EveningDropoffs.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:89f15dfc6fc5a4bf9b86720cff1dd0d57b2a7f31b31db0d63c0704d65ece9b4b"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "pd.set_option('display.mpl_style', 'default')\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfe = pd.read_json(\"EveningDropoffs.json\", orient=\"index\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 122
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfe[2010.0]=(dfe[2010.0]-dfe[2010.0].mean())/dfe[2010.0].std()\n",
      "dfe[2011.0]=(dfe[2011.0]-dfe[2011.0].mean())/dfe[2011.0].std()\n",
      "dfe[2012.0]=(dfe[2012.0]-dfe[2012.0].mean())/dfe[2012.0].std()\n",
      "dfe[2013.0]=(dfe[2013.0]-dfe[2013.0].mean())/dfe[2013.0].std()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 123
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfe.dropna(inplace=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 124
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#dfclean = dfe[dfe<dfe.quantile(0.95)]\n",
      "# dfclean\n",
      "dfclean.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>2010.0</th>\n",
        "      <th>2012.0</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0   </th>\n",
        "      <td>-0.220139</td>\n",
        "      <td>-0.232488</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1   </th>\n",
        "      <td>-0.148842</td>\n",
        "      <td>-0.139962</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10  </th>\n",
        "      <td>-0.216427</td>\n",
        "      <td>-0.229343</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100 </th>\n",
        "      <td>-0.219883</td>\n",
        "      <td>-0.233213</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1000</th>\n",
        "      <td>-0.220267</td>\n",
        "      <td>-0.233334</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 125,
       "text": [
        "          2010      2012\n",
        "0    -0.220139 -0.232488\n",
        "1    -0.148842 -0.139962\n",
        "10   -0.216427 -0.229343\n",
        "100  -0.219883 -0.233213\n",
        "1000 -0.220267 -0.233334"
       ]
      }
     ],
     "prompt_number": 125
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfclean= dfclean[[2010.0, 2012.0]]\n",
      "deltas = dfclean.pct_change(axis = 1)\n",
      "deltas = deltas.drop(2010.0, axis = 1)\n",
      "deltas.dropna()\n",
      "deltas.describe(percentiles=[0.05, 0.1, 0.25, 0.5, 0.75, 0.85, 0.9, 0.95])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>2012.0</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 54493.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>    -0.026169</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>    20.854337</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td> -2858.081395</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5%</th>\n",
        "      <td>    -0.253467</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10%</th>\n",
        "      <td>    -0.023984</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>     0.045951</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>     0.056550</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>     0.062546</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>85%</th>\n",
        "      <td>     0.071025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>90%</th>\n",
        "      <td>     0.085139</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>95%</th>\n",
        "      <td>     0.194839</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>  2072.038754</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 126,
       "text": [
        "               2012\n",
        "count  54493.000000\n",
        "mean      -0.026169\n",
        "std       20.854337\n",
        "min    -2858.081395\n",
        "5%        -0.253467\n",
        "10%       -0.023984\n",
        "25%        0.045951\n",
        "50%        0.056550\n",
        "75%        0.062546\n",
        "85%        0.071025\n",
        "90%        0.085139\n",
        "95%        0.194839\n",
        "max     2072.038754"
       ]
      }
     ],
     "prompt_number": 126
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "deltas"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>2011.0</th>\n",
        "      <th>2012.0</th>\n",
        "      <th>2013.0</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0    </th>\n",
        "      <td> 0.014684</td>\n",
        "      <td> 0.040813</td>\n",
        "      <td> 0.002940</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1    </th>\n",
        "      <td>-0.013650</td>\n",
        "      <td>-0.046643</td>\n",
        "      <td>-0.076238</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10   </th>\n",
        "      <td> 0.025349</td>\n",
        "      <td> 0.033482</td>\n",
        "      <td> 0.010703</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100  </th>\n",
        "      <td> 0.015865</td>\n",
        "      <td> 0.044062</td>\n",
        "      <td> 0.000354</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1000 </th>\n",
        "      <td> 0.011335</td>\n",
        "      <td> 0.047453</td>\n",
        "      <td> 0.003581</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10000</th>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10002</th>\n",
        "      <td> 0.016771</td>\n",
        "      <td> 0.036662</td>\n",
        "      <td> 0.006484</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10003</th>\n",
        "      <td>-0.189307</td>\n",
        "      <td> 0.208189</td>\n",
        "      <td>-0.172140</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10004</th>\n",
        "      <td> 0.014494</td>\n",
        "      <td> 0.041770</td>\n",
        "      <td> 0.004651</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10005</th>\n",
        "      <td> 0.011156</td>\n",
        "      <td> 0.045145</td>\n",
        "      <td> 0.003061</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10006</th>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10007</th>\n",
        "      <td> 0.006157</td>\n",
        "      <td> 0.034697</td>\n",
        "      <td> 0.013675</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10008</th>\n",
        "      <td> 0.027159</td>\n",
        "      <td> 0.042047</td>\n",
        "      <td> 0.011605</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10009</th>\n",
        "      <td> 0.146849</td>\n",
        "      <td> 0.189621</td>\n",
        "      <td>-0.021351</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1001 </th>\n",
        "      <td> 0.010855</td>\n",
        "      <td> 0.045363</td>\n",
        "      <td> 0.004551</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10010</th>\n",
        "      <td> 0.024877</td>\n",
        "      <td> 0.041188</td>\n",
        "      <td> 0.005415</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10012</th>\n",
        "      <td> 0.007813</td>\n",
        "      <td> 0.044105</td>\n",
        "      <td> 0.005387</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10013</th>\n",
        "      <td> 0.011192</td>\n",
        "      <td> 0.044089</td>\n",
        "      <td> 0.006765</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10014</th>\n",
        "      <td> 0.015894</td>\n",
        "      <td> 0.028527</td>\n",
        "      <td>-0.004235</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10015</th>\n",
        "      <td>-0.017560</td>\n",
        "      <td> 0.065604</td>\n",
        "      <td> 0.016650</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10016</th>\n",
        "      <td> 0.013767</td>\n",
        "      <td> 0.040987</td>\n",
        "      <td> 0.006066</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10017</th>\n",
        "      <td>-0.003352</td>\n",
        "      <td> 0.047013</td>\n",
        "      <td> 0.014169</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10018</th>\n",
        "      <td> 0.022368</td>\n",
        "      <td> 0.049736</td>\n",
        "      <td> 0.009240</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10019</th>\n",
        "      <td> 0.013431</td>\n",
        "      <td> 0.044550</td>\n",
        "      <td>-0.001734</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1002 </th>\n",
        "      <td> 0.015258</td>\n",
        "      <td> 0.035183</td>\n",
        "      <td> 0.007437</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10020</th>\n",
