3UwAuwrpvcWZUmSpIFmo0vSMNkLvKiYPg6YbLXS6Ogoy5cvB2BkZIQVK1awurhK\nPD5j/Jfp8szl0+WJiYk5rT+99dXT67coP/L0089sr8P6e6amGB8fn70+y5YP8/LOnTuZnGykgpnH\nqyQNjMzs+aux2fps27Ytr7jiilrrzMy8bOnSTKjtdelRR9VaX0KuWbas9s9VC0ORB3qVU26n8Qzq\nicDGYt5a4JIW65aKb/369aXXK7tu2Zwwl2PZY1Cam17mnrm+6j7/malMrlqzZs1ALy+TRzvl0E7L\nzauqSrv8Y0cakgZWRBwZEZ8DTgM+CywH7oiILcDpwKf7GJ4kSVIp3l4oaWBl5gHgdTNmbwfe04dw\nJEmSumKjS5IG2OOPP85YiR5EFy9ezLp166oPSJLa2LN1a9uelvc8/HB9wcyiU17tlE83bNjA1NRU\n1+/X4clGlyQNsEWLFpVqdJVZR5KqNrJvH2MPPDDr8suXLq0xmtY65dVO+XRqampe79fhqeMzXRHx\nwojYERFPRMQRxbwrI2JLRFwbEYuqD1OSJKl/ImJZROyJiE0RcWu/45E0XMp0pPEocD7wRYCIOBFY\nnZnnAPeBFnESAAANAklEQVQCF1cXniRJ0sD4m8w8PzNf3+9AJA2Xjo2uzNyfmXubZq3kmaFkbgPO\nqiAuSZKkQXN+RGyOiN/qdyCShks3XcaPAPuK6b1FWZIkaSF7GHg5cB7w2og4tc/xSBoi3XSksRd4\nUTF9HDDZaqXR0VGWL18OwMjICCtWrKhsRPodO3bw0EMPHay7ihHvW5UP1lf8XF1xue76DpZr+jwt\nD3d5enpiYgJJWmgy8wfADwAi4mbgVOCrM9er8/xnZnliYoLx8fG26+/Zs+dgrFUsf2T//meWFz9X\nN5WftbyL9zeb9/JO53ezLN+1cSNj4+NMTDZOgZePNK4/TJcfX7QIxsYG5v+z5erKO3fuZLL4vXc8\n/5lt1OSZL+B2GlfGTgQ2FvPWApe0WLeWUZ+nbdu2La+44opa68wsN2p6L1+dRliv4uWo7eoWbUZl\nr/JVNv+sX7++9Hpl1y2bE+ZyLF+2dGlP90da6KrKPcCxTdPXAq9psU49OzmLMnlgzZo1lS7vlAc7\n5bQyebRTDu20vFMMnT7HNcuWee6kltrln45XuiLiSOAW4DTgs8DvAndExBbgm8D7Om1DklStXbt2\nOZ6XVK1zIuKdwBSwJTO39zsgScOjY6MrMw8Ar5sxezvwnkoikiTN2ZIlSxzPS6pQZt5C40toSZqz\nbjrSkCRJkiSVZKNLkiRJkirUTe+FktQ3EbEM2Ab8LbA/HaRUkjQHj+7bx1jRA10rW775TcbavH9q\naqrXIekwYKNL0jD6m8z8lX4HIUkaPiccOMDY5s2zLr986dK2z79e/oEPVBCVFjpvL5Q0jM6PiM0R\n8Vv9DkSSJKkTr3RJGjYPAy8HngQ+ExGfz8xnDVBaZnDSaWUGG53L+tNbXz29fovyI08//cz2Oqz/\nyP79HQc7nev+lNmeZcvDUp7T4KSS1C+zDeA1nxcOjlzJy8GRNUyoYXBk4NeAy2fMKxXfQhscudOA\npc37Iy1kdeSe2V51n//M5ODIvVk+3xg9dzp8tcs/XulSW48//nit4/o4cKs6iYhjM/P/FcWzgWv6\nGY8k1WXDhg1tO3HYvXv3vOvYs3Vr204mHrznHsbaXFHcv3fvvGMYdmXOnTqd73T6Xc/3fKkX2++0\njfvvv59XvvKV86pjIbHRpbYWLVpUa6PLgVtVwjkR8U5gCtiSmdv7HZAk1WFqaqrt/8nR0dF51zGy\nbx9jDzww6/K/Peqotp1Q/NJRR807hmFX5typ0/JOv+v5ni/1Yvtl/h6r3IdhY6NL0lDJzFuAW/od\nR106dW08bc/DD5fa3q5du0r9ozvcvoGUJKlKNrokaYB16tp42uVLl5ba3pIlS0o1ug63byAlSaqS\nXcZLkiRJUoVsdEmSJElShby9UJIkSbXo9Jzq4dL7Yafna3vRE+V8lHn+t98xDhsbXZIkSapFp+dU\nD5feDzs9X9uLnijno8zzv/2Ocdh4e6EkSZIkVchGlyRJkiRVyNsLJamDXRs3MjY+3nG9w+VZBEmS\nNDc2uiSpgyWPPsrYl7/ccb3D5VkESZI0Nza6JEmSBsCGDRuYmpqadbm9xQ2HTj00Aux5+OF51dGp\nd8H777+fV77ylbMuH4S/pfnuw+LFi1m3bl0FkVXDRpckSdIAmJqaGuge7VROpx4aAS5funRedZTp\n/XDQ/5bmuw+delccNF03uiLivcBKYEdmvr13IXXnoYceqr3OR/bvr7e+p5+utT6ofx8nJiZqrQ9g\nfHyc1R2+kRr2Ovuxj1UZtNxTtbLHfZlvVqH8t6tlxmiZVvbbxoX0d7iQ9gUW3v5UZdDzz549e/od\nQkf9OJeZq7rPfboxDL/rYYixztzXVaMrIn4MWJKZ50bEf42IMzJzR49jm5Nvf/vbtddZ90H5vcxa\n6wOYeOyxUidyvfKlr3+9trqmvetd72K8RCcJvXTnnXfa6OrCIOaeqpU97st8swrlv10tM0bLtLLr\nLZS/Q1hY+wILb3+qMAz5ZxhOcvtxLjNXNrp6YxhiHPhGF/BPgc8V058HzgIGKvGoN57z9NOlTuR6\n5dPHH19bXdMOHDhQ+yVqT266Zu6R1C/mH0ld67bRNQJ8o5jeC7x65goXXXRRtzHN2eTkJDkE35xI\nmreOuaesLVu2lMpT3//+93lFt5