forOXTWest_CIs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Computing confidence intervals for OXT West to determine the importance of sample size, confidence in estimates\n",
    "# In order to compute the correct standard errors, I use the 80 household and individual-level replicate weights\n",
    "# from the American Community Survey data (as described here by IPUMS: https://usa.ipums.org/usa/)\n",
    "# see https://stharrold.github.io/20160110-etl-census-with-python.html for computation of the standard erorrs using\n",
    "    # replicate weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: pylab import has clobbered these variables: ['test']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "% pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mypath = '/Users/laura/Dropbox/posting/OXTWest'\n",
    "path_acs_internet = os.path.join(mypath, 'oxtwest_wrepwts.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "oxt = pd.read_csv(path_acs_internet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>year</th>\n",
       "      <th>datanum</th>\n",
       "      <th>serial</th>\n",
       "      <th>hhwt</th>\n",
       "      <th>repwt</th>\n",
       "      <th>city</th>\n",
       "      <th>gq</th>\n",
       "      <th>rentgrs</th>\n",
       "      <th>foodstmp</th>\n",
       "      <th>lingisol</th>\n",
       "      <th>...</th>\n",
       "      <th>repwtp71</th>\n",
       "      <th>repwtp72</th>\n",
       "      <th>repwtp73</th>\n",
       "      <th>repwtp74</th>\n",
       "      <th>repwtp75</th>\n",
       "      <th>repwtp76</th>\n",
       "      <th>repwtp77</th>\n",
       "      <th>repwtp78</th>\n",
       "      <th>repwtp79</th>\n",
       "      <th>repwtp80</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 2013</td>\n",
       "      <td> 1</td>\n",
       "      <td> 66410</td>\n",
       "      <td> 97</td>\n",
       "      <td> 1</td>\n",
       "      <td>       Oakland, CA</td>\n",
       "      <td> Households under 1970 definition</td>\n",
       "      <td>  790</td>\n",
       "      <td> Yes</td>\n",
       "      <td>     Linguistically isolated</td>\n",
       "      <td>...</td>\n",
       "      <td> 176</td>\n",
       "      <td> 108</td>\n",
       "      <td> 164</td>\n",
       "      <td> 172</td>\n",
       "      <td> 29</td>\n",
       "      <td> 92</td>\n",
       "      <td> 28</td>\n",
       "      <td> 104</td>\n",
       "      <td>  84</td>\n",
       "      <td>  92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 2013</td>\n",
       "      <td> 1</td>\n",
       "      <td> 66410</td>\n",
       "      <td> 97</td>\n",
       "      <td> 1</td>\n",
       "      <td>       Oakland, CA</td>\n",
       "      <td> Households under 1970 definition</td>\n",
       "      <td>  790</td>\n",
       "      <td> Yes</td>\n",
       "      <td>     Linguistically isolated</td>\n",
       "      <td>...</td>\n",
       "      <td> 136</td>\n",
       "      <td>  94</td>\n",
       "      <td> 132</td>\n",
       "      <td> 144</td>\n",
       "      <td> 24</td>\n",
       "      <td> 71</td>\n",
       "      <td> 26</td>\n",
       "      <td>  98</td>\n",
       "      <td>  72</td>\n",
       "      <td>  78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 2013</td>\n",
       "      <td> 1</td>\n",
       "      <td> 66434</td>\n",
       "      <td> 66</td>\n",
       "      <td> 1</td>\n",
       "      <td> San Francisco, CA</td>\n",
       "      <td> Households under 1970 definition</td>\n",
       "      <td>    0</td>\n",
       "      <td>  No</td>\n",
       "      <td> Not linguistically isolated</td>\n",
       "      <td>...</td>\n",
       "      <td>  22</td>\n",
       "      <td>  19</td>\n",
       "      <td>  71</td>\n",
       "      <td>  67</td>\n",
       "      <td> 60</td>\n",
       "      <td> 59</td>\n",
       "      <td> 95</td>\n",
       "      <td>  19</td>\n",
       "      <td>  68</td>\n",
       "      <td>  18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 2013</td>\n",
       "      <td> 1</td>\n",
       "      <td> 66436</td>\n",
       "      <td> 61</td>\n",
       "      <td> 1</td>\n",
       "      <td> San Francisco, CA</td>\n",
       "      <td> Households under 1970 definition</td>\n",
       "      <td> 2170</td>\n",
       "      <td> Yes</td>\n",
       "      <td> Not linguistically isolated</td>\n",
       "      <td>...</td>\n",
       "      <td>  65</td>\n",
       "      <td>  60</td>\n",
       "      <td>  18</td>\n",
       "      <td>  53</td>\n",
       "      <td> 16</td>\n",
       "      <td> 66</td>\n",
       "      <td> 80</td>\n",
       "      <td>  68</td>\n",
       "      <td> 113</td>\n",
       "      <td> 112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 2013</td>\n",
       "      <td> 1</td>\n",
       "      <td> 66436</td>\n",
       "      <td> 61</td>\n",
       "      <td> 1</td>\n",
       "      <td> San Francisco, CA</td>\n",
       "      <td> Households under 1970 definition</td>\n",
       "      <td> 2170</td>\n",
       "      <td> Yes</td>\n",
       "      <td> Not linguistically isolated</td>\n",