        "      <td> 0.033993</td>\n",
        "      <td> 0.076611</td>\n",
        "      <td>-0.005124</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10021</th>\n",
        "      <td> 0.033334</td>\n",
        "      <td>-0.001804</td>\n",
        "      <td> 0.047883</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10022</th>\n",
        "      <td> 0.009575</td>\n",
        "      <td> 0.045281</td>\n",
        "      <td> 0.004594</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10023</th>\n",
        "      <td> 0.016648</td>\n",
        "      <td> 0.029271</td>\n",
        "      <td>-0.002108</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10024</th>\n",
        "      <td> 0.006855</td>\n",
        "      <td> 0.039998</td>\n",
        "      <td> 0.011391</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9970 </th>\n",
        "      <td> 0.004354</td>\n",
        "      <td> 0.044159</td>\n",
        "      <td> 0.014579</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9971 </th>\n",
        "      <td> 0.014419</td>\n",
        "      <td> 0.042259</td>\n",
        "      <td> 0.003626</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9972 </th>\n",
        "      <td>-0.060746</td>\n",
        "      <td>-0.359245</td>\n",
        "      <td>-0.579853</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9973 </th>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9974 </th>\n",
        "      <td> 0.016535</td>\n",
        "      <td> 0.038695</td>\n",
        "      <td> 0.002309</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9975 </th>\n",
        "      <td>-0.420985</td>\n",
        "      <td>-0.006865</td>\n",
        "      <td>-0.033266</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9976 </th>\n",
        "      <td> 0.018749</td>\n",
        "      <td> 0.042927</td>\n",
        "      <td> 0.001960</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9977 </th>\n",
        "      <td> 0.026029</td>\n",
        "      <td> 0.042042</td>\n",
        "      <td> 0.010703</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9978 </th>\n",
        "      <td>-0.234363</td>\n",
        "      <td> 0.230049</td>\n",
        "      <td> 0.149616</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9979 </th>\n",
        "      <td> 0.015045</td>\n",
        "      <td> 0.037966</td>\n",
        "      <td> 0.005639</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>998  </th>\n",
        "      <td> 0.011421</td>\n",
        "      <td> 0.044594</td>\n",
        "      <td> 0.010600</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9980 </th>\n",
        "      <td>-0.122808</td>\n",
        "      <td>-0.137893</td>\n",
        "      <td>-0.329724</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9981 </th>\n",
        "      <td> 0.022152</td>\n",
        "      <td> 0.044653</td>\n",
        "      <td> 0.006484</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9982 </th>\n",
        "      <td> 0.011697</td>\n",
        "      <td> 0.034637</td>\n",
        "      <td> 0.009602</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9983 </th>\n",
        "      <td> 0.021638</td>\n",
        "      <td> 0.049773</td>\n",
        "      <td> 0.004102</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9984 </th>\n",
        "      <td> 0.044390</td>\n",
        "      <td> 0.046362</td>\n",
        "      <td> 0.028254</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9985 </th>\n",
        "      <td>-0.002549</td>\n",
        "      <td> 0.027768</td>\n",
        "      <td> 0.009627</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9986 </th>\n",
        "      <td> 0.014646</td>\n",
        "      <td> 0.044035</td>\n",
        "      <td> 0.001441</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9987 </th>\n",
        "      <td> 0.010020</td>\n",
        "      <td> 0.044089</td>\n",
        "      <td> 0.006230</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9988 </th>\n",
        "      <td> 0.007389</td>\n",
        "      <td> 0.046820</td>\n",
        "      <td>-0.006572</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999  </th>\n",
        "      <td>-0.016955</td>\n",
        "      <td> 0.061863</td>\n",
        "      <td> 0.025043</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9991 </th>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9992 </th>\n",
        "      <td> 0.017523</td>\n",
        "      <td> 0.042901</td>\n",
        "      <td> 0.005187</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9993 </th>\n",
        "      <td> 0.024492</td>\n",
        "      <td> 0.019418</td>\n",
        "      <td> 0.001439</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9994 </th>\n",
        "      <td>-0.038125</td>\n",
        "      <td>-0.181965</td>\n",
        "      <td>-0.028748</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9995 </th>\n",
        "      <td> 0.836381</td>\n",
        "      <td>-0.547225</td>\n",
        "      <td> 0.397028</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9996 </th>\n",
        "      <td> 0.040254</td>\n",
        "      <td> 0.001240</td>\n",
        "      <td> 0.021546</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9997 </th>\n",
        "      <td> 0.013356</td>\n",
        "      <td> 0.041795</td>\n",
        "      <td> 0.003031</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9998 </th>\n",
        "      <td> 0.007635</td>\n",
        "      <td> 0.060659</td>\n",
        "      <td> 0.004286</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9999 </th>\n",
        "      <td> 0.507325</td>\n",
        "      <td> 1.119112</td>\n",
        "      <td>-0.172419</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>57362 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "           2011      2012      2013\n",
        "0      0.014684  0.040813  0.002940\n",
        "1     -0.013650 -0.046643 -0.076238\n",
        "10     0.025349  0.033482  0.010703\n",
        "100    0.015865  0.044062  0.000354\n",
        "1000   0.011335  0.047453  0.003581\n",
        "10000       NaN       NaN       NaN\n",
        "10002  0.016771  0.036662  0.006484\n",
        "10003 -0.189307  0.208189 -0.172140\n",
        "10004  0.014494  0.041770  0.004651\n",
        "10005  0.011156  0.045145  0.003061\n",
        "10006       NaN       NaN       NaN\n",
        "10007  0.006157  0.034697  0.013675\n",
        "10008  0.027159  0.042047  0.011605\n",
        "10009  0.146849  0.189621 -0.021351\n",
        "1001   0.010855  0.045363  0.004551\n",
        "10010  0.024877  0.041188  0.005415\n",
        "10012  0.007813  0.044105  0.005387\n",
        "10013  0.011192  0.044089  0.006765\n",
        "10014  0.015894  0.028527 -0.004235\n",
        "10015 -0.017560  0.065604  0.016650\n",
        "10016  0.013767  0.040987  0.006066\n",
        "10017 -0.003352  0.047013  0.014169\n",
        "10018  0.022368  0.049736  0.009240\n",
        "10019  0.013431  0.044550 -0.001734\n",
        "1002   0.015258  0.035183  0.007437\n",
        "10020  0.033993  0.076611 -0.005124\n",
        "10021  0.033334 -0.001804  0.047883\n",
        "10022  0.009575  0.045281  0.004594\n",
        "10023  0.016648  0.029271 -0.002108\n",
        "10024  0.006855  0.039998  0.011391\n",
        "...         ...       ...       ...\n",
        "9970   0.004354  0.044159  0.014579\n",
        "9971   0.014419  0.042259  0.003626\n",
        "9972  -0.060746 -0.359245 -0.579853\n",
        "9973        NaN       NaN       NaN\n",
        "9974   0.016535  0.038695  0.002309\n",
        "9975  -0.420985 -0.006865 -0.033266\n",
        "9976   0.018749  0.042927  0.001960\n",
        "9977   0.026029  0.042042  0.010703\n",
        "9978  -0.234363  0.230049  0.149616\n",
        "9979   0.015045  0.037966  0.005639\n",
        "998    0.011421  0.044594  0.010600\n",
        "9980  -0.122808 -0.137893 -0.329724\n",
        "9981   0.022152  0.044653  0.006484\n",
        "9982   0.011697  0.034637  0.009602\n",
        "9983   0.021638  0.049773  0.004102\n",
        "9984   0.044390  0.046362  0.028254\n",
        "9985  -0.002549  0.027768  0.009627\n",
        "9986   0.014646  0.044035  0.001441\n",
        "9987   0.010020  0.044089  0.006230\n",
        "9988   0.007389  0.046820 -0.006572\n",
        "999   -0.016955  0.061863  0.025043\n",