VIWkhK5Z/Z8spznvMcPv7xj1cTmaSBF900ViLiN4BHMvOGiPjn\nwIsy88+bltsCkkRmRi+31yn3FOuYf6TDXK9zD5h/JJUzW/7p9krXF4C3AjcAFwAfLlOZJM1T29wD\n5h9JlTH/SOraEd28KTPvAZ6MiDuAA5l5d2/DkqRDmXsk9Yv5R9J8dHV7oSRJkiSpnK6udEmSJEmS\nyul5oysiXhgROyLiiYiovFEXEWdGxF0RcUdEXF11fUWdpxR1bo6ID9VRZ1Hv2yNiS011LYuIPRGx\nKSJuranON0fE54s6X1hDfT8dEbcXr4cj4udqqPM5EXFTUeenIuKoiutbFBEfi4jbImJDhfUcctxH\nxJURsSUiro2IRVXVPSOO9xa54H111NcrZT+/iHhjkXtujIhj+xt1a61yckSsHdJ9OSTXD+u+NGv+\nXzKs+9Pqf9Sw7ktZ88kTEXFeRGwt/hecVGGMXR//dcQ4n2O6rs+wKdY5H6c1/p67Pv7q/Bxjxnll\n34+XzOzpCzgaOB7YBBzR6+23qO8FwNHF9EeBU2qoc1HT9H8HzqihzqOBjwB3VF1XUd8y4C/rqKuo\n7yTgv9VVX4v6vwAcU0M9/xz4vWL6d4GLKq7vEuA/FNN/BvxoRfU867gHTgRuKpatBX6xhs/2x4AP\nFtP/tY7jsqbP77eBX6TR8dEdxfJLgSv7Hfcs+9Kck68Fzh3ifWnO9R8CzhzWfZnxt/aRIuZh/jt7\n1v+oYd6XOf7u5pInfgl4R7F8E3AM8BrgzyuMca7Hf60xdnFM1/4ZNv2uyx6n/fg9z/X460eMzzqv\nHIQYe34lKjP3Z+ZeoJYefDLzkcycHjr8B8BTNdTZXMeTwLeqrhN4C40DsE7nF98G/VYNdf00sKj4\nRuLPIqK2HqAi4iXAdzPziRqq+wawpJgeAR6tuL6XAvcW018BfrKKSpqO+2krgfFi+jYag4hWrdXA\npUOhw+c3vS8vB+7NzKep7zOdsxk5+QCNsYzGi/Kw7Utzrt8P/DBDui9Nmv+XDO3fWaH5f9Sw70tH\nXeSJzwNnRcRzgCcy84nM3A6cUmGMcz3+a42xi2O69s+wMJfjtF8xzuX460eMzeeV19BoQPU1xipv\n/6u1h46IOA14fmbeX1N9F0XEfTS+1an0xDkijgRWZeY4NTVmgYdp/DGeB7w2Ik6tuL6lwFGZeQHw\nD8DPV1xfs18APlVTXQ8APxkRX6VxJWZrxfV9HVhVTJ9Ho6FXhxFgXzG9t6Z6+1FnVVrty/Ez5h3f\nh7hKm87JwCRDvC8zcv2RDPe+zPxfMjP2Ydqf5v9RFwBnMLz70q0yeWJ63mNN76vj0Y+5HP+1xtjF\nMV13fN0cp3X/nrs5/uqOsfm88vE28dQW44LoSCMiTgCuAf5VXXVm5sbM/FHg28CFFVf3ZuC6iut4\nlsz8QWb+Q9H6vxmoutG1F9hcTG8CXlVxfc0uAm6sqa41wI2ZeSrwvyLiTRXXtxF4TkR8DpgCvltx\nfdP2AscV08fR+Me7EOusSqt9aT5pHOj9m5GT9zHE+zIj1z/FEO8Lh/4vaRX7UOzPjP9RN9G4i2CY\nfzfdKJsnmo9BqPiOoC6P/9pi7PKYrvMz7PY4rfMz7Pb4q/NzbD6vvB14Sb9jrLLRFdRwVaZ4EO6j\nNO7V/l7V9RV1Ht1U3EfjykyVXgH8ekTcApwSEf+24vqIZz9wfDaNA6pKW4HTiukVwIMV1wdARCwF\nnszM/1tHfTSOib8vpr9Pxd+8ZubTmfmbmfk6Gonjs1XWxzPH/HaeucJ2AfDFiuuFxnN5r625zl5r\n9/k9QOP4P4IB3r8WOXmY92Vmrj+CId2XQvP/klfTuCXo3GLZUO1Pi/9R/5vh/t3MxZzyRHHr/OKI\nWBIRZwJ/W1lgXR7/dcXY7TFd52dIl8dpzb/nro6/mj/HmeeVu/seYwUPrh1J47mKR4ufr+l1HTPq\nu5zGt/ebitdPVFlfUefP0bgv9HbgL6qub0bddXWk8QbgbuBO4N011fme4jP9OHBkTXW+FfiNGn9/\nxwO3Fvv5WWCk4vpOKur6PPArFdZzyHFPowONLTT+Adf1+/xTGg/F/lldv9M6Pz/gXwB30biC+dx+\nxz3LvhySk4d4Xw7J9TQewB66fWmxb3cM8/60+h81rPsyh33uOk/Q+EJqK41n206uMMauj/86YpzP\nMV3XZzgj3jkdpzX+nrs+/ur8HJlxXtnvGB0cWZIkSZIqtCCe6ZIkSZKkQWWjS5IkSZIqZKNLkiRJ\nkipko0uSJEmSKmSjS5IkSZIqZKNLkiRJkipko0uSJEmSKvT/ATfFt+8NVq2cAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16330b850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(hist1d_Supply_query,Area=(8001,8008,1),Min_Rooms=(1,6,1),Max_Rooms=(10,11,1),Min_Size=(10,100,10),Max_Size=(400,450,10),Min_Rent=(100,200,100),Max_Rent=(1000,6000,100),Supply_Threshold=(0,1,1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# def histsupply(Area,generator):\n",
    "#     generator(Area)\n",
    "    \n",