       "      <td>...</td>\n",
       "      <td> 103</td>\n",
       "      <td>  87</td>\n",
       "      <td>  24</td>\n",
       "      <td>  89</td>\n",
       "      <td> 29</td>\n",
       "      <td> 94</td>\n",
       "      <td> 86</td>\n",
       "      <td> 107</td>\n",
       "      <td> 148</td>\n",
       "      <td> 139</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 198 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  datanum  serial  hhwt  repwt               city  \\\n",
       "0  2013        1   66410    97      1        Oakland, CA   \n",
       "1  2013        1   66410    97      1        Oakland, CA   \n",
       "2  2013        1   66434    66      1  San Francisco, CA   \n",
       "3  2013        1   66436    61      1  San Francisco, CA   \n",
       "4  2013        1   66436    61      1  San Francisco, CA   \n",
       "\n",
       "                                 gq  rentgrs foodstmp  \\\n",
       "0  Households under 1970 definition      790      Yes   \n",
       "1  Households under 1970 definition      790      Yes   \n",
       "2  Households under 1970 definition        0       No   \n",
       "3  Households under 1970 definition     2170      Yes   \n",
       "4  Households under 1970 definition     2170      Yes   \n",
       "\n",
       "                      lingisol             ...              repwtp71 repwtp72  \\\n",
       "0      Linguistically isolated             ...                   176      108   \n",
       "1      Linguistically isolated             ...                   136       94   \n",
       "2  Not linguistically isolated             ...                    22       19   \n",
       "3  Not linguistically isolated             ...                    65       60   \n",
       "4  Not linguistically isolated             ...                   103       87   \n",
       "\n",
       "  repwtp73 repwtp74 repwtp75 repwtp76 repwtp77 repwtp78 repwtp79 repwtp80  \n",
       "0      164      172       29       92       28      104       84       92  \n",
       "1      132      144       24       71       26       98       72       78  \n",
       "2       71       67       60       59       95       19       68       18  \n",
       "3       18       53       16       66       80       68      113      112  \n",
       "4       24       89       29       94       86      107      148      139  \n",
       "\n",
       "[5 rows x 198 columns]"
      ]
     },
     "execution_count": 454,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oxt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "oxt['n'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# labeling the weights\n",
    "wt = 'repwtp'\n",
    "wts = [wt+str(inum) for inum in range(1, 81)] # ['repwt1', ..., 'repwt80']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cilaptop_ind\n",
      "0                58770\n",
      "1               209526\n",
      "Name: n, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# creating a NO laptop variable\n",
    "oxt = pd.concat([oxt, pd.get_dummies(oxt[\"cilaptop\"])[[2]]], axis = 1)\n",
    "# renaming\n",
    "oxt = oxt.rename(columns = {'Yes':'cilaptop_ind'})\n",
    "\n",
    "print oxt.groupby(by=['cilaptop_ind'])['n'].sum()\n",
    "\n",
    "oxt.loc[(oxt['cilaptop_ind'] == 1), 'no_comp'] = 0\n",
    "oxt.loc[(oxt['cilaptop_ind'] == 0), 'no_comp'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# What does access to Internet at home look like for poor Oakland residents based on age?\n",
    "# first creating variables needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cilaptop\n",
      "N/A (GQ)     2.557585\n",
      "No          20.738400\n",
      "Yes         83.519870\n",
      "Name: perwt, dtype: float64\n",
      "   N/A (GQ)  no_internet  Yes, with a subscription to an Internet Service  \\\n",
      "0         0            1                                                0   \n",
      "1         0            1                                                0   \n",
      "2         0            0                                                1   \n",
      "3         0            0                                                1   \n",
      "4         0            0                                                1   \n",
      "\n",
      "   Yes, without a subscription to an Internet Service  \n",
      "0                                                  0   \n",
      "1                                                  0   \n",
      "2                                                  0   \n",
      "3                                                  0   \n",
      "4                                                  0   \n"
     ]
    }
   ],
   "source": [
    "# internet access variable codebook\n",
    "# 0: N/A (GQ)\n",
    "# 1: Yes, with a subscription to an Internet Service\n",
    "# 2: Yes, without a subscription to an Internet Service\n",
    "# 3: No Internet access at this house, apartment, or mobile home\n",
    "\n",
    "print oxt.groupby(by=['cilaptop'])['perwt'].sum().div(oxt.hhwt.sum()) * 100 # response options\n",
    "\n",
    "onevar = pd.get_dummies(oxt['cinethh'])\n",