        "9991        NaN       NaN       NaN\n",
        "9992   0.017523  0.042901  0.005187\n",
        "9993   0.024492  0.019418  0.001439\n",
        "9994  -0.038125 -0.181965 -0.028748\n",
        "9995   0.836381 -0.547225  0.397028\n",
        "9996   0.040254  0.001240  0.021546\n",
        "9997   0.013356  0.041795  0.003031\n",
        "9998   0.007635  0.060659  0.004286\n",
        "9999   0.507325  1.119112 -0.172419\n",
        "\n",
        "[57362 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "high_change = deltas[deltas>=deltas.quantile(0.95)]\n",
      "high_change = high_change.dropna(how='all')\n",
      "high_change"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>2012.0</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>10009</th>\n",
        "      <td> 0.364316</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10025</th>\n",
        "      <td> 0.627035</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10029</th>\n",
        "      <td> 0.639122</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1004 </th>\n",
        "      <td> 0.673357</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10072</th>\n",
        "      <td> 0.363563</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10082</th>\n",
        "      <td> 1.318906</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10088</th>\n",
        "      <td> 0.222539</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10111</th>\n",
        "      <td> 0.328998</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10115</th>\n",
        "      <td> 1.294318</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10139</th>\n",
        "      <td> 1.322120</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10142</th>\n",
        "      <td> 1.109106</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10179</th>\n",
        "      <td> 0.675748</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10214</th>\n",
        "      <td> 0.288351</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1025 </th>\n",
        "      <td> 3.948371</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10250</th>\n",
        "      <td> 0.296667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>103  </th>\n",
        "      <td> 0.305306</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10315</th>\n",
        "      <td> 0.931568</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10366</th>\n",
        "      <td> 0.239306</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10370</th>\n",
        "      <td> 1.353453</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10401</th>\n",
        "      <td> 0.759782</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10403</th>\n",
        "      <td> 0.870546</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10435</th>\n",
        "      <td> 3.971379</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10489</th>\n",
        "      <td> 0.507887</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1051 </th>\n",
        "      <td> 0.464935</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10536</th>\n",
        "      <td> 2.165920</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10550</th>\n",
        "      <td> 0.613507</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10598</th>\n",
        "      <td> 0.237255</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10629</th>\n",
        "      <td> 0.308223</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10650</th>\n",
        "      <td> 1.096334</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10659</th>\n",
        "      <td> 0.243435</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9258 </th>\n",
        "      <td> 0.306613</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9278 </th>\n",
        "      <td> 0.244828</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9304 </th>\n",
        "      <td> 2.844885</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9328 </th>\n",
        "      <td> 2.633722</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9337 </th>\n",
        "      <td> 0.564517</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9353 </th>\n",
        "      <td> 0.219175</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9386 </th>\n",
        "      <td> 4.442883</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9401 </th>\n",
        "      <td> 1.162999</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9441 </th>\n",
        "      <td> 3.111492</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9481 </th>\n",
        "      <td> 4.082039</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9508 </th>\n",
        "      <td> 8.096281</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9519 </th>\n",
        "      <td> 0.263543</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>952  </th>\n",
        "      <td> 0.471671</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9540 </th>\n",
        "      <td> 0.359232</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9544 </th>\n",
        "      <td> 0.310467</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9589 </th>\n",
        "      <td> 2.083759</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9620 </th>\n",
        "      <td> 0.354468</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9627 </th>\n",
        "      <td> 0.371863</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9632 </th>\n",
        "      <td> 0.702362</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9634 </th>\n",
        "      <td> 7.089792</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>969  </th>\n",
        "      <td> 2.391492</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>973  </th>\n",
        "      <td> 0.205137</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9732 </th>\n",
        "      <td> 0.387200</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9778 </th>\n",
        "      <td> 0.226741</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9871 </th>\n",
        "      <td> 0.334081</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9875 </th>\n",
        "      <td> 0.383539</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9884 </th>\n",
        "      <td> 0.702568</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9887 </th>\n",
        "      <td> 3.147204</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9912 </th>\n",
        "      <td> 6.049852</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9999 </th>\n",
        "      <td> 2.194189</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>2725 rows \u00d7 1 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 127,
       "text": [
        "           2012\n",
        "10009  0.364316\n",
        "10025  0.627035\n",
        "10029  0.639122\n",
        "1004   0.673357\n",
        "10072  0.363563\n",
        "10082  1.318906\n",
        "10088  0.222539\n",
        "10111  0.328998\n",
        "10115  1.294318\n",
        "10139  1.322120\n",
        "10142  1.109106\n",
        "10179  0.675748\n",
        "10214  0.288351\n",
        "1025   3.948371\n",
        "10250  0.296667\n",
        "103    0.305306\n",
        "10315  0.931568\n",
        "10366  0.239306\n",
        "10370  1.353453\n",
        "10401  0.759782\n",
        "10403  0.870546\n",
        "10435  3.971379\n",
        "10489  0.507887\n",
        "1051   0.464935\n",
        "10536  2.165920\n",
        "10550  0.613507\n",
        "10598  0.237255\n",
        "10629  0.308223\n",
        "10650  1.096334\n",
        "10659  0.243435\n",
        "...         ...\n",
        "9258   0.306613\n",
        "9278   0.244828\n",
        "9304   2.844885\n",
        "9328   2.633722\n",
        "9337   0.564517\n",
        "9353   0.219175\n",
        "9386   4.442883\n",
        "9401   1.162999\n",
        "9441   3.111492\n",
        "9481   4.082039\n",
        "9508   8.096281\n",
        "9519   0.263543\n",
        "952    0.471671\n",
        "9540   0.359232\n",
        "9544   0.310467\n",
        "9589   2.083759\n",
        "9620   0.354468\n",
        "9627   0.371863\n",
        "9632   0.702362\n",
        "9634   7.089792\n",
        "969    2.391492\n",
        "973    0.205137\n",
        "9732   0.387200\n",
        "9778   0.226741\n",
        "9871   0.334081\n",
        "9875   0.383539\n",
        "9884   0.702568\n",
        "9887   3.147204\n",
        "9912   6.049852\n",