    "# interact(hist1d_Supply, Area=(8001,8009,1),\n",
    "#         generator={'Supply':hist1d_Supply\n",
    "#                    });"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**************************************************************\n",
      "Number of unique Supply Ads: 140.\n"
     ]
    },
    {
     "data": {
      "image/png": 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ak2oCzQnnxTivSz6N2coR1KPAJkE3xpg4E/bzOLcC8Ch3scHtP58oSjX+xn0A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\nSimX/D9uKgcn6lNeugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x162df8250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(hist1d_Supply, Area=(8001,8009,1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# interact(hist1d_Supply, Area=(8001,8009,1),\n",
    "#         generator={'Supply':hist1d_Supply\n",
    "#                    });"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4172\n"
     ]
    }
   ],
   "source": [
    "pathNY = '/Users/SVM/Dropbox/Applications/Crawlers/swiss-maps/topo/ch-plz.json'\n",
    "import fiona\n",
    "from shapely.geometry import MultiPoint\n",
    "CMAP = plt.get_cmap('RdYlBu_r')\n",
    "properties = []\n",
    "geometries = []\n",
    "zipids = []\n",
    "with fiona.open(pathNY) as f:\n",
    "    crs = f.crs\n",
    "    i = 0\n",
    "    for rec in f:\n",
    "        properties.append(rec['properties'].values())\n",
    "        zipids.append(int(rec['properties']['id']))\n",
    "        geometries.append(rec['geometry'])\n",
    "\n",
    "        i+=1\n",
    "print i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11f73e790>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YwGXWyvMSEjgsFxICVK/Onm2LFuzZ+tKK5uhotv34caBGjbSPV6nCRXwbNvBK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\nAK+UUjalAV4ppWzq/wEe/wFf+23wrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124f58a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pathSwissborder = '/Users/SVM/Dropbox/Applications/Crawlers/swiss-maps/switzerland.geojson'\n",
    "from shapely.geometry import MultiPoint\n",
    "CMAP = plt.get_cmap('RdYlBu_r')\n",
    "properties = []\n",
    "swissborder = []\n",
    "zipids = []\n",
    "with fiona.open(pathSwissborder) as f:\n",
    "    crs = f.crs\n",
    "    i = 0\n",
    "    for rec in f:\n",
    "        swissborder.append(rec['geometry'])\n",
    "\n",
    "        i+=1\n",
    "print i\n",
    "# for g in swissborder[0]['coordinates'][:]:\n",
    "ar = np.asarray(swissborder[0]['coordinates'][0])\n",
    "plt.plot(ar[:,0],ar[:,1],'-')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(swissborder[0]['coordinates'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def spatial_query_supply(Min_Rooms,Max_Rooms,Min_Size,Max_Size,Min_Rent,Max_Rent,Supply_Threshold,whattoplot='percentofcoverage'):\n",