    "onevar.rename(columns={'No Internet access at this house, apartment, or mobile home':'no_internet'}, inplace=True)\n",
    "print onevar.head()\n",
    "# keeping just the second column (\"No Internet access at this house, apartment, or mobile home\", now \"no_internet\"), \n",
    "# merging to main data set\n",
    "onevar = onevar[['no_internet']]\n",
    "oxt = pd.concat([oxt, onevar], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age\n",
      "1      2930\n",
      "10     2958\n",
      "11     2828\n",
      "12     2848\n",
      "13     2827\n",
      "14     2775\n",
      "15     2886\n",
      "16     3001\n",
      "17     3118\n",
      "18     3750\n",
      "19     3744\n",
      "2      2842\n",
      "20     3771\n",
      "21     3699\n",
      "22     4062\n",
      "...\n",
      "82                           1014\n",
      "83                            972\n",
      "84                            955\n",
      "85                            867\n",
      "86                            878\n",
      "87                            724\n",
      "88                            653\n",
      "89                            539\n",
      "9                            2798\n",
      "90 (90+ in 1980 and 1990)     459\n",
      "91                            238\n",
      "93                            103\n",
      "94                            753\n",
      "95                            800\n",
      "Less than 1 year old         2915\n",
      "Name: n, Length: 95, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Creating the age range variable\n",
    "print oxt.groupby(by=['age'])['n'].sum() # absolute/sampled numbers\n",
    "oxt.age[oxt.age == 'Less than 1 year old'] = 0 # recoding\n",
    "oxt.age[oxt.age == '90 (90+ in 1980 and 1990)'] = 91 # recoding\n",
    "oxt.age = oxt.age.astype(int)\n",
    "\n",
    "# Age variable\n",
    "oxt.loc[(oxt['age'] >= 0) & (oxt['age'] <= 10), 'age_ind'] = '0-10'\n",
    "oxt.loc[(oxt['age'] >= 11) & (oxt['age'] <= 14), 'age_ind'] = '11-14'\n",
    "oxt.loc[(oxt['age'] >= 15) & (oxt['age'] <= 18), 'age_ind'] = '15-18'\n",
    "oxt.loc[(oxt['age'] >= 19) & (oxt['age'] <= 22), 'age_ind'] = '19-22'\n",
    "oxt.loc[(oxt['age'] >= 22) & (oxt['age'] <= 25), 'age_ind'] = '22-25'\n",
    "oxt.loc[(oxt['age'] >= 21) & (oxt['age'] <= 29), 'age_ind'] = '26-29'\n",
    "oxt.loc[(oxt['age'] >= 30) & (oxt['age'] <= 39), 'age_ind'] = '30-39'\n",
    "oxt.loc[(oxt['age'] >= 40) & (oxt['age'] <= 49), 'age_ind'] = '40-49'\n",
    "oxt.loc[(oxt['age'] >= 50) & (oxt['age'] <= 59), 'age_ind'] = '50-59'\n",
    "oxt.loc[(oxt['age'] >= 61), 'age_ind'] = '61+'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "268296\n"
     ]
    }
   ],
   "source": [
    "# just Oakland:\n",
    "oxt_oakland = oxt[oxt['city'] == 'Oakland, CA']\n",
    "print len(oxt.index)\n",
    "\n",
    "# just poor in Oakland:\n",
    "oxt_oakland_poor = oxt_oakland[oxt_oakland.poverty < 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age_ind\n",
      "0-10       0.317152\n",
      "11-14      0.329046\n",
      "15-18      0.180771\n",
      "19-22      0.155227\n",
      "26-29      0.220203\n",
      "30-39      0.273447\n",
      "40-49      0.264167\n",
      "50-59      0.351251\n",
      "61+        0.478935\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# defining the function to summarize weighted internet access in the home by age group\n",
    "grouped = oxt_oakland_poor.groupby('age_ind')\n",
    "minifun = lambda x: np.average(x.no_internet, weights = x.perwt)\n",
    "print str(grouped.apply(minifun)) # proportion of poor Oaklanders without internet access in their home"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# adding confidence intervals to weighted means using replicate weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "age_grp = ['0-10', '11-14', '15-18', '19-22', '26-29', '30-39', '40-49', '50-59', '61+']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.317562545005\n",
      "0.327655631392\n",
      "0.181473949301\n",
      "0.155636709839\n",
      "0.220018740074\n",
      "0.273441168994\n",
      "0.263988716171\n",
      "0.35104464947\n",
      "0.478908082798\n"
     ]
    }
   ],
   "source": [
    "# Saving the replicate-weighted means\n",
    "means = []\n",
    "\n",
    "for age in age_grp:\n",
    "    age_se_mean = []\n",
    "    for weight in wts:\n",
    "        age_se_mean.append(float((np.average(oxt_oakland_poor[oxt_oakland_poor['age_ind'] == age][\"no_internet\"], \n",
    "                                                 weights = oxt_oakland_poor[oxt_oakland['age_ind'] == age][weight]))))\n",
    "        age_meandiff = np.sum(age_se_mean) / 80\n",
    "    means.append(float(age_meandiff))\n",
    "    print age_meandiff\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  age_grp     means\n",
      "0    0-10  0.317563\n",
      "1   11-14  0.327656\n",
      "2   15-18  0.181474\n",
      "3   19-22  0.155637\n",
      "4   26-29  0.220019\n",
      "5   30-39  0.273441\n",
      "6   40-49  0.263989\n",
      "7   50-59  0.351045\n",
      "8     61+  0.478908\n"
     ]
    }
   ],
   "source": [
    "# making data frame of age groups and means...\n",
    "age_df = pd.DataFrame({'age_grp': age_grp, 'means': means})\n",
    "print age_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0483297914402\n",
      "0.0654660467234\n",
      "0.0477533490329\n",
      "0.050011414039\n",
      "0.0363654264178\n",
      "0.0410976333568\n",
      "0.0416232763455\n",
      "0.0437529009122\n",
      "0.0381072231031\n",
      "[0.048329791440175705, 0.06546604672337289, 0.04775334903287616, 0.05001141403896587, 0.0363654264178082, 0.041097633356773536, 0.04162327634554945, 0.043752900912232044, 0.03810722310307676]\n"
     ]
    }
   ],
   "source": [
    "# Computing the standard errors for internet access in each age group...\n",
    "\n",
    "age_se_all = [] # for holding the 80 differences\n",
    "age_ses = [] # for holding the standard errors before averaging\n",
    "\n",
    "for age in age_grp:\n",
    "    for weight in wts:\n",
    "        age_se_all.append(float((np.average(oxt_oakland_poor[oxt_oakland_poor['age_ind'] == age][\"no_internet\"], \n",
    "                                                 weights = oxt_oakland_poor[oxt_oakland_poor['age_ind'] == age][weight])) - \n",
    "                                                      age_df[age_df['age_grp'] == age][\"means\"]) ** 2)\n",
    "    se = (np.sum(age_se_all) * .05) ** .5\n",
    "    print se\n",
    "    age_se_all = [] # clearing the list of 80\n",
    "    age_ses.append(float(se))\n",
    "print age_ses    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age_ind\n",
      "0-10       32063\n",
      "11-14      11278\n",
      "15-18      12755\n",
      "19-22       7515\n",
      "26-29      39100\n",
      "30-39      39324\n",
      "40-49      34531\n",
      "50-59      35782\n",
      "61+        52508\n",
      "Name: n, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print oxt.groupby(by = ['age_ind'])['n'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# data frame with means and standard errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  age_grp     means        se\n",
      "0    0-10  0.317563  0.048330\n",
      "1   11-14  0.327656  0.065466\n",
      "2   15-18  0.181474  0.047753\n",
      "3   19-22  0.155637  0.050011\n",
      "4   26-29  0.220019  0.036365\n",
      "5   30-39  0.273441  0.041098\n",
      "6   40-49  0.263989  0.041623\n",
      "7   50-59  0.351045  0.043753\n",
      "8     61+  0.478908  0.038107\n"
     ]
    }
   ],
   "source": [
    "age_df = pd.DataFrame({'age_grp': age_grp, 'means': means, 'se': age_ses})\n",
    "print age_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  age_grp     means        se     lower     upper\n",
      "0    0-10  0.317563  0.048330  0.222836  0.412289\n",
      "1   11-14  0.327656  0.065466  0.199342  0.455969\n",
      "2   15-18  0.181474  0.047753  0.087877  0.275071\n",
      "3   19-22  0.155637  0.050011  0.057614  0.253659\n",
      "4   26-29  0.220019  0.036365  0.148743  0.291295\n",
      "5   30-39  0.273441  0.041098  0.192890  0.353993\n",
      "6   40-49  0.263989  0.041623  0.182407  0.345570\n",
      "7   50-59  0.351045  0.043753  0.265289  0.436800\n",
      "8     61+  0.478908  0.038107  0.404218  0.553598\n"
     ]
    }
   ],
   "source": [
    "# computing the confidence intervals\n",
    "age_df['lower'] = age_df['means'] - (1.96 * age_df['se'])\n",
    "age_df['upper'] = age_df['means'] + (1.96 * age_df['se'])\n",
    "print age_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "age_df_graph = age_df\n",
    "\n",
    "# multiplying means and se by 100 to get proportion rather than percent\n",
    "age_df_graph[\"means\"] = age_df_graph[\"means\"] * 100\n",
    "age_df_graph[\"se\"] = age_df_graph[\"se\"] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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2Aj4PfB3AzP4CbAzM0wTZgiAIggZRbwXtucA7wOzAS2b2raYKFmacIAhKoFPN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f797850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graphing\n",
    "age_df_graph.index = age_df_graph[\"age_grp\"]\n",
    "ax = age_df_graph['means'].plot(kind = 'bar', yerr = (age_df_graph['se']) * 1.96)\n",
    "ax.set_ylabel(\"No internet (%)\")\n",
    "ax.set_xlabel(\"Age ranges\")\n",
    "ax.grid(b = False)\n",
    "labels = ax.get_xticklabels()\n",
    "plt.setp(labels, rotation=30, fontsize=10)\n",
    "plt.title('Percent of poor Oakland residents \\n without internet at home by age group', size=16) # household level\n",
    "plt.savefig('/Users/laura/Dropbox/posting/OXTWest/age.png', bbox_inches='tight', dpi = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# How does computer ownership of poor residents in Oakland compare with other US cities?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# keeping just poor\n",
    "oxt_poor = oxt[oxt.poverty < 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "city\n",
      "Chicago, IL          0.463431\n",
      "Cleveland, OH        0.546172\n",
      "Detroit, MI          0.504711\n",
      "Flint, MI            0.559227\n",
      "Manchester, NH       0.376820\n",
      "Miami, FL            0.470422\n",
      "Minneapolis, MN      0.397932\n",
      "New Orleans, LA      0.577662\n",
      "New York, NY         0.383169\n",
      "Oakland, CA          0.434421\n",
      "Providence, RI       0.555197\n",
      "San Francisco, CA    0.381865\n",
      "Seattle, WA          0.356036\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# function to compute proportion without a computer by group\n",
    "grouped = oxt_poor.groupby('city')\n",
    "minifun = lambda x: np.average(x.no_comp, weights = x.perwt)\n",
    "print str(grouped.apply(minifun))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# computing standard errors for each city mean...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "city\n",
      "Chicago, IL          0.463431\n",
      "Cleveland, OH        0.546172\n",
      "Detroit, MI          0.504711\n",
      "Flint, MI            0.559227\n",
      "Manchester, NH       0.376820\n",
      "Miami, FL            0.470422\n",
      "Minneapolis, MN      0.397932\n",
      "New Orleans, LA      0.577662\n",
      "New York, NY         0.383169\n",
      "Oakland, CA          0.434421\n",
      "Providence, RI       0.555197\n",
      "San Francisco, CA    0.381865\n",
      "Seattle, WA          0.356036\n",
      "dtype: float64\n",
      "['Chicago, IL', 'Cleveland, OH', 'Detroit, MI', 'Flint, MI', 'Manchester, NH', 'Miami, FL', 'Minneapolis, MN', 'New Orleans, LA', 'New York, NY', 'Oakland, CA', 'Providence, RI', 'San Francisco, CA', 'Seattle, WA']\n"
     ]
    }
   ],
   "source": [
    "mini_cities = grouped.apply(minifun)\n",
    "print mini_cities\n",
    "city_list = mini_cities.index.values.tolist()\n",
    "print city_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chicago, IL = 0.463415157627\n",
      "Cleveland, OH = 0.546390377356\n",
      "Detroit, MI = 0.50479516452\n",
      "Flint, MI = 0.557815507116\n",
      "Manchester, NH = 0.377633434154\n",
      "Miami, FL = 0.470315785062\n",
      "Minneapolis, MN = 0.39829477716\n",
      "New Orleans, LA = 0.577234884952\n",
      "New York, NY = 0.383163739904\n",
      "Oakland, CA = 0.434282010988\n",
      "Providence, RI = 0.554278442952\n",
      "San Francisco, CA = 0.381893440409\n",
      "Seattle, WA = 0.35636592331\n"
     ]
    }
   ],
   "source": [
    "# Saving the replicate-weighted means for each city\n",
    "city_means = []\n",
    "\n",
    "for city in city_list:\n",
    "    city_se_mean = []\n",
    "    for weight in wts:\n",
    "        city_se_mean.append(float((np.average(oxt_poor[oxt_poor['city'] == city][\"no_comp\"], \n",
    "                                                 weights = oxt_poor[oxt_poor['city'] == city][weight]))))\n",
    "        city_meandiff = np.sum(city_se_mean) / 80\n",
    "    city_means.append(city_meandiff)\n",
    "    print str(city) + \" = \" + str(city_meandiff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 city     means\n",
      "0         Chicago, IL  0.463415\n",
      "1       Cleveland, OH  0.546390\n",
      "2         Detroit, MI  0.504795\n",
      "3           Flint, MI  0.557816\n",
      "4      Manchester, NH  0.377633\n",
      "5           Miami, FL  0.470316\n",
      "6     Minneapolis, MN  0.398295\n",
      "7     New Orleans, LA  0.577235\n",
      "8        New York, NY  0.383164\n",
      "9         Oakland, CA  0.434282\n",
      "10     Providence, RI  0.554278\n",
      "11  San Francisco, CA  0.381893\n",
      "12        Seattle, WA  0.356366\n"
     ]
    }
   ],
   "source": [
    "# making data frame of age groups and means...\n",
    "city_df = pd.DataFrame({'city': city_list, 'means': city_means})\n",
    "print city_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0108863152875\n",
      "0.0227630673181\n",
      "0.0177552244864\n",
      "0.0464320660241\n",
      "0.0528620098412\n",
      "0.0175222635073\n",
      "0.0265577266899\n",
      "0.0197373759945\n",
      "0.00540260762714\n",
      "0.0243048288588\n",
      "0.0288893991624\n",
      "0.0185023648112\n",
      "0.0198967110244\n",
      "[0.010886315287513007, 0.022763067318143582, 0.01775522448642626, 0.046432066024134154, 0.05286200984117941, 0.017522263507314086, 0.026557726689872257, 0.01973737599451846, 0.005402607627138789, 0.02430482885884329, 0.02888939916243583, 0.018502364811156743, 0.019896711024438532]\n"
     ]
    }
   ],
   "source": [
    "# Computing the standard errors for each city's mean\n",
    "\n",
    "city_se_all = [] # for holding the 80 differences\n",
    "city_ses = [] # for holding the standard errors\n",
    "\n",
    "for city in city_list:\n",
    "    for weight in wts:\n",
    "        city_se_all.append(float((np.average(oxt_poor[oxt_poor['city'] == city][\"no_comp\"], \n",
    "                                                 weights = oxt_poor[oxt_poor['city'] == city][weight])) - \n",
    "                                                      city_df[city_df['city'] == city][\"means\"]) ** 2)\n",
    "    se = (np.sum(city_se_all) * .05) ** .5\n",
    "    print se\n",
    "    city_se_all = [] # clearing the list of 80\n",
    "    city_ses.append(float(se))\n",
    "print city_ses   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 city     means        se\n",
      "0         Chicago, IL  0.463415  0.010886\n",
      "1       Cleveland, OH  0.546390  0.022763\n",
      "2         Detroit, MI  0.504795  0.017755\n",
      "3           Flint, MI  0.557816  0.046432\n",
      "4      Manchester, NH  0.377633  0.052862\n",
      "5           Miami, FL  0.470316  0.017522\n",
      "6     Minneapolis, MN  0.398295  0.026558\n",
      "7     New Orleans, LA  0.577235  0.019737\n",
      "8        New York, NY  0.383164  0.005403\n",
      "9         Oakland, CA  0.434282  0.024305\n",
      "10     Providence, RI  0.554278  0.028889\n",
      "11  San Francisco, CA  0.381893  0.018502\n",
      "12        Seattle, WA  0.356366  0.019897\n"
     ]
    }
   ],
   "source": [
    "city_df = pd.DataFrame({'city': city_list, 'means': city_means, 'se': city_ses})\n",
    "print city_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 city     means        se     lower     upper\n",
      "0         Chicago, IL  0.463415  0.010886  0.442078  0.484752\n",
      "1       Cleveland, OH  0.546390  0.022763  0.501775  0.591006\n",
      "2         Detroit, MI  0.504795  0.017755  0.469995  0.539595\n",
      "3           Flint, MI  0.557816  0.046432  0.466809  0.648822\n",
      "4      Manchester, NH  0.377633  0.052862  0.274024  0.481243\n",
      "5           Miami, FL  0.470316  0.017522  0.435972  0.504659\n",
      "6     Minneapolis, MN  0.398295  0.026558  0.346242  0.450348\n",
      "7     New Orleans, LA  0.577235  0.019737  0.538550  0.615920\n",
      "8        New York, NY  0.383164  0.005403  0.372575  0.393753\n",
      "9         Oakland, CA  0.434282  0.024305  0.386645  0.481919\n",
      "10     Providence, RI  0.554278  0.028889  0.497655  0.610902\n",
      "11  San Francisco, CA  0.381893  0.018502  0.345629  0.418158\n",
      "12        Seattle, WA  0.356366  0.019897  0.317368  0.395363\n"
     ]
    }
   ],
   "source": [
    "# computing the confidence intervals\n",
    "city_df['lower'] = city_df['means'] - (1.96 * city_df['se'])\n",
    "city_df['upper'] = city_df['means'] + (1.96 * city_df['se'])\n",
    "print city_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 city     means        se     lower     upper\n",
      "7     New Orleans, LA  0.577235  0.019737  0.538550  0.615920\n",
      "3           Flint, MI  0.557816  0.046432  0.466809  0.648822\n",
      "10     Providence, RI  0.554278  0.028889  0.497655  0.610902\n",
      "1       Cleveland, OH  0.546390  0.022763  0.501775  0.591006\n",
      "2         Detroit, MI  0.504795  0.017755  0.469995  0.539595\n",
      "5           Miami, FL  0.470316  0.017522  0.435972  0.504659\n",
      "0         Chicago, IL  0.463415  0.010886  0.442078  0.484752\n",
      "9         Oakland, CA  0.434282  0.024305  0.386645  0.481919\n",
      "6     Minneapolis, MN  0.398295  0.026558  0.346242  0.450348\n",
      "8        New York, NY  0.383164  0.005403  0.372575  0.393753\n",
      "11  San Francisco, CA  0.381893  0.018502  0.345629  0.418158\n",
      "4      Manchester, NH  0.377633  0.052862  0.274024  0.481243\n",
      "12        Seattle, WA  0.356366  0.019897  0.317368  0.395363\n"
     ]
    }
   ],
   "source": [
    "city_df = city_df.sort(['means'], ascending = [0])\n",
    "print city_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# multiplying means and se by 100...\n",
    "city_df_graph = city_df\n",
    "# to get proportion rather than percent\n",
    "city_df_graph[\"means\"] = city_df_graph[\"means\"] * 100\n",
    "city_df_graph[\"se\"] = city_df_graph[\"se\"] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kJY+k+DjbALOB64FbWxESBEEQNGIkEZJao5nkJV/HY9oviQdBO13S11qWFARB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\nB2txs26NZEcEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111c3b890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "city_df_graph.index = city_df_graph[\"city\"]\n",
    "ax = city_df_graph['means'].plot(kind = 'bar', yerr = (city_df_graph['se']) * 1.96,\n",
    "                                color = ['b', 'b', 'b', 'b', 'b', 'b', 'b', 'r', 'b', 'b', 'b', 'b', 'b'])\n",
    "ax.set_ylabel(\"No computer (%)\")\n",
    "ax.set_xlabel(\"City\")\n",
    "ax.grid(b = False)\n",
    "plt.title('Percent of the poor \\n without a computer at home, by city', size = 16) # household level\n",
    "plt.savefig('/Users/laura/Dropbox/posting/OXTWest/city.png', bbox_inches='tight', dpi = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# What does computer ownership look like with the employment status (EMPSTAT) variable\n",
    "# NOTE: I am doing this just for Oakland"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "empstat\n",
      "Employed              0.120885\n",
      "Not in labor force    0.302571\n",
      "Unemployed            0.253451\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Proportion of each empstat category with computer\n",
    "# first creating the no_comp variable:\n",
    "oxt_oakland = oxt[oxt['city'] == 'Oakland, CA']\n",
    "grouped = oxt_oakland.groupby('empstat')\n",
    "minifun = lambda x: np.average(x.no_comp, weights = x.perwt)\n",
    "empstats = grouped.apply(minifun)\n",
    "print str(grouped.apply(minifun)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.253451442152\n",
      "0.120884531353\n"
     ]
    }
   ],
   "source": [
    "# saving the relavent means...\n",
    "\n",
    "emp_mean = empstats.iloc[0] # employed\n",
    "unemp_mean = empstats.iloc[2] # unemployed\n",
    "\n",
    "print unemp_mean\n",
    "print emp_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "empstat\n",
      "Employed              4493\n",
      "Not in labor force    2587\n",
      "Unemployed             443\n",
      "Name: n, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print oxt_oakland.groupby(by = ['empstat'])['n'].sum() # quickly looking at sample sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.120749312977\n"
     ]
    }
   ],
   "source": [
    "# Computing the proportion of employed Oakland residents with no computer (using replicate weights)\n",
    "oxt_oakland_emp = oxt_oakland[oxt_oakland['empstat'] == 'Employed']\n",
    "empstat_se = []\n",
    "\n",
    "empstat_se_mean = []\n",
    "\n",
    "for weight in wts:\n",
    "    empstat_se_mean.append(float((np.average(oxt_oakland_emp[\"no_comp\"], weights = oxt_oakland_emp[weight]))))\n",
    "emp_meandiff = np.sum(empstat_se_mean) / 80\n",
    "print emp_meandiff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.253637302145\n"
     ]
    }
   ],
   "source": [
    "# Computing the proportion of unemployed Oakland residents with no computer (using replicate weights)\n",
    "oxt_oakland_unemp = oxt_oakland[oxt_oakland['empstat'] == 'Unemployed']\n",
    "unempstat_se = []\n",
    "\n",
    "unempstat_se_mean = []\n",
    "\n",
    "for weight in wts:\n",
    "    unempstat_se_mean.append(float((np.average(oxt_oakland_unemp[\"no_comp\"], weights = oxt_oakland_unemp[weight]))))\n",
    "unemp_meandiff = np.sum(unempstat_se_mean) / 80\n",
    "print unemp_meandiff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00805891188154\n",
      "Standard error for proportion employed without a computer = 0.00805891188154\n"
     ]
    }
   ],
   "source": [
    "# Standard error for the employed\n",
    "for weight in wts:\n",
    "    empstat_se.append(float((np.average(oxt_oakland_emp[\"no_comp\"], weights = oxt_oakland_emp[weight]) - emp_meandiff) ** 2))\n",
    "empstat_ses = (np.sum(empstat_se) * .05) ** .5\n",
    "\n",
    "print empstat_ses\n",
    "\n",
    "print \"Standard error for proportion employed without a computer = \" + str(empstat_ses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0247170047675\n",
      "Standard error for proportion unemployed without a computer = 0.0247170047675\n"
     ]
    }
   ],
   "source": [
    "# Standard error for the unemployed\n",
    "for weight in wts:\n",
    "    unempstat_se.append(float((np.average(oxt_oakland_unemp[\"no_comp\"], weights = oxt_oakland_unemp[weight]) - unemp_meandiff) ** 2))\n",
    "unempstat_ses = (np.sum(unempstat_se) * .05) ** .5\n",
    "\n",
    "print unempstat_ses\n",
    "\n",
    "print \"Standard error for proportion unemployed without a computer = \" + str(unempstat_ses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent of employed without computer = 12.1% (CI = 10.5% , 13.7%)\n",
      "Percent of unemployed without computer = 25.3% (CI = 20.5% , 30.2%)\n"
     ]
    }
   ],
   "source": [
    "# What does computer ownership look like with the employment status (EMPSTAT) variable\n",
    "# NOTE: I am doing this just for Oakland\n",
    "\n",
    "print \"Percent of employed without computer = \" + str(round(emp_mean * 100, 1)) + \\\n",
    "    \"% (CI = \" + str(round(((emp_mean - (1.96*empstat_ses)) * 100), 1)) + \"% , \" + \\\n",
    "    str(round(((emp_mean + (1.96*empstat_ses)) * 100), 1)) + \"%)\"\n",
    "\n",
    "print \"Percent of unemployed without computer = \" + str(round(unemp_mean * 100, 1)) + \\\n",
    "    \"% (CI = \" + str(round(((unemp_mean - (1.96*unempstat_ses)) * 100), 1)) + \"% , \" + \\\n",
    "    str(round(((unemp_mean + (1.96*unempstat_ses)) * 100), 1)) + \"%)\"   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# What does Internet and computer access look like by race/ethnicity?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "race_var\n",
      "Black only    1882\n",
      "Hispanic      2118\n",
      "Other         2110\n",
      "White only    2983\n",
      "Name: n, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/laura/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:5: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame\n"
     ]
    }
   ],
   "source": [
    "# variable for Black only, white only, Hispanic origin only, other (including mixed race) - only in Oakland\n",
    "oxt_oakland.loc[(oxt_oakland['race'] == \"Black/Negro\"), 'race_var'] = 'Black only'\n",
    "oxt_oakland.loc[(oxt_oakland['race'] == \"White\"), 'race_var'] = 'White only'\n",
    "oxt_oakland.loc[(oxt_oakland['hispan'] != \"Not Hispanic\"), 'race_var'] = 'Hispanic'\n",
    "oxt_oakland['race_var'].fillna('Other', inplace = True)\n",
    "\n",
    "print oxt_oakland.groupby(by = ['race_var'])['n'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "race_list = ['Black only', 'Hispanic', 'Other', 'White only']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.232411474228\n",
      "0.219767069167\n",
      "0.112896282138\n",
      "0.0494528725257\n"
     ]
    }
   ],
   "source": [
    "# Saving the replicate-weighted means for each race/ethnicity\n",
    "race_means = []\n",
    "\n",
    "for race in race_list:\n",
    "    race_se_mean = []\n",
    "    for weight in wts:\n",
    "        race_se_mean.append(float((np.average(oxt_oakland[oxt_oakland['race_var'] == race][\"no_internet\"], \n",
    "                                                 weights = oxt_oakland[oxt_oakland['race_var'] == race][weight]))))\n",
    "        race_meandiff = np.sum(race_se_mean) / 80\n",
    "    race_means.append(float(race_meandiff))\n",
    "    print race_meandiff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      means        race\n",
      "0  0.232411  Black only\n",
      "1  0.219767    Hispanic\n",
      "2  0.112896       Other\n",
      "3  0.049453  White only\n"
     ]
    }
   ],
   "source": [
    "# making data frame of age groups and means...\n",
    "race_df = pd.DataFrame({'race': race_list, 'means': race_means})\n",
    "print race_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0197341909634\n",
      "0.0198668849235\n",
      "0.0112672410218\n",
      "0.00458884510785\n",
      "[0.019734190963415145, 0.019866884923549127, 0.011267241021763281, 0.004588845107852961]\n"
     ]
    }
   ],
   "source": [
    "# Computing the standard errors for internet access in each race/ethnicity...\n",
    "\n",
    "race_se_all = [] # for holding the 80 differences\n",
    "race_ses = [] # for holding the standard errors\n",
    "\n",
    "for race in race_list:\n",
    "    for weight in wts:\n",
    "        race_se_all.append(float((np.average(oxt_oakland[oxt_oakland['race_var'] == race][\"no_internet\"], \n",
    "                                                 weights = oxt_oakland[oxt_oakland['race_var'] == race][weight])) - \n",
    "                                                      race_df[race_df['race'] == race][\"means\"]) ** 2)\n",
    "    se = (np.sum(race_se_all) * .05) ** .5\n",
    "    print se\n",
    "    race_se_all = [] # clearing the list of 80\n",
    "    race_ses.append(float(se))\n",
    "print race_ses  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      means        race        se\n",
      "0  0.232411  Black only  0.019734\n",
      "1  0.219767    Hispanic  0.019867\n",
      "2  0.112896       Other  0.011267\n",
      "3  0.049453  White only  0.004589\n"
     ]
    }
   ],
   "source": [
    "race_df = pd.DataFrame({'race': race_list, 'means': race_means, 'se': race_ses})\n",
    "print race_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      means        race        se     lower     upper\n",
      "0  0.232411  Black only  0.019734  0.193732  0.271090\n",
      "1  0.219767    Hispanic  0.019867  0.180828  0.258706\n",
      "2  0.112896       Other  0.011267  0.090812  0.134980\n",
      "3  0.049453  White only  0.004589  0.040459  0.058447\n"
     ]
    }
   ],
   "source": [
    "# computing the confidence intervals\n",
    "race_df['lower'] = race_df['means'] - (1.96 * race_df['se'])\n",
    "race_df['upper'] = race_df['means'] + (1.96 * race_df['se'])\n",
    "print race_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      means        race        se     lower     upper\n",
      "0  0.232411  Black only  0.019734  0.193732  0.271090\n",
      "1  0.219767    Hispanic  0.019867  0.180828  0.258706\n",
      "2  0.112896       Other  0.011267  0.090812  0.134980\n",
      "3  0.049453  White only  0.004589  0.040459  0.058447\n"
     ]
    }
   ],
   "source": [
    "# graphing\n",
    "race_df = race_df.sort(['means'], ascending = [0])\n",
    "print race_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Graphing internet access by race/ethnicity\n",
    "race_df_graph = race_df\n",
    "# percent rather than proportion...\n",
    "race_df_graph[\"means\"] = race_df_graph[\"means\"] * 100\n",
    "race_df_graph[\"se\"] = race_df_graph[\"se\"] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WSfvm6a+XdJGkuyVdKGnxTuXBzMxG6uSZ/kvAARHxVmAD4HOS3gIcCFwUEasB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\ncJ2VQG7O9pIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ccbd650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "race_df_graph.index = race_df_graph[\"race\"]\n",
    "ax = race_df_graph['means'].plot(kind = 'bar', yerr = (race_df_graph['se']) * 1.96)\n",
    "ax.set_ylabel(\"No internet (%)\")\n",
    "ax.set_xlabel(\"Race/ethnicity\")\n",
    "ax.grid(b = False)\n",
    "labels = ax.get_xticklabels()\n",
    "plt.setp(labels, rotation = 0, fontsize = 10)\n",
    "\n",
    "plt.title('Percent of Oakland residents with no \\n internet access at home by race/ethnicity', size=16) \n",
    "plt.savefig('/Users/laura/Dropbox/posting/OXTWest/raceeth.png', bbox_inches='tight', dpi = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}