        "9999   2.194189\n",
        "\n",
        "[2725 rows x 1 columns]"
       ]
      }
     ],
     "prompt_number": 127
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "intersections = pd.read_csv('intersections.csv', header = None)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 128
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "intersections.index.name = 'int_id'\n",
      "\n",
      "high_change.index.name = 'int_id'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 129
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with_int = high_change.join(intersections)\n",
      "with_int.to_csv('deltaswithintnorm20102012.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 130
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(intersections)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 133,
       "text": [
        "62469"
       ]
      }
     ],
     "prompt_number": 133
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "deltas.to_csv('eveningdropoffschange.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with_int.to_csv()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>2012.0</th>\n",
        "      <th>0.0</th>\n",
        "      <th>1.0</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>int_id</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>10009</th>\n",
        "      <td> 0.364316</td>\n",
        "      <td> 40.82145</td>\n",
        "      <td>-73.95038</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10025</th>\n",
        "      <td> 0.627035</td>\n",
        "      <td> 40.71165</td>\n",
        "      <td>-73.96723</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10029</th>\n",
        "      <td> 0.639122</td>\n",
        "      <td> 40.84888</td>\n",
        "      <td>-73.93916</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1004 </th>\n",
        "      <td> 0.673357</td>\n",
        "      <td> 40.80130</td>\n",
        "      <td>-73.95011</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10072</th>\n",
        "      <td> 0.363563</td>\n",
        "      <td> 40.71215</td>\n",
        "      <td>-73.96698</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 117,
       "text": [
        "            2012      0         1   \n",
        "int_id                              \n",
        "10009   0.364316  40.82145 -73.95038\n",
        "10025   0.627035  40.71165 -73.96723\n",
        "10029   0.639122  40.84888 -73.93916\n",
        "1004    0.673357  40.80130 -73.95011\n",
        "10072   0.363563  40.71215 -73.96698"
       ]
      }
     ],
     "prompt_number": 117
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with_int = with_int.rename(columns={2011.0: '2011change', 2012.0: '2012change', 2013.0: '2013change', 0.0: 'y', 1.0: 'x'})\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfe.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>2010.0</th>\n",
        "      <th>2011.0</th>\n",
        "      <th>2012.0</th>\n",
        "      <th>2013.0</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0   </th>\n",
        "      <td>-0.220139</td>\n",
        "      <td>-0.223371</td>\n",
        "      <td>-0.232488</td>\n",
        "      <td>-0.233171</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1   </th>\n",
        "      <td>-0.148842</td>\n",
        "      <td>-0.146810</td>\n",
        "      <td>-0.139962</td>\n",
        "      <td>-0.129292</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10  </th>\n",
        "      <td>-0.216427</td>\n",
        "      <td>-0.221913</td>\n",
        "      <td>-0.229343</td>\n",
        "      <td>-0.231798</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100 </th>\n",
        "      <td>-0.219883</td>\n",
        "      <td>-0.223371</td>\n",
        "      <td>-0.233213</td>\n",
        "      <td>-0.233296</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1000</th>\n",
        "      <td>-0.220267</td>\n",
        "      <td>-0.222763</td>\n",
        "      <td>-0.233334</td>\n",
        "      <td>-0.234170</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "          2010      2011      2012      2013\n",
        "0    -0.220139 -0.223371 -0.232488 -0.233171\n",
        "1    -0.148842 -0.146810 -0.139962 -0.129292\n",
        "10   -0.216427 -0.221913 -0.229343 -0.231798\n",
        "100  -0.219883 -0.223371 -0.233213 -0.233296\n",
        "1000 -0.220267 -0.222763 -0.233334 -0.234170"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "norm = with_int.join(dfe)\n",
      "norm.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>2011change</th>\n",
        "      <th>2012change</th>\n",
        "      <th>2013change</th>\n",
        "      <th>y</th>\n",
        "      <th>x</th>\n",
        "      <th>2010.0</th>\n",
        "      <th>2011.0</th>\n",
        "      <th>2012.0</th>\n",
        "      <th>2013.0</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>int_id</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>10003</th>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0.208189</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 40.70157</td>\n",
        "      <td>-73.99332</td>\n",
        "      <td> 0.065050</td>\n",
        "      <td> 0.052736</td>\n",
        "      <td> 0.063714</td>\n",
        "      <td> 0.052747</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10009</th>\n",
        "      <td> 0.146849</td>\n",
        "      <td> 0.189621</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 40.82145</td>\n",
        "      <td>-73.95038</td>\n",
        "      <td> 0.188188</td>\n",
        "      <td> 0.215823</td>\n",
        "      <td> 0.256748</td>\n",
        "      <td> 0.251266</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10025</th>\n",
        "      <td> 0.145567</td>\n",
        "      <td> 0.420287</td>\n",
        "      <td> 1.348825</td>\n",
        "      <td> 40.71165</td>\n",
        "      <td>-73.96723</td>\n",
        "      <td> 0.211100</td>\n",
        "      <td> 0.241830</td>\n",
        "      <td> 0.343468</td>\n",
        "      <td> 0.806745</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10029</th>\n",
        "      <td> 0.204015</td>\n",
        "      <td> 0.361380</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 40.84888</td>\n",
        "      <td>-73.93916</td>\n",
        "      <td> 0.448544</td>\n",
        "      <td> 0.540054</td>\n",
        "      <td> 0.735219</td>\n",
        "      <td> 0.668281</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10032</th>\n",
        "      <td> 0.366168</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 40.81980</td>\n",
        "      <td>-73.93679</td>\n",
        "      <td>-0.077928</td>\n",
        "      <td>-0.106463</td>\n",
        "      <td>-0.083721</td>\n",
        "      <td>-0.076228</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "        2011change  2012change  2013change         y         x    2010.0  \\\n",
        "int_id                                                                     \n",
        "10003          NaN    0.208189         NaN  40.70157 -73.99332  0.065050   \n",
        "10009     0.146849    0.189621         NaN  40.82145 -73.95038  0.188188   \n",
        "10025     0.145567    0.420287    1.348825  40.71165 -73.96723  0.211100   \n",
        "10029     0.204015    0.361380         NaN  40.84888 -73.93916  0.448544   \n",
        "10032     0.366168         NaN         NaN  40.81980 -73.93679 -0.077928   \n",
        "\n",
        "          2011.0    2012.0    2013.0  \n",
        "int_id                                \n",
        "10003   0.052736  0.063714  0.052747  \n",
        "10009   0.215823  0.256748  0.251266  \n",
        "10025   0.241830  0.343468  0.806745  \n",
        "10029   0.540054  0.735219  0.668281  \n",
        "10032  -0.106463 -0.083721 -0.076228  "
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_not = pd.read_json(\"EveningDropoffs.json\", orient=\"index\")\n",
      "df_not.dropna(inplace=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_not.index.name = 'int_id'\n",
      "df_not.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>2010.0</th>\n",
        "      <th>2011.0</th>\n",
        "      <th>2012.0</th>\n",
        "      <th>2013.0</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>int_id</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0   </th>\n",
        "      <td>   8</td>\n",
        "      <td>   8</td>\n",
        "      <td>  11</td>\n",
        "      <td>  13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1   </th>\n",
        "      <td> 565</td>\n",
        "      <td> 638</td>\n",
        "      <td> 776</td>\n",
        "      <td> 845</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10  </th>\n",
        "      <td>  37</td>\n",
        "      <td>  20</td>\n",
        "      <td>  37</td>\n",
        "      <td>  24</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100 </th>\n",
        "      <td>  10</td>\n",
        "      <td>   8</td>\n",
        "      <td>   5</td>\n",
        "      <td>  12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1000</th>\n",
        "      <td>   7</td>\n",
        "      <td>  13</td>\n",
        "      <td>   4</td>\n",
        "      <td>   5</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "        2010  2011  2012  2013\n",
        "int_id                        \n",
        "0          8     8    11    13\n",
        "1        565   638   776   845\n",
        "10        37    20    37    24\n",
        "100       10     8     5    12\n",
        "1000       7    13     4     5"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total = with_int.join(df_not)\n",
      "total.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>2011change</th>\n",
        "      <th>2012change</th>\n",
        "      <th>2013change</th>\n",
        "      <th>y</th>\n",
        "      <th>x</th>\n",
        "      <th>2010.0</th>\n",
        "      <th>2011.0</th>\n",
        "      <th>2012.0</th>\n",
        "      <th>2013.0</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>int_id</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>10003</th>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0.208189</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 40.70157</td>\n",
        "      <td>-73.99332</td>\n",
        "      <td> 2236</td>\n",
        "      <td> 2280</td>\n",
        "      <td> 2460</td>\n",
        "      <td> 2303</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10009</th>\n",
        "      <td> 0.146849</td>\n",
        "      <td> 0.189621</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 40.82145</td>\n",
        "      <td>-73.95038</td>\n",
        "      <td> 3198</td>\n",
        "      <td> 3622</td>\n",
        "      <td> 4056</td>\n",
        "      <td> 3893</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10025</th>\n",
        "      <td> 0.145567</td>\n",
        "      <td> 0.420287</td>\n",
        "      <td> 1.348825</td>\n",
        "      <td> 40.71165</td>\n",
        "      <td>-73.96723</td>\n",
        "      <td> 3377</td>\n",
        "      <td> 3836</td>\n",
        "      <td> 4773</td>\n",
        "      <td> 8342</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10029</th>\n",
        "      <td> 0.204015</td>\n",
        "      <td> 0.361380</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 40.84888</td>\n",
        "      <td>-73.93916</td>\n",
        "      <td> 5232</td>\n",
        "      <td> 6290</td>\n",
        "      <td> 8012</td>\n",
        "      <td> 7233</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10032</th>\n",
        "      <td> 0.366168</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 40.81980</td>\n",
        "      <td>-73.93679</td>\n",
        "      <td> 1119</td>\n",
        "      <td>  970</td>\n",
        "      <td> 1241</td>\n",
        "      <td> 1270</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "        2011change  2012change  2013change         y         x  2010.0  \\\n",
        "int_id                                                                   \n",
        "10003          NaN    0.208189         NaN  40.70157 -73.99332    2236   \n",
        "10009     0.146849    0.189621         NaN  40.82145 -73.95038    3198   \n",
        "10025     0.145567    0.420287    1.348825  40.71165 -73.96723    3377   \n",
        "10029     0.204015    0.361380         NaN  40.84888 -73.93916    5232   \n",
        "10032     0.366168         NaN         NaN  40.81980 -73.93679    1119   \n",
        "\n",
        "        2011.0  2012.0  2013.0  \n",
        "int_id                          \n",
        "10003     2280    2460    2303  \n",
        "10009     3622    4056    3893  \n",
        "10025     3836    4773    8342  \n",
        "10029     6290    8012    7233  \n",
        "10032      970    1241    1270  "
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total.to_csv('deltaswithcounts.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "elchange = with_int.drop('2012change', axis = 1)\n",
      "elchange = elchange.drop('2013change', axis = 1)\n",
      "elchange= elchange.dropna(how='any')\n",
      "elchange.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>2011change</th>\n",
        "      <th>y</th>\n",
        "      <th>x</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>int_id</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>10009</th>\n",
        "      <td> 0.146849</td>\n",
        "      <td> 40.82145</td>\n",
        "      <td>-73.95038</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10025</th>\n",
        "      <td> 0.145567</td>\n",
        "      <td> 40.71165</td>\n",
        "      <td>-73.96723</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10029</th>\n",
        "      <td> 0.204015</td>\n",
        "      <td> 40.84888</td>\n",
        "      <td>-73.93916</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10032</th>\n",
        "      <td> 0.366168</td>\n",
        "      <td> 40.81980</td>\n",
        "      <td>-73.93679</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10034</th>\n",
        "      <td> 0.683089</td>\n",
        "      <td> 40.75352</td>\n",
        "      <td>-73.93447</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "        2011change         y         x\n",
        "int_id                                \n",
        "10009     0.146849  40.82145 -73.95038\n",
        "10025     0.145567  40.71165 -73.96723\n",
        "10029     0.204015  40.84888 -73.93916\n",
        "10032     0.366168  40.81980 -73.93679\n",
        "10034     0.683089  40.75352 -73.93447"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.hist(np.log(elchange['2011change']), bins = 40)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "(array([ 332.,  349.,  292.,  243.,  237.,  252.,  206.,  160.,  143.,\n",
        "         119.,   88.,   63.,   52.,   39.,   41.,   18.,   17.,   16.,\n",
        "          11.,   11.,    8.,    4.,    7.,    6.,    0.,    1.,    1.,\n",
        "           2.,    2.,    2.,    0.,    0.,    0.,    0.,    0.,    1.,\n",
        "           0.,    0.,    0.,    2.]),\n",
        " array([-2.31172113, -2.06092206, -1.81012299, -1.55932392, -1.30852485,\n",
        "        -1.05772578, -0.80692671, -0.55612764, -0.30532857, -0.0545295 ,\n",
        "         0.19626957,  0.44706864,  0.69786771,  0.94866679,  1.19946586,\n",
        "         1.45026493,  1.701064  ,  1.95186307,  2.20266214,  2.45346121,\n",
        "         2.70426028,  2.95505935,  3.20585842,  3.45665749,  3.70745656,\n",
        "         3.95825563,  4.2090547 ,  4.45985377,  4.71065284,  4.96145191,\n",
        "         5.21225098,  5.46305005,  5.71384912,  5.96464819,  6.21544726,\n",
        "         6.46624633,  6.7170454 ,  6.96784447,  7.21864354,  7.46944261,\n",
        "         7.72024168]),\n",
        " <a list of 40 Patch objects>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEBCAYAAACHTjUfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAAAAAAAAAAAAAAAwCv8PLCBzhtIc6+gAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107c20a90>"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "re_filter = pd.read_csv('re_filter.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 102
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "re_filter.head()"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "re_withints = re_filter.join(intersections)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 118
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test1 = list(re_withints['Unnamed: 0'])\n",
      "#with_int.head()\n",
      "test1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 148,
       "text": [
        "[28786.0,\n",
        " 61120.0,\n",
        " 11144.0,\n",
        " 48765.0,\n",
        " 13215.0,\n",
        " 24815.0,\n",
        " 47473.0,\n",