    "    \n",
    "    \n",
    "#     official_codes = pd.read_csv('/Users/SVM/Dropbox/Applications/realmatch360/Data/CH_Zip.csv')\n",
    "#     ind_canton = official_codes['admin code1']==canton_abrv\n",
    "#     zip_canton = official_codes.ix[ind_canton]['zip'].values[:]\n",
    "    \n",
    "    cmapname=\"RdYlBu_r\"\n",
    "#     cmapname=\"B\"\n",
    "    import itertools\n",
    "    import numpy as np    \n",
    "    \n",
    "    if Min_Rooms >= Max_Rooms:\n",
    "            Max_Rooms = Min_Rooms\n",
    "    if Min_Size>=Max_Size:\n",
    "            Max_Size= Min_Size\n",
    "    if Min_Rent>=Max_Rent:\n",
    "            Max_Rent= Min_Rent\n",
    "            \n",
    "    def check_search(df):\n",
    "        \n",
    "        amn = df['Rooms'].values[:]>=Min_Rooms\n",
    "        amx = df['Rooms'].values[:]<=Max_Rooms\n",
    "        \n",
    "        \n",
    "        \n",
    "        bmn = df['Living space'].values[:]>=Min_Size\n",
    "        bmx = df['Living space'].values[:]<=Max_Size\n",
    "        \n",
    "        \n",
    "        cmn = df['Rent'].values[:]>=Min_Rent\n",
    "        cmx = df['Rent'].values[:]<=Max_Rent\n",
    "        \n",
    "        return (amn*bmn*cmn*amx*bmx*cmx).sum()\n",
    "    \n",
    "   \n",
    "    Complete_data_zip = listing.copy()\n",
    "    Complete_data_zip.index = Complete_data_zip['ZIP']\n",
    "    geo_info = []\n",
    "    total_demand = []\n",
    "    percentofchange = []\n",
    "    \n",
    "#     print Complete_data_zip.shape\n",
    "#     Complete_data_zip_specific = Complete_data_zip\n",
    "    \n",
    "    long_lat_zip_all = Complete_data_zip.groupby(by='ZIP')['ZIP','lng','lat'].first()\n",
    "    \n",
    "    zip_GB = Complete_data_zip.groupby(by='ZIP')\n",
    "    long_lat_zip_specific = zip_GB['ZIP','lng','lat'].first()\n",
    "    \n",
    "    ind_zip = zip_GB.size()>Supply_Threshold\n",
    "#     print zip_GB.size()\n",
    "#     print ind_zip\n",
    "    long_lat_zip_sel = long_lat_zip_specific.ix[ind_zip]\n",
    "    \n",
    "    total_specific_supply=zip_GB.size()[ind_zip]\n",
    "    total_supply = total_specific_supply.values[:]\n",
    "    \n",
    "#     total_interest_in_property0 = Complete_data_zip_specific.ix[ind_zip].groupby(by='zip').apply(check_search)\n",
    "    \n",
    "#     print zip_GB.size()\n",
    "#     print Complete_data_zip.ix[ind_zip]\n",
    "    \n",
    "    total_interest_in_property1 = Complete_data_zip.ix[ind_zip].groupby(by='ZIP').apply(check_search)\n",
    "#     print total_interest_in_property1/(total_specific_demand.values[:]).astype(float)\n",
    "\n",
    "#     percentofchange = 100*(total_interest_in_property1.values[:]-total_interest_in_property0.values[:])/(total_specific_demand.values[:]).astype(float)\n",
