2:7:16 - 2014 San Francisco Energy Score by Zip.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"datasets/Existing_Commercial_Buildings_Energy_Performance_Ordinance_Report.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Which postal code in SF has the most energy efficient buildings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['﻿Parcel(s)', 'Building Name', 'Building Address', 'Postal Code',\n",
       "       'Floor Area', 'Property Type - Self Selected', 'PIM Link (description)',\n",
       "       'PIM Link', 'Benchmark 2010 Status', 'Benchmark 2011 Status',\n",
       "       'Benchmark 2012 Status', 'Benchmark 2013 Status',\n",
       "       'Benchmark 2014 Status', 'Energy Audit Due Date', 'Energy Audit Status',\n",
       "       'Full.Address (address)', '2011 ENERGY STAR Score',\n",
       "       'Full.Address (city)', 'Full.Address', '2011 Site EUI (kBtu/ft2)',\n",
       "       '2011 Source EUI (kBtu/ft2)', 'Full.Address (state)',\n",
       "       'Full.Address (zip)',\n",
       "       '2011 Percent Better than National Median Site EUI',\n",
       "       '2011 Percent Better than National Median Source EUI',\n",
       "       '2011 Total GHG Emissions (Metric Tons CO2e)',\n",
       "       '2011 Total GHG Emissions Intensity (kgCO2e/ft2)',\n",
       "       '2011 Weather Normalized Site EUI (kBtu/ft2)',\n",
       "       '2011 Weather Normalized Source EUI (kBtu/ft2)',\n",
       "       '2012 ENERGY STAR Score', '2012 Site EUI (kBtu/ft2)',\n",
       "       '2012 Source EUI (kBtu/ft2)',\n",
       "       '2012 Percent Better than National Median Site EUI',\n",
       "       '2012 Percent Better than National Median Source EUI',\n",
       "       '2012 Total GHG Emissions (Metric Tons CO2e)',\n",
       "       '2012 Total GHG Emissions Intensity (kgCO2e/ft2)',\n",
       "       '2012 Weather Normalized Site EUI (kBtu/ft2)',\n",
       "       '2012 Weather Normalized Source EUI (kBtu/sq.ft)',\n",
       "       '2013 ENERGY STAR Score', '2013 Site EUI (kBtu/ft2)',\n",
       "       '2013 Source EUI (kBtu/ft2)',\n",
       "       '2013 Percent Better than National Median Site EUI',\n",
       "       '2013 Percent Better than National Median Source EUI',\n",
       "       '2013 Total GHG Emissions (Metric Tons CO2e)',\n",
       "       '2013 Total GHG Emissions Intensity (kgCO2e/ft2)',\n",
       "       '2013 Weather Normalized Site EUI (kBtu/ft2)',\n",
       "       '2013 Weather Normalized Source EUI (kBtu/ft2)',\n",
       "       '2014 ENERGY STAR Score', '2014 Site EUI (kBtu/ft2)',\n",
       "       '2014 Source EUI (kBtu/ft2)',\n",
       "       '2014 Percent Better than National Median Site EUI',\n",
       "       '2014 Percent Better than National Median Source EUI',\n",
       "       '2014 Total GHG Emissions (Metric Tons CO2e)',\n",
       "       '2014 Total GHG Emissions Intensity (kgCO2e/ft2)',\n",
       "       '2014 Weather Normalized Site EUI (kBtu/ft2)',\n",
       "       '2014 Weather Normalized Source EUI (kBtu/ft2)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "energy_zip = pd.concat([data['2014 ENERGY STAR Score'], data['Postal Code']], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### page 185 in Python for Data Analysis tells you how to concatonate series objects\n",
    "#### new remove N/As"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cleaned_energy_zip = energy_zip.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014 ENERGY STAR Score    714\n",
       "Postal Code               714\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_energy_zip.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2014 ENERGY STAR Score</th>\n",
       "      <th>Postal Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>94</td>\n",
       "      <td>94104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14</td>\n",
       "      <td>94109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>80</td>\n",
       "      <td>94102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>98</td>\n",
       "      <td>94105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>73</td>\n",
       "      <td>94115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>94111</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>89</td>\n",
       "      <td>94122</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>75</td>\n",
       "      <td>94109</td>\n",
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       "      <th>21</th>\n",
       "      <td>99</td>\n",
       "      <td>94105</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>99</td>\n",
       "      <td>94107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>93</td>\n",
       "      <td>94103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2</td>\n",
       "      <td>94103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>75</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>95</td>\n",
       "      <td>94124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>91</td>\n",
       "      <td>94104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>100</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>79</td>\n",
       "      <td>94104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>92</td>\n",
       "      <td>94105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>88</td>\n",
       "      <td>94104</td>\n",
       "    </tr>\n",
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       "      <th>53</th>\n",
       "      <td>85</td>\n",
       "      <td>94111</td>\n",
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       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>77</td>\n",
       "      <td>94111</td>\n",
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       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>99</td>\n",
       "      <td>94108</td>\n",
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       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>87</td>\n",
       "      <td>94102</td>\n",
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       "      <th>65</th>\n",
       "      <td>80</td>\n",
       "      <td>94105</td>\n",
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       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>65</td>\n",
       "      <td>94103</td>\n",
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       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>8</td>\n",
       "      <td>94109</td>\n",
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       "      <th>76</th>\n",
       "      <td>93</td>\n",
       "      <td>94103</td>\n",
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       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>96</td>\n",
       "      <td>94103</td>\n",
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       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>57</td>\n",
       "      <td>94107</td>\n",
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       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>19</td>\n",
       "      <td>94107</td>\n",
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       "      <th>...</th>\n",
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       "      <th>1674</th>\n",
       "      <td>86</td>\n",
       "      <td>94103</td>\n",
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       "      <th>1675</th>\n",
       "      <td>91</td>\n",
       "      <td>94103</td>\n",
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       "      <th>1684</th>\n",
       "      <td>96</td>\n",
       "      <td>94105</td>\n",
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       "      <th>1686</th>\n",
       "      <td>48</td>\n",
       "      <td>94102</td>\n",