        " 9118.0,\n",
        " 34070.0,\n",
        " 20520.0,\n",
        " 23456.0,\n",
        " 20694.0,\n",
        " 27475.0,\n",
        " 59085.0,\n",
        " 54701.0,\n",
        " 6589.0,\n",
        " 11789.0,\n",
        " 30565.0,\n",
        " 36996.0,\n",
        " 5554.0,\n",
        " 57783.0,\n",
        " 43850.0,\n",
        " 7271.0,\n",
        " 23752.0,\n",
        " 18570.0,\n",
        " 13525.0,\n",
        " 28776.0,\n",
        " 29502.0,\n",
        " 43340.0,\n",
        " 21436.0,\n",
        " 11983.0,\n",
        " 58201.0,\n",
        " 1795.0,\n",
        " 22081.0,\n",
        " 21278.0,\n",
        " 33362.0,\n",
        " 51178.0,\n",
        " 36359.0,\n",
        " 52543.0,\n",
        " 1950.0,\n",
        " 8580.0,\n",
        " 3762.0,\n",
        " 21209.0,\n",
        " 59398.0,\n",
        " 51927.0,\n",
        " 9895.0,\n",
        " 6471.0,\n",
        " 25982.0,\n",
        " 6195.0,\n",
        " 11474.0,\n",
        " 31718.0,\n",
        " 59990.0,\n",
        " 47434.0,\n",
        " 25264.0,\n",
        " 1912.0,\n",
        " 19476.0,\n",
        " 27614.0,\n",
        " 22537.0,\n",
        " 60976.0,\n",
        " 3187.0,\n",
        " 14897.0,\n",
        " 37196.0,\n",
        " 29643.0,\n",
        " 19739.0,\n",
        " 52151.0,\n",
        " 22051.0,\n",
        " 16149.0,\n",
        " 25255.0,\n",
        " 40781.0,\n",
        " 45442.0,\n",
        " 45016.0,\n",
        " 31328.0,\n",
        " 55699.0,\n",
        " 4635.0,\n",
        " 43212.0,\n",
        " 44237.0,\n",
        " 9844.0,\n",
        " 54425.0,\n",
        " 15573.0,\n",
        " 180.0,\n",
        " 8929.0,\n",
        " 48759.0,\n",
        " 4544.0,\n",
        " 31213.0,\n",
        " 31702.0,\n",
        " 30971.0,\n",
        " 38155.0,\n",
        " 6473.0,\n",
        " 20416.0,\n",
        " 19275.0,\n",
        " 37589.0,\n",
        " 9826.0,\n",
        " 25551.0,\n",
        " 25972.0,\n",
        " 50268.0,\n",
        " 16422.0,\n",
        " 61701.0,\n",
        " 57575.0,\n",
        " 42454.0,\n",
        " 49959.0,\n",
        " 50284.0,\n",
        " 14586.0,\n",
        " 16583.0,\n",
        " 30802.0,\n",
        " 36753.0,\n",
        " 27042.0,\n",
        " 45020.0,\n",
        " 28210.0,\n",
        " 60742.0,\n",
        " 59855.0,\n",
        " 34160.0,\n",
        " 17387.0,\n",
        " 53025.0,\n",
        " 51395.0,\n",
        " 59187.0,\n",
        " 50998.0,\n",
        " 23141.0,\n",
        " 54019.0,\n",
        " 20343.0,\n",
        " 24459.0,\n",
        " 61742.0,\n",
        " 53129.0,\n",
        " 27032.0,\n",
        " 56771.0,\n",
        " 15199.0,\n",
        " 11639.0,\n",
        " 35137.0,\n",
        " 29453.0,\n",
        " 50760.0,\n",
        " 23426.0,\n",
        " 51204.0,\n",
        " 20926.0,\n",
        " 43044.0,\n",
        " 31220.0,\n",
        " 34640.0,\n",
        " 26103.0,\n",
        " 29593.0,\n",
        " 56480.0,\n",
        " 11631.0,\n",
        " 24416.0,\n",
        " 10413.0,\n",
        " 54121.0,\n",
        " 58290.0,\n",
        " 31825.0,\n",
        " 6869.0,\n",
        " 37931.0,\n",
        " 38838.0,\n",
        " 21401.0,\n",
        " 59051.0,\n",
        " 48083.0,\n",
        " 58388.0,\n",
        " 45966.0,\n",
        " 38771.0,\n",
        " 54541.0,\n",
        " 43204.0,\n",
        " 51539.0,\n",
        " 1721.0,\n",
        " 54695.0,\n",
        " 5076.0,\n",
        " 50716.0,\n",
        " 43066.0,\n",
        " 5090.0,\n",
        " 45032.0,\n",
        " 61983.0,\n",
        " 51921.0,\n",
        " 24256.0,\n",
        " 27095.0,\n",
        " 4666.0,\n",
        " 12556.0,\n",
        " 22812.0,\n",
        " 33374.0,\n",
        " 22036.0,\n",
        " 13333.0,\n",
        " 12192.0,\n",
        " 49289.0,\n",
        " 25115.0,\n",
        " 16499.0,\n",
        " 51619.0,\n",
        " 25784.0,\n",
        " 61709.0,\n",
        " 51275.0,\n",
        " 32372.0,\n",
        " 34084.0,\n",
        " 53636.0,\n",
        " 57398.0,\n",
        " 13704.0,\n",
        " 18671.0,\n",
        " 45530.0,\n",
        " 2015.0,\n",
        " 28250.0,\n",
        " 57836.0,\n",
        " 18307.0,\n",
        " 1099.0,\n",
        " 46563.0,\n",
        " 42792.0,\n",
        " 17524.0,\n",
        " 36925.0,\n",
        " 35831.0,\n",
        " 54913.0,\n",
        " 52430.0,\n",
        " 60975.0,\n",
        " 23521.0,\n",
        " 36444.0,\n",
        " 42596.0,\n",
        " 9052.0,\n",
        " 26545.0,\n",
        " 59804.0,\n",
        " 6681.0,\n",
        " 55860.0,\n",
        " 30978.0,\n",
        " 3018.0,\n",
        " 41745.0,\n",
        " 42386.0,\n",
        " 56170.0,\n",
        " 17229.0,\n",
        " 29775.0,\n",
        " 46056.0,\n",
        " 2026.0,\n",
        " 28095.0,\n",
        " 17740.0,\n",
        " 28700.0,\n",
        " 17640.0,\n",
        " 41097.0,\n",
        " 20994.0,\n",
        " 29154.0,\n",
        " 11380.0,\n",
        " 52513.0,\n",
        " 24931.0,\n",
        " 46285.0,\n",
        " 28135.0,\n",
        " 54365.0,\n",
        " 20327.0,\n",
        " 31359.0,\n",
        " 22371.0,\n",
        " 35915.0,\n",
        " 56441.0,\n",
        " 41449.0,\n",
        " 15686.0,\n",
        " 36127.0,\n",
        " 54064.0,\n",
        " 13342.0,\n",
        " 47315.0,\n",
        " 32073.0,\n",
        " 47138.0,\n",
        " 51978.0,\n",
        " 52923.0,\n",
        " 42414.0,\n",
        " 10773.0,\n",
        " 3453.0,\n",
        " 31476.0,\n",
        " 29676.0,\n",
        " 42934.0,\n",
        " 10708.0,\n",
        " 7103.0,\n",
        " 30910.0,\n",
        " 41547.0,\n",
        " 22341.0,\n",
        " 33722.0,\n",
        " 24061.0,\n",
        " 27708.0,\n",
        " 21958.0,\n",
        " 45897.0,\n",
        " 21096.0,\n",
        " 36694.0,\n",
        " 42728.0,\n",
        " 52041.0,\n",
        " 21485.0,\n",
        " 30919.0,\n",
        " 33912.0,\n",
        " 46948.0,\n",
        " 27720.0,\n",
        " 1115.0,\n",
        " 1078.0,\n",
        " 28676.0,\n",
        " 14167.0,\n",
        " 61778.0,\n",
        " 20949.0,\n",
        " 25389.0,\n",
        " 20151.0,\n",
        " 13130.0,\n",
        " 16251.0,\n",
        " 30559.0,\n",
        " 13361.0,\n",
        " 61456.0,\n",
        " 18646.0,\n",
        " 26865.0,\n",
        " 36484.0,\n",
        " 42440.0,\n",
        " 20223.0,\n",
        " 51590.0,\n",
        " 29946.0,\n",
        " 38249.0,\n",
        " 20109.0,\n",
        " 11825.0,\n",
        " 31827.0,\n",
        " 29156.0,\n",
        " 5350.0,\n",
        " 36565.0,\n",
        " 30314.0,\n",
        " 20439.0,\n",
        " 12423.0,\n",
        " 38383.0,\n",
        " 3021.0,\n",
        " 51038.0,\n",
        " 2832.0,\n",
        " 17648.0,\n",
        " 18481.0,\n",
        " 228.0,\n",
        " 47235.0,\n",
        " 34312.0,\n",
        " 19351.0,\n",
        " 21763.0,\n",
        " 44136.0,\n",
        " 31271.0,\n",
        " 46678.0,\n",
        " 31361.0,\n",
        " 52967.0,\n",
        " 47675.0,\n",
        " 39159.0,\n",
        " 7674.0,\n",
        " 49930.0,\n",
        " 23008.0,\n",
        " 19737.0,\n",
        " 10423.0,\n",
        " 50928.0,\n",
        " 53600.0,\n",
        " 6604.0,\n",
        " 21637.0,\n",
        " 49316.0,\n",
        " 15672.0,\n",
        " 38797.0,\n",
        " 13491.0,\n",
        " 58072.0,\n",
        " 40240.0,\n",
        " 51770.0,\n",
        " 14852.0,\n",
        " 56413.0,\n",
        " 29738.0,\n",
        " 40620.0,\n",
        " 62116.0,\n",
        " 7867.0,\n",
        " 19920.0,\n",
        " 24833.0,\n",
        " 28283.0,\n",
        " 38406.0,\n",
        " 39150.0,\n",
        " 13346.0,\n",
        " 52613.0,\n",
        " 52976.0,\n",
        " 1651.0,\n",
        " 41509.0,\n",
        " 17592.0,\n",
        " 25084.0,\n",
        " 36680.0,\n",
        " 1014.0,\n",
        " 59560.0,\n",
        " 13519.0,\n",
        " 21616.0,\n",
        " 59179.0,\n",
        " 24898.0,\n",
        " 26392.0,\n",
        " 5635.0,\n",
        " 10552.0,\n",
        " 16891.0,\n",
        " 52730.0,\n",
        " 16727.0,\n",
        " 29496.0,\n",
        " 25402.0,\n",
        " 2650.0,\n",
        " 8585.0,\n",
        " 18974.0,\n",
        " 52750.0,\n",
        " 57307.0,\n",
        " 34130.0,\n",
        " 25239.0,\n",
        " 59424.0,\n",
        " 9464.0,\n",
        " 22455.0,\n",
        " 41572.0,\n",
        " 49342.0,\n",
        " 29578.0,\n",
        " 52656.0,\n",
        " 26000.0,\n",
        " 43110.0,\n",
        " 7043.0,\n",
        " 44222.0,\n",
        " 13981.0,\n",
        " 39980.0,\n",
        " 2150.0,\n",
        " 35868.0,\n",
        " 57877.0,\n",
        " 34969.0,\n",
        " 26742.0,\n",
        " 34587.0,\n",
        " 28495.0,\n",
        " 49397.0,\n",
        " 17884.0,\n",
        " 36953.0,\n",
        " 11197.0,\n",
        " 9092.0,\n",
        " 20424.0,\n",
        " 40812.0,\n",
        " 1405.0,\n",
        " 61784.0,\n",
        " 18608.0,\n",
        " 13987.0,\n",
        " 39213.0,\n",
        " 5693.0,\n",
        " 8224.0,\n",
        " 30399.0,\n",
        " 38616.0,\n",
        " 14462.0,\n",
        " 34251.0,\n",
        " 11754.0,\n",
        " 30394.0,\n",
        " 58169.0,\n",
        " 19715.0,\n",
        " 3884.0,\n",
        " 40802.0,\n",
        " 25724.0,\n",
        " 35947.0,\n",
        " 39134.0,\n",
        " 28433.0,\n",
        " 46885.0,\n",
        " 30243.0,\n",
        " 30481.0,\n",
        " 61735.0,\n",
        " 43203.0,\n",
        " 17008.0,\n",
        " 44355.0,\n",
        " 22339.0,\n",
        " 2837.0,\n",
        " 53187.0,\n",
        " 15182.0,\n",
        " 144.0,\n",
        " 29208.0,\n",
        " 53870.0,\n",
        " 45554.0,\n",
        " 27660.0,\n",
        " 28941.0,\n",
        " 18471.0,\n",
        " 16949.0,\n",
        " 41177.0,\n",
        " 9512.0,\n",
        " 26437.0,\n",
        " 27444.0,\n",
        " 29002.0,\n",
        " 37320.0,\n",
        " 43101.0,\n",
        " 23250.0,\n",
        " 26056.0,\n",
        " 58854.0,\n",
        " 33826.0,\n",
        " 32390.0,\n",
        " 56069.0,\n",
        " 7075.0,\n",
        " 11242.0,\n",
        " 30838.0,\n",
        " 60438.0,\n",
        " 5016.0,\n",
        " 55788.0,\n",
        " 41600.0,\n",
        " 49631.0,\n",
        " 19652.0,\n",
        " 32611.0,\n",
        " 38456.0,\n",
        " 31969.0,\n",
        " 50930.0,\n",
        " 10162.0,\n",
        " 27387.0,\n",
        " 6955.0,\n",
        " 51489.0,\n",
        " 12012.0,\n",
        " 53680.0,\n",
        " 32757.0,\n",
        " 33016.0,\n",
        " 35989.0,\n",
        " 12668.0,\n",
        " 8931.0,\n",
        " 18983.0,\n",
        " 31673.0,\n",
        " 18702.0,\n",
        " 32348.0,\n",
        " 3060.0,\n",
        " 15604.0,\n",
        " 11562.0,\n",
        " 2803.0,\n",
        " 4577.0,\n",
        " 16130.0,\n",
        " 11223.0,\n",
        " 28542.0,\n",
        " 43688.0,\n",
        " 11255.0,\n",
        " 45293.0,\n",
        " 56520.0,\n",
        " 59250.0,\n",
        " 32812.0,\n",
        " 42499.0,\n",
        " 42707.0,\n",
        " 21476.0,\n",
        " 34318.0,\n",
        " 33781.0,\n",
        " 8991.0,\n",
        " 8620.0,\n",
        " 41238.0,\n",
        " 37248.0,\n",
        " 1283.0,\n",
        " 17443.0,\n",
        " 42548.0,\n",
        " 31001.0,\n",
        " 35867.0,\n",
        " 53470.0,\n",
        " 43723.0,\n",
        " 45030.0,\n",
        " 54347.0,\n",
        " 9140.0,\n",
        " 32499.0,\n",
        " 48955.0,\n",
        " 1025.0,\n",
        " 41707.0,\n",
        " 17650.0,\n",
        " 48053.0,\n",
        " 18061.0,\n",
        " 31512.0,\n",
        " 24529.0,\n",
        " 1155.0,\n",
        " 43855.0,\n",
        " 29776.0,\n",
        " 57080.0,\n",
        " 56452.0,\n",
        " 41578.0,\n",
        " 37532.0,\n",
        " 29873.0,\n",
        " 55598.0,\n",
        " 49106.0,\n",
        " 9593.0,\n",
        " 10372.0,\n",
        " 13515.0,\n",
        " 44842.0,\n",
        " 10171.0,\n",
        " 8579.0,\n",
        " 32942.0,\n",
        " 24364.0,\n",
        " 56700.0,\n",
        " 14903.0,\n",
        " 26579.0,\n",
        " 58552.0,\n",
        " 37079.0,\n",
        " 43327.0,\n",
        " 41032.0,\n",
        " 41280.0,\n",
        " 4955.0,\n",
        " 61361.0,\n",
        " 17577.0,\n",
        " 59845.0,\n",
        " 36019.0,\n",
        " 33730.0,\n",
        " 12088.0,\n",
        " 47532.0,\n",
        " 33419.0,\n",
        " 22151.0,\n",
        " 10878.0,\n",
        " 23432.0,\n",
        " 32246.0,\n",
        " 38343.0,\n",
        " 6849.0,\n",
        " 20946.0,\n",
        " 19155.0,\n",
        " 17537.0,\n",
        " 29400.0,\n",
        " 5780.0,\n",
        " 12703.0,\n",
        " 31485.0,\n",
        " 18802.0,\n",
        " 12475.0,\n",
        " 4083.0,\n",
        " 2288.0,\n",
        " 27163.0,\n",
        " 41079.0,\n",
        " 27077.0,\n",
        " 33641.0,\n",
        " 29715.0,\n",
        " 61451.0,\n",
        " 47178.0,\n",
        " 3831.0,\n",
        " 12761.0,\n",
        " 54737.0,\n",
        " 54496.0,\n",
        " 27886.0,\n",
        " 14273.0,\n",
        " 13357.0,\n",
        " 46138.0,\n",
        " 15973.0,\n",
        " 34095.0,\n",
        " 14366.0,\n",
        " 18527.0,\n",
        " 50070.0,\n",
        " 11928.0,\n",
        " 42647.0,\n",
        " 22078.0,\n",
        " 12182.0,\n",
        " 35247.0,\n",
        " 47533.0,\n",
        " 21085.0,\n",
        " 50500.0,\n",
        " 2716.0,\n",
        " 7149.0,\n",
        " 25282.0,\n",
        " 50908.0,\n",
        " 51197.0,\n",
        " 61145.0,\n",
        " 39533.0,\n",
        " 7148.0,\n",
        " 21736.0,\n",
        " 57058.0,\n",
        " 53497.0,\n",
        " 7741.0,\n",
        " 14502.0,\n",
        " 48880.0,\n",
        " 29178.0,\n",
        " 5879.0,\n",
        " 35381.0,\n",
        " 12204.0,\n",
        " 59803.0,\n",
        " 17359.0,\n",
        " 25001.0,\n",
        " 23171.0,\n",
        " 14164.0,\n",
        " 16440.0,\n",
        " 31871.0,\n",
        " 1748.0,\n",
        " 29605.0,\n",
        " 53927.0,\n",
        " 26687.0,\n",
        " 16332.0,\n",
        " 50906.0,\n",
        " 53433.0,\n",
        " 15955.0,\n",
        " 2583.0,\n",
        " 52741.0,\n",
        " 34475.0,\n",
        " 39791.0,\n",
        " 39939.0,\n",
        " 18246.0,\n",
        " 34467.0,\n",
        " 17825.0,\n",
        " 919.0,\n",
        " 41810.0,\n",
        " 46036.0,\n",
        " 2959.0,\n",
        " 17014.0,\n",
        " 21982.0,\n",
        " 8084.0,\n",
        " 42070.0,\n",
        " 16430.0,\n",
        " 14641.0,\n",
        " 16223.0,\n",
        " 9834.0,\n",
        " 28161.0,\n",
        " 2041.0,\n",
        " 35982.0,\n",
        " 61869.0,\n",
        " 11007.0,\n",
        " 58920.0,\n",
        " 42869.0,\n",
        " 21479.0,\n",
        " 12776.0,\n",
        " 55943.0,\n",
        " 35053.0,\n",
        " 31584.0,\n",
        " 60753.0,\n",
        " 30913.0,\n",
        " 55285.0,\n",
        " 40398.0,\n",
        " 22337.0,\n",
        " 17075.0,\n",
        " 10393.0,\n",
        " 15788.0,\n",
        " 31386.0,\n",
        " 55947.0,\n",
        " 20314.0,\n",
        " 43885.0,\n",
        " 52110.0,\n",
        " 48095.0,\n",
        " 16220.0,\n",
        " 9175.0,\n",
        " 15012.0,\n",
        " 27875.0,\n",
        " 40081.0,\n",
        " 41524.0,\n",
        " 15747.0,\n",
        " 59908.0,\n",
        " 47215.0,\n",
        " 43122.0,\n",
        " 59088.0,\n",
        " 36950.0,\n",
        " 13899.0,\n",
        " 4876.0,\n",
        " 29412.0,\n",
        " 8306.0,\n",
        " 4790.0,\n",
        " 55352.0,\n",
        " 49731.0,\n",
        " 29885.0,\n",
        " 16389.0,\n",
        " 49172.0,\n",
        " 25151.0,\n",
        " 57173.0,\n",
        " 52536.0,\n",
        " 48024.0,\n",
        " 29789.0,\n",
        " 19840.0,\n",
        " 28724.0,\n",
        " 15057.0,\n",
        " 40195.0,\n",
        " 6939.0,\n",
        " 60863.0,\n",
        " 45002.0,\n",
        " 17341.0,\n",
        " 28277.0,\n",
        " 31561.0,\n",
        " 11247.0,\n",
        " 54149.0,\n",
        " 60650.0,\n",
        " 33349.0,\n",
        " 54788.0,\n",
        " 21605.0,\n",
        " 7459.0,\n",
        " 20145.0,\n",
        " 10989.0,\n",
        " 26646.0,\n",
        " 34736.0,\n",
        " 23489.0,\n",
        " 53124.0,\n",
        " 33310.0,\n",
        " 45254.0,\n",
        " 50359.0,\n",
        " 13151.0,\n",
        " 8364.0,\n",
        " 44653.0,\n",
        " 300.0,\n",
        " 49783.0,\n",
        " 20612.0,\n",
        " 32693.0,\n",
        " 1477.0,\n",
        " 7742.0,\n",
        " 36383.0,\n",
        " 29486.0,\n",
        " 56428.0,\n",
        " 57289.0,\n",
        " 37445.0,\n",
        " 60644.0,\n",
        " 36437.0,\n",
        " 44871.0,\n",
        " 14199.0,\n",
        " 59143.0,\n",
        " 44246.0,\n",
        " 38521.0,\n",
        " 621.0,\n",
        " 13172.0,\n",
        " 54035.0,\n",
        " 56733.0,\n",
        " 55268.0,\n",
        " 37862.0,\n",
        " 36609.0,\n",
        " 22215.0,\n",
        " 46430.0,\n",
        " 45049.0,\n",
        " 53119.0,\n",
        " 53768.0,\n",
        " 8568.0,\n",
        " 5489.0,\n",
        " 55433.0,\n",
        " 44663.0,\n",
        " 6714.0,\n",
        " 15180.0,\n",
        " 37066.0,\n",
        " 54625.0,\n",
        " 5406.0,\n",
        " 61455.0,\n",
        " 45691.0,\n",
        " 52743.0,\n",
        " 7785.0,\n",
        " 11191.0,\n",
        " 53313.0,\n",
        " 6677.0,\n",
        " 36900.0,\n",
        " 18302.0,\n",
        " 13542.0,\n",
        " 28920.0,\n",
        " 59331.0,\n",
        " 50798.0,\n",
        " 38840.0,\n",
        " 25548.0,\n",
        " 53565.0,\n",
        " 46400.0,\n",
        " 46629.0,\n",
        " 62069.0,\n",
        " 41228.0,\n",
        " 30598.0,\n",
        " 51742.0,\n",
        " 59967.0,\n",
        " 20041.0,\n",
        " 26827.0,\n",
        " 43365.0,\n",
        " 40366.0,\n",
        " 18621.0,\n",
        " 2581.0,\n",
        " 47027.0,\n",
        " 34834.0,\n",
        " 39995.0,\n",
        " 11360.0,\n",
        " 19048.0,\n",
        " 5906.0,\n",
        " 1285.0,\n",
        " 17048.0,\n",
        " 35128.0,\n",
        " 43930.0,\n",
        " 49498.0,\n",
        " 34219.0,\n",
        " 3327.0,\n",
        " 16296.0,\n",
        " 46518.0,\n",
        " 48269.0,\n",
        " 54158.0,\n",
        " 53075.0,\n",
        " 48338.0,\n",
        " 3988.0,\n",
        " 23403.0,\n",
        " 36560.0,\n",
        " 24358.0,\n",
        " 9015.0,\n",
        " 28571.0,\n",
        " 22385.0,\n",
        " 41764.0,\n",
        " 16817.0,\n",
        " 32943.0,\n",
        " 42629.0,\n",
        " 61948.0,\n",
        " 59520.0,\n",
        " 35846.0,\n",
        " 53908.0,\n",
        " 36100.0,\n",
        " 48089.0,\n",
        " 18904.0,\n",
        " 44468.0,\n",
        " 52914.0,\n",
        " 54794.0,\n",
        " 388.0,\n",
        " 45309.0,\n",
        " 12521.0,\n",
        " 34297.0,\n",
        " 2682.0,\n",
        " 13459.0,\n",
        " 30030.0,\n",
        " 48464.0,\n",
        " 38541.0,\n",
        " 33874.0,\n",
        " 28670.0,\n",
        " 61330.0,\n",
        " 48048.0,\n",
        " 26973.0,\n",
        " 56006.0,\n",
        " 51186.0,\n",
        " 38167.0,\n",
        " 19527.0,\n",
        " 59366.0,\n",
        " 4712.0,\n",
        " 38544.0,\n",
        " 44797.0,\n",
        " 59765.0,\n",
        " 43426.0,\n",
        " 39494.0,\n",
        " 27066.0,\n",
        " 42125.0,\n",
        " 36304.0,\n",
        " 35736.0,\n",
        " 58738.0,\n",
        " 61683.0,\n",
        " 9337.0,\n",
        " 36604.0,\n",
        " 51925.0,\n",
        " 34580.0,\n",
        " 49710.0,\n",
        " 52369.0,\n",
        " 38509.0,\n",
        " 43584.0,\n",
        " 32869.0,\n",
        " 49711.0,\n",
        " 52062.0,\n",
        " 33169.0,\n",
        " 37382.0,\n",
        " 51118.0,\n",
        " 19428.0,\n",
        " 47091.0,\n",
        " 38729.0,\n",
        " 7030.0,\n",
        " 44608.0,\n",
        " 49821.0,\n",
        " 24790.0,\n",
        " 24339.0,\n",
        " 18659.0,\n",
        " 595.0,\n",
        " 26077.0,\n",
        " 35239.0,\n",
        " 40353.0,\n",
        " 26740.0,\n",
        " 38750.0,\n",
        " 14631.0,\n",
        " 8108.0,\n",
        " 13740.0,\n",
        " 44877.0,\n",
        " 24042.0,\n",
        " 58433.0,\n",
        " 24913.0,\n",
        " 3709.0,\n",
        " 30643.0,\n",
        " 48708.0,\n",
        " 21705.0,\n",
        " 24214.0,\n",
        " 20679.0,\n",
        " 40115.0,\n",
        " 30691.0,\n",
        " 29998.0,\n",
        " 46237.0,\n",
        " 41815.0,\n",
        " 6105.0,\n",
        " 52152.0,\n",
        " 31880.0,\n",
        " 8398.0,\n",
        " 52221.0,\n",
        " 57957.0,\n",
        " 29914.0,\n",
        " 26799.0,\n",
        " 24967.0,\n",
        " 34129.0,\n",
        " 41693.0,\n",
        " 5747.0,\n",
        " 51155.0,\n",
        " 42960.0,\n",
        " 10492.0,\n",
        " 17120.0,\n",
        " 13763.0,\n",
        " 19664.0,\n",
        " 52284.0,\n",
        " 40973.0,\n",
        " 52059.0,\n",
        " 11091.0,\n",
        " 40301.0,\n",
        " 5905.0,\n",
        " 10412.0,\n",
        " 19376.0,\n",
        " 57519.0,\n",
        " 25370.0,\n",
        " 57757.0,\n",
        " 6565.0,\n",
        " 44807.0,\n",
        " 42355.0,\n",
        " 48050.0,\n",
        " 52090.0,\n",
        " 20526.0,\n",
        " 10729.0,\n",
        " 18170.0,\n",
        " 54490.0,\n",
        " 53899.0,\n",
        " 40980.0,\n",
        " 61718.0,\n",
        " 11243.0,\n",
        " 528.0,\n",
        " 19874.0,\n",
        " 23663.0,\n",
        " 28423.0,\n",
        " 43376.0,\n",
        " 35791.0,\n",
        " 11136.0,\n",
        " 13838.0,\n",
        " 47308.0,\n",
        " 45271.0,\n",
        " 47836.0,\n",
        " 40396.0,\n",
        " 37637.0,\n",
        " 10052.0,\n",
        " 42432.0,\n",
        " 38413.0,\n",
        " 40773.0,\n",
        " 17036.0,\n",
        " 10025.0,\n",
        " 13539.0,\n",
        " 37046.0,\n",
        " 27458.0,\n",
        " 7831.0,\n",
        " 29079.0,\n",
        " 52432.0,\n",
        " 46726.0,\n",
        " 45107.0,\n",
        " 41568.0,\n",
        " 26084.0,\n",
        " 58428.0,\n",
        " 45215.0,\n",
        " 20101.0,\n",
        " ...]"
       ]
      }
     ],
     "prompt_number": 148
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "re_withints.to_csv('re_filter_with_int.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "change_ints = list(with_int.index)\n",
      "len(set(change_ints).intersection(test1))\n",
      "\n",
      "# in_both = re_withints[re_withints['Unnamed: 0'].isin(change_ints)]\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 151,
       "text": [
        "337"
       ]
      }
     ],
     "prompt_number": 151
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(in_both)\n",
      "#in_both.drop([2010.0])\n",
      "#in_both.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 120,
       "text": [
        "337"
       ]
      }
     ],
     "prompt_number": 120
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "in_both.to_csv('intsinboth.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 121
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.hist(with_int[2012.0], bins = 30)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 74,
       "text": [
        "(array([  2.70700000e+03,   6.00000000e+00,   3.00000000e+00,\n",
        "          2.00000000e+00,   2.00000000e+00,   0.00000000e+00,\n",
        "          1.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
        "          0.00000000e+00,   1.00000000e+00,   0.00000000e+00,\n",
        "          2.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
        "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
        "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
        "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
        "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
        "          0.00000000e+00,   0.00000000e+00,   1.00000000e+00]),\n",
        " array([  1.95210166e-01,   6.92566616e+01,   1.38318113e+02,\n",
        "          2.07379565e+02,   2.76441016e+02,   3.45502467e+02,\n",
        "          4.14563919e+02,   4.83625370e+02,   5.52686822e+02,\n",
        "          6.21748273e+02,   6.90809725e+02,   7.59871176e+02,\n",
        "          8.28932628e+02,   8.97994079e+02,   9.67055531e+02,\n",
        "          1.03611698e+03,   1.10517843e+03,   1.17423988e+03,\n",
        "          1.24330134e+03,   1.31236279e+03,   1.38142424e+03,\n",
        "          1.45048569e+03,   1.51954714e+03,   1.58860859e+03,\n",
        "          1.65767005e+03,   1.72673150e+03,   1.79579295e+03,\n",
        "          1.86485440e+03,   1.93391585e+03,   2.00297730e+03,\n",
        "          2.07203875e+03]),\n",
        " <a list of 30 Patch objects>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEBCAYAAAB4wNK4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAAAAVOj/AUYtz+Ehy7VuAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10dd74cd0>"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}