    "    \n",
    "    total_cases = total_interest_in_property1.values[:]\n",
    "    percentofcoverage = 100*total_cases/(total_specific_supply.values[:]).astype(float)\n",
    "    \n",
    "    \n",
    "    \n",
    "    #To Plot\n",
    "    fig = plt.figure(figsize=(12,8))\n",
    "    \n",
    "    if whattoplot=='total_cases':\n",
    "        \n",
    "    \n",
    "        ax = fig.add_subplot(1,1,1)\n",
    "        swissborderarr = np.asarray(swissborder[0]['coordinates'][0])\n",
    "        plt.plot(swissborderarr[:,0],swissborderarr[:,1],'-k')\n",
    "#         sc = plt.scatter(long_lat_zip_all.lng,long_lat_zip_all.lat,c='None',s=40,marker='.',edgecolor='gray',linewidth=.3, cmap=cmapname ,alpha=.4)\n",
    "        \n",
    "        \n",
    "        eps = .004\n",
    "        X_mn= swissborderarr[:,0].min()*(1-eps)\n",
    "        Y_mn= swissborderarr[:,1].min()*(1-eps)\n",
    "        X_mx= swissborderarr[:,0].max()*(1+eps)\n",
    "        Y_mx= swissborderarr[:,1].max()*(1+eps)\n",
    "    \n",
    "    \n",
    "    \n",
    "        md = np.median(total_cases)\n",
    "        sd = np.std(total_cases)\n",
    "        mn = md-2*sd\n",
    "        mx= md+2.5*sd\n",
    "        mn = np.min(total_cases)\n",
    "#         total_cases_canton = total_interest_in_property1.ix[zip_canton].values[:]\n",
    "    #     sc = plt.scatter(long_lat_zip_sel.lng.ix[zip_canton],long_lat_zip_sel.lat.ix[zip_canton],c=total_cases_canton,s=20,vmin=mn,vmax=mx,marker='o',edgecolor='None', cmap=cmapname ,alpha=1)\n",
    "    \n",
    "        sc = plt.scatter(long_lat_zip_sel.lng,long_lat_zip_sel.lat,c=total_cases,s=40,vmin=mn,vmax=mx,marker='.',edgecolor='None', cmap=cmapname ,alpha=1)\n",
    "    \n",
    "\n",
    "        plt.xlim(X_mn,X_mx)\n",
    "        plt.ylim(Y_mn,Y_mx)\n",
    "    \n",
    "    \n",
    "        ticklabels = np.round(np.linspace(mn,mx,5),decimals=3).astype(int).astype(str)\n",
    "        ticklabels[-1]=\">\"+ticklabels[-1]\n",
    "    \n",
    "        cbar = plt.colorbar(sc,ticks=np.round(np.linspace(mn,mx,5),decimals=3).astype(int),shrink=0.6)\n",
    "        cbar.ax.set_yticklabels(ticklabels)\n",
    "    \n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "        plt.title(\"Total number of available cases\")\n",
    "        plt.axis('off')\n",
    "    \n",
    "    \n",
    "#     ax = fig.add_subplot(2,2,2)\n",
    "#     md = np.median(total_cases)\n",
    "#     sd = np.std(total_cases)\n",
    "#     mn = md-2*sd\n",
    "#     mx= md+2.5*sd\n",
    "#     mn = np.min(total_cases)\n",
    "#     import fiona\n",
    "#     CMAP = plt.get_cmap('RdYlBu_r')\n",
    "    \n",
    "#     for i,z in enumerate(zipids):\n",
    "# #         if z in zip_canton:\n",
    "#         try:\n",