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       "      <th>1689</th>\n",
       "      <td>94</td>\n",
       "      <td>94111</td>\n",
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       "    <tr>\n",
       "      <th>1693</th>\n",
       "      <td>86</td>\n",
       "      <td>94103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1695</th>\n",
       "      <td>88</td>\n",
       "      <td>94105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1698</th>\n",
       "      <td>92</td>\n",
       "      <td>94105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>91</td>\n",
       "      <td>94108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>55</td>\n",
       "      <td>94121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>100</td>\n",
       "      <td>94105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>79</td>\n",
       "      <td>94104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>12</td>\n",
       "      <td>94111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1707</th>\n",
       "      <td>94</td>\n",
       "      <td>94107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1708</th>\n",
       "      <td>78</td>\n",
       "      <td>94111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1711</th>\n",
       "      <td>89</td>\n",
       "      <td>94104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1712</th>\n",
       "      <td>84</td>\n",
       "      <td>94111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1713</th>\n",
       "      <td>98</td>\n",
       "      <td>94133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1714</th>\n",
       "      <td>39</td>\n",
       "      <td>94133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1715</th>\n",
       "      <td>77</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1718</th>\n",
       "      <td>95</td>\n",
       "      <td>94107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1719</th>\n",
       "      <td>85</td>\n",
       "      <td>94124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1721</th>\n",
       "      <td>62</td>\n",
       "      <td>94123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1728</th>\n",
       "      <td>84</td>\n",
       "      <td>94108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1729</th>\n",
       "      <td>62</td>\n",
       "      <td>94123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1731</th>\n",
       "      <td>85</td>\n",
       "      <td>94111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1732</th>\n",
       "      <td>24</td>\n",
       "      <td>94103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1733</th>\n",
       "      <td>98</td>\n",
       "      <td>94103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1735</th>\n",
       "      <td>99</td>\n",
       "      <td>94109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1737</th>\n",
       "      <td>95</td>\n",
       "      <td>94111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>714 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      2014 ENERGY STAR Score Postal Code\n",
       "4                         94       94104\n",
       "9                         14       94109\n",
       "12                        80       94102\n",
       "15                        98       94105\n",
       "17                        73       94115\n",
       "18                         1       94111\n",
       "19                        89       94122\n",
       "20                        75       94109\n",
       "21                        99       94105\n",
       "24                        99       94107\n",
       "25                        93       94103\n",
       "35                         2       94103\n",
       "37                        75       94110\n",
       "38                        95       94124\n",
       "39                        91       94104\n",
       "42                       100       94110\n",
       "47                        79       94104\n",
       "50                        92       94105\n",
       "52                        88       94104\n",
       "53                        85       94111\n",
       "56                        77       94111\n",
       "57                        99       94108\n",
       "61                        87       94102\n",
       "65                        80       94105\n",
       "66                        65       94103\n",
       "71                         8       94109\n",
       "76                        93       94103\n",
       "78                        96       94103\n",
       "79                        57       94107\n",
       "80                        19       94107\n",
       "...                      ...         ...\n",
       "1674                      86       94103\n",
       "1675                      91       94103\n",
       "1684                      96       94105\n",
       "1686                      48       94102\n",
       "1689                      94       94111\n",
       "1693                      86       94103\n",
       "1695                      88       94105\n",
       "1698                      92       94105\n",
       "1699                      91       94108\n",
       "1700                      55       94121\n",
       "1701                     100       94105\n",
       "1702                      79       94104\n",
       "1703                      12       94111\n",
       "1707                      94       94107\n",
       "1708                      78       94111\n",
       "1711                      89       94104\n",
       "1712                      84       94111\n",
       "1713                      98       94133\n",
       "1714                      39       94133\n",
       "1715                      77       94110\n",
       "1718                      95       94107\n",
       "1719                      85       94124\n",
       "1721                      62       94123\n",
       "1728                      84       94108\n",
       "1729                      62       94123\n",
       "1731                      85       94111\n",
       "1732                      24       94103\n",
       "1733                      98       94103\n",
       "1735                      99       94109\n",
       "1737                      95       94111\n",
       "\n",
       "[714 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_energy_zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "zip_grouped = cleaned_energy_zip['2014 ENERGY STAR Score'].groupby(cleaned_energy_zip['Postal Code'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Postal Code\n",
       "94102    62.531915\n",
       "94103    72.526882\n",
       "94104    84.384615\n",
       "94105    80.477778\n",
       "94107    75.528571\n",
       "94108    67.589286\n",
       "94109    69.424242\n",
       "94110    80.500000\n",
       "94111    72.267442\n",
       "94112    69.200000\n",
       "94114    38.000000\n",
       "94115    61.666667\n",
       "94116    60.400000\n",
       "94117    62.833333\n",
       "94118    48.300000\n",
       "94121    64.200000\n",
       "94122    60.750000\n",
       "94123    47.933333\n",
       "94124    61.000000\n",
       "94127    58.000000\n",
       "94131     9.000000\n",
       "94132    63.666667\n",
       "94133    67.380952\n",
       "94134    69.600000\n",
       "94158    40.000000\n",
       "Name: 2014 ENERGY STAR Score, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zip_grouped.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "zip_mean = zip_grouped.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "zip_mean.to_csv('energy_zip_data.csv')"
   ]
  }
 ],
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