{
 "metadata": {
  "name": "EPA_to_DF"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Converting EPA Airquality Data into Pandas DataFrames by County\n",
      "---------------------------------------------------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "from pandas import DataFrame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Airquality Data comes from https://ofmext.epa.gov/AQDMRS/aqdmrs.html\n",
      "#This query includes the ozone data for the year 2011 in the state of california\n",
      "a = open('AQDM_507410362.txt')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.read_csv(a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 5 entries, 0 to 4\n",
        "Data columns:\n",
        "Latitude                   5  non-null values\n",
        "Longitude                  5  non-null values\n",
        "Datum                      5  non-null values\n",
        "Horizontal Accuracy        5  non-null values\n",
        "State Code                 5  non-null values\n",
        "County Code                5  non-null values\n",
        "Site Num                   5  non-null values\n",
        "Parameter Code             5  non-null values\n",
        "POC                        5  non-null values\n",
        "AQS Parameter Desc         5  non-null values\n",
        "Date Local                 5  non-null values\n",
        "24 Hour Local              5  non-null values\n",
        "Date GMT                   5  non-null values\n",
        "24 Hour GMT                5  non-null values\n",
        "Year GMT                   5  non-null values\n",
        "Day In Year GMT            5  non-null values\n",
        "Sample Measurement         5  non-null values\n",
        "Units of Measure           5  non-null values\n",
        "Sample Duration            5  non-null values\n",
        "Sample Frequency           0  non-null values\n",
        "Detection Limit            5  non-null values\n",
        "Measurement Uncertainty    0  non-null values\n",
        "Qualifier Description      0  non-null values\n",
        "Method Type                5  non-null values\n",
        "Method Description         5  non-null values\n",
        "dtypes: float64(13), object(12)"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(df)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "2995068"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Group all measurements by measurement site (identified by countycode and site number) and create a new DataFrame with data averaged over time per measurement site"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "county_df = df.groupby(['County Code','Site Num', 'Latitude', 'Longitude'])\n",
      "\n",
      "county_code = list()\n",
      "sitenum = list()\n",
      "lat = list()\n",
      "lon = list()\n",
      "mean_meas = list()\n",
      "max_meas = list()\n",
      "min_meas = list()\n",
      "median_meas = list()\n",
      "\n",
      "for grpname,grp in county_df:\n",
      "    county_code.append(grpname[0])\n",
      "    sitenum.append(grpname[1])\n",
      "    lat.append(float(grpname[2]))\n",
      "    lon.append(float(grpname[3]))\n",
      "    mean_meas.append(grp['Sample Measurement'].mean())\n",
      "    max_meas.append(grp['Sample Measurement'].max())\n",
      "    min_meas.append(grp['Sample Measurement'].min())\n",
      "    median_meas.append(grp['Sample Measurement'].median())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sites_dict = {'County Code': county_code, 'Site Num': sitenum, 'Mean Ozone': mean_meas, 'Max Ozone': max_meas, 'Min Ozone': min_meas, 'Median Ozone': median_meas, 'Latitude': lat, 'Longitude': lon}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sites_df = DataFrame(sites_dict)\n",
      "sites_df.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>County Code</th>\n",
        "      <th>Latitude</th>\n",
        "      <th>Longitude</th>\n",
        "      <th>Max Ozone</th>\n",
        "      <th>Mean Ozone</th>\n",
        "      <th>Median Ozone</th>\n",
        "      <th>Min Ozone</th>\n",
        "      <th>Site Num</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td> 37.687526</td>\n",
        "      <td>-121.784217</td>\n",
        "      <td> 0.115</td>\n",
        "      <td> 0.023289</td>\n",
        "      <td> 0.023</td>\n",
        "      <td> 0.000</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 1</td>\n",
        "      <td> 37.687526</td>\n",
        "      <td>-121.784217</td>\n",
        "      <td> 0.063</td>\n",
        "      <td> 0.033426</td>\n",
        "      <td> 0.035</td>\n",
        "      <td> 0.003</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 1</td>\n",
        "      <td> 37.743065</td>\n",
        "      <td>-122.169935</td>\n",
        "      <td> 0.091</td>\n",
        "      <td> 0.018510</td>\n",
        "      <td> 0.018</td>\n",
        "      <td> 0.000</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 1</td>\n",
        "      <td> 37.743065</td>\n",
        "      <td>-122.169935</td>\n",
        "      <td> 0.051</td>\n",
        "      <td> 0.027224</td>\n",
        "      <td> 0.028</td>\n",
        "      <td> 0.002</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 1</td>\n",
        "      <td> 37.814781</td>\n",
        "      <td>-122.282347</td>\n",
        "      <td> 0.057</td>\n",
        "      <td> 0.018001</td>\n",
        "      <td> 0.018</td>\n",
        "      <td> 0.000</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "   County Code   Latitude   Longitude  Max Ozone  Mean Ozone  Median Ozone  Min Ozone  \\\n",
        "0            1  37.687526 -121.784217      0.115    0.023289         0.023      0.000   \n",
        "1            1  37.687526 -121.784217      0.063    0.033426         0.035      0.003   \n",
        "2            1  37.743065 -122.169935      0.091    0.018510         0.018      0.000   \n",
        "3            1  37.743065 -122.169935      0.051    0.027224         0.028      0.002   \n",
        "4            1  37.814781 -122.282347      0.057    0.018001         0.018      0.000   \n",
        "\n",
        "   Site Num  \n",
        "0         7  \n",
        "1         7  \n",
        "2         9  \n",
        "3         9  \n",
        "4        11  "
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Look at some overall stats for our new DataFrame"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'Std ', sites_df['Mean Ozone'].std()\n",
      "print 'Mean ',sites_df['Mean Ozone'].mean()\n",
      "print 'Median',sites_df['Mean Ozone'].median()\n",
      "print 'Lat Max',sites_df['Latitude'].max()\n",
      "print 'Lat Min',sites_df['Latitude'].min()\n",
      "print 'Lon Max',sites_df['Longitude'].max()\n",
      "print 'Lon Min',sites_df['Longitude'].min()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Std  0.00994831782491\n",
        "Mean  0.035125235605\n",
        "Median 0.0332733333333\n",
        "Lat Max 41.726892\n",
        "Lat Min 32.552164\n",
        "Lon Max -114.602886\n",
        "Lon Min -124.20139\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot all measureing stations to see if they are evenly distributed"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(sites_df['Longitude'],sites_df['Latitude'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<matplotlib.collections.PathCollection at 0x11126e6c>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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4OfnlWl/2nXfe0btmrhHYIlBNevToc8eYYmJiROtDn6yfM03AR3x9q+pVSvbS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\ngg==\n"
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Convert the 'County Code' into a FIPS, ie add on the '06' California prefix"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#sites_df['FIPS'] = sites_df['County Code'].apply(lambda x: '0' + (str(6000 + int(x) )))\n",
      "sites_df['FIPS'] = sites_df['County Code'].apply(lambda x: 6000 + int(x) )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sites_df['FIPS'][100:110]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "100    6037\n",
        "101    6037\n",
        "102    6037\n",
        "103    6037\n",
        "104    6037\n",
        "105    6037\n",
        "106    6037\n",
        "107    6037\n",
        "108    6039\n",
        "109    6039\n",
        "Name: FIPS"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next Up: Calculate county wide averages\n",
      "---------------------------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "county_averages = sites_df.groupby('FIPS')['Mean Ozone'].mean()\n",
      "county_averages.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "Int64Index([6001, 6005, 6007, 6009, 6011, 6013, 6017, 6019, 6021, 6023, 6025, 6027, 6029, 6031, 6033, 6037, 6039, 6041, 6043, 6045, 6047, 6053, 6055, 6057, 6059, 6061, 6065, 6067, 6069, 6071, 6073, 6075, 6077, 6079, 6081, 6083, 6085, 6087, 6089, 6093, 6095, 6097, 6099, 6101, 6103, 6107, 6109, 6111, 6113], dtype=int64)"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "county_averages.hist(bins = 20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0xae410cc>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "county_averages.mean(), county_averages.std()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "(0.033272136153468475, 0.006327595808016743)"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "med = pd.read_csv(open('FIPSAsthmaCounty.csv'), sep=',')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "med = med.set_index('FIPS')\n",
      "med.columns = ['county', 'prevalence']\n",
      "med.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "Int64Index([6005, 6011, 6027, 6001, 6003, 6007, 6009, 6013, 6015, 6017, 6019, 6021, 6023, 6025, 6029, 6031, 6033, 6035, 6037, 6039, 6041, 6043, 6045, 6047, 6049, 6051, 6053, 6055, 6057, 6059, 6061, 6063, 6065, 6067, 6071, 6073, 6075, 6077, 6079, 6081, 6083, 6085, 6087, 6089, 6091, 6093, 6095, 6097, 6099, 6101, 6103, 6105, 6107, 6109, 6111, 6113, 6115], dtype=int64)"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ozone = DataFrame(county_averages)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "both = pd.concat([med, ozone], axis = 1)\n",
      "                                               "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "both[:10]"
     ],
     "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>county</th>\n",
        "      <th>prevalence</th>\n",
        "      <th>Mean Ozone</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FIPS</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>6001</th>\n",
        "      <td>      Alameda</td>\n",
        "      <td>  9.8</td>\n",
        "      <td> 0.025886</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6003</th>\n",
        "      <td>       Alpine</td>\n",
        "      <td>  8.7</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6005</th>\n",
        "      <td>       Amador</td>\n",
        "      <td>  8.7</td>\n",
        "      <td> 0.031071</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6007</th>\n",
        "      <td>        Butte</td>\n",
        "      <td>  9.7</td>\n",
        "      <td> 0.039171</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6009</th>\n",
        "      <td>   Calaveras </td>\n",
        "      <td>  8.7</td>\n",
        "      <td> 0.036388</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6011</th>\n",
        "      <td>       Colusa</td>\n",
        "      <td> 13.7</td>\n",
        "      <td> 0.030787</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6013</th>\n",
        "      <td> Contra Costa</td>\n",
        "      <td>  9.6</td>\n",
        "      <td> 0.027290</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6015</th>\n",
        "      <td>    Del Norte</td>\n",
        "      <td>  9.2</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6017</th>\n",
        "      <td>    El Dorado</td>\n",
        "      <td>  8.3</td>\n",
        "      <td> 0.040176</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6019</th>\n",
        "      <td>       Fresno</td>\n",
        "      <td> 11.3</td>\n",
        "      <td> 0.040742</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "            county  prevalence  Mean Ozone\n",
        "FIPS                                      \n",
        "6001       Alameda         9.8    0.025886\n",
        "6003        Alpine         8.7         NaN\n",
        "6005        Amador         8.7    0.031071\n",
        "6007         Butte         9.7    0.039171\n",
        "6009    Calaveras          8.7    0.036388\n",
        "6011        Colusa        13.7    0.030787\n",
        "6013  Contra Costa         9.6    0.027290\n",
        "6015     Del Norte         9.2         NaN\n",
        "6017     El Dorado         8.3    0.040176\n",
        "6019        Fresno        11.3    0.040742"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "both = both.dropna()\n",
      "both[:10]"
     ],
     "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>county</th>\n",
        "      <th>prevalence</th>\n",
        "      <th>Mean Ozone</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FIPS</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>6001</th>\n",
        "      <td>      Alameda</td>\n",
        "      <td>  9.8</td>\n",
        "      <td> 0.025886</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6005</th>\n",
        "      <td>       Amador</td>\n",
        "      <td>  8.7</td>\n",
        "      <td> 0.031071</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6007</th>\n",
        "      <td>        Butte</td>\n",
        "      <td>  9.7</td>\n",
        "      <td> 0.039171</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6009</th>\n",
        "      <td>   Calaveras </td>\n",
        "      <td>  8.7</td>\n",
        "      <td> 0.036388</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6011</th>\n",
        "      <td>       Colusa</td>\n",
        "      <td> 13.7</td>\n",
        "      <td> 0.030787</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6013</th>\n",
        "      <td> Contra Costa</td>\n",
        "      <td>  9.6</td>\n",
        "      <td> 0.027290</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6017</th>\n",
        "      <td>    El Dorado</td>\n",
        "      <td>  8.3</td>\n",
        "      <td> 0.040176</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6019</th>\n",
        "      <td>       Fresno</td>\n",
        "      <td> 11.3</td>\n",
        "      <td> 0.040742</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6021</th>\n",
        "      <td>        Glenn</td>\n",
        "      <td> 13.7</td>\n",
        "      <td> 0.032210</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6023</th>\n",
        "      <td>     Humboldt</td>\n",
        "      <td>  7.0</td>\n",
        "      <td> 0.023115</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "            county  prevalence  Mean Ozone\n",
        "FIPS                                      \n",
        "6001       Alameda         9.8    0.025886\n",
        "6005        Amador         8.7    0.031071\n",
        "6007         Butte         9.7    0.039171\n",
        "6009    Calaveras          8.7    0.036388\n",
        "6011        Colusa        13.7    0.030787\n",
        "6013  Contra Costa         9.6    0.027290\n",
        "6017     El Dorado         8.3    0.040176\n",
        "6019        Fresno        11.3    0.040742\n",
        "6021         Glenn        13.7    0.032210\n",
        "6023      Humboldt         7.0    0.023115"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(both['Mean Ozone'], both['prevalence'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "<matplotlib.collections.PathCollection at 0x20c592ac>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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06TMQO3cG48KF5fj22zwMHz62xlfmXb16Ff37h2PfvuH4/ff38MknxxEdPUui1Pd36dIl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\nRKRgLPJERAr2/wFdpk3WC52XcQAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z = np.polyfit(both['Mean Ozone'], both['prevalence'], 1)\n",
      "p = np.poly1d(z)\n",
      "plt.scatter(both['Mean Ozone'], both['prevalence'])\n",
      "plt.plot(both['Mean Ozone'],p(both['Mean Ozone']),\"r--\")\n",
      "#plt.ylim(both['prevalence'].min(), both['prevalence'].max())\n",
      "#plt.xlim(both['Mean Ozone'].min(), both['Mean Ozone'].max())\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "[<matplotlib.lines.Line2D at 0x23eb33ac>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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pvGNV6p78L7/8QvPmzfH398fR0ZFx48axYcOGAm02btzIlClTAOjW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\nl+sAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'r= '+str(both['Mean Ozone'].corr(both['prevalence']))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "r= 0.218297247024\n"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As we are dealing with atmosphere and not a physical law, this is pretty good.\n",
      "r should be higher once we use the data for 2007"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}