    "#             val = total_interest_in_property1.ix[z]/mx\n",
    "#         except:\n",
    "#             val=-1\n",
    "#         try:\n",
    "#             if val>=0:\n",
    "#                 plot_multipolygon(ax, shape(geometries[i]),fcolor=CMAP(val),ecolor='none')\n",
    "#             else:\n",
    "#                 plot_multipolygon(ax, shape(geometries[i]),fcolor='none',ecolor='gray')\n",
    "#         except:\n",
    "#             continue\n",
    "# #         else:\n",
    "# #             continue\n",
    "# #             try:\n",
    "# #                 plot_multipolygon(ax, shape(geometries[i]),fcolor='none',ecolor='gray')\n",
    "# #             except:\n",
    "# #                 continue\n",
    "\n",
    "    \n",
    "   \n",
    "    \n",
    "#     ticklabels = np.round(np.linspace(mn,mx,5),decimals=3).astype(int).astype(str)\n",
    "#     ticklabels[-1]=\">\"+ticklabels[-1]\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "#     cbar = plt.colorbar(sc,ticks=np.round(np.linspace(mn,mx,5),decimals=3).astype(int),shrink=0.6)\n",
    "#     cbar.ax.set_yticklabels(ticklabels)\n",
    "    \n",
    "#     plt.xticks([])\n",
    "#     plt.yticks([])\n",
    "#     plt.title(\"Total number of available cases\")\n",
    "#     plt.axis('off')\n",
    "    if whattoplot=='percentofcoverage':\n",
    "        ax = fig.add_subplot(1,1,1)\n",
    "#         swissborderarr = np.asarray(swissborder[0]['coordinates'][0])\n",
    "# #         plt.plot(swissborderarr[:,0],swissborderarr[:,1],'-k')\n",
    "        \n",
    "        \n",
    "#         eps = .004\n",
    "#         X_mn= swissborderarr[:,0].min()*(1-eps)\n",
    "#         Y_mn= swissborderarr[:,1].min()*(1-eps)\n",
    "#         X_mx= swissborderarr[:,0].max()*(1+eps)\n",
    "#         Y_mx= swissborderarr[:,1].max()*(1+eps)\n",
    "        \n",
    "# #         sc = plt.scatter(long_lat_zip_all.lng,long_lat_zip_all.lat,c='None',s=20,marker='.',edgecolor='gray',linewidth=.3, cmap=cmapname ,alpha=.4)\n",
    "#         mn = np.min(percentofcoverage)  \n",
    "#         mx = np.max(percentofcoverage)\n",
    "#     #     mn = 0\n",
    "#     #     mx =100\n",
    "# #         percentofcoverage_canton = 100*total_cases_canton/(total_specific_supply.ix[zip_canton].values[:]).astype(float)\n",
    "#     #     sc = plt.scatter(long_lat_zip_sel.lng.ix[zip_canton],long_lat_zip_sel.lat.ix[zip_canton],c=percentofcoverage_canton,s=20,vmin=mn,vmax=mx,marker='o',edgecolor='None', cmap=cmapname ,alpha=1)\n",
    "#         sc = plt.scatter(long_lat_zip_sel.lng,long_lat_zip_sel.lat,c=percentofcoverage,s=40,vmin=mn,vmax=mx,marker='.',edgecolor='None', cmap=cmapname ,alpha=1)\n",
    "#         plt.xlim(X_mn,X_mx)\n",
    "#         plt.ylim(Y_mn,Y_mx)\n",
    "#         if mn == mx:\n",
    "#             mx = mn + 6\n",
    "#         plt.colorbar(sc,ticks=np.round(np.linspace(mn,mx,5),decimals=3).astype(int),shrink=0.6)\n",
    "#         plt.xticks([])\n",
    "#         plt.yticks([])\n",
    "#         plt.title(\"Percent of available cases based on the selected query\")\n",
    "#         plt.axis('off')\n",
    "#         plt.close()\n",
    "        \n",
    "        \n",
    "    #     ax = fig.add_subplot(2,2,4)\n",
    "        md = np.median(total_cases)\n",
    "        sd = np.std(total_cases)\n",
    "        mn = md-2*sd\n",
    "        mx= 1\n",
    "        mn = 0\n",
    "        import fiona\n",
    "        CMAP = plt.get_cmap('RdYlBu_r')\n",
    "    \n",
    "        for i,z in enumerate(zipids):\n",
    "#         if z in zip_canton:\n",
    "            try:\n",
    "                nomin  = total_interest_in_property1.ix[z]\n",
    "                denom = total_specific_supply.ix[z]\n",
    "                val = nomin/denom.astype(float)\n",
    "            except:\n",
    "                val=-1\n",
    "            try:\n",
    "                if val>=0:\n",
    "                    cax = plot_multipolygon(ax, shape(geometries[i]),fcolor=CMAP(val),ecolor='none')\n",
    "                else:\n",
    "                    cax = plot_multipolygon(ax, shape(geometries[i]),fcolor='gray',ecolor='none',alpha=.1)\n",
    "            except:\n",
    "                continue\n",
    "#         else:\n",
    "#             continue\n",
    "#             try:\n",
    "#                 plot_multipolygon(ax, shape(geometries[i]),fcolor='none',ecolor='gray')\n",
    "#             except:\n",
    "#                 continue\n",
    "            \n",
    "    \n",
    "        mn = 0\n",
    "        mx = 100\n",
    "    \n",
    "        ticklabels = np.round(np.linspace(mn,mx,5),decimals=3).astype(int).astype(str)\n",
    "#         cbar = plt.colorbar(cax,ticks=np.round(np.linspace(mn,mx,5),decimals=3).astype(int),shrink=0.6)\n",
    "#         cbar.ax.set_yticklabels(ticklabels)\n",
    "    \n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "#         plt.title(\"Percent of available cases based on your query\")\n",
    "        plt.axis('off')\n",
    "    \n",
    "\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    font = {'size'   : 12}\n",
    "    plt.rc('font', **font)\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    path = '/Users/SVM/Dropbox/Applications/Crawlers/images/'\n",
    "    filename = path + 'swiss_sensitivity_{}_Min_Rooms_{}_Max_Rooms_{}_Min_Size_{}_Max_Size_{}_Min_Rent_{}_Max_Rent_{}_.png'.format(whattoplot,Min_Rooms,Max_Rooms,Min_Size,Max_Size,Min_Rent,Max_Rent)\n",
    "    fig.savefig(filename, dpi=200)\n",
    "#     return total_cases.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {