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kikiliu/gist:11389972

Created April 29, 2014 03:24
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gistfile1.txt
{
"metadata": {
"name": "",
"signature": "sha256:1549383cfcfadd0f9557cc366906b339383ea0da3b94dc44bc0dd7850df611ba"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Examining Gentrification in Oakland"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Gentrification is a hot topic in the Bay Area, and much of the conversation centers around its effects in Oakland. We set out to develop an understanding of what was happening in Oakland based on freely available census, city-collected, and real estate data. Our group considered some of the vectors of gentrification in Oakland over time including property values, income, changing race demographics, and crime statistics. We\u2019ve learned a lot about the challenges of measuring a phenomenon as complex as gentrification, and we have some interesting insights to share. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, we import all libraries needed for the data manipulation and analysis. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab --no-import-all inline"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from pandas import DataFrame, Series, Index\n",
"import pandas as pd\n",
"from itertools import islice"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#import census modules\n",
"import census\n",
"import us\n",
"\n",
"#import API key\n",
"import settings"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To run this notebook, download an API key from the census and add it to a file called settings.py in the same directory as your notebook: http://www.census.gov/developers/tos/key_request.html"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#access API key\n",
"c = census.Census(key=settings.CENSUS_KEY)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#create function to call California census tracts for 2000 (adapted from Raymond Yee's function)\n",
"year = 2000\n",
"def places(variables=\"NAME\", year=year):\n",
" \n",
" states_fips = set([s.fips for s in us.states.STATES])\n",
" geo={'for':'place:*',\n",
" 'in':'state:06'}\n",
" \n",
" for place in c.sf1.get(variables, geo=geo, year=year):\n",
" yield place\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#P001001/Total Pop, P010004/African-American Not Hispanic, P011001/Hispanic, \n",
"#P010006/Asian, not Hispanic P010003/White, not Hispanic \n",
"ca_places_2000 = [place for place in places(variables=\"NAME,P001001,P010004,P011001,P010006,P010003\")]\n",
"\n",
"#put list into dataframe\n",
"ca_places_2000_df = pd.DataFrame(ca_places_2000)\n",
"ca_places_2000_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>NAME</th>\n",
" <th>P001001</th>\n",
" <th>P010003</th>\n",
" <th>P010004</th>\n",
" <th>P010006</th>\n",
" <th>P011001</th>\n",
" <th>place</th>\n",
" <th>state</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> Acton CDP</td>\n",
" <td> 2390</td>\n",
" <td> 2058</td>\n",
" <td> 20</td>\n",
" <td> 53</td>\n",
" <td> 263</td>\n",
" <td> 212</td>\n",
" <td> 6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> Adelanto city</td>\n",
" <td> 18130</td>\n",
" <td> 6964</td>\n",
" <td> 2477</td>\n",
" <td> 390</td>\n",
" <td> 8299</td>\n",
" <td> 296</td>\n",
" <td> 6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> Agoura Hills city</td>\n",
" <td> 20537</td>\n",
" <td> 17419</td>\n",
" <td> 318</td>\n",
" <td> 1571</td>\n",
" <td> 1407</td>\n",
" <td> 394</td>\n",
" <td> 6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> Alameda city</td>\n",
" <td> 72259</td>\n",
" <td> 40770</td>\n",
" <td> 5181</td>\n",
" <td> 20534</td>\n",
" <td> 6725</td>\n",
" <td> 562</td>\n",
" <td> 6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> Alamo CDP</td>\n",
" <td> 15626</td>\n",
" <td> 13919</td>\n",
" <td> 95</td>\n",
" <td> 1100</td>\n",
" <td> 616</td>\n",
" <td> 618</td>\n",
" <td> 6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 8 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 24,
"text": [
" NAME P001001 P010003 P010004 P010006 P011001 place state\n",
"0 Acton CDP 2390 2058 20 53 263 212 6\n",
"1 Adelanto city 18130 6964 2477 390 8299 296 6\n",
"2 Agoura Hills city 20537 17419 318 1571 1407 394 6\n",
"3 Alameda city 72259 40770 5181 20534 6725 562 6\n",
"4 Alamo CDP 15626 13919 95 1100 616 618 6\n",
"\n",
"[5 rows x 8 columns]"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pop_vars_00 = ['P001001', 'P010003', 'P010004', 'P010006', 'P011001']\n",
"\n",
"#turn numbers into integers\n",
"ca_places_2000_df[(pop_vars_00)] = ca_places_2000_df[(pop_vars_00)].astype(int)\n",
"\n",
"#sort by total population, largest first\n",
"ca_places_2000_df[['NAME','P001001','P010003', 'P010004','P010006', \\\n",
" 'P011001']].sort('P001001', ascending=False).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>NAME</th>\n",
" <th>P001001</th>\n",
" <th>P010003</th>\n",
" <th>P010004</th>\n",
" <th>P010006</th>\n",
" <th>P011001</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>559</th>\n",
" <td> Los Angeles city</td>\n",
" <td> 3694820</td>\n",
" <td> 1167030</td>\n",
" <td> 422819</td>\n",
" <td> 396352</td>\n",
" <td> 1719073</td>\n",
" </tr>\n",
" <tr>\n",
" <th>839</th>\n",
" <td> San Diego city</td>\n",
" <td> 1223400</td>\n",
" <td> 632533</td>\n",
" <td> 103508</td>\n",
" <td> 184105</td>\n",
" <td> 310752</td>\n",
" </tr>\n",
" <tr>\n",
" <th>850</th>\n",
" <td> San Jose city</td>\n",
" <td> 894943</td>\n",
" <td> 343088</td>\n",
" <td> 33571</td>\n",
" <td> 252818</td>\n",
" <td> 269989</td>\n",
" </tr>\n",
" <tr>\n",
" <th>843</th>\n",
" <td> San Francisco city</td>\n",
" <td> 776733</td>\n",
" <td> 356374</td>\n",
" <td> 64070</td>\n",
" <td> 250364</td>\n",
" <td> 109504</td>\n",
" </tr>\n",
" <tr>\n",
" <th>553</th>\n",
" <td> Long Beach city</td>\n",
" <td> 461522</td>\n",
" <td> 161584</td>\n",
" <td> 70935</td>\n",
" <td> 61438</td>\n",
" <td> 165092</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 6 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 25,
"text": [
" NAME P001001 P010003 P010004 P010006 P011001\n",
"559 Los Angeles city 3694820 1167030 422819 396352 1719073\n",
"839 San Diego city 1223400 632533 103508 184105 310752\n",
"850 San Jose city 894943 343088 33571 252818 269989\n",
"843 San Francisco city 776733 356374 64070 250364 109504\n",
"553 Long Beach city 461522 161584 70935 61438 165092\n",
"\n",
"[5 rows x 6 columns]"
]
}
],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#create new descriptively named columns with values population by race/ethnicity\n",
"ca_places_2000_df['African-American, not Hispanic'] = ca_places_2000_df['P010004']\n",
"ca_places_2000_df['White, not Hispanic'] = ca_places_2000_df['P010003']\n",
"ca_places_2000_df['Asian, not Hispanic'] = ca_places_2000_df['P010006']\n",
"ca_places_2000_df['Total Pop'] = ca_places_2000_df['P001001']\n",
"ca_places_2000_df['Hispanic'] = ca_places_2000_df['P011001']\n",
"\n",
"#show only columns that have legible names; set index by tract\n",
"alameda_places_df_2000 = ca_places_2000_df[['place','NAME','Total Pop','African-American, not Hispanic', \\\n",
" 'Asian, not Hispanic', 'Hispanic', 'White, not Hispanic']] \n",
"\n",
"alameda_places_df_2000.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>place</th>\n",
" <th>NAME</th>\n",
" <th>Total Pop</th>\n",
" <th>African-American, not Hispanic</th>\n",
" <th>Asian, not Hispanic</th>\n",
" <th>Hispanic</th>\n",
" <th>White, not Hispanic</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 212</td>\n",
" <td> Acton CDP</td>\n",
" <td> 2390</td>\n",
" <td> 20</td>\n",
" <td> 53</td>\n",
" <td> 263</td>\n",
" <td> 2058</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 296</td>\n",
" <td> Adelanto city</td>\n",
" <td> 18130</td>\n",
" <td> 2477</td>\n",
" <td> 390</td>\n",
" <td> 8299</td>\n",
" <td> 6964</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 394</td>\n",
" <td> Agoura Hills city</td>\n",
" <td> 20537</td>\n",
" <td> 318</td>\n",
" <td> 1571</td>\n",
" <td> 1407</td>\n",
" <td> 17419</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 562</td>\n",
" <td> Alameda city</td>\n",
" <td> 72259</td>\n",
" <td> 5181</td>\n",
" <td> 20534</td>\n",
" <td> 6725</td>\n",
" <td> 40770</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 618</td>\n",
" <td> Alamo CDP</td>\n",
" <td> 15626</td>\n",
" <td> 95</td>\n",
" <td> 1100</td>\n",
" <td> 616</td>\n",
" <td> 13919</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 7 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 26,
"text": [
" place NAME Total Pop African-American, not Hispanic \\\n",
"0 212 Acton CDP 2390 20 \n",
"1 296 Adelanto city 18130 2477 \n",
"2 394 Agoura Hills city 20537 318 \n",
"3 562 Alameda city 72259 5181 \n",
"4 618 Alamo CDP 15626 95 \n",
"\n",
" Asian, not Hispanic Hispanic White, not Hispanic \n",
"0 53 263 2058 \n",
"1 390 8299 6964 \n",
"2 1571 1407 17419 \n",
"3 20534 6725 40770 \n",
"4 1100 616 13919 \n",
"\n",
"[5 rows x 7 columns]"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ca_places_2010 = [place for place in places(variables=\"NAME,P0010001,P0050004,P0050010,P0050006,P0050003\", year=2010)]\n",
"\n",
"#put list into dataframe\n",
"ca_places_2010_df = pd.DataFrame(ca_places_2010)\n",
"ca_places_2010_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>NAME</th>\n",
" <th>P0010001</th>\n",
" <th>P0050003</th>\n",
" <th>P0050004</th>\n",
" <th>P0050006</th>\n",
" <th>P0050010</th>\n",
" <th>place</th>\n",
" <th>state</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> Acalanes Ridge CDP</td>\n",
" <td> 1137</td>\n",
" <td> 908</td>\n",
" <td> 5</td>\n",
" <td> 125</td>\n",
" <td> 50</td>\n",
" <td> 00135</td>\n",
" <td> 06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> Acampo CDP</td>\n",
" <td> 341</td>\n",
" <td> 113</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 199</td>\n",
" <td> 00156</td>\n",
" <td> 06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> Acton CDP</td>\n",
" <td> 7596</td>\n",
" <td> 5782</td>\n",
" <td> 54</td>\n",
" <td> 151</td>\n",
" <td> 1373</td>\n",
" <td> 00212</td>\n",
" <td> 06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> Adelanto city</td>\n",
" <td> 31765</td>\n",
" <td> 5395</td>\n",
" <td> 6196</td>\n",
" <td> 522</td>\n",
" <td> 18513</td>\n",
" <td> 00296</td>\n",
" <td> 06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> Adin CDP</td>\n",
" <td> 272</td>\n",
" <td> 224</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 32</td>\n",
" <td> 00310</td>\n",
" <td> 06</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 8 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 27,
"text": [
" NAME P0010001 P0050003 P0050004 P0050006 P0050010 place \\\n",
"0 Acalanes Ridge CDP 1137 908 5 125 50 00135 \n",
"1 Acampo CDP 341 113 0 3 199 00156 \n",
"2 Acton CDP 7596 5782 54 151 1373 00212 \n",
"3 Adelanto city 31765 5395 6196 522 18513 00296 \n",
"4 Adin CDP 272 224 2 0 32 00310 \n",
"\n",
" state \n",
"0 06 \n",
"1 06 \n",
"2 06 \n",
"3 06 \n",
"4 06 \n",
"\n",
"[5 rows x 8 columns]"
]
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pop_vars_10 = ['P0010001','P0050003','P0050004','P0050006','P0050010']\n",
"\n",
"#turn numbers into integers\n",
"ca_places_2010_df[(pop_vars_10)] = ca_places_2010_df[(pop_vars_10)].astype(int)\n",
"\n",
"#sort CA cities by total population\n",
"ca_places_2010_df.sort('P0010001', ascending=False).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>NAME</th>\n",
" <th>P0010001</th>\n",
" <th>P0050003</th>\n",
" <th>P0050004</th>\n",
" <th>P0050006</th>\n",
" <th>P0050010</th>\n",
" <th>place</th>\n",
" <th>state</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>789 </th>\n",
" <td> Los Angeles city</td>\n",
" <td> 3792621</td>\n",
" <td> 1086908</td>\n",
" <td> 347380</td>\n",
" <td> 420212</td>\n",
" <td> 1838822</td>\n",
" <td> 44000</td>\n",
" <td> 06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1187</th>\n",
" <td> San Diego city</td>\n",
" <td> 1307402</td>\n",
" <td> 589702</td>\n",
" <td> 82497</td>\n",
" <td> 204347</td>\n",
" <td> 376020</td>\n",
" <td> 66000</td>\n",
" <td> 06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1197</th>\n",
" <td> San Jose city</td>\n",
" <td> 945942</td>\n",
" <td> 271382</td>\n",
" <td> 27508</td>\n",
" <td> 300022</td>\n",
" <td> 313636</td>\n",
" <td> 68000</td>\n",
" <td> 06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1191</th>\n",
" <td> San Francisco city</td>\n",
" <td> 805235</td>\n",
" <td> 337451</td>\n",
" <td> 46781</td>\n",
" <td> 265700</td>\n",
" <td> 121774</td>\n",
" <td> 67000</td>\n",
" <td> 06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>500 </th>\n",
" <td> Fresno city</td>\n",
" <td> 494665</td>\n",
" <td> 148598</td>\n",
" <td> 37885</td>\n",
" <td> 60939</td>\n",
" <td> 232055</td>\n",
" <td> 27000</td>\n",
" <td> 06</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 8 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 28,
"text": [
" NAME P0010001 P0050003 P0050004 P0050006 P0050010 \\\n",
"789 Los Angeles city 3792621 1086908 347380 420212 1838822 \n",
"1187 San Diego city 1307402 589702 82497 204347 376020 \n",
"1197 San Jose city 945942 271382 27508 300022 313636 \n",
"1191 San Francisco city 805235 337451 46781 265700 121774 \n",
"500 Fresno city 494665 148598 37885 60939 232055 \n",
"\n",
" place state \n",
"789 44000 06 \n",
"1187 66000 06 \n",
"1197 68000 06 \n",
"1191 67000 06 \n",
"500 27000 06 \n",
"\n",
"[5 rows x 8 columns]"
]
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#create new descriptively named columns with values population by race/ethnicity\n",
"ca_places_2010_df['African-American, not Hispanic'] = ca_places_2010_df['P0050004']\n",
"ca_places_2010_df['White, not Hispanic'] = ca_places_2010_df['P0050003']\n",
"ca_places_2010_df['Asian, not Hispanic'] = ca_places_2010_df['P0050006']\n",
"ca_places_2010_df['Total Pop'] = ca_places_2010_df['P0010001']\n",
"ca_places_2010_df['Hispanic'] = ca_places_2010_df['P0050010']\n",
"\n",
"#show only columns that have legible names; set index by tract\n",
"alameda_places_df_2010 = ca_places_2010_df[['place','NAME','Total Pop','African-American, not Hispanic', \\\n",
" 'Asian, not Hispanic', 'Hispanic', 'White, not Hispanic']] \n",
"\n",
"alameda_places_df_2010.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>place</th>\n",
" <th>NAME</th>\n",
" <th>Total Pop</th>\n",
" <th>African-American, not Hispanic</th>\n",
" <th>Asian, not Hispanic</th>\n",
" <th>Hispanic</th>\n",
" <th>White, not Hispanic</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 00135</td>\n",
" <td> Acalanes Ridge CDP</td>\n",
" <td> 1137</td>\n",
" <td> 5</td>\n",
" <td> 125</td>\n",
" <td> 50</td>\n",
" <td> 908</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 00156</td>\n",
" <td> Acampo CDP</td>\n",
" <td> 341</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 199</td>\n",
" <td> 113</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 00212</td>\n",
" <td> Acton CDP</td>\n",
" <td> 7596</td>\n",
" <td> 54</td>\n",
" <td> 151</td>\n",
" <td> 1373</td>\n",
" <td> 5782</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 00296</td>\n",
" <td> Adelanto city</td>\n",
" <td> 31765</td>\n",
" <td> 6196</td>\n",
" <td> 522</td>\n",
" <td> 18513</td>\n",
" <td> 5395</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 00310</td>\n",
" <td> Adin CDP</td>\n",
" <td> 272</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 32</td>\n",
" <td> 224</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 7 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 30,
"text": [
" place NAME Total Pop African-American, not Hispanic \\\n",
"0 00135 Acalanes Ridge CDP 1137 5 \n",
"1 00156 Acampo CDP 341 0 \n",
"2 00212 Acton CDP 7596 54 \n",
"3 00296 Adelanto city 31765 6196 \n",
"4 00310 Adin CDP 272 2 \n",
"\n",
" Asian, not Hispanic Hispanic White, not Hispanic \n",
"0 125 50 908 \n",
"1 3 199 113 \n",
"2 151 1373 5782 \n",
"3 522 18513 5395 \n",
"4 0 32 224 \n",
"\n",
"[5 rows x 7 columns]"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#find place ID of Oakland by sorting on towns in CA starting with O by population\n",
"o_towns_2000 = alameda_places_df_2000[alameda_places_df_2000['NAME'].str.startswith('O')]\n",
"o_towns_2000_new = o_towns_2000.sort('Total Pop', ascending=False).set_index(['place'])\n",
"o_towns_2000_new.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>NAME</th>\n",
" <th>Total Pop</th>\n",
" <th>African-American, not Hispanic</th>\n",
" <th>Asian, not Hispanic</th>\n",
" <th>Hispanic</th>\n",
" <th>White, not Hispanic</th>\n",
" </tr>\n",
" <tr>\n",
" <th>place</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>53000</th>\n",
" <td> Oakland city</td>\n",
" <td> 399484</td>\n",
" <td> 146510</td>\n",
" <td> 65267</td>\n",
" <td> 87467</td>\n",
" <td> 101996</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54652</th>\n",
" <td> Oxnard city</td>\n",
" <td> 170358</td>\n",
" <td> 6541</td>\n",
" <td> 13793</td>\n",
" <td> 112807</td>\n",
" <td> 37354</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53322</th>\n",
" <td> Oceanside city</td>\n",
" <td> 161029</td>\n",
" <td> 10914</td>\n",
" <td> 11082</td>\n",
" <td> 48691</td>\n",
" <td> 90451</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53896</th>\n",
" <td> Ontario city</td>\n",
" <td> 158007</td>\n",
" <td> 12107</td>\n",
" <td> 6863</td>\n",
" <td> 94610</td>\n",
" <td> 44183</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53980</th>\n",
" <td> Orange city</td>\n",
" <td> 128821</td>\n",
" <td> 2216</td>\n",
" <td> 13070</td>\n",
" <td> 41434</td>\n",
" <td> 72481</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 6 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 35,
"text": [
" NAME Total Pop African-American, not Hispanic \\\n",
"place \n",
"53000 Oakland city 399484 146510 \n",
"54652 Oxnard city 170358 6541 \n",
"53322 Oceanside city 161029 10914 \n",
"53896 Ontario city 158007 12107 \n",
"53980 Orange city 128821 2216 \n",
"\n",
" Asian, not Hispanic Hispanic White, not Hispanic \n",
"place \n",
"53000 65267 87467 101996 \n",
"54652 13793 112807 37354 \n",
"53322 11082 48691 90451 \n",
"53896 6863 94610 44183 \n",
"53980 13070 41434 72481 \n",
"\n",
"[5 rows x 6 columns]"
]
}
],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#find place ID of Oakland by sorting on towns in CA starting with O by population\n",
"o_towns_2010 = ca_places_2010_df[ca_places_2010_df['NAME'].str.startswith('O')]\n",
"o_towns_2010_new = o_towns_2010[['place','NAME','Total Pop','African-American, not Hispanic', \\\n",
" 'Asian, not Hispanic', 'Hispanic', 'White, not Hispanic']].sort('Total Pop', ascending=False).set_index(['place'])\n",
"\n",
"o_towns_2010_new.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>NAME</th>\n",
" <th>Total Pop</th>\n",
" <th>African-American, not Hispanic</th>\n",
" <th>Asian, not Hispanic</th>\n",
" <th>Hispanic</th>\n",
" <th>White, not Hispanic</th>\n",
" </tr>\n",
" <tr>\n",
" <th>place</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>53000</th>\n",
" <td> Oakland city</td>\n",
" <td> 390724</td>\n",
" <td> 106637</td>\n",
" <td> 65127</td>\n",
" <td> 99068</td>\n",
" <td> 101308</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54652</th>\n",
" <td> Oxnard city</td>\n",
" <td> 197899</td>\n",
" <td> 4754</td>\n",
" <td> 14084</td>\n",
" <td> 145551</td>\n",
" <td> 29410</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53322</th>\n",
" <td> Oceanside city</td>\n",
" <td> 167086</td>\n",
" <td> 7101</td>\n",
" <td> 10638</td>\n",
" <td> 59947</td>\n",
" <td> 80849</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53896</th>\n",
" <td> Ontario city</td>\n",
" <td> 163924</td>\n",
" <td> 9598</td>\n",
" <td> 8078</td>\n",
" <td> 113085</td>\n",
" <td> 29898</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53980</th>\n",
" <td> Orange city</td>\n",
" <td> 136416</td>\n",
" <td> 1895</td>\n",
" <td> 15116</td>\n",
" <td> 52014</td>\n",
" <td> 63805</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 6 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 33,
"text": [
" NAME Total Pop African-American, not Hispanic \\\n",
"place \n",
"53000 Oakland city 390724 106637 \n",
"54652 Oxnard city 197899 4754 \n",
"53322 Oceanside city 167086 7101 \n",
"53896 Ontario city 163924 9598 \n",
"53980 Orange city 136416 1895 \n",
"\n",
" Asian, not Hispanic Hispanic White, not Hispanic \n",
"place \n",
"53000 65127 99068 101308 \n",
"54652 14084 145551 29410 \n",
"53322 10638 59947 80849 \n",
"53896 8078 113085 29898 \n",
"53980 15116 52014 63805 \n",
"\n",
"[5 rows x 6 columns]"
]
}
],
"prompt_number": 33
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Overall Population Change in Oakland"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Total Oakland population decrease from 2000 to 2010\n",
"o_pop_change = o_towns_2010_new.ix[['53000']]['Total Pop'] - o_towns_2000_new.ix[['53000']]['Total Pop']\n",
"o_pop_change"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 37,
"text": [
"53000 -8760\n",
"Name: Total Pop, dtype: int64"
]
}
],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#change over 2000 total population is an overall decrease by 2% since 2000\n",
"o_pop_percent_change = o_pop_change/o_towns_2000_new.ix[['53000']]['Total Pop']\n",
"o_pop_percent_change"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 38,
"text": [
"53000 -0.021928\n",
"Name: Total Pop, dtype: float64"
]
}
],
"prompt_number": 38
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Population Change in Oakland's African-American Community"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#change in African-American population between 2000 and 2010\n",
"af_am_total_change = o_towns_2010_new.ix[['53000']]['African-American, not Hispanic'] - \\\n",
"o_towns_2000_new.ix[['53000']]['African-American, not Hispanic']\n",
"\n",
"af_am_total_change"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 39,
"text": [
"53000 -39873\n",
"Name: African-American, not Hispanic, dtype: int64"
]
}
],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Percentage of African-Americans in 2000\n",
"af_am_percent_2000 = o_towns_2000_new.ix[['53000']]['African-American, not Hispanic']\\\n",
"/o_towns_2000_new.ix[['53000']]['Total Pop']\n",
"\n",
"af_am_percent_2000"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 52,
"text": [
"53000 0.366748\n",
"dtype: float64"
]
}
],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Percentage of African-Americans in 2010\n",
"af_am_percent_2010 = o_towns_2010_new.ix[['53000']]['African-American, not Hispanic']\\\n",
"/o_towns_2010_new.ix[['53000']]['Total Pop']\n",
"\n",
"af_am_percent_2010"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 53,
"text": [
"53000 0.272922\n",
"dtype: float64"
]
}
],
"prompt_number": 53
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#9.4% decrease in percentage of African-American community in ten year span\n",
"af_am_percent_change = af_am_percent_2010 - af_am_percent_2000 \n",
"af_am_percent_change"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 54,
"text": [
"53000 -0.093827\n",
"dtype: float64"
]
}
],
"prompt_number": 54
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pop_changes = [af_am_percent_change[0]*100, o_pop_percent_change[0]*100]\n",
"\n",
"communities = ('African-American Community', 'All Oakland')\n",
"pos = np.arange(len(communities)) + .5\n",
"error = np.random.rand(len(communities))\n",
"\n",
"plt.barh(pos, pop_changes, align='center', color=\"#669999\", alpha=0.7)\n",
"plt.yticks(pos, communities)\n",
"plt.grid()\n",
"plt.xlabel('Population Change (%)')\n",
"plt.title('Oakland between 2000-2010')\n",
"\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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XsH//fsTExMDb2xsAcO/ePTRs2FCT51KSY8eOwc/PDzY2NgAevfvdt28fBgwY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\n7ERERDrCYiciItKR/wdT9pF+qkqX1wAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x108f8d710>"
]
}
],
"prompt_number": 177
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Demographic Changes in Oakland of White, Asian, and Hispanic Communities"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#change in White population between 2000 and 2010\n",
"white_total_change = o_towns_2010_new.ix[['53000']]['White, not Hispanic'] - \\\n",
"o_towns_2000_new.ix[['53000']]['White, not Hispanic']\n",
"\n",
"white_total_change"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 44,
"text": [
"53000 -688\n",
"Name: White, not Hispanic, dtype: int64"
]
}
],
"prompt_number": 44
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#change in Asian population between 2000 and 2010\n",
"asian_total_change = o_towns_2010_new.ix[['53000']]['Asian, not Hispanic'] - \\\n",
"o_towns_2000_new.ix[['53000']]['Asian, not Hispanic']\n",
"\n",
"asian_total_change"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 45,
"text": [
"53000 -140\n",
"Name: Asian, not Hispanic, dtype: int64"
]
}
],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#change in Hispanic population between 2000 and 2010\n",
"hisp_total_change = o_towns_2010_new.ix[['53000']]['Hispanic'] - \\\n",
"o_towns_2000_new.ix[['53000']]['Hispanic']\n",
"\n",
"hisp_total_change"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 46,
"text": [
"53000 11601\n",
"Name: Hispanic, dtype: int64"
]
}
],
"prompt_number": 46
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All Oakland Total Change of Each Community in Numbers"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#compare total numbers for each demographic group changing in Oakland over 10 year period\n",
"all_race_changes = [af_am_total_change, asian_total_change, hisp_total_change, white_total_change]\n",
"\n",
"\n",
"diff_df = DataFrame(all_race_changes)\n",
"diff_df.rename(columns={'53000': 'Oakland'}, inplace=True)\n",
"diff_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>Oakland</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>African-American, not Hispanic</th>\n",
" <td>-39873</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Asian, not Hispanic</th>\n",
" <td> -140</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hispanic</th>\n",
" <td> 11601</td>\n",
" </tr>\n",
" <tr>\n",
" <th>White, not Hispanic</th>\n",
" <td> -688</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4 rows \u00d7 1 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 162,
"text": [
" Oakland\n",
"African-American, not Hispanic -39873\n",
"Asian, not Hispanic -140\n",
"Hispanic 11601\n",
"White, not Hispanic -688\n",
"\n",
"[4 rows x 1 columns]"
]
}
],
"prompt_number": 162
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Percentage of Asians in 2000\n",
"asian_percent_2000 = o_towns_2000_new.ix[['53000']]['Asian, not Hispanic']\\\n",
"/o_towns_2000_new.ix[['53000']]['Total Pop']\n",
"\n",
"#Percentage of Asians in 2010\n",
"asian_percent_2010 = o_towns_2010_new.ix[['53000']]['Asian, not Hispanic']\\\n",
"/o_towns_2010_new.ix[['53000']]['Total Pop']\n",
"\n",
"#less than 1% increase in percentage of Asian community in ten year span\n",
"asian_percent_change = asian_percent_2010 - asian_percent_2000 \n",
"asian_percent_change"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 57,
"text": [
"53000 0.003305\n",
"dtype: float64"
]
}
],
"prompt_number": 57
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Percentage of Hispanics in 2000\n",
"hispanic_percent_2000 = o_towns_2000_new.ix[['53000']]['Hispanic']\\\n",
"/o_towns_2000_new.ix[['53000']]['Total Pop']\n",
"\n",
"#Percentage of Hispanics in 2010\n",
"hispanic_percent_2010 = o_towns_2010_new.ix[['53000']]['Hispanic']\\\n",
"/o_towns_2010_new.ix[['53000']]['Total Pop']\n",
"\n",
"#3% increase in percentage of Hispanic community in ten year span\n",
"hispanic_percent_change = hispanic_percent_2010 - hispanic_percent_2000 \n",
"hispanic_percent_change"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 58,
"text": [
"53000 0.0346\n",
"dtype: float64"
]
}
],
"prompt_number": 58
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Percentage of Whites in 2000\n",
"white_percent_2000 = o_towns_2000_new.ix[['53000']]['White, not Hispanic']\\\n",
"/o_towns_2000_new.ix[['53000']]['Total Pop']\n",
"\n",
"#Percentage of Whites in 2010\n",
"white_percent_2010 = o_towns_2010_new.ix[['53000']]['White, not Hispanic']\\\n",
"/o_towns_2010_new.ix[['53000']]['Total Pop']\n",
"\n",
"#9.4% decrease in percentage of White community in ten year span\n",
"white_percent_change = white_percent_2010 - white_percent_2000 \n",
"white_percent_change"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 67,
"text": [
"pandas.core.series.Series"
]
}
],
"prompt_number": 67
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"changes_over_time = {\"Hispanic\": Series([hispanic_percent_2000[0], hispanic_percent_2010[0]], index=['2000','2010']),\\\n",
" \"Asian\": Series([asian_percent_2000[0], asian_percent_2010[0]], index=['2000','2010']),\\\n",
" \"African-American\": Series([af_am_percent_2000[0], af_am_percent_2010[0]], index=['2000','2010']),\\\n",
" \"White\": Series([white_percent_2000[0], white_percent_2010[0]], index=['2000','2010'])}\n",
" \n",
" \n",
"race_perc_df = DataFrame(changes_over_time)\n",
"race_perc_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>African-American</th>\n",
" <th>Asian</th>\n",
" <th>Hispanic</th>\n",
" <th>White</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000</th>\n",
" <td> 0.366748</td>\n",
" <td> 0.163378</td>\n",
" <td> 0.21895</td>\n",
" <td> 0.255319</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010</th>\n",
" <td> 0.272922</td>\n",
" <td> 0.166683</td>\n",
" <td> 0.25355</td>\n",
" <td> 0.259283</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows \u00d7 4 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 88,
"text": [
" African-American Asian Hispanic White\n",
"2000 0.366748 0.163378 0.21895 0.255319\n",
"2010 0.272922 0.166683 0.25355 0.259283\n",
"\n",
"[2 rows x 4 columns]"
]
}
],
"prompt_number": 88
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"race_perc_df.ix['2000']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 89,
"text": [
"African-American 0.366748\n",
"Asian 0.163378\n",
"Hispanic 0.218950\n",
"White 0.255319\n",
"Name: 2000, dtype: float64"
]
}
],
"prompt_number": 89
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"race_perc_df.columns\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 91,
"text": [
"Index([u'African-American', u'Asian', u'Hispanic', u'White'], dtype='object')"
]
}
],
"prompt_number": 91
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#http://matplotlib.org/examples/api/barchart_demo.html\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"N = 4\n",
"\n",
"#x locations for the groups, width of the bars\n",
"ind = np.arange(N)\n",
"width = 0.3 \n",
"\n",
"perc_00 = race_perc_df.ix['2000']*100\n",
"fig, ax = plt.subplots()\n",
"rects1 = ax.bar(ind, perc_00, width, edgecolor='#ede5e5', facecolor='#b04c4c', align='center')\n",
"\n",
"perc_10 = race_perc_df.ix['2010']*100\n",
"rects2 = ax.bar(ind+width, perc_10, width, edgecolor='#e5eeee', facecolor='#99bbbb', align='center')\n",
"\n",
"# add labeling\n",
"ax.set_ylabel('Percent')\n",
"ax.set_title('Percent of Total Population by Census Categories for Race/Ethnicity')\n",
"ax.set_xticks(ind+width)\n",
"ax.set_xticklabels(race_perc_df.columns)\n",
"ax.legend( (rects1[0], rects2[0]), ('2000', '2010') )\n",
"\n",
"\n",
"def autolabel(rects):\n",
" # attach some text labels\n",
" for rect in rects:\n",
" height = rect.get_height()\n",
" ax.text(rect.get_x()+rect.get_width()/2., 1.025*height, '%d'%int(height),\n",
" ha='center', va='bottom')\n",
"\n",
"autolabel(rects1)\n",
"autolabel(rects2)\n",
"\n",
"plt.show()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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yODg4wMfHBz4+Pjhy5Iguw2KMMSYxnf5m1Lx5c8TGxqp01X3q1CkIgoDZs2dj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\nFrenMrq4alEoFMjOzsa5c+f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4uDjExMTg3LlzaN68Ofr16yfePjIwMBDHK79MX1VU4Sfk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\njBhjjEmOkxFjjDHJ/X9v8aLAksnVZgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x108902fd0>"
]
}
],
"prompt_number": 205
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#hmmm, there's greater than 100% population in Oakland in 2000? Must be due to rounding. \n",
"df.ix['2000'].sum()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 129,
"text": [
"1.0043956704148351"
]
}
],
"prompt_number": 129
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#The missing 5% percent must be people we didn't count, like American Indians. Perhaps Oakland is overall more diverse in 2010 \n",
"#than in 2000 in the sense that there are more represented groups in the population.\n",
"df.ix['2010'].sum()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 207,
"text": [
"0.95243701436307981"
]
}
],
"prompt_number": 207
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def tracts(variables=\"NAME\", year=2010):\n",
" \n",
" states_fips = set([s.fips for s in us.states.STATES])\n",
" geo={'for':'tract:*',\n",
" 'in':'state:06 county:001'}\n",
" \n",
" for tract in c.sf1.get(variables, geo=geo, year=year):\n",
" yield tract\n",
"\n",
" \n",
"tracts().next()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 209,
"text": [
"{u'NAME': u'Census Tract 4001',\n",
" u'county': u'001',\n",
" u'state': u'06',\n",
" u'tract': u'400100'}"
]
}
],
"prompt_number": 209
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#P001001/Total Pop, P010004/African-American Not Hispanic, P011001/Hispanic, P010006/Asian, not Hispanic \n",
"#P010003/White, not Hispanic \n",
"o_tracts_2000 = [tract for tract in tracts(variables=\"NAME,P001001,P010004,P011001,P010006,P010003\", year=2000)]\n",
"\n",
"#put list into dataframe\n",
"tracts_2000_df = pd.DataFrame(o_tracts_2000)\n",
"\n",
"populations = ['P001001', 'P010004', 'P011001', 'P010006', 'P010003']\n",
"tracts_2000_df[(populations)] = tracts_2000_df[(populations)].astype(int)\n",
"tracts_2000_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>NAME</th>\n",
" <th>P001001</th>\n",
" <th>P010003</th>\n",
" <th>P010004</th>\n",
" <th>P010006</th>\n",
" <th>P011001</th>\n",
" <th>county</th>\n",
" <th>state</th>\n",
" <th>tract</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> Census Tract 4001</td>\n",
" <td> 2498</td>\n",
" <td> 1987</td>\n",
" <td> 125</td>\n",
" <td> 305</td>\n",
" <td> 97</td>\n",
" <td> 1</td>\n",
" <td> 6</td>\n",
" <td> 400100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> Census Tract 4002</td>\n",
" <td> 1910</td>\n",
" <td> 1567</td>\n",
" <td> 71</td>\n",
" <td> 177</td>\n",
" <td> 117</td>\n",
" <td> 1</td>\n",
" <td> 6</td>\n",
" <td> 400200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> Census Tract 4003</td>\n",
" <td> 4878</td>\n",
" <td> 3401</td>\n",
" <td> 768</td>\n",
" <td> 418</td>\n",
" <td> 314</td>\n",
" <td> 1</td>\n",
" <td> 6</td>\n",
" <td> 400300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> Census Tract 4004</td>\n",
" <td> 3659</td>\n",
" <td> 2494</td>\n",
" <td> 671</td>\n",
" <td> 308</td>\n",
" <td> 241</td>\n",
" <td> 1</td>\n",
" <td> 6</td>\n",
" <td> 400400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> Census Tract 4005</td>\n",
" <td> 3410</td>\n",
" <td> 1387</td>\n",
" <td> 1510</td>\n",
" <td> 216</td>\n",
" <td> 363</td>\n",
" <td> 1</td>\n",
" <td> 6</td>\n",
" <td> 400500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 9 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 212,
"text": [
" NAME P001001 P010003 P010004 P010006 P011001 county \\\n",
"0 Census Tract 4001 2498 1987 125 305 97 1 \n",
"1 Census Tract 4002 1910 1567 71 177 117 1 \n",
"2 Census Tract 4003 4878 3401 768 418 314 1 \n",
"3 Census Tract 4004 3659 2494 671 308 241 1 \n",
"4 Census Tract 4005 3410 1387 1510 216 363 1 \n",
"\n",
" state tract \n",
"0 6 400100 \n",
"1 6 400200 \n",
"2 6 400300 \n",
"3 6 400400 \n",
"4 6 400500 \n",
"\n",
"[5 rows x 9 columns]"
]
}
],
"prompt_number": 212
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#create new descriptively named columns with values population by race/ethnicity\n",
"tracts_2000_df['African-American, not Hispanic'] = tracts_2000_df['P010004']\n",
"tracts_2000_df['White, not Hispanic'] = tracts_2000_df['P010003']\n",
"tracts_2000_df['Asian, not Hispanic'] = tracts_2000_df['P010006']\n",
"tracts_2000_df['Total Pop'] = tracts_2000_df['P001001']\n",
"tracts_2000_df['Hispanic'] = tracts_2000_df['P011001']\n",
"\n",
"#show only columns that have legible names\n",
"alameda_tracts_2000_df = tracts_2000_df[['tract','Total Pop','African-American, not Hispanic',\\\n",
" 'White, not Hispanic', 'Asian, not Hispanic', 'Hispanic']]\n",
"\n",
"alameda_tracts_2000_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>tract</th>\n",
" <th>Total Pop</th>\n",
" <th>African-American, not Hispanic</th>\n",
" <th>White, not Hispanic</th>\n",
" <th>Asian, not Hispanic</th>\n",
" <th>Hispanic</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 400100</td>\n",
" <td> 2498</td>\n",
" <td> 125</td>\n",
" <td> 1987</td>\n",
" <td> 305</td>\n",
" <td> 97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 400200</td>\n",
" <td> 1910</td>\n",
" <td> 71</td>\n",
" <td> 1567</td>\n",
" <td> 177</td>\n",
" <td> 117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 400300</td>\n",
" <td> 4878</td>\n",
" <td> 768</td>\n",
" <td> 3401</td>\n",
" <td> 418</td>\n",
" <td> 314</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 400400</td>\n",
" <td> 3659</td>\n",
" <td> 671</td>\n",
" <td> 2494</td>\n",
" <td> 308</td>\n",
" <td> 241</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 400500</td>\n",
" <td> 3410</td>\n",
" <td> 1510</td>\n",
" <td> 1387</td>\n",
" <td> 216</td>\n",
" <td> 363</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 6 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 220,
"text": [
" tract Total Pop African-American, not Hispanic White, not Hispanic \\\n",
"0 400100 2498 125 1987 \n",
"1 400200 1910 71 1567 \n",
"2 400300 4878 768 3401 \n",
"3 400400 3659 671 2494 \n",
"4 400500 3410 1510 1387 \n",
"\n",
" Asian, not Hispanic Hispanic \n",
"0 305 97 \n",
"1 177 117 \n",
"2 418 314 \n",
"3 308 241 \n",
"4 216 363 \n",
"\n",
"[5 rows x 6 columns]"
]
}
],
"prompt_number": 220
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"alameda_tracts_2000_df['AfAm_ratio_2000'] = tracts_2000_df['P010004']/tracts_2000_df['P001001']\n",
"alameda_tracts_2000_df['White_ratio_2000'] = tracts_2000_df['P010003']/tracts_2000_df['P001001']\n",
"alameda_tracts_2000_df['Asian_ratio_2000'] = tracts_2000_df['P010006']/tracts_2000_df['P001001']\n",
"alameda_tracts_2000_df['Hispanic_ratio_2000'] = tracts_2000_df['P011001']/tracts_2000_df['P001001']\n",
"\n",
"alameda_tracts_2000_df.head() #.set_index(['tract']).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>tract</th>\n",
" <th>Total Pop</th>\n",
" <th>African-American, not Hispanic</th>\n",
" <th>White, not Hispanic</th>\n",
" <th>Asian, not Hispanic</th>\n",
" <th>Hispanic</th>\n",
" <th>AfAm_ratio_2000</th>\n",
" <th>White_ratio_2000</th>\n",
" <th>Asian_ratio_2000</th>\n",
" <th>Hispanic_ratio_2000</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 400100</td>\n",
" <td> 2498</td>\n",
" <td> 125</td>\n",
" <td> 1987</td>\n",
" <td> 305</td>\n",
" <td> 97</td>\n",
" <td> 0.050040</td>\n",
" <td> 0.795436</td>\n",
" <td> 0.122098</td>\n",
" <td> 0.038831</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 400200</td>\n",
" <td> 1910</td>\n",
" <td> 71</td>\n",
" <td> 1567</td>\n",
" <td> 177</td>\n",
" <td> 117</td>\n",
" <td> 0.037173</td>\n",
" <td> 0.820419</td>\n",
" <td> 0.092670</td>\n",
" <td> 0.061257</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 400300</td>\n",
" <td> 4878</td>\n",
" <td> 768</td>\n",
" <td> 3401</td>\n",
" <td> 418</td>\n",
" <td> 314</td>\n",
" <td> 0.157442</td>\n",
" <td> 0.697212</td>\n",
" <td> 0.085691</td>\n",
" <td> 0.064371</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 400400</td>\n",
" <td> 3659</td>\n",
" <td> 671</td>\n",
" <td> 2494</td>\n",
" <td> 308</td>\n",
" <td> 241</td>\n",
" <td> 0.183383</td>\n",
" <td> 0.681607</td>\n",
" <td> 0.084176</td>\n",
" <td> 0.065865</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 400500</td>\n",
" <td> 3410</td>\n",
" <td> 1510</td>\n",
" <td> 1387</td>\n",
" <td> 216</td>\n",
" <td> 363</td>\n",
" <td> 0.442815</td>\n",
" <td> 0.406745</td>\n",
" <td> 0.063343</td>\n",
" <td> 0.106452</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 10 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 221,
"text": [
" tract Total Pop African-American, not Hispanic White, not Hispanic \\\n",
"0 400100 2498 125 1987 \n",
"1 400200 1910 71 1567 \n",
"2 400300 4878 768 3401 \n",
"3 400400 3659 671 2494 \n",
"4 400500 3410 1510 1387 \n",
"\n",
" Asian, not Hispanic Hispanic AfAm_ratio_2000 White_ratio_2000 \\\n",
"0 305 97 0.050040 0.795436 \n",
"1 177 117 0.037173 0.820419 \n",
"2 418 314 0.157442 0.697212 \n",
"3 308 241 0.183383 0.681607 \n",
"4 216 363 0.442815 0.406745 \n",
"\n",
" Asian_ratio_2000 Hispanic_ratio_2000 \n",
"0 0.122098 0.038831 \n",
"1 0.092670 0.061257 \n",
"2 0.085691 0.064371 \n",
"3 0.084176 0.065865 \n",
"4 0.063343 0.106452 \n",
"\n",
"[5 rows x 10 columns]"
]
}
],
"prompt_number": 221
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = range(0,1)\n",
"y = alameda_tracts_2000_df['tract']\n",
"alameda_tracts_2000_df['AfAm_ratio_2000'].plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 224,
"text": [
"<matplotlib.axes.AxesSubplot at 0x108f78190>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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UgVdrODSTwRsJvBmLxqoZfGuregYfbiPrNdcAr71mroomUYONhePBiyoagSB2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\nH0UIvEAgEPRRhMALBAJBH+X/A/iIZV+w5BrpAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x108960d90>"
]
}
],
"prompt_number": 224
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from numpy.random import randn\n",
"\n",
"\n",
"for item in alameda_tracts_2000_df['AfAm_ratio_2000']:\n",
" \n",
"\n",
"spacing = len(alameda_tracts_2000_df['AfAm_ratio_2000'])\n",
"width = .35\n",
"fig = plt.figure()\n",
"\n",
"ax1 = fig.add_subplot(2, 2, 1)\n",
"ax2 = fig.add_subplot(2, 2, 2)\n",
"ax3 = fig.add_subplot(2, 2, 3)\n",
"ax4 = fig.add_subplot(2, 2, 4)\n",
"\n",
"ax1.bar(spacing, test, width, edgecolor='#ede5e5', facecolor='#327676', align='center')\n",
"ax2.scatter(np.arange(30), np.arange(30) + 3 * randn(30))\n",
"ax3.plot(randn(50).cumsum(), 'k--')\n",
"ax4.plot(randn(50).cumsum(), 'k--')\n",
"\n",
"\n",
"fig.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "AssertionError",
"evalue": "incompatible sizes: argument 'height' must be length 1 or scalar",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-235-e50fa6d6ecd6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0max4\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspacing\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medgecolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'#ede5e5'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfacecolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'#327676'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malign\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'center'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0max2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m3\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0max3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcumsum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'k--'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m//anaconda/lib/python2.7/site-packages/matplotlib/axes.pyc\u001b[0m in \u001b[0;36mbar\u001b[0;34m(self, left, height, width, bottom, **kwargs)\u001b[0m\n\u001b[1;32m 4997\u001b[0m assert len(height) == nbars, (\"incompatible sizes: argument 'height' \"\n\u001b[1;32m 4998\u001b[0m \u001b[0;34m\"must be length %d or scalar\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4999\u001b[0;31m nbars)\n\u001b[0m\u001b[1;32m 5000\u001b[0m assert len(width) == nbars, (\"incompatible sizes: argument 'width' \"\n\u001b[1;32m 5001\u001b[0m \u001b[0;34m\"must be length %d or scalar\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAssertionError\u001b[0m: incompatible sizes: argument 'height' must be length 1 or scalar"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x1093c5b10>"
]
}
],
"prompt_number": 235
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#sort by highest African-American populations\n",
"tracts_w_ratio_2000 = alameda_tracts_2000_df.sort('AfAm_ratio_2000', ascending=False)\n",
"\n",
"#52 tracts have an African-American population greater than 40% \n",
"tracts_w_ratio_2000[(tracts_w_ratio_2000['AfAm_ratio_2000']>.4) & (tracts_w_ratio_2000['Total Pop'] > 1)] "
],
"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>tract</th>\n",
" <th>Total Pop</th>\n",
" <th>African-American, not Hispanic</th>\n",
" <th>White, not Hispanic</th>\n",
" <th>Asian, not Hispanic</th>\n",
" <th>Hispanic</th>\n",
" <th>AfAm_ratio_2000</th>\n",
" <th>White_ratio_2000</th>\n",
" <th>Asian_ratio_2000</th>\n",
" <th>Hispanic_ratio_2000</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>22 </th>\n",
" <td> 402300</td>\n",
" <td> 453</td>\n",
" <td> 364</td>\n",
" <td> 58</td>\n",
" <td> 19</td>\n",
" <td> 15</td>\n",
" <td> 0.803532</td>\n",
" <td> 0.128035</td>\n",
" <td> 0.041943</td>\n",
" <td> 0.033113</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20 </th>\n",
" <td> 402100</td>\n",
" <td> 1161</td>\n",
" <td> 927</td>\n",
" <td> 21</td>\n",
" <td> 161</td>\n",
" <td> 58</td>\n",
" <td> 0.798450</td>\n",
" <td> 0.018088</td>\n",
" <td> 0.138674</td>\n",
" <td> 0.049957</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99 </th>\n",
" <td> 409800</td>\n",
" <td> 3250</td>\n",
" <td> 2542</td>\n",
" <td> 370</td>\n",
" <td> 97</td>\n",
" <td> 249</td>\n",
" <td> 0.782154</td>\n",
" <td> 0.113846</td>\n",
" <td> 0.029846</td>\n",
" <td> 0.076615</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24 </th>\n",
" <td> 402500</td>\n",
" <td> 1779</td>\n",
" <td> 1369</td>\n",
" <td> 86</td>\n",
" <td> 231</td>\n",
" <td> 108</td>\n",
" <td> 0.769533</td>\n",
" <td> 0.048342</td>\n",
" <td> 0.129848</td>\n",
" <td> 0.060708</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23 </th>\n",
" <td> 402400</td>\n",
" <td> 2588</td>\n",
" <td> 1978</td>\n",
" <td> 166</td>\n",
" <td> 298</td>\n",
" <td> 171</td>\n",
" <td> 0.764297</td>\n",
" <td> 0.064142</td>\n",
" <td> 0.115147</td>\n",
" <td> 0.066074</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17 </th>\n",
" <td> 401800</td>\n",
" <td> 1953</td>\n",
" <td> 1490</td>\n",
" <td> 88</td>\n",
" <td> 39</td>\n",
" <td> 308</td>\n",
" <td> 0.762929</td>\n",
" <td> 0.045059</td>\n",
" <td> 0.019969</td>\n",
" <td> 0.157706</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> 401000</td>\n",
" <td> 5709</td>\n",
" <td> 4176</td>\n",
" <td> 649</td>\n",
" <td> 404</td>\n",
" <td> 547</td>\n",
" <td> 0.731477</td>\n",
" <td> 0.113680</td>\n",
" <td> 0.070765</td>\n",
" <td> 0.095814</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14 </th>\n",
" <td> 401500</td>\n",
" <td> 2413</td>\n",
" <td> 1717</td>\n",
" <td> 314</td>\n",
" <td> 142</td>\n",
" <td> 255</td>\n",
" <td> 0.711562</td>\n",
" <td> 0.130128</td>\n",
" <td> 0.058848</td>\n",
" <td> 0.105678</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td> 410100</td>\n",
" <td> 2784</td>\n",
" <td> 1968</td>\n",
" <td> 268</td>\n",
" <td> 122</td>\n",
" <td> 448</td>\n",
" <td> 0.706897</td>\n",
" <td> 0.096264</td>\n",
" <td> 0.043822</td>\n",
" <td> 0.160920</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> 400700</td>\n",
" <td> 4451</td>\n",
" <td> 3104</td>\n",
" <td> 879</td>\n",
" <td> 221</td>\n",
" <td> 299</td>\n",
" <td> 0.697371</td>\n",
" <td> 0.197484</td>\n",
" <td> 0.049652</td>\n",
" <td> 0.067176</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92 </th>\n",
" <td> 409100</td>\n",
" <td> 2163</td>\n",
" <td> 1489</td>\n",
" <td> 30</td>\n",
" <td> 39</td>\n",
" <td> 607</td>\n",
" <td> 0.688396</td>\n",
" <td> 0.013870</td>\n",
" <td> 0.018031</td>\n",
" <td> 0.280629</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13 </th>\n",
" <td> 401400</td>\n",
" <td> 4765</td>\n",
" <td> 3167</td>\n",
" <td> 342</td>\n",
" <td> 558</td>\n",
" <td> 706</td>\n",
" <td> 0.664638</td>\n",
" <td> 0.071773</td>\n",
" <td> 0.117104</td>\n",
" <td> 0.148164</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78 </th>\n",
" <td> 407700</td>\n",
" <td> 4599</td>\n",
" <td> 3036</td>\n",
" <td> 826</td>\n",
" <td> 226</td>\n",
" <td> 530</td>\n",
" <td> 0.660144</td>\n",
" <td> 0.179604</td>\n",
" <td> 0.049141</td>\n",
" <td> 0.115242</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85 </th>\n",
" <td> 408400</td>\n",
" <td> 3782</td>\n",
" <td> 2496</td>\n",
" <td> 138</td>\n",
" <td> 91</td>\n",
" <td> 1026</td>\n",
" <td> 0.659968</td>\n",
" <td> 0.036489</td>\n",
" <td> 0.024061</td>\n",
" <td> 0.271285</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83 </th>\n",
" <td> 408200</td>\n",
" <td> 4388</td>\n",
" <td> 2882</td>\n",
" <td> 753</td>\n",
" <td> 206</td>\n",
" <td> 560</td>\n",
" <td> 0.656791</td>\n",
" <td> 0.171604</td>\n",
" <td> 0.046946</td>\n",
" <td> 0.127621</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td> 410200</td>\n",
" <td> 3550</td>\n",
" <td> 2298</td>\n",
" <td> 183</td>\n",
" <td> 158</td>\n",
" <td> 891</td>\n",
" <td> 0.647324</td>\n",
" <td> 0.051549</td>\n",
" <td> 0.044507</td>\n",
" <td> 0.250986</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91 </th>\n",
" <td> 409000</td>\n",
" <td> 3327</td>\n",
" <td> 2132</td>\n",
" <td> 141</td>\n",
" <td> 79</td>\n",
" <td> 968</td>\n",
" <td> 0.640818</td>\n",
" <td> 0.042381</td>\n",
" <td> 0.023745</td>\n",
" <td> 0.290953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> 400900</td>\n",
" <td> 2456</td>\n",
" <td> 1570</td>\n",
" <td> 575</td>\n",
" <td> 135</td>\n",
" <td> 202</td>\n",
" <td> 0.639251</td>\n",
" <td> 0.234121</td>\n",
" <td> 0.054967</td>\n",
" <td> 0.082248</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98 </th>\n",
" <td> 409700</td>\n",
" <td> 5208</td>\n",
" <td> 3281</td>\n",
" <td> 222</td>\n",
" <td> 180</td>\n",
" <td> 1471</td>\n",
" <td> 0.629992</td>\n",
" <td> 0.042627</td>\n",
" <td> 0.034562</td>\n",
" <td> 0.282450</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td> 409900</td>\n",
" <td> 3499</td>\n",
" <td> 2199</td>\n",
" <td> 994</td>\n",
" <td> 210</td>\n",
" <td> 149</td>\n",
" <td> 0.628465</td>\n",
" <td> 0.284081</td>\n",
" <td> 0.060017</td>\n",
" <td> 0.042584</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21 </th>\n",
" <td> 402200</td>\n",
" <td> 1844</td>\n",
" <td> 1138</td>\n",
" <td> 181</td>\n",
" <td> 117</td>\n",
" <td> 411</td>\n",
" <td> 0.617137</td>\n",
" <td> 0.098156</td>\n",
" <td> 0.063449</td>\n",
" <td> 0.222885</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26 </th>\n",
" <td> 402700</td>\n",
" <td> 1946</td>\n",
" <td> 1200</td>\n",
" <td> 105</td>\n",
" <td> 180</td>\n",
" <td> 450</td>\n",
" <td> 0.616650</td>\n",
" <td> 0.053957</td>\n",
" <td> 0.092497</td>\n",
" <td> 0.231244</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87 </th>\n",
" <td> 408600</td>\n",
" <td> 5232</td>\n",
" <td> 3218</td>\n",
" <td> 188</td>\n",
" <td> 147</td>\n",
" <td> 1623</td>\n",
" <td> 0.615061</td>\n",
" <td> 0.035933</td>\n",
" <td> 0.028096</td>\n",
" <td> 0.310206</td>\n",
" </tr>\n",
" <tr>\n",
" <th>144</th>\n",
" <td> 424002</td>\n",
" <td> 1998</td>\n",
" <td> 1228</td>\n",
" <td> 389</td>\n",
" <td> 150</td>\n",
" <td> 268</td>\n",
" <td> 0.614615</td>\n",
" <td> 0.194695</td>\n",
" <td> 0.075075</td>\n",
" <td> 0.134134</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> 400600</td>\n",
" <td> 1707</td>\n",
" <td> 1037</td>\n",
" <td> 446</td>\n",
" <td> 98</td>\n",
" <td> 148</td>\n",
" <td> 0.607499</td>\n",
" <td> 0.261277</td>\n",
" <td> 0.057411</td>\n",
" <td> 0.086702</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15 </th>\n",
" <td> 401600</td>\n",
" <td> 1933</td>\n",
" <td> 1170</td>\n",
" <td> 263</td>\n",
" <td> 164</td>\n",
" <td> 322</td>\n",
" <td> 0.605277</td>\n",
" <td> 0.136058</td>\n",
" <td> 0.084842</td>\n",
" <td> 0.166580</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> 400800</td>\n",
" <td> 3368</td>\n",
" <td> 1990</td>\n",
" <td> 792</td>\n",
" <td> 336</td>\n",
" <td> 301</td>\n",
" <td> 0.590855</td>\n",
" <td> 0.235154</td>\n",
" <td> 0.099762</td>\n",
" <td> 0.089371</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27 </th>\n",
" <td> 402800</td>\n",
" <td> 1910</td>\n",
" <td> 1122</td>\n",
" <td> 354</td>\n",
" <td> 280</td>\n",
" <td> 154</td>\n",
" <td> 0.587435</td>\n",
" <td> 0.185340</td>\n",
" <td> 0.146597</td>\n",
" <td> 0.080628</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84 </th>\n",
" <td> 408300</td>\n",
" <td> 4799</td>\n",
" <td> 2816</td>\n",
" <td> 1075</td>\n",
" <td> 249</td>\n",
" <td> 712</td>\n",
" <td> 0.586789</td>\n",
" <td> 0.224005</td>\n",
" <td> 0.051886</td>\n",
" <td> 0.148364</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93 </th>\n",
" <td> 409200</td>\n",
" <td> 3111</td>\n",
" <td> 1786</td>\n",
" <td> 78</td>\n",
" <td> 142</td>\n",
" <td> 1057</td>\n",
" <td> 0.574092</td>\n",
" <td> 0.025072</td>\n",
" <td> 0.045644</td>\n",
" <td> 0.339762</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86 </th>\n",
" <td> 408500</td>\n",
" <td> 5307</td>\n",
" <td> 3037</td>\n",
" <td> 139</td>\n",
" <td> 156</td>\n",
" <td> 1924</td>\n",
" <td> 0.572263</td>\n",
" <td> 0.026192</td>\n",
" <td> 0.029395</td>\n",
" <td> 0.362540</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88 </th>\n",
" <td> 408700</td>\n",
" <td> 7504</td>\n",
" <td> 4270</td>\n",
" <td> 402</td>\n",
" <td> 294</td>\n",
" <td> 2541</td>\n",
" <td> 0.569030</td>\n",
" <td> 0.053571</td>\n",
" <td> 0.039179</td>\n",
" <td> 0.338619</td>\n",
" </tr>\n",
" <tr>\n",
" <th>134</th>\n",
" <td> 423300</td>\n",
" <td> 3468</td>\n",
" <td> 1929</td>\n",
" <td> 960</td>\n",
" <td> 240</td>\n",
" <td> 405</td>\n",
" <td> 0.556228</td>\n",
" <td> 0.276817</td>\n",
" <td> 0.069204</td>\n",
" <td> 0.116782</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td> 410400</td>\n",
" <td> 3366</td>\n",
" <td> 1842</td>\n",
" <td> 224</td>\n",
" <td> 115</td>\n",
" <td> 1195</td>\n",
" <td> 0.547237</td>\n",
" <td> 0.066548</td>\n",
" <td> 0.034165</td>\n",
" <td> 0.355021</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97 </th>\n",
" <td> 409600</td>\n",
" <td> 5235</td>\n",
" <td> 2799</td>\n",
" <td> 144</td>\n",
" <td> 156</td>\n",
" <td> 2060</td>\n",
" <td> 0.534670</td>\n",
" <td> 0.027507</td>\n",
" <td> 0.029799</td>\n",
" <td> 0.393505</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12 </th>\n",
" <td> 401300</td>\n",
" <td> 2810</td>\n",
" <td> 1468</td>\n",
" <td> 782</td>\n",
" <td> 349</td>\n",
" <td> 249</td>\n",
" <td> 0.522420</td>\n",
" <td> 0.278292</td>\n",
" <td> 0.124199</td>\n",
" <td> 0.088612</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16 </th>\n",
" <td> 401700</td>\n",
" <td> 1878</td>\n",
" <td> 979</td>\n",
" <td> 263</td>\n",
" <td> 103</td>\n",
" <td> 568</td>\n",
" <td> 0.521299</td>\n",
" <td> 0.140043</td>\n",
" <td> 0.054846</td>\n",
" <td> 0.302449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td> 410300</td>\n",
" <td> 3728</td>\n",
" <td> 1938</td>\n",
" <td> 123</td>\n",
" <td> 133</td>\n",
" <td> 1490</td>\n",
" <td> 0.519850</td>\n",
" <td> 0.032994</td>\n",
" <td> 0.035676</td>\n",
" <td> 0.399678</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101</th>\n",
" <td> 410000</td>\n",
" <td> 2846</td>\n",
" <td> 1435</td>\n",
" <td> 1100</td>\n",
" <td> 189</td>\n",
" <td> 173</td>\n",
" <td> 0.504216</td>\n",
" <td> 0.386507</td>\n",
" <td> 0.066409</td>\n",
" <td> 0.060787</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79 </th>\n",
" <td> 407800</td>\n",
" <td> 2453</td>\n",
" <td> 1174</td>\n",
" <td> 794</td>\n",
" <td> 225</td>\n",
" <td> 282</td>\n",
" <td> 0.478598</td>\n",
" <td> 0.323685</td>\n",
" <td> 0.091724</td>\n",
" <td> 0.114961</td>\n",
" </tr>\n",
" <tr>\n",
" <th>143</th>\n",
" <td> 424001</td>\n",
" <td> 3875</td>\n",
" <td> 1821</td>\n",
" <td> 964</td>\n",
" <td> 323</td>\n",
" <td> 810</td>\n",
" <td> 0.469935</td>\n",
" <td> 0.248774</td>\n",
" <td> 0.083355</td>\n",
" <td> 0.209032</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90 </th>\n",
" <td> 408900</td>\n",
" <td> 3339</td>\n",
" <td> 1569</td>\n",
" <td> 135</td>\n",
" <td> 79</td>\n",
" <td> 1520</td>\n",
" <td> 0.469901</td>\n",
" <td> 0.040431</td>\n",
" <td> 0.023660</td>\n",
" <td> 0.455226</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77 </th>\n",
" <td> 407600</td>\n",
" <td> 6681</td>\n",
" <td> 3115</td>\n",
" <td> 691</td>\n",
" <td> 761</td>\n",
" <td> 2033</td>\n",
" <td> 0.466248</td>\n",
" <td> 0.103428</td>\n",
" <td> 0.113905</td>\n",
" <td> 0.304296</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89 </th>\n",
" <td> 408800</td>\n",
" <td> 5174</td>\n",
" <td> 2396</td>\n",
" <td> 133</td>\n",
" <td> 628</td>\n",
" <td> 1981</td>\n",
" <td> 0.463085</td>\n",
" <td> 0.025705</td>\n",
" <td> 0.121376</td>\n",
" <td> 0.382876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35 </th>\n",
" <td> 403600</td>\n",
" <td> 4400</td>\n",
" <td> 2028</td>\n",
" <td> 1609</td>\n",
" <td> 510</td>\n",
" <td> 316</td>\n",
" <td> 0.460909</td>\n",
" <td> 0.365682</td>\n",
" <td> 0.115909</td>\n",
" <td> 0.071818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25 </th>\n",
" <td> 402600</td>\n",
" <td> 977</td>\n",
" <td> 439</td>\n",
" <td> 75</td>\n",
" <td> 416</td>\n",
" <td> 46</td>\n",
" <td> 0.449335</td>\n",
" <td> 0.076766</td>\n",
" <td> 0.425793</td>\n",
" <td> 0.047083</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> 400500</td>\n",
" <td> 3410</td>\n",
" <td> 1510</td>\n",
" <td> 1387</td>\n",
" <td> 216</td>\n",
" <td> 363</td>\n",
" <td> 0.442815</td>\n",
" <td> 0.406745</td>\n",
" <td> 0.063343</td>\n",
" <td> 0.106452</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95 </th>\n",
" <td> 409400</td>\n",
" <td> 4455</td>\n",
" <td> 1929</td>\n",
" <td> 133</td>\n",
" <td> 62</td>\n",
" <td> 2255</td>\n",
" <td> 0.432997</td>\n",
" <td> 0.029854</td>\n",
" <td> 0.013917</td>\n",
" <td> 0.506173</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94 </th>\n",
" <td> 409300</td>\n",
" <td> 5492</td>\n",
" <td> 2355</td>\n",
" <td> 224</td>\n",
" <td> 178</td>\n",
" <td> 2679</td>\n",
" <td> 0.428806</td>\n",
" <td> 0.040787</td>\n",
" <td> 0.032411</td>\n",
" <td> 0.487800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57 </th>\n",
" <td> 405700</td>\n",
" <td> 3757</td>\n",
" <td> 1508</td>\n",
" <td> 392</td>\n",
" <td> 1229</td>\n",
" <td> 660</td>\n",
" <td> 0.401384</td>\n",
" <td> 0.104339</td>\n",
" <td> 0.327123</td>\n",
" <td> 0.175672</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96 </th>\n",
" <td> 409500</td>\n",
" <td> 3555</td>\n",
" <td> 1425</td>\n",
" <td> 116</td>\n",
" <td> 190</td>\n",
" <td> 1795</td>\n",
" <td> 0.400844</td>\n",
" <td> 0.032630</td>\n",
" <td> 0.053446</td>\n",
" <td> 0.504923</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34 </th>\n",
" <td> 403500</td>\n",
" <td> 6346</td>\n",
" <td> 2540</td>\n",
" <td> 2321</td>\n",
" <td> 1056</td>\n",
" <td> 515</td>\n",
" <td> 0.400252</td>\n",
" <td> 0.365742</td>\n",
" <td> 0.166404</td>\n",
" <td> 0.081153</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>52 rows \u00d7 10 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 241,
"text": [
" tract Total Pop African-American, not Hispanic White, not Hispanic \\\n",
"22 402300 453 364 58 \n",
"20 402100 1161 927 21 \n",
"99 409800 3250 2542 370 \n",
"24 402500 1779 1369 86 \n",
"23 402400 2588 1978 166 \n",
"17 401800 1953 1490 88 \n",
"9 401000 5709 4176 649 \n",
"14 401500 2413 1717 314 \n",
"102 410100 2784 1968 268 \n",
"6 400700 4451 3104 879 \n",
"92 409100 2163 1489 30 \n",
"13 401400 4765 3167 342 \n",
"78 407700 4599 3036 826 \n",
"85 408400 3782 2496 138 \n",
"83 408200 4388 2882 753 \n",
"103 410200 3550 2298 183 \n",
"91 409000 3327 2132 141 \n",
"8 400900 2456 1570 575 \n",
"98 409700 5208 3281 222 \n",
"100 409900 3499 2199 994 \n",
"21 402200 1844 1138 181 \n",
"26 402700 1946 1200 105 \n",
"87 408600 5232 3218 188 \n",
"144 424002 1998 1228 389 \n",
"5 400600 1707 1037 446 \n",
"15 401600 1933 1170 263 \n",
"7 400800 3368 1990 792 \n",
"27 402800 1910 1122 354 \n",
"84 408300 4799 2816 1075 \n",
"93 409200 3111 1786 78 \n",
"86 408500 5307 3037 139 \n",
"88 408700 7504 4270 402 \n",
"134 423300 3468 1929 960 \n",
"105 410400 3366 1842 224 \n",
"97 409600 5235 2799 144 \n",
"12 401300 2810 1468 782 \n",
"16 401700 1878 979 263 \n",
"104 410300 3728 1938 123 \n",
"101 410000 2846 1435 1100 \n",
"79 407800 2453 1174 794 \n",
"143 424001 3875 1821 964 \n",
"90 408900 3339 1569 135 \n",
"77 407600 6681 3115 691 \n",
"89 408800 5174 2396 133 \n",
"35 403600 4400 2028 1609 \n",
"25 402600 977 439 75 \n",
"4 400500 3410 1510 1387 \n",
"95 409400 4455 1929 133 \n",
"94 409300 5492 2355 224 \n",
"57 405700 3757 1508 392 \n",
"96 409500 3555 1425 116 \n",
"34 403500 6346 2540 2321 \n",
"\n",
" Asian, not Hispanic Hispanic AfAm_ratio_2000 White_ratio_2000 \\\n",
"22 19 15 0.803532 0.128035 \n",
"20 161 58 0.798450 0.018088 \n",
"99 97 249 0.782154 0.113846 \n",
"24 231 108 0.769533 0.048342 \n",
"23 298 171 0.764297 0.064142 \n",
"17 39 308 0.762929 0.045059 \n",
"9 404 547 0.731477 0.113680 \n",
"14 142 255 0.711562 0.130128 \n",
"102 122 448 0.706897 0.096264 \n",
"6 221 299 0.697371 0.197484 \n",
"92 39 607 0.688396 0.013870 \n",
"13 558 706 0.664638 0.071773 \n",
"78 226 530 0.660144 0.179604 \n",
"85 91 1026 0.659968 0.036489 \n",
"83 206 560 0.656791 0.171604 \n",
"103 158 891 0.647324 0.051549 \n",
"91 79 968 0.640818 0.042381 \n",
"8 135 202 0.639251 0.234121 \n",
"98 180 1471 0.629992 0.042627 \n",
"100 210 149 0.628465 0.284081 \n",
"21 117 411 0.617137 0.098156 \n",
"26 180 450 0.616650 0.053957 \n",
"87 147 1623 0.615061 0.035933 \n",
"144 150 268 0.614615 0.194695 \n",
"5 98 148 0.607499 0.261277 \n",
"15 164 322 0.605277 0.136058 \n",
"7 336 301 0.590855 0.235154 \n",
"27 280 154 0.587435 0.185340 \n",
"84 249 712 0.586789 0.224005 \n",
"93 142 1057 0.574092 0.025072 \n",
"86 156 1924 0.572263 0.026192 \n",
"88 294 2541 0.569030 0.053571 \n",
"134 240 405 0.556228 0.276817 \n",
"105 115 1195 0.547237 0.066548 \n",
"97 156 2060 0.534670 0.027507 \n",
"12 349 249 0.522420 0.278292 \n",
"16 103 568 0.521299 0.140043 \n",
"104 133 1490 0.519850 0.032994 \n",
"101 189 173 0.504216 0.386507 \n",
"79 225 282 0.478598 0.323685 \n",
"143 323 810 0.469935 0.248774 \n",
"90 79 1520 0.469901 0.040431 \n",
"77 761 2033 0.466248 0.103428 \n",
"89 628 1981 0.463085 0.025705 \n",
"35 510 316 0.460909 0.365682 \n",
"25 416 46 0.449335 0.076766 \n",
"4 216 363 0.442815 0.406745 \n",
"95 62 2255 0.432997 0.029854 \n",
"94 178 2679 0.428806 0.040787 \n",
"57 1229 660 0.401384 0.104339 \n",
"96 190 1795 0.400844 0.032630 \n",
"34 1056 515 0.400252 0.365742 \n",
"\n",
" Asian_ratio_2000 Hispanic_ratio_2000 \n",
"22 0.041943 0.033113 \n",
"20 0.138674 0.049957 \n",
"99 0.029846 0.076615 \n",
"24 0.129848 0.060708 \n",
"23 0.115147 0.066074 \n",
"17 0.019969 0.157706 \n",
"9 0.070765 0.095814 \n",
"14 0.058848 0.105678 \n",
"102 0.043822 0.160920 \n",
"6 0.049652 0.067176 \n",
"92 0.018031 0.280629 \n",
"13 0.117104 0.148164 \n",
"78 0.049141 0.115242 \n",
"85 0.024061 0.271285 \n",
"83 0.046946 0.127621 \n",
"103 0.044507 0.250986 \n",
"91 0.023745 0.290953 \n",
"8 0.054967 0.082248 \n",
"98 0.034562 0.282450 \n",
"100 0.060017 0.042584 \n",
"21 0.063449 0.222885 \n",
"26 0.092497 0.231244 \n",
"87 0.028096 0.310206 \n",
"144 0.075075 0.134134 \n",
"5 0.057411 0.086702 \n",
"15 0.084842 0.166580 \n",
"7 0.099762 0.089371 \n",
"27 0.146597 0.080628 \n",
"84 0.051886 0.148364 \n",
"93 0.045644 0.339762 \n",
"86 0.029395 0.362540 \n",
"88 0.039179 0.338619 \n",
"134 0.069204 0.116782 \n",
"105 0.034165 0.355021 \n",
"97 0.029799 0.393505 \n",
"12 0.124199 0.088612 \n",
"16 0.054846 0.302449 \n",
"104 0.035676 0.399678 \n",
"101 0.066409 0.060787 \n",
"79 0.091724 0.114961 \n",
"143 0.083355 0.209032 \n",
"90 0.023660 0.455226 \n",
"77 0.113905 0.304296 \n",
"89 0.121376 0.382876 \n",
"35 0.115909 0.071818 \n",
"25 0.425793 0.047083 \n",
"4 0.063343 0.106452 \n",
"95 0.013917 0.506173 \n",
"94 0.032411 0.487800 \n",
"57 0.327123 0.175672 \n",
"96 0.053446 0.504923 \n",
"34 0.166404 0.081153 \n",
"\n",
"[52 rows x 10 columns]"
]
}
],
"prompt_number": 241
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#P0010001/Total Pop, P0050004/African-American Not Hispanic, P0050010/Hispanic, \n",
"#P0050006/Asian, not Hispanic P0050003/White, not Hispanic \n",
"o_tracts_2010 = [tract for tract in tracts(variables=\"NAME,P0010001,P0050004,P0050010,P0050006,P0050003\")]\n",
"\n",
"#put list into dataframe\n",
"tract_df_2010 = pd.DataFrame(o_tracts_2010)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 245
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#make all population column values into integers\n",
"tract_df_2010.P0050004 = tract_df_2010.P0050004.astype(float)\n",
"tract_df_2010.P0050003 = tract_df_2010.P0050003.astype(float)\n",
"tract_df_2010.P0050006 = tract_df_2010.P0050006.astype(float)\n",
"tract_df_2010.P0010001 = tract_df_2010.P0010001.astype(float)\n",
"tract_df_2010.P0050010 = tract_df_2010.P0050010.astype(float)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 246
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#create new descriptively named columns with values population by race/ethnicity\n",
"tract_df_2010['African-American, not Hispanic'] = tract_df_2010['P0050004']\n",
"tract_df_2010['White, not Hispanic'] = tract_df_2010['P0050003']\n",
"tract_df_2010['Asian, not Hispanic'] = tract_df_2010['P0050006']\n",
"tract_df_2010['Total Pop'] = tract_df_2010['P0010001']\n",
"tract_df_2010['Hispanic'] = tract_df_2010['P0050010']\n",
"\n",
"#show only columns that have legible names; set index by tract\n",
"alameda_tracts_df_2010 = tract_df_2010[['NAME','tract','Total Pop','African-American, not Hispanic']] \n",
"\n",
"alameda_tracts_df_2010.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>NAME</th>\n",
" <th>tract</th>\n",
" <th>Total Pop</th>\n",
" <th>African-American, not Hispanic</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> Census Tract 4001</td>\n",
" <td> 400100</td>\n",
" <td> 2937</td>\n",
" <td> 140</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> Census Tract 4002</td>\n",
" <td> 400200</td>\n",
" <td> 1974</td>\n",
" <td> 31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> Census Tract 4003</td>\n",
" <td> 400300</td>\n",
" <td> 4865</td>\n",
" <td> 512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> Census Tract 4004</td>\n",
" <td> 400400</td>\n",
" <td> 3703</td>\n",
" <td> 448</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> Census Tract 4005</td>\n",
" <td> 400500</td>\n",
" <td> 3517</td>\n",
" <td> 933</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 4 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 247,
"text": [
" NAME tract Total Pop African-American, not Hispanic\n",
"0 Census Tract 4001 400100 2937 140\n",
"1 Census Tract 4002 400200 1974 31\n",
"2 Census Tract 4003 400300 4865 512\n",
"3 Census Tract 4004 400400 3703 448\n",
"4 Census Tract 4005 400500 3517 933\n",
"\n",
"[5 rows x 4 columns]"
]
}
],
"prompt_number": 247
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#create new column for ratio of African-American community in each tract\n",
"alameda_tracts_df_2010['AfAm_ratio_2010'] = tract_df_2010['P0050004']/tract_df_2010['P0010001']\n",
"\n",
"alameda_tracts_df_2010.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>NAME</th>\n",
" <th>tract</th>\n",
" <th>Total Pop</th>\n",
" <th>African-American, not Hispanic</th>\n",
" <th>AfAm_ratio_2010</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> Census Tract 4001</td>\n",
" <td> 400100</td>\n",
" <td> 2937</td>\n",
" <td> 140</td>\n",
" <td> 0.047668</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> Census Tract 4002</td>\n",
" <td> 400200</td>\n",
" <td> 1974</td>\n",
" <td> 31</td>\n",
" <td> 0.015704</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> Census Tract 4003</td>\n",
" <td> 400300</td>\n",
" <td> 4865</td>\n",
" <td> 512</td>\n",
" <td> 0.105242</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> Census Tract 4004</td>\n",
" <td> 400400</td>\n",
" <td> 3703</td>\n",
" <td> 448</td>\n",
" <td> 0.120983</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> Census Tract 4005</td>\n",
" <td> 400500</td>\n",
" <td> 3517</td>\n",
" <td> 933</td>\n",
" <td> 0.265283</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 5 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 248,
"text": [
" NAME tract Total Pop African-American, not Hispanic \\\n",
"0 Census Tract 4001 400100 2937 140 \n",
"1 Census Tract 4002 400200 1974 31 \n",
"2 Census Tract 4003 400300 4865 512 \n",
"3 Census Tract 4004 400400 3703 448 \n",
"4 Census Tract 4005 400500 3517 933 \n",
"\n",
" AfAm_ratio_2010 \n",
"0 0.047668 \n",
"1 0.015704 \n",
"2 0.105242 \n",
"3 0.120983 \n",
"4 0.265283 \n",
"\n",
"[5 rows x 5 columns]"
]
}
],
"prompt_number": 248
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#alameda_tracts_df_2010.set_index(['tract'])\n",
"\n",
"#highest proportion of African-American community in Oakland\n",
"common_tracts = set(alameda_tracts_df_2010['tract']) & set(alameda_tracts_2000_df['tract'])\n",
"\n",
"len(set(alameda_tracts_df_2010['tract']))\n",
"\n",
"len(set(alameda_tracts_2000_df['tract']))\n",
"\n",
"# in common but we have to check if they're geographically related to each other\n",
"len(common_tracts) \n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 249,
"text": [
"279"
]
}
],
"prompt_number": 249
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#sort by highest African-American populations in 2010 tracts\n",
"tracts_w_ratio_2010 = alameda_tracts_df_2010.sort('AfAm_ratio_2010', ascending=False)\n",
"\n",
"#32 tracts have an African-American population greater than 40% as compared to 52 in 2000 \n",
"tracts_w_ratio_2010[(tracts_w_ratio_2010['AfAm_ratio_2010']>.4) & (tracts_w_ratio_2010['Total Pop'] > 1)] "
],
"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>NAME</th>\n",
" <th>tract</th>\n",
" <th>Total Pop</th>\n",
" <th>African-American, not Hispanic</th>\n",
" <th>AfAm_ratio_2010</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>20 </th>\n",
" <td> Census Tract 4025</td>\n",
" <td> 402500</td>\n",
" <td> 1784</td>\n",
" <td> 1191</td>\n",
" <td> 0.667601</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td> Census Tract 4098</td>\n",
" <td> 409800</td>\n",
" <td> 2887</td>\n",
" <td> 1884</td>\n",
" <td> 0.652581</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109</th>\n",
" <td> Census Tract 4105</td>\n",
" <td> 410500</td>\n",
" <td> 2193</td>\n",
" <td> 1360</td>\n",
" <td> 0.620155</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td> Census Tract 4101</td>\n",
" <td> 410100</td>\n",
" <td> 2406</td>\n",
" <td> 1482</td>\n",
" <td> 0.615960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19 </th>\n",
" <td> Census Tract 4024</td>\n",
" <td> 402400</td>\n",
" <td> 2351</td>\n",
" <td> 1358</td>\n",
" <td> 0.577627</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17 </th>\n",
" <td> Census Tract 4018</td>\n",
" <td> 401800</td>\n",
" <td> 1703</td>\n",
" <td> 977</td>\n",
" <td> 0.573693</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22 </th>\n",
" <td> Census Tract 4027</td>\n",
" <td> 402700</td>\n",
" <td> 1569</td>\n",
" <td> 881</td>\n",
" <td> 0.561504</td>\n",
" </tr>\n",
" <tr>\n",
" <th>106</th>\n",
" <td> Census Tract 4102</td>\n",
" <td> 410200</td>\n",
" <td> 3062</td>\n",
" <td> 1642</td>\n",
" <td> 0.536251</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td> Census Tract 4099</td>\n",
" <td> 409900</td>\n",
" <td> 3308</td>\n",
" <td> 1756</td>\n",
" <td> 0.530834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14 </th>\n",
" <td> Census Tract 4015</td>\n",
" <td> 401500</td>\n",
" <td> 2630</td>\n",
" <td> 1392</td>\n",
" <td> 0.529278</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86 </th>\n",
" <td> Census Tract 4082</td>\n",
" <td> 408200</td>\n",
" <td> 4054</td>\n",
" <td> 2144</td>\n",
" <td> 0.528860</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88 </th>\n",
" <td> Census Tract 4084</td>\n",
" <td> 408400</td>\n",
" <td> 3323</td>\n",
" <td> 1702</td>\n",
" <td> 0.512188</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81 </th>\n",
" <td> Census Tract 4077</td>\n",
" <td> 407700</td>\n",
" <td> 4109</td>\n",
" <td> 2094</td>\n",
" <td> 0.509613</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> Census Tract 4010</td>\n",
" <td> 401000</td>\n",
" <td> 5678</td>\n",
" <td> 2848</td>\n",
" <td> 0.501585</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> Census Tract 4007</td>\n",
" <td> 400700</td>\n",
" <td> 4206</td>\n",
" <td> 2068</td>\n",
" <td> 0.491679</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101</th>\n",
" <td> Census Tract 4097</td>\n",
" <td> 409700</td>\n",
" <td> 4696</td>\n",
" <td> 2305</td>\n",
" <td> 0.490843</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td> Census Tract 4100</td>\n",
" <td> 410000</td>\n",
" <td> 2805</td>\n",
" <td> 1369</td>\n",
" <td> 0.488057</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13 </th>\n",
" <td> Census Tract 4014</td>\n",
" <td> 401400</td>\n",
" <td> 4314</td>\n",
" <td> 2090</td>\n",
" <td> 0.484469</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15 </th>\n",
" <td> Census Tract 4016</td>\n",
" <td> 401600</td>\n",
" <td> 2163</td>\n",
" <td> 1005</td>\n",
" <td> 0.464632</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89 </th>\n",
" <td> Census Tract 4085</td>\n",
" <td> 408500</td>\n",
" <td> 4972</td>\n",
" <td> 2265</td>\n",
" <td> 0.455551</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90 </th>\n",
" <td> Census Tract 4086</td>\n",
" <td> 408600</td>\n",
" <td> 5492</td>\n",
" <td> 2476</td>\n",
" <td> 0.450838</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87 </th>\n",
" <td> Census Tract 4083</td>\n",
" <td> 408300</td>\n",
" <td> 4167</td>\n",
" <td> 1875</td>\n",
" <td> 0.449964</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> Census Tract 4009</td>\n",
" <td> 400900</td>\n",
" <td> 2302</td>\n",
" <td> 1005</td>\n",
" <td> 0.436577</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91 </th>\n",
" <td> Census Tract 4087</td>\n",
" <td> 408700</td>\n",
" <td> 7207</td>\n",
" <td> 3142</td>\n",
" <td> 0.435965</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94 </th>\n",
" <td> Census Tract 4090</td>\n",
" <td> 409000</td>\n",
" <td> 3552</td>\n",
" <td> 1539</td>\n",
" <td> 0.433277</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td> Census Tract 4240.02</td>\n",
" <td> 424002</td>\n",
" <td> 2172</td>\n",
" <td> 906</td>\n",
" <td> 0.417127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92 </th>\n",
" <td> Census Tract 4088</td>\n",
" <td> 408800</td>\n",
" <td> 5547</td>\n",
" <td> 2295</td>\n",
" <td> 0.413737</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23 </th>\n",
" <td> Census Tract 4028</td>\n",
" <td> 402800</td>\n",
" <td> 3345</td>\n",
" <td> 1378</td>\n",
" <td> 0.411958</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95 </th>\n",
" <td> Census Tract 4091</td>\n",
" <td> 409100</td>\n",
" <td> 2255</td>\n",
" <td> 924</td>\n",
" <td> 0.409756</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> Census Tract 4008</td>\n",
" <td> 400800</td>\n",
" <td> 3594</td>\n",
" <td> 1463</td>\n",
" <td> 0.407067</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108</th>\n",
" <td> Census Tract 4104</td>\n",
" <td> 410400</td>\n",
" <td> 3792</td>\n",
" <td> 1540</td>\n",
" <td> 0.406118</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96 </th>\n",
" <td> Census Tract 4092</td>\n",
" <td> 409200</td>\n",
" <td> 3152</td>\n",
" <td> 1263</td>\n",
" <td> 0.400698</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>32 rows \u00d7 5 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 254,
"text": [
" NAME tract Total Pop African-American, not Hispanic \\\n",
"20 Census Tract 4025 402500 1784 1191 \n",
"102 Census Tract 4098 409800 2887 1884 \n",
"109 Census Tract 4105 410500 2193 1360 \n",
"105 Census Tract 4101 410100 2406 1482 \n",
"19 Census Tract 4024 402400 2351 1358 \n",
"17 Census Tract 4018 401800 1703 977 \n",
"22 Census Tract 4027 402700 1569 881 \n",
"106 Census Tract 4102 410200 3062 1642 \n",
"103 Census Tract 4099 409900 3308 1756 \n",
"14 Census Tract 4015 401500 2630 1392 \n",
"86 Census Tract 4082 408200 4054 2144 \n",
"88 Census Tract 4084 408400 3323 1702 \n",
"81 Census Tract 4077 407700 4109 2094 \n",
"9 Census Tract 4010 401000 5678 2848 \n",
"6 Census Tract 4007 400700 4206 2068 \n",
"101 Census Tract 4097 409700 4696 2305 \n",
"104 Census Tract 4100 410000 2805 1369 \n",
"13 Census Tract 4014 401400 4314 2090 \n",
"15 Census Tract 4016 401600 2163 1005 \n",
"89 Census Tract 4085 408500 4972 2265 \n",
"90 Census Tract 4086 408600 5492 2476 \n",
"87 Census Tract 4083 408300 4167 1875 \n",
"8 Census Tract 4009 400900 2302 1005 \n",
"91 Census Tract 4087 408700 7207 3142 \n",
"94 Census Tract 4090 409000 3552 1539 \n",
"148 Census Tract 4240.02 424002 2172 906 \n",
"92 Census Tract 4088 408800 5547 2295 \n",
"23 Census Tract 4028 402800 3345 1378 \n",
"95 Census Tract 4091 409100 2255 924 \n",
"7 Census Tract 4008 400800 3594 1463 \n",
"108 Census Tract 4104 410400 3792 1540 \n",
"96 Census Tract 4092 409200 3152 1263 \n",
"\n",
" AfAm_ratio_2010 \n",
"20 0.667601 \n",
"102 0.652581 \n",
"109 0.620155 \n",
"105 0.615960 \n",
"19 0.577627 \n",
"17 0.573693 \n",
"22 0.561504 \n",
"106 0.536251 \n",
"103 0.530834 \n",
"14 0.529278 \n",
"86 0.528860 \n",
"88 0.512188 \n",
"81 0.509613 \n",
"9 0.501585 \n",
"6 0.491679 \n",
"101 0.490843 \n",
"104 0.488057 \n",
"13 0.484469 \n",
"15 0.464632 \n",
"89 0.455551 \n",
"90 0.450838 \n",
"87 0.449964 \n",
"8 0.436577 \n",
"91 0.435965 \n",
"94 0.433277 \n",
"148 0.417127 \n",
"92 0.413737 \n",
"23 0.411958 \n",
"95 0.409756 \n",
"7 0.407067 \n",
"108 0.406118 \n",
"96 0.400698 \n",
"\n",
"[32 rows x 5 columns]"
]
}
],
"prompt_number": 254
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cols = ['tract', 'Total Pop_00', 'Total Pop_10','African-American, not Hispanic_00', \\\n",
" 'African-American, not Hispanic_10','AfAm_ratio_2000', 'AfAm_ratio_2010']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 92
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"combined_df = pd.merge(tracts_w_ratio_2010, tracts_w_ratio_2000, on='tract', sort=True,\n",
" suffixes=('_10', '_00'), copy=True)\n",
"\n",
"combined_df[(cols)].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>tract</th>\n",
" <th>Total Pop_00</th>\n",
" <th>Total Pop_10</th>\n",
" <th>African-American, not Hispanic_00</th>\n",
" <th>African-American, not Hispanic_10</th>\n",
" <th>AfAm_ratio_2000</th>\n",
" <th>AfAm_ratio_2010</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 400100</td>\n",
" <td> 2498</td>\n",
" <td> 2937</td>\n",
" <td> 125</td>\n",
" <td> 140</td>\n",
" <td> 0.050040</td>\n",
" <td> 0.047668</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 400200</td>\n",
" <td> 1910</td>\n",
" <td> 1974</td>\n",
" <td> 71</td>\n",
" <td> 31</td>\n",
" <td> 0.037173</td>\n",
" <td> 0.015704</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 400300</td>\n",
" <td> 4878</td>\n",
" <td> 4865</td>\n",
" <td> 768</td>\n",
" <td> 512</td>\n",
" <td> 0.157442</td>\n",
" <td> 0.105242</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 400400</td>\n",
" <td> 3659</td>\n",
" <td> 3703</td>\n",
" <td> 671</td>\n",
" <td> 448</td>\n",
" <td> 0.183383</td>\n",
" <td> 0.120983</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 400500</td>\n",
" <td> 3410</td>\n",
" <td> 3517</td>\n",
" <td> 1510</td>\n",
" <td> 933</td>\n",
" <td> 0.442815</td>\n",
" <td> 0.265283</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 7 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 93,
"text": [
" tract Total Pop_00 Total Pop_10 African-American, not Hispanic_00 \\\n",
"0 400100 2498 2937 125 \n",
"1 400200 1910 1974 71 \n",
"2 400300 4878 4865 768 \n",
"3 400400 3659 3703 671 \n",
"4 400500 3410 3517 1510 \n",
"\n",
" African-American, not Hispanic_10 AfAm_ratio_2000 AfAm_ratio_2010 \n",
"0 140 0.050040 0.047668 \n",
"1 31 0.037173 0.015704 \n",
"2 512 0.157442 0.105242 \n",
"3 448 0.183383 0.120983 \n",
"4 933 0.442815 0.265283 \n",
"\n",
"[5 rows x 7 columns]"
]
}
],
"prompt_number": 93
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"combined_df['percent change in AfAm'] = combined_df['AfAm_ratio_2010'] - \\\n",
"combined_df['AfAm_ratio_2000']\n",
"\n",
"cols.append('percent change in AfAm')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 94
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print cols"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"['tract', 'Total Pop_00', 'Total Pop_10', 'African-American, not Hispanic_00', 'African-American, not Hispanic_10', 'AfAm_ratio_2000', 'AfAm_ratio_2010', 'percent change in AfAm']\n"
]
}
],
"prompt_number": 95
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#showing percent change of AfAm community by tract\n",
"combined_df[(cols)].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>tract</th>\n",
" <th>Total Pop_00</th>\n",
" <th>Total Pop_10</th>\n",
" <th>African-American, not Hispanic_00</th>\n",
" <th>African-American, not Hispanic_10</th>\n",
" <th>AfAm_ratio_2000</th>\n",
" <th>AfAm_ratio_2010</th>\n",
" <th>percent change in AfAm</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 400100</td>\n",
" <td> 2498</td>\n",
" <td> 2937</td>\n",
" <td> 125</td>\n",
" <td> 140</td>\n",
" <td> 0.050040</td>\n",
" <td> 0.047668</td>\n",
" <td>-0.002372</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 400200</td>\n",
" <td> 1910</td>\n",
" <td> 1974</td>\n",
" <td> 71</td>\n",
" <td> 31</td>\n",
" <td> 0.037173</td>\n",
" <td> 0.015704</td>\n",
" <td>-0.021469</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 400300</td>\n",
" <td> 4878</td>\n",
" <td> 4865</td>\n",
" <td> 768</td>\n",
" <td> 512</td>\n",
" <td> 0.157442</td>\n",
" <td> 0.105242</td>\n",
" <td>-0.052200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 400400</td>\n",
" <td> 3659</td>\n",
" <td> 3703</td>\n",
" <td> 671</td>\n",
" <td> 448</td>\n",
" <td> 0.183383</td>\n",
" <td> 0.120983</td>\n",
" <td>-0.062400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 400500</td>\n",
" <td> 3410</td>\n",
" <td> 3517</td>\n",
" <td> 1510</td>\n",
" <td> 933</td>\n",
" <td> 0.442815</td>\n",
" <td> 0.265283</td>\n",
" <td>-0.177532</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 8 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 118,
"text": [
" tract Total Pop_00 Total Pop_10 African-American, not Hispanic_00 \\\n",
"0 400100 2498 2937 125 \n",
"1 400200 1910 1974 71 \n",
"2 400300 4878 4865 768 \n",
"3 400400 3659 3703 671 \n",
"4 400500 3410 3517 1510 \n",
"\n",
" African-American, not Hispanic_10 AfAm_ratio_2000 AfAm_ratio_2010 \\\n",
"0 140 0.050040 0.047668 \n",
"1 31 0.037173 0.015704 \n",
"2 512 0.157442 0.105242 \n",
"3 448 0.183383 0.120983 \n",
"4 933 0.442815 0.265283 \n",
"\n",
" percent change in AfAm \n",
"0 -0.002372 \n",
"1 -0.021469 \n",
"2 -0.052200 \n",
"3 -0.062400 \n",
"4 -0.177532 \n",
"\n",
"[5 rows x 8 columns]"
]
}
],
"prompt_number": 118
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#tract with greatest increase and decrease\n",
"change_range = [combined_df['percent change in AfAm'].max(), combined_df['percent change in AfAm'].min()]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 99,
"text": [
"(0.057652969187467429, -0.27863964908719824)"
]
}
],
"prompt_number": 99
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#tracts in which the negative percent change was the greatest\n",
"neg_perc_change = combined_df[(cols)].sort('percent change in AfAm', ascending=True)[:10]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 106
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Total population in 2010 and 2000 for the tracts where the AfAm community changed the most\n",
"neg_perc_change['Total Pop_10'].sum(), neg_perc_change['Total Pop_00'].sum()\n",
"\n",
"#Change is only about +100 people!"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 112,
"text": [
"(34195.0, 34088)"
]
}
],
"prompt_number": 112
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Total AfAm population in these tracts where the percentage change was greatest\n",
"neg_perc_change['African-American, not Hispanic_10'].sum(), \\\n",
"neg_perc_change['African-American, not Hispanic_00'].sum()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 116,
"text": [
"(13772.0, 21312)"
]
}
],
"prompt_number": 116
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#difference is more than 7 times the change of the overall population (and negative)\n",
"neg_perc_change['African-American, not Hispanic_10'].sum()-neg_perc_change['African-American, not Hispanic_00'].sum()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 117,
"text": [
"-7540.0"
]
}
],
"prompt_number": 117
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#positive percent change (greatest growth in AfAm community)\n",
"combined_df[(cols)].sort('percent change in AfAm', ascending=True).tail(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>tract</th>\n",
" <th>Total Pop_00</th>\n",
" <th>Total Pop_10</th>\n",
" <th>African-American, not Hispanic_00</th>\n",
" <th>African-American, not Hispanic_10</th>\n",
" <th>AfAm_ratio_2000</th>\n",
" <th>AfAm_ratio_2010</th>\n",
" <th>percent change in AfAm</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>195</th>\n",
" <td> 436700</td>\n",
" <td> 2989</td>\n",
" <td> 3284</td>\n",
" <td> 172</td>\n",
" <td> 280</td>\n",
" <td> 0.057544</td>\n",
" <td> 0.085262</td>\n",
" <td> 0.027718</td>\n",
" </tr>\n",
" <tr>\n",
" <th>189</th>\n",
" <td> 436300</td>\n",
" <td> 6378</td>\n",
" <td> 7129</td>\n",
" <td> 628</td>\n",
" <td> 906</td>\n",
" <td> 0.098463</td>\n",
" <td> 0.127087</td>\n",
" <td> 0.028623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>200</th>\n",
" <td> 437300</td>\n",
" <td> 3270</td>\n",
" <td> 3111</td>\n",
" <td> 344</td>\n",
" <td> 418</td>\n",
" <td> 0.105199</td>\n",
" <td> 0.134362</td>\n",
" <td> 0.029163</td>\n",
" </tr>\n",
" <tr>\n",
" <th>171</th>\n",
" <td> 433400</td>\n",
" <td> 6014</td>\n",
" <td> 6305</td>\n",
" <td> 428</td>\n",
" <td> 634</td>\n",
" <td> 0.071167</td>\n",
" <td> 0.100555</td>\n",
" <td> 0.029388</td>\n",
" </tr>\n",
" <tr>\n",
" <th>117</th>\n",
" <td> 422900</td>\n",
" <td> 2416</td>\n",
" <td> 4336</td>\n",
" <td> 135</td>\n",
" <td> 374</td>\n",
" <td> 0.055877</td>\n",
" <td> 0.086255</td>\n",
" <td> 0.030377</td>\n",
" </tr>\n",
" <tr>\n",
" <th>191</th>\n",
" <td> 436402</td>\n",
" <td> 2844</td>\n",
" <td> 2618</td>\n",
" <td> 270</td>\n",
" <td> 330</td>\n",
" <td> 0.094937</td>\n",
" <td> 0.126050</td>\n",
" <td> 0.031114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>169</th>\n",
" <td> 433200</td>\n",
" <td> 6562</td>\n",
" <td> 6897</td>\n",
" <td> 807</td>\n",
" <td> 1067</td>\n",
" <td> 0.122981</td>\n",
" <td> 0.154705</td>\n",
" <td> 0.031724</td>\n",
" </tr>\n",
" <tr>\n",
" <th>156</th>\n",
" <td> 430900</td>\n",
" <td> 4667</td>\n",
" <td> 4681</td>\n",
" <td> 246</td>\n",
" <td> 412</td>\n",
" <td> 0.052711</td>\n",
" <td> 0.088015</td>\n",
" <td> 0.035305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>161</th>\n",
" <td> 432200</td>\n",
" <td> 3939</td>\n",
" <td> 4080</td>\n",
" <td> 660</td>\n",
" <td> 831</td>\n",
" <td> 0.167555</td>\n",
" <td> 0.203676</td>\n",
" <td> 0.036121</td>\n",
" </tr>\n",
" <tr>\n",
" <th>158</th>\n",
" <td> 431100</td>\n",
" <td> 3137</td>\n",
" <td> 3225</td>\n",
" <td> 252</td>\n",
" <td> 445</td>\n",
" <td> 0.080332</td>\n",
" <td> 0.137984</td>\n",
" <td> 0.057653</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10 rows \u00d7 8 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 103,
"text": [
" tract Total Pop_00 Total Pop_10 African-American, not Hispanic_00 \\\n",
"195 436700 2989 3284 172 \n",
"189 436300 6378 7129 628 \n",
"200 437300 3270 3111 344 \n",
"171 433400 6014 6305 428 \n",
"117 422900 2416 4336 135 \n",
"191 436402 2844 2618 270 \n",
"169 433200 6562 6897 807 \n",
"156 430900 4667 4681 246 \n",
"161 432200 3939 4080 660 \n",
"158 431100 3137 3225 252 \n",
"\n",
" African-American, not Hispanic_10 AfAm_ratio_2000 AfAm_ratio_2010 \\\n",
"195 280 0.057544 0.085262 \n",
"189 906 0.098463 0.127087 \n",
"200 418 0.105199 0.134362 \n",
"171 634 0.071167 0.100555 \n",
"117 374 0.055877 0.086255 \n",
"191 330 0.094937 0.126050 \n",
"169 1067 0.122981 0.154705 \n",
"156 412 0.052711 0.088015 \n",
"161 831 0.167555 0.203676 \n",
"158 445 0.080332 0.137984 \n",
"\n",
" percent change in AfAm \n",
"195 0.027718 \n",
"189 0.028623 \n",
"200 0.029163 \n",
"171 0.029388 \n",
"117 0.030377 \n",
"191 0.031114 \n",
"169 0.031724 \n",
"156 0.035305 \n",
"161 0.036121 \n",
"158 0.057653 \n",
"\n",
"[10 rows x 8 columns]"
]
}
],
"prompt_number": 103
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"path = '../df_complete.csv'\n",
"\n",
"crime_df = pd.read_csv(path)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 196
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"del crime_df['Unnamed: 0']\n",
"crime_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>DEPARTMENT</th>\n",
" <th>TRACT00</th>\n",
" <th>TRACT10</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> PETTY THEFT</td>\n",
" <td> 409100</td>\n",
" <td> 409100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> OTHER</td>\n",
" <td> 409700</td>\n",
" <td> 409700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> WEAPONS</td>\n",
" <td> 401100</td>\n",
" <td> 401100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> STOLEN VEHICLE</td>\n",
" <td> 407100</td>\n",
" <td> 407102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> VANDALISM</td>\n",
" <td> 400700</td>\n",
" <td> 400700</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 3 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 197,
"text": [
" DEPARTMENT TRACT00 TRACT10\n",
"0 PETTY THEFT 409100 409100\n",
"1 OTHER 409700 409700\n",
"2 WEAPONS 401100 401100\n",
"3 STOLEN VEHICLE 407100 407102\n",
"4 VANDALISM 400700 400700\n",
"\n",
"[5 rows x 3 columns]"
]
}
],
"prompt_number": 197
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"crime_df['tract'] = crime_df['TRACT10']\n",
"crime_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>DEPARTMENT</th>\n",
" <th>TRACT00</th>\n",
" <th>TRACT10</th>\n",
" <th>tract</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> PETTY THEFT</td>\n",
" <td> 409100</td>\n",
" <td> 409100</td>\n",
" <td> 409100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> OTHER</td>\n",
" <td> 409700</td>\n",
" <td> 409700</td>\n",
" <td> 409700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> WEAPONS</td>\n",
" <td> 401100</td>\n",
" <td> 401100</td>\n",
" <td> 401100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> STOLEN VEHICLE</td>\n",
" <td> 407100</td>\n",
" <td> 407102</td>\n",
" <td> 407102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> VANDALISM</td>\n",
" <td> 400700</td>\n",
" <td> 400700</td>\n",
" <td> 400700</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 4 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 198,
"text": [
" DEPARTMENT TRACT00 TRACT10 tract\n",
"0 PETTY THEFT 409100 409100 409100\n",
"1 OTHER 409700 409700 409700\n",
"2 WEAPONS 401100 401100 401100\n",
"3 STOLEN VEHICLE 407100 407102 407102\n",
"4 VANDALISM 400700 400700 400700\n",
"\n",
"[5 rows x 4 columns]"
]
}
],
"prompt_number": 198
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"del crime_df['DEPARTMENT']\n",
"del crime_df['TRACT00']\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 199
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#agg_crime_2010_df = DataFrame(crime_df.groupby('tract').count())\n",
"crime_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>TRACT10</th>\n",
" <th>tract</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 409100</td>\n",
" <td> 409100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 409700</td>\n",
" <td> 409700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 401100</td>\n",
" <td> 401100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 407102</td>\n",
" <td> 407102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 400700</td>\n",
" <td> 400700</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 2 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 201,
"text": [
" TRACT10 tract\n",
"0 409100 409100\n",
"1 409700 409700\n",
"2 401100 401100\n",
"3 407102 407102\n",
"4 400700 400700\n",
"\n",
"[5 rows x 2 columns]"
]
}
],
"prompt_number": 201
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 203
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"crime_race_df = pd.merge(agg_crime_2010_df, combined_df, on='tract', left_index=True, sort=True,\n",
" suffixes=('_crime', '_race'), copy=True)\n",
"\n",
"crime_race_df.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <tbody>\n",
" <tr>\n",
" <td>Int64Index([], dtype='int64')</td>\n",
" <td>Empty DataFrame</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>0 rows \u00d7 18 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 204,
"text": [
"Empty DataFrame\n",
"Columns: [Total Crimes, tract, NAME_10, Total Pop_10, African-American, not Hispanic_10, Asian, not Hispanic_10, Hispanic_10, White, not Hispanic_10, AfAm_ratio_2010, NAME_00, Total Pop_00, African-American, not Hispanic_00, Asian, not Hispanic_00, Hispanic_00, White, not Hispanic_00, AfAm_ratio, AfAm_ratio_2000, percent change in AfAm]\n",
"Index: []\n",
"\n",
"[0 rows x 18 columns]"
]
}
],
"prompt_number": 204
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"combined_df[(cols)].set_index(['tract'])"
],
"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>Total Pop_00</th>\n",
" <th>Total Pop_10</th>\n",
" <th>African-American, not Hispanic_00</th>\n",
" <th>African-American, not Hispanic_10</th>\n",
" <th>AfAm_ratio_2000</th>\n",
" <th>AfAm_ratio_2010</th>\n",
" <th>percent change in AfAm</th>\n",
" </tr>\n",
" <tr>\n",
" <th>tract</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>400100</th>\n",
" <td> 2498</td>\n",
" <td> 2937</td>\n",
" <td> 125</td>\n",
" <td> 140</td>\n",
" <td> 0.050040</td>\n",
" <td> 0.047668</td>\n",
" <td>-0.002372</td>\n",
" </tr>\n",
" <tr>\n",
" <th>400200</th>\n",
" <td> 1910</td>\n",
" <td> 1974</td>\n",
" <td> 71</td>\n",
" <td> 31</td>\n",
" <td> 0.037173</td>\n",
" <td> 0.015704</td>\n",
" <td>-0.021469</td>\n",
" </tr>\n",
" <tr>\n",
" <th>400300</th>\n",
" <td> 4878</td>\n",
" <td> 4865</td>\n",
" <td> 768</td>\n",
" <td> 512</td>\n",
" <td> 0.157442</td>\n",
" <td> 0.105242</td>\n",
" <td>-0.052200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>400400</th>\n",
" <td> 3659</td>\n",
" <td> 3703</td>\n",
" <td> 671</td>\n",
" <td> 448</td>\n",
" <td> 0.183383</td>\n",
" <td> 0.120983</td>\n",
" <td>-0.062400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>400500</th>\n",
" <td> 3410</td>\n",
" <td> 3517</td>\n",
" <td> 1510</td>\n",
" <td> 933</td>\n",
" <td> 0.442815</td>\n",
" <td> 0.265283</td>\n",
" <td>-0.177532</td>\n",
" </tr>\n",
" <tr>\n",
" <th>400600</th>\n",
" <td> 1707</td>\n",
" <td> 1571</td>\n",
" <td> 1037</td>\n",
" <td> 615</td>\n",
" <td> 0.607499</td>\n",
" <td> 0.391470</td>\n",
" <td>-0.216028</td>\n",
" </tr>\n",
" <tr>\n",
" <th>400700</th>\n",
" <td> 4451</td>\n",
" <td> 4206</td>\n",
" <td> 3104</td>\n",
" <td> 2068</td>\n",
" <td> 0.697371</td>\n",
" <td> 0.491679</td>\n",
" <td>-0.205693</td>\n",
" </tr>\n",
" <tr>\n",
" <th>400800</th>\n",
" <td> 3368</td>\n",
" <td> 3594</td>\n",
" <td> 1990</td>\n",
" <td> 1463</td>\n",
" <td> 0.590855</td>\n",
" <td> 0.407067</td>\n",
" <td>-0.183788</td>\n",
" </tr>\n",
" <tr>\n",
" <th>400900</th>\n",
" <td> 2456</td>\n",
" <td> 2302</td>\n",
" <td> 1570</td>\n",
" <td> 1005</td>\n",
" <td> 0.639251</td>\n",
" <td> 0.436577</td>\n",
" <td>-0.202674</td>\n",
" </tr>\n",
" <tr>\n",
" <th>401000</th>\n",
" <td> 5709</td>\n",
" <td> 5678</td>\n",
" <td> 4176</td>\n",
" <td> 2848</td>\n",
" <td> 0.731477</td>\n",
" <td> 0.501585</td>\n",
" <td>-0.229892</td>\n",
" </tr>\n",
" <tr>\n",
" <th>401100</th>\n",
" <td> 4007</td>\n",
" <td> 4156</td>\n",
" <td> 1395</td>\n",
" <td> 975</td>\n",
" <td> 0.348141</td>\n",
" <td> 0.234601</td>\n",
" <td>-0.113540</td>\n",
" </tr>\n",
" <tr>\n",
" <th>401200</th>\n",
" <td> 2432</td>\n",
" <td> 2416</td>\n",
" <td> 589</td>\n",
" <td> 421</td>\n",
" <td> 0.242188</td>\n",
" <td> 0.174255</td>\n",
" <td>-0.067933</td>\n",
" </tr>\n",
" <tr>\n",
" <th>401300</th>\n",
" <td> 2810</td>\n",
" <td> 3528</td>\n",
" <td> 1468</td>\n",
" <td> 1255</td>\n",
" <td> 0.522420</td>\n",
" <td> 0.355726</td>\n",
" <td>-0.166694</td>\n",
" </tr>\n",
" <tr>\n",
" <th>401400</th>\n",
" <td> 4765</td>\n",
" <td> 4314</td>\n",
" <td> 3167</td>\n",
" <td> 2090</td>\n",
" <td> 0.664638</td>\n",
" <td> 0.484469</td>\n",
" <td>-0.180169</td>\n",
" </tr>\n",
" <tr>\n",
" <th>401500</th>\n",
" <td> 2413</td>\n",
" <td> 2630</td>\n",
" <td> 1717</td>\n",
" <td> 1392</td>\n",
" <td> 0.711562</td>\n",
" <td> 0.529278</td>\n",
" <td>-0.182285</td>\n",
" </tr>\n",
" <tr>\n",
" <th>401600</th>\n",
" <td> 1933</td>\n",
" <td> 2163</td>\n",
" <td> 1170</td>\n",
" <td> 1005</td>\n",
" <td> 0.605277</td>\n",
" <td> 0.464632</td>\n",
" <td>-0.140644</td>\n",
" </tr>\n",
" <tr>\n",
" <th>401700</th>\n",
" <td> 1878</td>\n",
" <td> 2667</td>\n",
" <td> 979</td>\n",
" <td> 884</td>\n",
" <td> 0.521299</td>\n",
" <td> 0.331459</td>\n",
" <td>-0.189841</td>\n",
" </tr>\n",
" <tr>\n",
" <th>401800</th>\n",
" <td> 1953</td>\n",
" <td> 1703</td>\n",
" <td> 1490</td>\n",
" <td> 977</td>\n",
" <td> 0.762929</td>\n",
" <td> 0.573693</td>\n",
" <td>-0.189235</td>\n",
" </tr>\n",
" <tr>\n",
" <th>402200</th>\n",
" <td> 1844</td>\n",
" <td> 2385</td>\n",
" <td> 1138</td>\n",
" <td> 868</td>\n",
" <td> 0.617137</td>\n",
" <td> 0.363941</td>\n",
" <td>-0.253195</td>\n",
" </tr>\n",
" <tr>\n",
" <th>402400</th>\n",
" <td> 2588</td>\n",
" <td> 2351</td>\n",
" <td> 1978</td>\n",
" <td> 1358</td>\n",
" <td> 0.764297</td>\n",
" <td> 0.577627</td>\n",
" <td>-0.186670</td>\n",
" </tr>\n",
" <tr>\n",
" <th>402500</th>\n",
" <td> 1779</td>\n",
" <td> 1784</td>\n",
" <td> 1369</td>\n",
" <td> 1191</td>\n",
" <td> 0.769533</td>\n",
" <td> 0.667601</td>\n",
" <td>-0.101933</td>\n",
" </tr>\n",
" <tr>\n",
" <th>402600</th>\n",
" <td> 977</td>\n",
" <td> 1151</td>\n",
" <td> 439</td>\n",
" <td> 340</td>\n",
" <td> 0.449335</td>\n",
" <td> 0.295395</td>\n",
" <td>-0.153939</td>\n",
" </tr>\n",
" <tr>\n",
" <th>402700</th>\n",
" <td> 1946</td>\n",
" <td> 1569</td>\n",
" <td> 1200</td>\n",
" <td> 881</td>\n",
" <td> 0.616650</td>\n",
" <td> 0.561504</td>\n",
" <td>-0.055145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>402800</th>\n",
" <td> 1910</td>\n",
" <td> 3345</td>\n",
" <td> 1122</td>\n",
" <td> 1378</td>\n",
" <td> 0.587435</td>\n",
" <td> 0.411958</td>\n",
" <td>-0.175476</td>\n",
" </tr>\n",
" <tr>\n",
" <th>402900</th>\n",
" <td> 1291</td>\n",
" <td> 1434</td>\n",
" <td> 419</td>\n",
" <td> 289</td>\n",
" <td> 0.324555</td>\n",
" <td> 0.201534</td>\n",
" <td>-0.123020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>403000</th>\n",
" <td> 2734</td>\n",
" <td> 2788</td>\n",
" <td> 115</td>\n",
" <td> 123</td>\n",
" <td> 0.042063</td>\n",
" <td> 0.044118</td>\n",
" <td> 0.002055</td>\n",
" </tr>\n",
" <tr>\n",
" <th>403100</th>\n",
" <td> 1647</td>\n",
" <td> 2238</td>\n",
" <td> 470</td>\n",
" <td> 679</td>\n",
" <td> 0.285367</td>\n",
" <td> 0.303396</td>\n",
" <td> 0.018029</td>\n",
" </tr>\n",
" <tr>\n",
" <th>403300</th>\n",
" <td> 2310</td>\n",
" <td> 4054</td>\n",
" <td> 207</td>\n",
" <td> 455</td>\n",
" <td> 0.089610</td>\n",
" <td> 0.112235</td>\n",
" <td> 0.022624</td>\n",
" </tr>\n",
" <tr>\n",
" <th>403400</th>\n",
" <td> 3697</td>\n",
" <td> 4146</td>\n",
" <td> 1128</td>\n",
" <td> 816</td>\n",
" <td> 0.305112</td>\n",
" <td> 0.196816</td>\n",
" <td>-0.108296</td>\n",
" </tr>\n",
" <tr>\n",
" <th>403600</th>\n",
" <td> 4400</td>\n",
" <td> 4482</td>\n",
" <td> 2028</td>\n",
" <td> 1699</td>\n",
" <td> 0.460909</td>\n",
" <td> 0.379072</td>\n",
" <td>-0.081837</td>\n",
" </tr>\n",
" <tr>\n",
" <th>403800</th>\n",
" <td> 3453</td>\n",
" <td> 3461</td>\n",
" <td> 605</td>\n",
" <td> 334</td>\n",
" <td> 0.175210</td>\n",
" <td> 0.096504</td>\n",
" <td>-0.078706</td>\n",
" </tr>\n",
" <tr>\n",
" <th>403900</th>\n",
" <td> 3794</td>\n",
" <td> 3584</td>\n",
" <td> 999</td>\n",
" <td> 573</td>\n",
" <td> 0.263310</td>\n",
" <td> 0.159877</td>\n",
" <td>-0.103433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>404000</th>\n",
" <td> 2885</td>\n",
" <td> 2819</td>\n",
" <td> 577</td>\n",
" <td> 401</td>\n",
" <td> 0.200000</td>\n",
" <td> 0.142249</td>\n",
" <td>-0.057751</td>\n",
" </tr>\n",
" <tr>\n",
" <th>404200</th>\n",
" <td> 3176</td>\n",
" <td> 3483</td>\n",
" <td> 133</td>\n",
" <td> 171</td>\n",
" <td> 0.041877</td>\n",
" <td> 0.049096</td>\n",
" <td> 0.007219</td>\n",
" </tr>\n",
" <tr>\n",
" <th>404300</th>\n",
" <td> 3089</td>\n",
" <td> 3218</td>\n",
" <td> 209</td>\n",
" <td> 141</td>\n",
" <td> 0.067659</td>\n",
" <td> 0.043816</td>\n",
" <td>-0.023843</td>\n",
" </tr>\n",
" <tr>\n",
" <th>404400</th>\n",
" <td> 4699</td>\n",
" <td> 5314</td>\n",
" <td> 234</td>\n",
" <td> 253</td>\n",
" <td> 0.049798</td>\n",
" <td> 0.047610</td>\n",
" <td>-0.002188</td>\n",
" </tr>\n",
" <tr>\n",
" <th>404501</th>\n",
" <td> 1575</td>\n",
" <td> 1677</td>\n",
" <td> 83</td>\n",
" <td> 72</td>\n",
" <td> 0.052698</td>\n",
" <td> 0.042934</td>\n",
" <td>-0.009765</td>\n",
" </tr>\n",
" <tr>\n",
" <th>404502</th>\n",
" <td> 5493</td>\n",
" <td> 5784</td>\n",
" <td> 396</td>\n",
" <td> 363</td>\n",
" <td> 0.072092</td>\n",
" <td> 0.062759</td>\n",
" <td>-0.009332</td>\n",
" </tr>\n",
" <tr>\n",
" <th>404600</th>\n",
" <td> 4296</td>\n",
" <td> 4353</td>\n",
" <td> 264</td>\n",
" <td> 261</td>\n",
" <td> 0.061453</td>\n",
" <td> 0.059959</td>\n",
" <td>-0.001494</td>\n",
" </tr>\n",
" <tr>\n",
" <th>404700</th>\n",
" <td> 1927</td>\n",
" <td> 1954</td>\n",
" <td> 221</td>\n",
" <td> 156</td>\n",
" <td> 0.114686</td>\n",
" <td> 0.079836</td>\n",
" <td>-0.034850</td>\n",
" </tr>\n",
" <tr>\n",
" <th>404800</th>\n",
" <td> 2683</td>\n",
" <td> 2684</td>\n",
" <td> 677</td>\n",
" <td> 530</td>\n",
" <td> 0.252329</td>\n",
" <td> 0.197466</td>\n",
" <td>-0.054863</td>\n",
" </tr>\n",
" <tr>\n",
" <th>404900</th>\n",
" <td> 4356</td>\n",
" <td> 4129</td>\n",
" <td> 546</td>\n",
" <td> 462</td>\n",
" <td> 0.125344</td>\n",
" <td> 0.111891</td>\n",
" <td>-0.013453</td>\n",
" </tr>\n",
" <tr>\n",
" <th>405000</th>\n",
" <td> 3204</td>\n",
" <td> 3136</td>\n",
" <td> 458</td>\n",
" <td> 302</td>\n",
" <td> 0.142946</td>\n",
" <td> 0.096301</td>\n",
" <td>-0.046645</td>\n",
" </tr>\n",
" <tr>\n",
" <th>405100</th>\n",
" <td> 4161</td>\n",
" <td> 4197</td>\n",
" <td> 750</td>\n",
" <td> 497</td>\n",
" <td> 0.180245</td>\n",
" <td> 0.118418</td>\n",
" <td>-0.061827</td>\n",
" </tr>\n",
" <tr>\n",
" <th>405200</th>\n",
" <td> 4991</td>\n",
" <td> 4597</td>\n",
" <td> 1088</td>\n",
" <td> 699</td>\n",
" <td> 0.217992</td>\n",
" <td> 0.152056</td>\n",
" <td>-0.065937</td>\n",
" </tr>\n",
" <tr>\n",
" <th>405500</th>\n",
" <td> 4147</td>\n",
" <td> 3643</td>\n",
" <td> 1046</td>\n",
" <td> 737</td>\n",
" <td> 0.252231</td>\n",
" <td> 0.202306</td>\n",
" <td>-0.049925</td>\n",
" </tr>\n",
" <tr>\n",
" <th>405600</th>\n",
" <td> 3734</td>\n",
" <td> 3137</td>\n",
" <td> 1095</td>\n",
" <td> 710</td>\n",
" <td> 0.293251</td>\n",
" <td> 0.226331</td>\n",
" <td>-0.066920</td>\n",
" </tr>\n",
" <tr>\n",
" <th>405700</th>\n",
" <td> 3757</td>\n",
" <td> 3243</td>\n",
" <td> 1508</td>\n",
" <td> 1047</td>\n",
" <td> 0.401384</td>\n",
" <td> 0.322849</td>\n",
" <td>-0.078535</td>\n",
" </tr>\n",
" <tr>\n",
" <th>405800</th>\n",
" <td> 4777</td>\n",
" <td> 3965</td>\n",
" <td> 1446</td>\n",
" <td> 768</td>\n",
" <td> 0.302700</td>\n",
" <td> 0.193695</td>\n",
" <td>-0.109006</td>\n",
" </tr>\n",
" <tr>\n",
" <th>406000</th>\n",
" <td> 3655</td>\n",
" <td> 3450</td>\n",
" <td> 451</td>\n",
" <td> 419</td>\n",
" <td> 0.123393</td>\n",
" <td> 0.121449</td>\n",
" <td>-0.001943</td>\n",
" </tr>\n",
" <tr>\n",
" <th>406100</th>\n",
" <td> 4301</td>\n",
" <td> 4381</td>\n",
" <td> 545</td>\n",
" <td> 460</td>\n",
" <td> 0.126715</td>\n",
" <td> 0.104999</td>\n",
" <td>-0.021716</td>\n",
" </tr>\n",
" <tr>\n",
" <th>406201</th>\n",
" <td> 5802</td>\n",
" <td> 4649</td>\n",
" <td> 934</td>\n",
" <td> 720</td>\n",
" <td> 0.160979</td>\n",
" <td> 0.154872</td>\n",
" <td>-0.006107</td>\n",
" </tr>\n",
" <tr>\n",
" <th>406202</th>\n",
" <td> 5084</td>\n",
" <td> 4718</td>\n",
" <td> 705</td>\n",
" <td> 561</td>\n",
" <td> 0.138670</td>\n",
" <td> 0.118906</td>\n",
" <td>-0.019764</td>\n",
" </tr>\n",
" <tr>\n",
" <th>406300</th>\n",
" <td> 4410</td>\n",
" <td> 4113</td>\n",
" <td> 1374</td>\n",
" <td> 749</td>\n",
" <td> 0.311565</td>\n",
" <td> 0.182106</td>\n",
" <td>-0.129459</td>\n",
" </tr>\n",
" <tr>\n",
" <th>406400</th>\n",
" <td> 2276</td>\n",
" <td> 2145</td>\n",
" <td> 886</td>\n",
" <td> 589</td>\n",
" <td> 0.389279</td>\n",
" <td> 0.274592</td>\n",
" <td>-0.114687</td>\n",
" </tr>\n",
" <tr>\n",
" <th>406500</th>\n",
" <td> 6253</td>\n",
" <td> 5930</td>\n",
" <td> 1670</td>\n",
" <td> 1167</td>\n",
" <td> 0.267072</td>\n",
" <td> 0.196796</td>\n",
" <td>-0.070276</td>\n",
" </tr>\n",
" <tr>\n",
" <th>406700</th>\n",
" <td> 5224</td>\n",
" <td> 5048</td>\n",
" <td> 789</td>\n",
" <td> 685</td>\n",
" <td> 0.151034</td>\n",
" <td> 0.135697</td>\n",
" <td>-0.015336</td>\n",
" </tr>\n",
" <tr>\n",
" <th>406800</th>\n",
" <td> 3611</td>\n",
" <td> 3428</td>\n",
" <td> 667</td>\n",
" <td> 519</td>\n",
" <td> 0.184713</td>\n",
" <td> 0.151400</td>\n",
" <td>-0.033313</td>\n",
" </tr>\n",
" <tr>\n",
" <th>406900</th>\n",
" <td> 3695</td>\n",
" <td> 3719</td>\n",
" <td> 1030</td>\n",
" <td> 810</td>\n",
" <td> 0.278755</td>\n",
" <td> 0.217800</td>\n",
" <td>-0.060955</td>\n",
" </tr>\n",
" <tr>\n",
" <th>407000</th>\n",
" <td> 6652</td>\n",
" <td> 5885</td>\n",
" <td> 1827</td>\n",
" <td> 1488</td>\n",
" <td> 0.274654</td>\n",
" <td> 0.252846</td>\n",
" <td>-0.021808</td>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>279 rows \u00d7 7 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 122,
"text": [
" Total Pop_00 Total Pop_10 African-American, not Hispanic_00 \\\n",
"tract \n",
"400100 2498 2937 125 \n",
"400200 1910 1974 71 \n",
"400300 4878 4865 768 \n",
"400400 3659 3703 671 \n",
"400500 3410 3517 1510 \n",
"400600 1707 1571 1037 \n",
"400700 4451 4206 3104 \n",
"400800 3368 3594 1990 \n",
"400900 2456 2302 1570 \n",
"401000 5709 5678 4176 \n",
"401100 4007 4156 1395 \n",
"401200 2432 2416 589 \n",
"401300 2810 3528 1468 \n",
"401400 4765 4314 3167 \n",
"401500 2413 2630 1717 \n",
"401600 1933 2163 1170 \n",
"401700 1878 2667 979 \n",
"401800 1953 1703 1490 \n",
"402200 1844 2385 1138 \n",
"402400 2588 2351 1978 \n",
"402500 1779 1784 1369 \n",
"402600 977 1151 439 \n",
"402700 1946 1569 1200 \n",
"402800 1910 3345 1122 \n",
"402900 1291 1434 419 \n",
"403000 2734 2788 115 \n",
"403100 1647 2238 470 \n",
"403300 2310 4054 207 \n",
"403400 3697 4146 1128 \n",
"403600 4400 4482 2028 \n",
"403800 3453 3461 605 \n",
"403900 3794 3584 999 \n",
"404000 2885 2819 577 \n",
"404200 3176 3483 133 \n",
"404300 3089 3218 209 \n",
"404400 4699 5314 234 \n",
"404501 1575 1677 83 \n",
"404502 5493 5784 396 \n",
"404600 4296 4353 264 \n",
"404700 1927 1954 221 \n",
"404800 2683 2684 677 \n",
"404900 4356 4129 546 \n",
"405000 3204 3136 458 \n",
"405100 4161 4197 750 \n",
"405200 4991 4597 1088 \n",
"405500 4147 3643 1046 \n",
"405600 3734 3137 1095 \n",
"405700 3757 3243 1508 \n",
"405800 4777 3965 1446 \n",
"406000 3655 3450 451 \n",
"406100 4301 4381 545 \n",
"406201 5802 4649 934 \n",
"406202 5084 4718 705 \n",
"406300 4410 4113 1374 \n",
"406400 2276 2145 886 \n",
"406500 6253 5930 1670 \n",
"406700 5224 5048 789 \n",
"406800 3611 3428 667 \n",
"406900 3695 3719 1030 \n",
"407000 6652 5885 1827 \n",
" ... ... ... \n",
"\n",
" African-American, not Hispanic_10 AfAm_ratio_2000 AfAm_ratio_2010 \\\n",
"tract \n",
"400100 140 0.050040 0.047668 \n",
"400200 31 0.037173 0.015704 \n",
"400300 512 0.157442 0.105242 \n",
"400400 448 0.183383 0.120983 \n",
"400500 933 0.442815 0.265283 \n",
"400600 615 0.607499 0.391470 \n",
"400700 2068 0.697371 0.491679 \n",
"400800 1463 0.590855 0.407067 \n",
"400900 1005 0.639251 0.436577 \n",
"401000 2848 0.731477 0.501585 \n",
"401100 975 0.348141 0.234601 \n",
"401200 421 0.242188 0.174255 \n",
"401300 1255 0.522420 0.355726 \n",
"401400 2090 0.664638 0.484469 \n",
"401500 1392 0.711562 0.529278 \n",
"401600 1005 0.605277 0.464632 \n",
"401700 884 0.521299 0.331459 \n",
"401800 977 0.762929 0.573693 \n",
"402200 868 0.617137 0.363941 \n",
"402400 1358 0.764297 0.577627 \n",
"402500 1191 0.769533 0.667601 \n",
"402600 340 0.449335 0.295395 \n",
"402700 881 0.616650 0.561504 \n",
"402800 1378 0.587435 0.411958 \n",
"402900 289 0.324555 0.201534 \n",
"403000 123 0.042063 0.044118 \n",
"403100 679 0.285367 0.303396 \n",
"403300 455 0.089610 0.112235 \n",
"403400 816 0.305112 0.196816 \n",
"403600 1699 0.460909 0.379072 \n",
"403800 334 0.175210 0.096504 \n",
"403900 573 0.263310 0.159877 \n",
"404000 401 0.200000 0.142249 \n",
"404200 171 0.041877 0.049096 \n",
"404300 141 0.067659 0.043816 \n",
"404400 253 0.049798 0.047610 \n",
"404501 72 0.052698 0.042934 \n",
"404502 363 0.072092 0.062759 \n",
"404600 261 0.061453 0.059959 \n",
"404700 156 0.114686 0.079836 \n",
"404800 530 0.252329 0.197466 \n",
"404900 462 0.125344 0.111891 \n",
"405000 302 0.142946 0.096301 \n",
"405100 497 0.180245 0.118418 \n",
"405200 699 0.217992 0.152056 \n",
"405500 737 0.252231 0.202306 \n",
"405600 710 0.293251 0.226331 \n",
"405700 1047 0.401384 0.322849 \n",
"405800 768 0.302700 0.193695 \n",
"406000 419 0.123393 0.121449 \n",
"406100 460 0.126715 0.104999 \n",
"406201 720 0.160979 0.154872 \n",
"406202 561 0.138670 0.118906 \n",
"406300 749 0.311565 0.182106 \n",
"406400 589 0.389279 0.274592 \n",
"406500 1167 0.267072 0.196796 \n",
"406700 685 0.151034 0.135697 \n",
"406800 519 0.184713 0.151400 \n",
"406900 810 0.278755 0.217800 \n",
"407000 1488 0.274654 0.252846 \n",
" ... ... ... \n",
"\n",
" percent change in AfAm \n",
"tract \n",
"400100 -0.002372 \n",
"400200 -0.021469 \n",
"400300 -0.052200 \n",
"400400 -0.062400 \n",
"400500 -0.177532 \n",
"400600 -0.216028 \n",
"400700 -0.205693 \n",
"400800 -0.183788 \n",
"400900 -0.202674 \n",
"401000 -0.229892 \n",
"401100 -0.113540 \n",
"401200 -0.067933 \n",
"401300 -0.166694 \n",
"401400 -0.180169 \n",
"401500 -0.182285 \n",
"401600 -0.140644 \n",
"401700 -0.189841 \n",
"401800 -0.189235 \n",
"402200 -0.253195 \n",
"402400 -0.186670 \n",
"402500 -0.101933 \n",
"402600 -0.153939 \n",
"402700 -0.055145 \n",
"402800 -0.175476 \n",
"402900 -0.123020 \n",
"403000 0.002055 \n",
"403100 0.018029 \n",
"403300 0.022624 \n",
"403400 -0.108296 \n",
"403600 -0.081837 \n",
"403800 -0.078706 \n",
"403900 -0.103433 \n",
"404000 -0.057751 \n",
"404200 0.007219 \n",
"404300 -0.023843 \n",
"404400 -0.002188 \n",
"404501 -0.009765 \n",
"404502 -0.009332 \n",
"404600 -0.001494 \n",
"404700 -0.034850 \n",
"404800 -0.054863 \n",
"404900 -0.013453 \n",
"405000 -0.046645 \n",
"405100 -0.061827 \n",
"405200 -0.065937 \n",
"405500 -0.049925 \n",
"405600 -0.066920 \n",
"405700 -0.078535 \n",
"405800 -0.109006 \n",
"406000 -0.001943 \n",
"406100 -0.021716 \n",
"406201 -0.006107 \n",
"406202 -0.019764 \n",
"406300 -0.129459 \n",
"406400 -0.114687 \n",
"406500 -0.070276 \n",
"406700 -0.015336 \n",
"406800 -0.033313 \n",
"406900 -0.060955 \n",
"407000 -0.021808 \n",
" ... \n",
"\n",
"[279 rows x 7 columns]"
]
}
],
"prompt_number": 122
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#now merge 2000 and 2010 tract dataframes\n",
"#add column that gives percent of African-Americans in each tract, and change over the \n",
"# time period. \n",
"#Maybe add in something about density. \n",
"#Then think about same questions for income. "
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#sort by African-American population\n",
"tracts_2000_df.sort('P010004', ascending=False)[['NAME','tract','Total Pop','African-American, not Hispanic', \\\n",
" 'Asian, not Hispanic', 'Hispanic', 'White, not Hispanic']].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>NAME</th>\n",
" <th>tract</th>\n",
" <th>Total Pop</th>\n",
" <th>African-American, not Hispanic</th>\n",
" <th>Asian, not Hispanic</th>\n",
" <th>Hispanic</th>\n",
" <th>White, not Hispanic</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>88</th>\n",
" <td> Census Tract 4087</td>\n",
" <td> 408700</td>\n",
" <td> 7504</td>\n",
" <td> 4270</td>\n",
" <td> 294</td>\n",
" <td> 2541</td>\n",
" <td> 402</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> Census Tract 4010</td>\n",
" <td> 401000</td>\n",
" <td> 5709</td>\n",
" <td> 4176</td>\n",
" <td> 404</td>\n",
" <td> 547</td>\n",
" <td> 649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td> Census Tract 4097</td>\n",
" <td> 409700</td>\n",
" <td> 5208</td>\n",
" <td> 3281</td>\n",
" <td> 180</td>\n",
" <td> 1471</td>\n",
" <td> 222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td> Census Tract 4086</td>\n",
" <td> 408600</td>\n",
" <td> 5232</td>\n",
" <td> 3218</td>\n",
" <td> 147</td>\n",
" <td> 1623</td>\n",
" <td> 188</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> Census Tract 4014</td>\n",
" <td> 401400</td>\n",
" <td> 4765</td>\n",
" <td> 3167</td>\n",
" <td> 558</td>\n",
" <td> 706</td>\n",
" <td> 342</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 7 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 31,
"text": [
" NAME tract Total Pop African-American, not Hispanic \\\n",
"88 Census Tract 4087 408700 7504 4270 \n",
"9 Census Tract 4010 401000 5709 4176 \n",
"98 Census Tract 4097 409700 5208 3281 \n",
"87 Census Tract 4086 408600 5232 3218 \n",
"13 Census Tract 4014 401400 4765 3167 \n",
"\n",
" Asian, not Hispanic Hispanic White, not Hispanic \n",
"88 294 2541 402 \n",
"9 404 547 649 \n",
"98 180 1471 222 \n",
"87 147 1623 188 \n",
"13 558 706 342 \n",
"\n",
"[5 rows x 7 columns]"
]
}
],
"prompt_number": 31
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#sort by Hispanic population\n",
"tracts_2000_df.sort('P011001', ascending=False)[['NAME','tract','Total Pop','African-American, not Hispanic', \\\n",
" 'Asian, not Hispanic', 'Hispanic', 'White, not Hispanic']].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>NAME</th>\n",
" <th>tract</th>\n",
" <th>Total Pop</th>\n",
" <th>African-American, not Hispanic</th>\n",
" <th>Asian, not Hispanic</th>\n",
" <th>Hispanic</th>\n",
" <th>White, not Hispanic</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>236</th>\n",
" <td> Census Tract 4402</td>\n",
" <td> 440200</td>\n",
" <td> 6346</td>\n",
" <td> 162</td>\n",
" <td> 504</td>\n",
" <td> 5165</td>\n",
" <td> 546</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73 </th>\n",
" <td> Census Tract 4072</td>\n",
" <td> 407200</td>\n",
" <td> 7039</td>\n",
" <td> 603</td>\n",
" <td> 792</td>\n",
" <td> 5060</td>\n",
" <td> 513</td>\n",
" </tr>\n",
" <tr>\n",
" <th>226</th>\n",
" <td> Census Tract 4377</td>\n",
" <td> 437700</td>\n",
" <td> 8827</td>\n",
" <td> 951</td>\n",
" <td> 1366</td>\n",
" <td> 4838</td>\n",
" <td> 1342</td>\n",
" </tr>\n",
" <tr>\n",
" <th>203</th>\n",
" <td> Census Tract 4356</td>\n",
" <td> 435600</td>\n",
" <td> 9524</td>\n",
" <td> 1027</td>\n",
" <td> 721</td>\n",
" <td> 4071</td>\n",
" <td> 3659</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72 </th>\n",
" <td> Census Tract 4071</td>\n",
" <td> 407100</td>\n",
" <td> 8376</td>\n",
" <td> 2086</td>\n",
" <td> 1559</td>\n",
" <td> 3896</td>\n",
" <td> 826</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 7 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 32,
"text": [
" NAME tract Total Pop African-American, not Hispanic \\\n",
"236 Census Tract 4402 440200 6346 162 \n",
"73 Census Tract 4072 407200 7039 603 \n",
"226 Census Tract 4377 437700 8827 951 \n",
"203 Census Tract 4356 435600 9524 1027 \n",
"72 Census Tract 4071 407100 8376 2086 \n",
"\n",
" Asian, not Hispanic Hispanic White, not Hispanic \n",
"236 504 5165 546 \n",
"73 792 5060 513 \n",
"226 1366 4838 1342 \n",
"203 721 4071 3659 \n",
"72 1559 3896 826 \n",
"\n",
"[5 rows x 7 columns]"
]
}
],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#sort by White population\n",
"tracts_2000_df.sort('P010003', ascending=False)[['NAME','tract','Total Pop','African-American, not Hispanic', \\\n",
" 'Asian, not Hispanic', 'Hispanic', 'White, not Hispanic']].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>NAME</th>\n",
" <th>tract</th>\n",
" <th>Total Pop</th>\n",
" <th>African-American, not Hispanic</th>\n",
" <th>Asian, not Hispanic</th>\n",
" <th>Hispanic</th>\n",
" <th>White, not Hispanic</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>320</th>\n",
" <td> Census Tract 4517.02</td>\n",
" <td> 451702</td>\n",
" <td> 7828</td>\n",
" <td> 107</td>\n",
" <td> 471</td>\n",
" <td> 712</td>\n",
" <td> 6563</td>\n",
" </tr>\n",
" <tr>\n",
" <th>304</th>\n",
" <td> Census Tract 4507.22</td>\n",
" <td> 450722</td>\n",
" <td> 9326</td>\n",
" <td> 238</td>\n",
" <td> 2010</td>\n",
" <td> 813</td>\n",
" <td> 6361</td>\n",
" </tr>\n",
" <tr>\n",
" <th>315</th>\n",
" <td> Census Tract 4515.02</td>\n",
" <td> 451502</td>\n",
" <td> 8009</td>\n",
" <td> 231</td>\n",
" <td> 499</td>\n",
" <td> 996</td>\n",
" <td> 6353</td>\n",
" </tr>\n",
" <tr>\n",
" <th>295</th>\n",
" <td> Census Tract 4506.02</td>\n",
" <td> 450602</td>\n",
" <td> 7890</td>\n",
" <td> 131</td>\n",
" <td> 942</td>\n",
" <td> 538</td>\n",
" <td> 6341</td>\n",
" </tr>\n",
" <tr>\n",
" <th>302</th>\n",
" <td> Census Tract 4507.03</td>\n",
" <td> 450703</td>\n",
" <td> 7656</td>\n",
" <td> 115</td>\n",
" <td> 1383</td>\n",
" <td> 616</td>\n",
" <td> 5615</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 7 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 33,
"text": [
" NAME tract Total Pop African-American, not Hispanic \\\n",
"320 Census Tract 4517.02 451702 7828 107 \n",
"304 Census Tract 4507.22 450722 9326 238 \n",
"315 Census Tract 4515.02 451502 8009 231 \n",
"295 Census Tract 4506.02 450602 7890 131 \n",
"302 Census Tract 4507.03 450703 7656 115 \n",
"\n",
" Asian, not Hispanic Hispanic White, not Hispanic \n",
"320 471 712 6563 \n",
"304 2010 813 6361 \n",
"315 499 996 6353 \n",
"295 942 538 6341 \n",
"302 1383 616 5615 \n",
"\n",
"[5 rows x 7 columns]"
]
}
],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#sort by Asian population\n",
"tracts_2000_df.sort('P010006', ascending=False)[['NAME','tract','Total Pop','African-American, not Hispanic', \\\n",
" 'Asian, not Hispanic', 'Hispanic', 'White, not Hispanic']].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>NAME</th>\n",
" <th>tract</th>\n",
" <th>Total Pop</th>\n",
" <th>African-American, not Hispanic</th>\n",
" <th>Asian, not Hispanic</th>\n",
" <th>Hispanic</th>\n",
" <th>White, not Hispanic</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>254</th>\n",
" <td> Census Tract 4415.03</td>\n",
" <td> 441503</td>\n",
" <td> 10783</td>\n",
" <td> 323</td>\n",
" <td> 7677</td>\n",
" <td> 544</td>\n",
" <td> 2406</td>\n",
" </tr>\n",
" <tr>\n",
" <th>261</th>\n",
" <td> Census Tract 4419.01</td>\n",
" <td> 441901</td>\n",
" <td> 11485</td>\n",
" <td> 490</td>\n",
" <td> 4923</td>\n",
" <td> 1437</td>\n",
" <td> 4835</td>\n",
" </tr>\n",
" <tr>\n",
" <th>277</th>\n",
" <td> Census Tract 4431.01</td>\n",
" <td> 443101</td>\n",
" <td> 9329</td>\n",
" <td> 198</td>\n",
" <td> 4875</td>\n",
" <td> 512</td>\n",
" <td> 3885</td>\n",
" </tr>\n",
" <tr>\n",
" <th>238</th>\n",
" <td> Census Tract 4403.02</td>\n",
" <td> 440302</td>\n",
" <td> 7432</td>\n",
" <td> 645</td>\n",
" <td> 4377</td>\n",
" <td> 922</td>\n",
" <td> 1546</td>\n",
" </tr>\n",
" <tr>\n",
" <th>255</th>\n",
" <td> Census Tract 4415.21</td>\n",
" <td> 441521</td>\n",
" <td> 6100</td>\n",
" <td> 244</td>\n",
" <td> 3740</td>\n",
" <td> 373</td>\n",
" <td> 1941</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 7 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 34,
"text": [
" NAME tract Total Pop African-American, not Hispanic \\\n",
"254 Census Tract 4415.03 441503 10783 323 \n",
"261 Census Tract 4419.01 441901 11485 490 \n",
"277 Census Tract 4431.01 443101 9329 198 \n",
"238 Census Tract 4403.02 440302 7432 645 \n",
"255 Census Tract 4415.21 441521 6100 244 \n",
"\n",
" Asian, not Hispanic Hispanic White, not Hispanic \n",
"254 7677 544 2406 \n",
"261 4923 1437 4835 \n",
"277 4875 512 3885 \n",
"238 4377 922 1546 \n",
"255 3740 373 1941 \n",
"\n",
"[5 rows x 7 columns]"
]
}
],
"prompt_number": 34
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#transpose so tracts are columns \n",
"alameda_tracts_2000_df.transpose().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>tract</th>\n",
" <th>400100</th>\n",
" <th>400200</th>\n",
" <th>400300</th>\n",
" <th>400400</th>\n",
" <th>400500</th>\n",
" <th>400600</th>\n",
" <th>400700</th>\n",
" <th>400800</th>\n",
" <th>400900</th>\n",
" <th>401000</th>\n",
" <th>401100</th>\n",
" <th>401200</th>\n",
" <th>401300</th>\n",
" <th>401400</th>\n",
" <th>401500</th>\n",
" <th>401600</th>\n",
" <th>401700</th>\n",
" <th>401800</th>\n",
" <th>401900</th>\n",
" <th>402000</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>NAME</th>\n",
" <td> Census Tract 4001</td>\n",
" <td> Census Tract 4002</td>\n",
" <td> Census Tract 4003</td>\n",
" <td> Census Tract 4004</td>\n",
" <td> Census Tract 4005</td>\n",
" <td> Census Tract 4006</td>\n",
" <td> Census Tract 4007</td>\n",
" <td> Census Tract 4008</td>\n",
" <td> Census Tract 4009</td>\n",
" <td> Census Tract 4010</td>\n",
" <td> Census Tract 4011</td>\n",
" <td> Census Tract 4012</td>\n",
" <td> Census Tract 4013</td>\n",
" <td> Census Tract 4014</td>\n",
" <td> Census Tract 4015</td>\n",
" <td> Census Tract 4016</td>\n",
" <td> Census Tract 4017</td>\n",
" <td> Census Tract 4018</td>\n",
" <td> Census Tract 4019</td>\n",
" <td> Census Tract 4020</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Total Pop</th>\n",
" <td> 2498</td>\n",
" <td> 1910</td>\n",
" <td> 4878</td>\n",
" <td> 3659</td>\n",
" <td> 3410</td>\n",
" <td> 1707</td>\n",
" <td> 4451</td>\n",
" <td> 3368</td>\n",
" <td> 2456</td>\n",
" <td> 5709</td>\n",
" <td> 4007</td>\n",
" <td> 2432</td>\n",
" <td> 2810</td>\n",
" <td> 4765</td>\n",
" <td> 2413</td>\n",
" <td> 1933</td>\n",
" <td> 1878</td>\n",
" <td> 1953</td>\n",
" <td> 759</td>\n",
" <td> 28</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>African-American, not Hispanic</th>\n",
" <td> 125</td>\n",
" <td> 71</td>\n",
" <td> 768</td>\n",
" <td> 671</td>\n",
" <td> 1510</td>\n",
" <td> 1037</td>\n",
" <td> 3104</td>\n",
" <td> 1990</td>\n",
" <td> 1570</td>\n",
" <td> 4176</td>\n",
" <td> 1395</td>\n",
" <td> 589</td>\n",
" <td> 1468</td>\n",
" <td> 3167</td>\n",
" <td> 1717</td>\n",
" <td> 1170</td>\n",
" <td> 979</td>\n",
" <td> 1490</td>\n",
" <td> 204</td>\n",
" <td> 7</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Asian, not Hispanic</th>\n",
" <td> 305</td>\n",
" <td> 177</td>\n",
" <td> 418</td>\n",
" <td> 308</td>\n",
" <td> 216</td>\n",
" <td> 98</td>\n",
" <td> 221</td>\n",
" <td> 336</td>\n",
" <td> 135</td>\n",
" <td> 404</td>\n",
" <td> 568</td>\n",
" <td> 326</td>\n",
" <td> 349</td>\n",
" <td> 558</td>\n",
" <td> 142</td>\n",
" <td> 164</td>\n",
" <td> 103</td>\n",
" <td> 39</td>\n",
" <td> 69</td>\n",
" <td> 1</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hispanic</th>\n",
" <td> 97</td>\n",
" <td> 117</td>\n",
" <td> 314</td>\n",
" <td> 241</td>\n",
" <td> 363</td>\n",
" <td> 148</td>\n",
" <td> 299</td>\n",
" <td> 301</td>\n",
" <td> 202</td>\n",
" <td> 547</td>\n",
" <td> 472</td>\n",
" <td> 185</td>\n",
" <td> 249</td>\n",
" <td> 706</td>\n",
" <td> 255</td>\n",
" <td> 322</td>\n",
" <td> 568</td>\n",
" <td> 308</td>\n",
" <td> 386</td>\n",
" <td> 10</td>\n",
" <td>...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 321 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 35,
"text": [
"tract 400100 400200 \\\n",
"NAME Census Tract 4001 Census Tract 4002 \n",
"Total Pop 2498 1910 \n",
"African-American, not Hispanic 125 71 \n",
"Asian, not Hispanic 305 177 \n",
"Hispanic 97 117 \n",
"\n",
"tract 400300 400400 \\\n",
"NAME Census Tract 4003 Census Tract 4004 \n",
"Total Pop 4878 3659 \n",
"African-American, not Hispanic 768 671 \n",
"Asian, not Hispanic 418 308 \n",
"Hispanic 314 241 \n",
"\n",
"tract 400500 400600 \\\n",
"NAME Census Tract 4005 Census Tract 4006 \n",
"Total Pop 3410 1707 \n",
"African-American, not Hispanic 1510 1037 \n",
"Asian, not Hispanic 216 98 \n",
"Hispanic 363 148 \n",
"\n",
"tract 400700 400800 \\\n",
"NAME Census Tract 4007 Census Tract 4008 \n",
"Total Pop 4451 3368 \n",
"African-American, not Hispanic 3104 1990 \n",
"Asian, not Hispanic 221 336 \n",
"Hispanic 299 301 \n",
"\n",
"tract 400900 401000 \\\n",
"NAME Census Tract 4009 Census Tract 4010 \n",
"Total Pop 2456 5709 \n",
"African-American, not Hispanic 1570 4176 \n",
"Asian, not Hispanic 135 404 \n",
"Hispanic 202 547 \n",
"\n",
"tract 401100 401200 \\\n",
"NAME Census Tract 4011 Census Tract 4012 \n",
"Total Pop 4007 2432 \n",
"African-American, not Hispanic 1395 589 \n",
"Asian, not Hispanic 568 326 \n",
"Hispanic 472 185 \n",
"\n",
"tract 401300 401400 \\\n",
"NAME Census Tract 4013 Census Tract 4014 \n",
"Total Pop 2810 4765 \n",
"African-American, not Hispanic 1468 3167 \n",
"Asian, not Hispanic 349 558 \n",
"Hispanic 249 706 \n",
"\n",
"tract 401500 401600 \\\n",
"NAME Census Tract 4015 Census Tract 4016 \n",
"Total Pop 2413 1933 \n",
"African-American, not Hispanic 1717 1170 \n",
"Asian, not Hispanic 142 164 \n",
"Hispanic 255 322 \n",
"\n",
"tract 401700 401800 \\\n",
"NAME Census Tract 4017 Census Tract 4018 \n",
"Total Pop 1878 1953 \n",
"African-American, not Hispanic 979 1490 \n",
"Asian, not Hispanic 103 39 \n",
"Hispanic 568 308 \n",
"\n",
"tract 401900 402000 \n",
"NAME Census Tract 4019 Census Tract 4020 ... \n",
"Total Pop 759 28 ... \n",
"African-American, not Hispanic 204 7 ... \n",
"Asian, not Hispanic 69 1 ... \n",
"Hispanic 386 10 ... \n",
"\n",
"[5 rows x 321 columns]"
]
}
],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#http://api.census.gov/data/2000/sf1?get=P001001&for=block+group:1&in=state:06+county:001+tract:400100\n",
"#http://api.census.gov/data/2000/sf1?get=P001001&for=block+group:*&in=state:06+county:001+tract:400100\n",
"#http://api.census.gov/data/2000/sf1?get=P001001&for=block+group:*&in=state:06+county:001\n",
"\n",
"def block_groups(variables='NAME'):\n",
" for state in us.states.STATES:\n",
" \n",
" # handy to print out state to monitor progress\n",
" # print state.fips, state\n",
" counties_in_state={'for':'county:*',\n",
" 'in':'state:{fips}'.format(fips=state.fips)}\n",
" \n",
" for county in c.sf1.get('NAME', geo=counties_in_state, year=2000):\n",
" \n",
" # print county['state'], county['NAME']\n",
" tracts_in_county = {'for':'tract:*',\n",
" 'in': 'state:{s_fips} county:{c_fips}'.format(s_fips=state.fips, \n",
" c_fips=county['county'])}\n",
" \n",
" for tract in c.sf1.get(variables,geo=tracts_in_county, year=2000):\n",
" \n",
" block_group_in_tract = {'for': 'block+group:*', 'in': 'state:{s_fips} county:{c_fips} tract:{t_fips}'.format(s_fips=state.fips, \n",
" c_fips=county['county'], t_fips=400100)}\n",
" \n",
" for block_group in c.sf1.get(variables,geo=block_group_in_tract, year=2000):\n",
" \n",
" yield block_group"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#example of list comprehension that worked before: \n",
"#show_me_counties = [county for county in counties2(variables='NAME') if county['state'] == '06']\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#list comprehension for grabbing total & African-American pop from above function by block group\n",
"#bgs = [bg for bg in block_groups(variables=\"NAME,P001001,P010004\")]\n",
"\n",
"bgs = []\n",
"for group in block_groups(variables=\"NAME,P001001,P010004\"):\n",
" bgs.append(group)\n",
"\n",
"#put list into dataframe\n",
"bg_df = pd.DataFrame(bgs)\n",
"bg_df.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "CensusException",
"evalue": "error: invalid 'for' argument",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mCensusException\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-26-281141b2d33c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mbgs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mgroup\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mblock_groups\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"NAME,P001001,P010004\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mbgs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-24-dc581758eb6c>\u001b[0m in \u001b[0;36mblock_groups\u001b[0;34m(variables)\u001b[0m\n\u001b[1;32m 25\u001b[0m c_fips=county['county'], t_fips=400100)}\n\u001b[1;32m 26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mblock_group\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mgeo\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mblock_group_in_tract\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myear\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mblock_group\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m//anaconda/lib/python2.7/site-packages/census/core.pyc\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, fields, geo, year)\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 135\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mCensusException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mCensusException\u001b[0m: error: invalid 'for' argument"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# oaktown_zips = ['94607','94612','94610','94607','94618','94611','94606','94602','94601','94605','94619','94621']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 70
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<pre>\n",
"&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
"Index: 1769 entries, 93637 to 96061\n",
"Data columns (total 4 columns):\n",
"NAME 1769 non-null values\n",
"Total/P0010001 1769 non-null values\n",
"African-American/P0050004 1769 non-null values\n",
"state 1769 non-null values\n",
"dtypes: object(4)\n",
"</pre>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 73,
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 1769 entries, 93637 to 96061\n",
"Data columns (total 4 columns):\n",
"NAME 1769 non-null values\n",
"Total/P0010001 1769 non-null values\n",
"African-American/P0050004 1769 non-null values\n",
"state 1769 non-null values\n",
"dtypes: object(4)"
]
}
],
"prompt_number": 73
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#zip_df.xs(key='94607', axis=0) \n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 74,
"text": [
"NAME ZCTA5 94607\n",
"P0010001 24978\n",
"P0050004 9445\n",
"state 06\n",
"Name: 94607, dtype: object"
]
}
],
"prompt_number": 74
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"oaktown_zip_list = {}\n",
"for code in oaktown_zips:\n",
" oaktown_zip_list[code] = zip_df.ix[code]\n",
" \n",
"oaktown_zip_df_2010 = pd.DataFrame(oaktown_zip_list)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 79
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"from pandas import Series, DataFrame\n",
"from itertools import islice\n",
"\n",
"%pylab --no-import-all inline\n",
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt\n",
"from pylab import figure, show"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import os\n",
"import glob\n",
"'''Set the files in the directory which shares parent with current directory.'''\n",
"Zillow_DIR = os.path.join(os.pardir, \"OpenData\", \"Neighborhood\")\n",
"\n",
"assert os.path.exists(Zillow_DIR)\n",
"# glob.glob(Zillow_DIR + \"/*\")[:5]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def generateDFfromFilename(name):\n",
" '''Pass unique name string as parameters, generate dataframe from the file'''\n",
" Zillow_file = os.path.join(Zillow_DIR, '%s.csv' % name)\n",
" df = pd.read_csv(Zillow_file)\n",
" return df.fillna(0)\n",
"def cleanedOakland(df, drop=[]):\n",
" oakland_df = df[(df.State=='CA') & (df.City=='Oakland')]\n",
"# oakland_df.dropna(how='all') vs. df.dropna() for any row w/ NaN\n",
" cleaned_oakland_df = oakland_df.drop(drop,axis=1) \n",
" return cleaned_oakland_df#.set_index(\"RegionName\", inplace=True)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"city_homevalue = generateDFfromFilename(\"City_Zhvi_AllHomes\")\n",
"# oak_homevalue = cleanedOakland(city_homevalue)\n",
"oak_history = city_homevalue[(city_homevalue.State=='CA')& (city_homevalue.RegionName==\"Oakland\")].set_index(\"RegionName\").drop([\"State\", \"Metro\", \"CountyName\"], axis=1).transpose()\n",
"oak_history.index = pd.to_datetime(oak_history.index)\n",
"oak_history"
],
"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>RegionName</th>\n",
" <th>Oakland</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1996-04-01</th>\n",
" <td> 143700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1996-05-01</th>\n",
" <td> 144300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1996-06-01</th>\n",
" <td> 144700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1996-07-01</th>\n",
" <td> 144400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1996-08-01</th>\n",
" <td> 144300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1996-09-01</th>\n",
" <td> 144400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1996-10-01</th>\n",
" <td> 144700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1996-11-01</th>\n",
" <td> 145100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1996-12-01</th>\n",
" <td> 145600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1997-01-01</th>\n",
" <td> 146200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1997-02-01</th>\n",
" <td> 147200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1997-03-01</th>\n",
" <td> 148000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1997-04-01</th>\n",
" <td> 148700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1997-05-01</th>\n",
" <td> 149200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1997-06-01</th>\n",
" <td> 149900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1997-07-01</th>\n",
" <td> 150700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1997-08-01</th>\n",
" <td> 151400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1997-09-01</th>\n",
" <td> 152200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1997-10-01</th>\n",
" <td> 153400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1997-11-01</th>\n",
" <td> 154700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1997-12-01</th>\n",
" <td> 156100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1998-01-01</th>\n",
" <td> 157800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1998-02-01</th>\n",
" <td> 159800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1998-03-01</th>\n",
" <td> 161100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1998-04-01</th>\n",
" <td> 161800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1998-05-01</th>\n",
" <td> 162100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1998-06-01</th>\n",
" <td> 162000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1998-07-01</th>\n",
" <td> 162200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1998-08-01</th>\n",
" <td> 163200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1998-09-01</th>\n",
" <td> 164400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1998-10-01</th>\n",
" <td> 164800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1998-11-01</th>\n",
" <td> 164600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1998-12-01</th>\n",
" <td> 164500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-01-01</th>\n",
" <td> 165500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-02-01</th>\n",
" <td> 166800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-03-01</th>\n",
" <td> 168000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-04-01</th>\n",
" <td> 169700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-05-01</th>\n",
" <td> 172200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-06-01</th>\n",
" <td> 175600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-07-01</th>\n",
" <td> 180500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-08-01</th>\n",
" <td> 185400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-09-01</th>\n",
" <td> 189200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-10-01</th>\n",
" <td> 192400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-11-01</th>\n",
" <td> 195700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-12-01</th>\n",
" <td> 199400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-01</th>\n",
" <td> 204200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-02-01</th>\n",
" <td> 209500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-03-01</th>\n",
" <td> 215400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-04-01</th>\n",
" <td> 221600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-05-01</th>\n",
" <td> 228600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-06-01</th>\n",
" <td> 235600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-07-01</th>\n",
" <td> 241600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-08-01</th>\n",
" <td> 246400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-09-01</th>\n",
" <td> 250800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-10-01</th>\n",
" <td> 255100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-11-01</th>\n",
" <td> 259900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-12-01</th>\n",
" <td> 264900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td> 269300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-02-01</th>\n",
" <td> 273200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-03-01</th>\n",
" <td> 275800</td>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <td>...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>216 rows \u00d7 1 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 77,
"text": [
"RegionName Oakland\n",
"1996-04-01 143700\n",
"1996-05-01 144300\n",
"1996-06-01 144700\n",
"1996-07-01 144400\n",
"1996-08-01 144300\n",
"1996-09-01 144400\n",
"1996-10-01 144700\n",
"1996-11-01 145100\n",
"1996-12-01 145600\n",
"1997-01-01 146200\n",
"1997-02-01 147200\n",
"1997-03-01 148000\n",
"1997-04-01 148700\n",
"1997-05-01 149200\n",
"1997-06-01 149900\n",
"1997-07-01 150700\n",
"1997-08-01 151400\n",
"1997-09-01 152200\n",
"1997-10-01 153400\n",
"1997-11-01 154700\n",
"1997-12-01 156100\n",
"1998-01-01 157800\n",
"1998-02-01 159800\n",
"1998-03-01 161100\n",
"1998-04-01 161800\n",
"1998-05-01 162100\n",
"1998-06-01 162000\n",
"1998-07-01 162200\n",
"1998-08-01 163200\n",
"1998-09-01 164400\n",
"1998-10-01 164800\n",
"1998-11-01 164600\n",
"1998-12-01 164500\n",
"1999-01-01 165500\n",
"1999-02-01 166800\n",
"1999-03-01 168000\n",
"1999-04-01 169700\n",
"1999-05-01 172200\n",
"1999-06-01 175600\n",
"1999-07-01 180500\n",
"1999-08-01 185400\n",
"1999-09-01 189200\n",
"1999-10-01 192400\n",
"1999-11-01 195700\n",
"1999-12-01 199400\n",
"2000-01-01 204200\n",
"2000-02-01 209500\n",
"2000-03-01 215400\n",
"2000-04-01 221600\n",
"2000-05-01 228600\n",
"2000-06-01 235600\n",
"2000-07-01 241600\n",
"2000-08-01 246400\n",
"2000-09-01 250800\n",
"2000-10-01 255100\n",
"2000-11-01 259900\n",
"2000-12-01 264900\n",
"2001-01-01 269300\n",
"2001-02-01 273200\n",
"2001-03-01 275800\n",
" ...\n",
"\n",
"[216 rows x 1 columns]"
]
}
],
"prompt_number": 77
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(list(oak_history.index),oak_history['Oakland'].tolist())\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 85,
"text": [
"[<matplotlib.lines.Line2D at 0xbe7ce10>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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hGTZsmLz11ltSXl7u+cxjjz0mGRkZIvJdg3lNTY3s3LlTwsLCPA3m8fHxsmHD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\ntZgmD6WUUi2myUMppVSL/X/m/dBtH1ZDwAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x14fe3160>"
]
}
],
"prompt_number": 85
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"test = DataFrame([2000, 2011, 2011, 2008, 2000])\n",
"test.groupby(0).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></th>\n",
" <th>0</th>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000</th>\n",
" <th>0</th>\n",
" <td> 2000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">2011</th>\n",
" <th>1</th>\n",
" <td> 2011</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 2011</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2008</th>\n",
" <th>3</th>\n",
" <td> 2008</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000</th>\n",
" <th>4</th>\n",
" <td> 2000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 1 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 45,
"text": [
" 0\n",
"0 \n",
"2000 0 2000\n",
"2011 1 2011\n",
" 2 2011\n",
"2008 3 2008\n",
"2000 4 2000\n",
"\n",
"[5 rows x 1 columns]"
]
}
],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"homevalue = generateDFfromFilename(\"Neighborhood_Zhvi_AllHomes\")\n",
"oak_homevalue = cleanedOakland(homevalue,[\"City\", \"State\", \"Metro\", \"CountyName\"])\n",
"oak_homevalue"
],
"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>RegionName</th>\n",
" <th>1996-04</th>\n",
" <th>1996-05</th>\n",
" <th>1996-06</th>\n",
" <th>1996-07</th>\n",
" <th>1996-08</th>\n",
" <th>1996-09</th>\n",
" <th>1996-10</th>\n",
" <th>1996-11</th>\n",
" <th>1996-12</th>\n",
" <th>1997-01</th>\n",
" <th>1997-02</th>\n",
" <th>1997-03</th>\n",
" <th>1997-04</th>\n",
" <th>1997-05</th>\n",
" <th>1997-06</th>\n",
" <th>1997-07</th>\n",
" <th>1997-08</th>\n",
" <th>1997-09</th>\n",
" <th>1997-10</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1307</th>\n",
" <td> Redwood Heights</td>\n",
" <td> 185500</td>\n",
" <td> 185400</td>\n",
" <td> 184000</td>\n",
" <td> 182400</td>\n",
" <td> 181300</td>\n",
" <td> 180700</td>\n",
" <td> 180600</td>\n",
" <td> 181100</td>\n",
" <td> 182000</td>\n",
" <td> 183700</td>\n",
" <td> 185500</td>\n",
" <td> 186300</td>\n",
" <td> 186700</td>\n",
" <td> 187600</td>\n",
" <td> 188600</td>\n",
" <td> 190700</td>\n",
" <td> 194000</td>\n",
" <td> 196200</td>\n",
" <td> 196900</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1481</th>\n",
" <td> Adams Point</td>\n",
" <td> 113800</td>\n",
" <td> 110900</td>\n",
" <td> 108300</td>\n",
" <td> 106300</td>\n",
" <td> 105300</td>\n",
" <td> 104500</td>\n",
" <td> 103400</td>\n",
" <td> 102600</td>\n",
" <td> 103800</td>\n",
" <td> 105500</td>\n",
" <td> 106900</td>\n",
" <td> 108600</td>\n",
" <td> 109900</td>\n",
" <td> 109900</td>\n",
" <td> 110700</td>\n",
" <td> 112300</td>\n",
" <td> 114700</td>\n",
" <td> 118500</td>\n",
" <td> 123000</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1531</th>\n",
" <td> Clinton</td>\n",
" <td> 125200</td>\n",
" <td> 125400</td>\n",
" <td> 125400</td>\n",
" <td> 125100</td>\n",
" <td> 125300</td>\n",
" <td> 125200</td>\n",
" <td> 125200</td>\n",
" <td> 125500</td>\n",
" <td> 125800</td>\n",
" <td> 125300</td>\n",
" <td> 125200</td>\n",
" <td> 125500</td>\n",
" <td> 125500</td>\n",
" <td> 125200</td>\n",
" <td> 125200</td>\n",
" <td> 125700</td>\n",
" <td> 125900</td>\n",
" <td> 125900</td>\n",
" <td> 126200</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1582</th>\n",
" <td> Cleveland Heights</td>\n",
" <td> 209900</td>\n",
" <td> 211300</td>\n",
" <td> 209100</td>\n",
" <td> 206200</td>\n",
" <td> 205500</td>\n",
" <td> 204900</td>\n",
" <td> 203400</td>\n",
" <td> 200400</td>\n",
" <td> 196400</td>\n",
" <td> 194200</td>\n",
" <td> 194000</td>\n",
" <td> 193000</td>\n",
" <td> 191800</td>\n",
" <td> 191600</td>\n",
" <td> 191900</td>\n",
" <td> 192300</td>\n",
" <td> 194500</td>\n",
" <td> 197200</td>\n",
" <td> 199200</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1677</th>\n",
" <td> Havenscourt</td>\n",
" <td> 94300</td>\n",
" <td> 96100</td>\n",
" <td> 98200</td>\n",
" <td> 100100</td>\n",
" <td> 101300</td>\n",
" <td> 101300</td>\n",
" <td> 101000</td>\n",
" <td> 100800</td>\n",
" <td> 100900</td>\n",
" <td> 102300</td>\n",
" <td> 104000</td>\n",
" <td> 104700</td>\n",
" <td> 105100</td>\n",
" <td> 106300</td>\n",
" <td> 107600</td>\n",
" <td> 108400</td>\n",
" <td> 108400</td>\n",
" <td> 108900</td>\n",
" <td> 109800</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1740</th>\n",
" <td> Bushrod</td>\n",
" <td> 169100</td>\n",
" <td> 168900</td>\n",
" <td> 168800</td>\n",
" <td> 167700</td>\n",
" <td> 166000</td>\n",
" <td> 164500</td>\n",
" <td> 163300</td>\n",
" <td> 163000</td>\n",
" <td> 162400</td>\n",
" <td> 162100</td>\n",
" <td> 162600</td>\n",
" <td> 162400</td>\n",
" <td> 160800</td>\n",
" <td> 159500</td>\n",
" <td> 159100</td>\n",
" <td> 158900</td>\n",
" <td> 158800</td>\n",
" <td> 159900</td>\n",
" <td> 161100</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1760</th>\n",
" <td> Piedmont Avenue</td>\n",
" <td> 144700</td>\n",
" <td> 142600</td>\n",
" <td> 142500</td>\n",
" <td> 142200</td>\n",
" <td> 142400</td>\n",
" <td> 143400</td>\n",
" <td> 145200</td>\n",
" <td> 147500</td>\n",
" <td> 150000</td>\n",
" <td> 150400</td>\n",
" <td> 150200</td>\n",
" <td> 151600</td>\n",
" <td> 153300</td>\n",
" <td> 156000</td>\n",
" <td> 158700</td>\n",
" <td> 162800</td>\n",
" <td> 167900</td>\n",
" <td> 170900</td>\n",
" <td> 171700</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1820</th>\n",
" <td> Upper Dimond</td>\n",
" <td> 170600</td>\n",
" <td> 172900</td>\n",
" <td> 173100</td>\n",
" <td> 173600</td>\n",
" <td> 175200</td>\n",
" <td> 176300</td>\n",
" <td> 177700</td>\n",
" <td> 179000</td>\n",
" <td> 179800</td>\n",
" <td> 181500</td>\n",
" <td> 184500</td>\n",
" <td> 186800</td>\n",
" <td> 187600</td>\n",
" <td> 188500</td>\n",
" <td> 189900</td>\n",
" <td> 192000</td>\n",
" <td> 194000</td>\n",
" <td> 195700</td>\n",
" <td> 196800</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1826</th>\n",
" <td> St. Elizabeth</td>\n",
" <td> 106400</td>\n",
" <td> 104500</td>\n",
" <td> 103700</td>\n",
" <td> 104300</td>\n",
" <td> 104900</td>\n",
" <td> 104800</td>\n",
" <td> 105200</td>\n",
" <td> 106300</td>\n",
" <td> 107000</td>\n",
" <td> 107600</td>\n",
" <td> 108800</td>\n",
" <td> 110500</td>\n",
" <td> 111800</td>\n",
" <td> 112200</td>\n",
" <td> 112000</td>\n",
" <td> 111800</td>\n",
" <td> 111800</td>\n",
" <td> 111900</td>\n",
" <td> 111800</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1972</th>\n",
" <td> Longfellow</td>\n",
" <td> 134800</td>\n",
" <td> 130900</td>\n",
" <td> 128800</td>\n",
" <td> 127700</td>\n",
" <td> 126200</td>\n",
" <td> 125000</td>\n",
" <td> 124500</td>\n",
" <td> 124800</td>\n",
" <td> 125700</td>\n",
" <td> 126800</td>\n",
" <td> 127500</td>\n",
" <td> 128600</td>\n",
" <td> 130200</td>\n",
" <td> 130400</td>\n",
" <td> 129800</td>\n",
" <td> 129100</td>\n",
" <td> 129900</td>\n",
" <td> 131800</td>\n",
" <td> 134000</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1990</th>\n",
" <td> Upper Rockridge</td>\n",
" <td> 350300</td>\n",
" <td> 353500</td>\n",
" <td> 358300</td>\n",
" <td> 360900</td>\n",
" <td> 362100</td>\n",
" <td> 364100</td>\n",
" <td> 365800</td>\n",
" <td> 367100</td>\n",
" <td> 369300</td>\n",
" <td> 374000</td>\n",
" <td> 379300</td>\n",
" <td> 382900</td>\n",
" <td> 386200</td>\n",
" <td> 389800</td>\n",
" <td> 390600</td>\n",
" <td> 392500</td>\n",
" <td> 398000</td>\n",
" <td> 403100</td>\n",
" <td> 406000</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000</th>\n",
" <td> Fremont</td>\n",
" <td> 104800</td>\n",
" <td> 106800</td>\n",
" <td> 108100</td>\n",
" <td> 108900</td>\n",
" <td> 110000</td>\n",
" <td> 111300</td>\n",
" <td> 112600</td>\n",
" <td> 112800</td>\n",
" <td> 112300</td>\n",
" <td> 112600</td>\n",
" <td> 113600</td>\n",
" <td> 114000</td>\n",
" <td> 113800</td>\n",
" <td> 114300</td>\n",
" <td> 114900</td>\n",
" <td> 115200</td>\n",
" <td> 115000</td>\n",
" <td> 114600</td>\n",
" <td> 113900</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024</th>\n",
" <td> Meadow Brook</td>\n",
" <td> 114800</td>\n",
" <td> 113800</td>\n",
" <td> 112400</td>\n",
" <td> 112200</td>\n",
" <td> 113400</td>\n",
" <td> 114000</td>\n",
" <td> 114400</td>\n",
" <td> 115100</td>\n",
" <td> 116600</td>\n",
" <td> 118200</td>\n",
" <td> 119600</td>\n",
" <td> 119200</td>\n",
" <td> 117700</td>\n",
" <td> 117300</td>\n",
" <td> 118900</td>\n",
" <td> 120300</td>\n",
" <td> 120300</td>\n",
" <td> 119500</td>\n",
" <td> 118600</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2030</th>\n",
" <td> Webster</td>\n",
" <td> 90200</td>\n",
" <td> 93000</td>\n",
" <td> 95800</td>\n",
" <td> 97700</td>\n",
" <td> 98700</td>\n",
" <td> 99200</td>\n",
" <td> 99500</td>\n",
" <td> 99800</td>\n",
" <td> 99900</td>\n",
" <td> 100400</td>\n",
" <td> 101300</td>\n",
" <td> 101500</td>\n",
" <td> 101400</td>\n",
" <td> 100900</td>\n",
" <td> 100600</td>\n",
" <td> 100400</td>\n",
" <td> 100200</td>\n",
" <td> 100300</td>\n",
" <td> 101100</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2054</th>\n",
" <td> Seminary</td>\n",
" <td> 90300</td>\n",
" <td> 92400</td>\n",
" <td> 93600</td>\n",
" <td> 94700</td>\n",
" <td> 96200</td>\n",
" <td> 96400</td>\n",
" <td> 95900</td>\n",
" <td> 95600</td>\n",
" <td> 95100</td>\n",
" <td> 95000</td>\n",
" <td> 96200</td>\n",
" <td> 97300</td>\n",
" <td> 97800</td>\n",
" <td> 98600</td>\n",
" <td> 99300</td>\n",
" <td> 98700</td>\n",
" <td> 97800</td>\n",
" <td> 98200</td>\n",
" <td> 99300</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2062</th>\n",
" <td> Rancho San Antonio</td>\n",
" <td> 109800</td>\n",
" <td> 109700</td>\n",
" <td> 109400</td>\n",
" <td> 109800</td>\n",
" <td> 111100</td>\n",
" <td> 112000</td>\n",
" <td> 112100</td>\n",
" <td> 112700</td>\n",
" <td> 113900</td>\n",
" <td> 114900</td>\n",
" <td> 115200</td>\n",
" <td> 114500</td>\n",
" <td> 114000</td>\n",
" <td> 115100</td>\n",
" <td> 116700</td>\n",
" <td> 116600</td>\n",
" <td> 115200</td>\n",
" <td> 113700</td>\n",
" <td> 113800</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2085</th>\n",
" <td> Maxwell Park</td>\n",
" <td> 129200</td>\n",
" <td> 130900</td>\n",
" <td> 133300</td>\n",
" <td> 134600</td>\n",
" <td> 135100</td>\n",
" <td> 136000</td>\n",
" <td> 137500</td>\n",
" <td> 138600</td>\n",
" <td> 139600</td>\n",
" <td> 140800</td>\n",
" <td> 141900</td>\n",
" <td> 142900</td>\n",
" <td> 143800</td>\n",
" <td> 144500</td>\n",
" <td> 144900</td>\n",
" <td> 145900</td>\n",
" <td> 147200</td>\n",
" <td> 148400</td>\n",
" <td> 149200</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2099</th>\n",
" <td> Glenview</td>\n",
" <td> 201300</td>\n",
" <td> 207400</td>\n",
" <td> 211500</td>\n",
" <td> 213600</td>\n",
" <td> 216300</td>\n",
" <td> 219300</td>\n",
" <td> 221500</td>\n",
" <td> 221700</td>\n",
" <td> 221300</td>\n",
" <td> 222400</td>\n",
" <td> 225800</td>\n",
" <td> 228400</td>\n",
" <td> 228700</td>\n",
" <td> 228000</td>\n",
" <td> 227500</td>\n",
" <td> 228200</td>\n",
" <td> 229800</td>\n",
" <td> 231300</td>\n",
" <td> 232200</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2199</th>\n",
" <td> Arroyo Viejo</td>\n",
" <td> 88200</td>\n",
" <td> 91300</td>\n",
" <td> 95000</td>\n",
" <td> 97400</td>\n",
" <td> 98500</td>\n",
" <td> 98700</td>\n",
" <td> 99300</td>\n",
" <td> 100300</td>\n",
" <td> 100600</td>\n",
" <td> 100800</td>\n",
" <td> 102000</td>\n",
" <td> 103700</td>\n",
" <td> 104300</td>\n",
" <td> 103800</td>\n",
" <td> 102800</td>\n",
" <td> 102100</td>\n",
" <td> 101900</td>\n",
" <td> 102900</td>\n",
" <td> 104600</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2298</th>\n",
" <td> Allendale</td>\n",
" <td> 104800</td>\n",
" <td> 105400</td>\n",
" <td> 105700</td>\n",
" <td> 106000</td>\n",
" <td> 105900</td>\n",
" <td> 106000</td>\n",
" <td> 107000</td>\n",
" <td> 108600</td>\n",
" <td> 109900</td>\n",
" <td> 111600</td>\n",
" <td> 113400</td>\n",
" <td> 114400</td>\n",
" <td> 114300</td>\n",
" <td> 114000</td>\n",
" <td> 114200</td>\n",
" <td> 114700</td>\n",
" <td> 115000</td>\n",
" <td> 115000</td>\n",
" <td> 115100</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2377</th>\n",
" <td> Grand Lake</td>\n",
" <td> 117300</td>\n",
" <td> 116900</td>\n",
" <td> 117500</td>\n",
" <td> 117700</td>\n",
" <td> 116600</td>\n",
" <td> 115300</td>\n",
" <td> 114500</td>\n",
" <td> 114800</td>\n",
" <td> 116000</td>\n",
" <td> 116900</td>\n",
" <td> 118600</td>\n",
" <td> 120800</td>\n",
" <td> 122700</td>\n",
" <td> 125200</td>\n",
" <td> 128400</td>\n",
" <td> 129600</td>\n",
" <td> 130200</td>\n",
" <td> 131400</td>\n",
" <td> 134100</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2381</th>\n",
" <td> Prescott</td>\n",
" <td> 136600</td>\n",
" <td> 134800</td>\n",
" <td> 133500</td>\n",
" <td> 131600</td>\n",
" <td> 129300</td>\n",
" <td> 128300</td>\n",
" <td> 127200</td>\n",
" <td> 124700</td>\n",
" <td> 123200</td>\n",
" <td> 123200</td>\n",
" <td> 123900</td>\n",
" <td> 126000</td>\n",
" <td> 129300</td>\n",
" <td> 131900</td>\n",
" <td> 134400</td>\n",
" <td> 137100</td>\n",
" <td> 140300</td>\n",
" <td> 143300</td>\n",
" <td> 144500</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2394</th>\n",
" <td> Lakeshore</td>\n",
" <td> 268900</td>\n",
" <td> 277300</td>\n",
" <td> 283900</td>\n",
" <td> 286400</td>\n",
" <td> 287400</td>\n",
" <td> 288000</td>\n",
" <td> 289000</td>\n",
" <td> 290700</td>\n",
" <td> 292800</td>\n",
" <td> 296000</td>\n",
" <td> 298900</td>\n",
" <td> 299900</td>\n",
" <td> 298500</td>\n",
" <td> 297100</td>\n",
" <td> 297500</td>\n",
" <td> 300800</td>\n",
" <td> 304900</td>\n",
" <td> 308300</td>\n",
" <td> 309900</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2579</th>\n",
" <td> Temescal</td>\n",
" <td> 166200</td>\n",
" <td> 168600</td>\n",
" <td> 168900</td>\n",
" <td> 168000</td>\n",
" <td> 167900</td>\n",
" <td> 168200</td>\n",
" <td> 168500</td>\n",
" <td> 168800</td>\n",
" <td> 169500</td>\n",
" <td> 170600</td>\n",
" <td> 172400</td>\n",
" <td> 173600</td>\n",
" <td> 173000</td>\n",
" <td> 173000</td>\n",
" <td> 174500</td>\n",
" <td> 176800</td>\n",
" <td> 178500</td>\n",
" <td> 179700</td>\n",
" <td> 180700</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2611</th>\n",
" <td> Highland Terrace</td>\n",
" <td> 113900</td>\n",
" <td> 112600</td>\n",
" <td> 112200</td>\n",
" <td> 112900</td>\n",
" <td> 113700</td>\n",
" <td> 114600</td>\n",
" <td> 114600</td>\n",
" <td> 114000</td>\n",
" <td> 113800</td>\n",
" <td> 114100</td>\n",
" <td> 114200</td>\n",
" <td> 113700</td>\n",
" <td> 113200</td>\n",
" <td> 113600</td>\n",
" <td> 114200</td>\n",
" <td> 114300</td>\n",
" <td> 114500</td>\n",
" <td> 114500</td>\n",
" <td> 115300</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2670</th>\n",
" <td> Sequoyah</td>\n",
" <td> 223100</td>\n",
" <td> 222700</td>\n",
" <td> 223800</td>\n",
" <td> 226100</td>\n",
" <td> 228500</td>\n",
" <td> 229900</td>\n",
" <td> 230600</td>\n",
" <td> 231500</td>\n",
" <td> 232700</td>\n",
" <td> 235000</td>\n",
" <td> 237300</td>\n",
" <td> 239000</td>\n",
" <td> 241300</td>\n",
" <td> 244900</td>\n",
" <td> 248100</td>\n",
" <td> 250800</td>\n",
" <td> 253000</td>\n",
" <td> 254100</td>\n",
" <td> 254700</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2682</th>\n",
" <td> Montclair</td>\n",
" <td> 326000</td>\n",
" <td> 325000</td>\n",
" <td> 322100</td>\n",
" <td> 319000</td>\n",
" <td> 318800</td>\n",
" <td> 319200</td>\n",
" <td> 318500</td>\n",
" <td> 318500</td>\n",
" <td> 320000</td>\n",
" <td> 322000</td>\n",
" <td> 324400</td>\n",
" <td> 325600</td>\n",
" <td> 325900</td>\n",
" <td> 326700</td>\n",
" <td> 328200</td>\n",
" <td> 329300</td>\n",
" <td> 329700</td>\n",
" <td> 331400</td>\n",
" <td> 335000</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2734</th>\n",
" <td> North Stonehurst</td>\n",
" <td> 91800</td>\n",
" <td> 94500</td>\n",
" <td> 96400</td>\n",
" <td> 97200</td>\n",
" <td> 97300</td>\n",
" <td> 97600</td>\n",
" <td> 98100</td>\n",
" <td> 98700</td>\n",
" <td> 99400</td>\n",
" <td> 100400</td>\n",
" <td> 101400</td>\n",
" <td> 102200</td>\n",
" <td> 102800</td>\n",
" <td> 102800</td>\n",
" <td> 102300</td>\n",
" <td> 102200</td>\n",
" <td> 102000</td>\n",
" <td> 101900</td>\n",
" <td> 102800</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2749</th>\n",
" <td> Harrington</td>\n",
" <td> 107900</td>\n",
" <td> 107200</td>\n",
" <td> 107100</td>\n",
" <td> 107400</td>\n",
" <td> 107700</td>\n",
" <td> 107700</td>\n",
" <td> 108800</td>\n",
" <td> 110000</td>\n",
" <td> 110500</td>\n",
" <td> 111000</td>\n",
" <td> 112100</td>\n",
" <td> 113100</td>\n",
" <td> 113600</td>\n",
" <td> 113600</td>\n",
" <td> 114000</td>\n",
" <td> 115100</td>\n",
" <td> 116000</td>\n",
" <td> 116200</td>\n",
" <td> 116100</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2786</th>\n",
" <td> Jefferson</td>\n",
" <td> 114300</td>\n",
" <td> 114000</td>\n",
" <td> 114700</td>\n",
" <td> 115600</td>\n",
" <td> 116000</td>\n",
" <td> 116500</td>\n",
" <td> 117700</td>\n",
" <td> 119300</td>\n",
" <td> 120700</td>\n",
" <td> 121800</td>\n",
" <td> 122600</td>\n",
" <td> 122800</td>\n",
" <td> 123400</td>\n",
" <td> 124700</td>\n",
" <td> 125700</td>\n",
" <td> 126600</td>\n",
" <td> 128100</td>\n",
" <td> 128600</td>\n",
" <td> 127200</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2891</th>\n",
" <td> Iveywood</td>\n",
" <td> 97600</td>\n",
" <td> 98400</td>\n",
" <td> 100200</td>\n",
" <td> 101700</td>\n",
" <td> 102300</td>\n",
" <td> 102700</td>\n",
" <td> 102700</td>\n",
" <td> 103000</td>\n",
" <td> 103700</td>\n",
" <td> 105200</td>\n",
" <td> 106200</td>\n",
" <td> 106600</td>\n",
" <td> 106700</td>\n",
" <td> 106600</td>\n",
" <td> 106600</td>\n",
" <td> 107100</td>\n",
" <td> 107800</td>\n",
" <td> 108700</td>\n",
" <td> 110300</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2903</th>\n",
" <td> Fairview Park</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 242200</td>\n",
" <td> 241800</td>\n",
" <td> 241900</td>\n",
" <td> 239700</td>\n",
" <td> 236400</td>\n",
" <td> 236600</td>\n",
" <td> 239500</td>\n",
" <td> 242300</td>\n",
" <td> 243900</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2906</th>\n",
" <td> Chabot Park</td>\n",
" <td> 238600</td>\n",
" <td> 240500</td>\n",
" <td> 241100</td>\n",
" <td> 242200</td>\n",
" <td> 243700</td>\n",
" <td> 243800</td>\n",
" <td> 243200</td>\n",
" <td> 243100</td>\n",
" <td> 242700</td>\n",
" <td> 243300</td>\n",
" <td> 245600</td>\n",
" <td> 246700</td>\n",
" <td> 246500</td>\n",
" <td> 247400</td>\n",
" <td> 248500</td>\n",
" <td> 249200</td>\n",
" <td> 249900</td>\n",
" <td> 252200</td>\n",
" <td> 255400</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2963</th>\n",
" <td> Merriwood</td>\n",
" <td> 304500</td>\n",
" <td> 300100</td>\n",
" <td> 296000</td>\n",
" <td> 293200</td>\n",
" <td> 291200</td>\n",
" <td> 291100</td>\n",
" <td> 290300</td>\n",
" <td> 289000</td>\n",
" <td> 289000</td>\n",
" <td> 291500</td>\n",
" <td> 293300</td>\n",
" <td> 293300</td>\n",
" <td> 293000</td>\n",
" <td> 294200</td>\n",
" <td> 296900</td>\n",
" <td> 301200</td>\n",
" <td> 305400</td>\n",
" <td> 307700</td>\n",
" <td> 310600</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2988</th>\n",
" <td> Eastmont Hills</td>\n",
" <td> 142500</td>\n",
" <td> 141600</td>\n",
" <td> 141000</td>\n",
" <td> 141000</td>\n",
" <td> 141300</td>\n",
" <td> 141300</td>\n",
" <td> 141000</td>\n",
" <td> 140900</td>\n",
" <td> 141200</td>\n",
" <td> 142000</td>\n",
" <td> 142800</td>\n",
" <td> 142400</td>\n",
" <td> 141700</td>\n",
" <td> 141700</td>\n",
" <td> 142000</td>\n",
" <td> 141900</td>\n",
" <td> 141500</td>\n",
" <td> 141700</td>\n",
" <td> 142300</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2991</th>\n",
" <td> Shafter</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 245800</td>\n",
" <td> 245200</td>\n",
" <td> 241500</td>\n",
" <td> 238100</td>\n",
" <td> 236900</td>\n",
" <td> 239700</td>\n",
" <td> 244800</td>\n",
" <td> 247200</td>\n",
" <td> 247100</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3035</th>\n",
" <td> Eastmont</td>\n",
" <td> 93800</td>\n",
" <td> 93700</td>\n",
" <td> 94800</td>\n",
" <td> 96000</td>\n",
" <td> 97600</td>\n",
" <td> 99500</td>\n",
" <td> 100500</td>\n",
" <td> 101100</td>\n",
" <td> 101900</td>\n",
" <td> 102900</td>\n",
" <td> 104300</td>\n",
" <td> 106200</td>\n",
" <td> 108300</td>\n",
" <td> 109800</td>\n",
" <td> 110900</td>\n",
" <td> 110900</td>\n",
" <td> 108900</td>\n",
" <td> 106800</td>\n",
" <td> 106700</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3043</th>\n",
" <td> Castlemont</td>\n",
" <td> 111300</td>\n",
" <td> 109900</td>\n",
" <td> 110200</td>\n",
" <td> 110300</td>\n",
" <td> 110400</td>\n",
" <td> 111300</td>\n",
" <td> 112000</td>\n",
" <td> 112200</td>\n",
" <td> 111900</td>\n",
" <td> 111800</td>\n",
" <td> 111600</td>\n",
" <td> 111000</td>\n",
" <td> 110600</td>\n",
" <td> 110700</td>\n",
" <td> 111000</td>\n",
" <td> 111500</td>\n",
" <td> 112000</td>\n",
" <td> 112400</td>\n",
" <td> 113600</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3062</th>\n",
" <td> Rockridge</td>\n",
" <td> 287800</td>\n",
" <td> 297700</td>\n",
" <td> 304000</td>\n",
" <td> 307300</td>\n",
" <td> 308900</td>\n",
" <td> 311400</td>\n",
" <td> 314400</td>\n",
" <td> 316000</td>\n",
" <td> 315600</td>\n",
" <td> 317300</td>\n",
" <td> 320700</td>\n",
" <td> 322400</td>\n",
" <td> 322700</td>\n",
" <td> 324900</td>\n",
" <td> 328600</td>\n",
" <td> 331800</td>\n",
" <td> 334100</td>\n",
" <td> 336800</td>\n",
" <td> 339800</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3084</th>\n",
" <td> Cox</td>\n",
" <td> 100700</td>\n",
" <td> 101300</td>\n",
" <td> 101900</td>\n",
" <td> 102200</td>\n",
" <td> 102400</td>\n",
" <td> 102700</td>\n",
" <td> 103000</td>\n",
" <td> 102900</td>\n",
" <td> 102500</td>\n",
" <td> 103200</td>\n",
" <td> 104100</td>\n",
" <td> 104100</td>\n",
" <td> 103900</td>\n",
" <td> 103900</td>\n",
" <td> 104100</td>\n",
" <td> 104900</td>\n",
" <td> 105700</td>\n",
" <td> 106300</td>\n",
" <td> 107500</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3097</th>\n",
" <td> Frick</td>\n",
" <td> 130200</td>\n",
" <td> 131100</td>\n",
" <td> 131700</td>\n",
" <td> 131800</td>\n",
" <td> 131600</td>\n",
" <td> 131800</td>\n",
" <td> 132700</td>\n",
" <td> 133500</td>\n",
" <td> 133700</td>\n",
" <td> 134400</td>\n",
" <td> 134800</td>\n",
" <td> 134400</td>\n",
" <td> 133700</td>\n",
" <td> 133900</td>\n",
" <td> 134700</td>\n",
" <td> 135900</td>\n",
" <td> 137300</td>\n",
" <td> 138700</td>\n",
" <td> 139400</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3107</th>\n",
" <td> Trestle Glen</td>\n",
" <td> 274300</td>\n",
" <td> 280600</td>\n",
" <td> 287000</td>\n",
" <td> 291400</td>\n",
" <td> 294100</td>\n",
" <td> 296400</td>\n",
" <td> 298900</td>\n",
" <td> 301800</td>\n",
" <td> 303700</td>\n",
" <td> 306400</td>\n",
" <td> 309600</td>\n",
" <td> 310800</td>\n",
" <td> 310600</td>\n",
" <td> 311900</td>\n",
" <td> 314300</td>\n",
" <td> 318600</td>\n",
" <td> 323300</td>\n",
" <td> 326400</td>\n",
" <td> 328800</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3119</th>\n",
" <td> Sobrante Park</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 106900</td>\n",
" <td> 106800</td>\n",
" <td> 106400</td>\n",
" <td> 107000</td>\n",
" <td> 108600</td>\n",
" <td> 109900</td>\n",
" <td> 110800</td>\n",
" <td> 111100</td>\n",
" <td> 111000</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3129</th>\n",
" <td> Santa Fe</td>\n",
" <td> 152800</td>\n",
" <td> 148900</td>\n",
" <td> 146000</td>\n",
" <td> 142700</td>\n",
" <td> 140500</td>\n",
" <td> 139200</td>\n",
" <td> 138300</td>\n",
" <td> 137500</td>\n",
" <td> 135800</td>\n",
" <td> 135000</td>\n",
" <td> 134800</td>\n",
" <td> 133800</td>\n",
" <td> 132100</td>\n",
" <td> 131300</td>\n",
" <td> 131300</td>\n",
" <td> 131500</td>\n",
" <td> 131200</td>\n",
" <td> 131500</td>\n",
" <td> 132300</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3202</th>\n",
" <td> Brookfield Village</td>\n",
" <td> 83600</td>\n",
" <td> 82600</td>\n",
" <td> 82300</td>\n",
" <td> 82600</td>\n",
" <td> 82800</td>\n",
" <td> 83200</td>\n",
" <td> 83700</td>\n",
" <td> 84400</td>\n",
" <td> 85300</td>\n",
" <td> 86500</td>\n",
" <td> 87600</td>\n",
" <td> 88500</td>\n",
" <td> 89200</td>\n",
" <td> 89900</td>\n",
" <td> 89800</td>\n",
" <td> 88800</td>\n",
" <td> 88200</td>\n",
" <td> 88800</td>\n",
" <td> 90100</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3258</th>\n",
" <td> Coliseum</td>\n",
" <td> 83500</td>\n",
" <td> 83800</td>\n",
" <td> 84900</td>\n",
" <td> 86000</td>\n",
" <td> 86400</td>\n",
" <td> 86200</td>\n",
" <td> 85600</td>\n",
" <td> 85000</td>\n",
" <td> 85200</td>\n",
" <td> 85900</td>\n",
" <td> 86800</td>\n",
" <td> 87600</td>\n",
" <td> 88300</td>\n",
" <td> 89100</td>\n",
" <td> 89800</td>\n",
" <td> 90400</td>\n",
" <td> 91400</td>\n",
" <td> 93300</td>\n",
" <td> 95300</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3266</th>\n",
" <td> Lakewide</td>\n",
" <td> 147100</td>\n",
" <td> 144800</td>\n",
" <td> 149000</td>\n",
" <td> 152600</td>\n",
" <td> 154600</td>\n",
" <td> 157700</td>\n",
" <td> 160000</td>\n",
" <td> 160500</td>\n",
" <td> 160700</td>\n",
" <td> 160300</td>\n",
" <td> 159800</td>\n",
" <td> 161100</td>\n",
" <td> 168200</td>\n",
" <td> 173800</td>\n",
" <td> 172800</td>\n",
" <td> 173600</td>\n",
" <td> 179300</td>\n",
" <td> 184300</td>\n",
" <td> 186900</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3271</th>\n",
" <td> Chinatown</td>\n",
" <td> 132400</td>\n",
" <td> 128100</td>\n",
" <td> 126200</td>\n",
" <td> 125900</td>\n",
" <td> 125300</td>\n",
" <td> 125600</td>\n",
" <td> 126900</td>\n",
" <td> 128000</td>\n",
" <td> 128900</td>\n",
" <td> 129400</td>\n",
" <td> 130100</td>\n",
" <td> 131600</td>\n",
" <td> 132900</td>\n",
" <td> 133600</td>\n",
" <td> 134500</td>\n",
" <td> 134500</td>\n",
" <td> 135300</td>\n",
" <td> 136300</td>\n",
" <td> 136300</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3286</th>\n",
" <td> Clawson</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3433</th>\n",
" <td> Piedmont Pines</td>\n",
" <td> 332400</td>\n",
" <td> 330200</td>\n",
" <td> 328100</td>\n",
" <td> 325700</td>\n",
" <td> 324600</td>\n",
" <td> 325800</td>\n",
" <td> 328000</td>\n",
" <td> 329900</td>\n",
" <td> 332500</td>\n",
" <td> 336400</td>\n",
" <td> 339600</td>\n",
" <td> 341600</td>\n",
" <td> 344500</td>\n",
" <td> 348600</td>\n",
" <td> 352100</td>\n",
" <td> 354700</td>\n",
" <td> 357000</td>\n",
" <td> 358700</td>\n",
" <td> 360400</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3504</th>\n",
" <td> Upper Peralta Creek</td>\n",
" <td> 120300</td>\n",
" <td> 119500</td>\n",
" <td> 117900</td>\n",
" <td> 116100</td>\n",
" <td> 115300</td>\n",
" <td> 115800</td>\n",
" <td> 116400</td>\n",
" <td> 116700</td>\n",
" <td> 116900</td>\n",
" <td> 117200</td>\n",
" <td> 117400</td>\n",
" <td> 118000</td>\n",
" <td> 118900</td>\n",
" <td> 119900</td>\n",
" <td> 121200</td>\n",
" <td> 122400</td>\n",
" <td> 122700</td>\n",
" <td> 122700</td>\n",
" <td> 122900</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3629</th>\n",
" <td> Las Palmas</td>\n",
" <td> 110800</td>\n",
" <td> 111400</td>\n",
" <td> 113400</td>\n",
" <td> 114100</td>\n",
" <td> 114400</td>\n",
" <td> 115500</td>\n",
" <td> 115600</td>\n",
" <td> 114700</td>\n",
" <td> 114300</td>\n",
" <td> 114800</td>\n",
" <td> 115200</td>\n",
" <td> 115400</td>\n",
" <td> 115400</td>\n",
" <td> 115000</td>\n",
" <td> 114800</td>\n",
" <td> 115600</td>\n",
" <td> 116400</td>\n",
" <td> 117000</td>\n",
" <td> 118300</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3645</th>\n",
" <td> Caballo Hills</td>\n",
" <td> 244000</td>\n",
" <td> 242600</td>\n",
" <td> 242100</td>\n",
" <td> 244600</td>\n",
" <td> 247900</td>\n",
" <td> 249000</td>\n",
" <td> 247500</td>\n",
" <td> 248000</td>\n",
" <td> 251000</td>\n",
" <td> 255400</td>\n",
" <td> 260300</td>\n",
" <td> 263900</td>\n",
" <td> 265800</td>\n",
" <td> 266900</td>\n",
" <td> 268700</td>\n",
" <td> 270300</td>\n",
" <td> 272000</td>\n",
" <td> 273500</td>\n",
" <td> 275300</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3781</th>\n",
" <td> Produce &amp; Waterfront</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3863</th>\n",
" <td> Oakmore</td>\n",
" <td> 278700</td>\n",
" <td> 279100</td>\n",
" <td> 281100</td>\n",
" <td> 284300</td>\n",
" <td> 285800</td>\n",
" <td> 286100</td>\n",
" <td> 287100</td>\n",
" <td> 289300</td>\n",
" <td> 291700</td>\n",
" <td> 295100</td>\n",
" <td> 298100</td>\n",
" <td> 300400</td>\n",
" <td> 302500</td>\n",
" <td> 304600</td>\n",
" <td> 306700</td>\n",
" <td> 309200</td>\n",
" <td> 312700</td>\n",
" <td> 316600</td>\n",
" <td> 321400</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3921</th>\n",
" <td> Fairfax</td>\n",
" <td> 111300</td>\n",
" <td> 112100</td>\n",
" <td> 114100</td>\n",
" <td> 115900</td>\n",
" <td> 117100</td>\n",
" <td> 118400</td>\n",
" <td> 120500</td>\n",
" <td> 122200</td>\n",
" <td> 122900</td>\n",
" <td> 124100</td>\n",
" <td> 125700</td>\n",
" <td> 126500</td>\n",
" <td> 126900</td>\n",
" <td> 127800</td>\n",
" <td> 128600</td>\n",
" <td> 128600</td>\n",
" <td> 128900</td>\n",
" <td> 129600</td>\n",
" <td> 129000</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3942</th>\n",
" <td> Toler Heights</td>\n",
" <td> 141700</td>\n",
" <td> 140400</td>\n",
" <td> 138000</td>\n",
" <td> 134900</td>\n",
" <td> 132600</td>\n",
" <td> 131400</td>\n",
" <td> 130900</td>\n",
" <td> 131300</td>\n",
" <td> 131600</td>\n",
" <td> 131300</td>\n",
" <td> 130800</td>\n",
" <td> 130500</td>\n",
" <td> 131400</td>\n",
" <td> 132800</td>\n",
" <td> 134100</td>\n",
" <td> 135700</td>\n",
" <td> 137000</td>\n",
" <td> 138400</td>\n",
" <td> 140300</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3970</th>\n",
" <td> Forestland</td>\n",
" <td> 335100</td>\n",
" <td> 333200</td>\n",
" <td> 333000</td>\n",
" <td> 333300</td>\n",
" <td> 332300</td>\n",
" <td> 331100</td>\n",
" <td> 330200</td>\n",
" <td> 329200</td>\n",
" <td> 327100</td>\n",
" <td> 327500</td>\n",
" <td> 327700</td>\n",
" <td> 327900</td>\n",
" <td> 328500</td>\n",
" <td> 330600</td>\n",
" <td> 330000</td>\n",
" <td> 329700</td>\n",
" <td> 331000</td>\n",
" <td> 335300</td>\n",
" <td> 341600</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3994</th>\n",
" <td> Golden Gate</td>\n",
" <td> 160500</td>\n",
" <td> 155300</td>\n",
" <td> 153100</td>\n",
" <td> 152100</td>\n",
" <td> 150000</td>\n",
" <td> 147000</td>\n",
" <td> 145400</td>\n",
" <td> 145100</td>\n",
" <td> 144500</td>\n",
" <td> 143900</td>\n",
" <td> 143600</td>\n",
" <td> 143700</td>\n",
" <td> 144600</td>\n",
" <td> 146500</td>\n",
" <td> 150100</td>\n",
" <td> 154000</td>\n",
" <td> 156500</td>\n",
" <td> 157300</td>\n",
" <td> 157700</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4015</th>\n",
" <td> Durant Manor</td>\n",
" <td> 126900</td>\n",
" <td> 127000</td>\n",
" <td> 125900</td>\n",
" <td> 124100</td>\n",
" <td> 123400</td>\n",
" <td> 123500</td>\n",
" <td> 123400</td>\n",
" <td> 123400</td>\n",
" <td> 123700</td>\n",
" <td> 124600</td>\n",
" <td> 125500</td>\n",
" <td> 125800</td>\n",
" <td> 125800</td>\n",
" <td> 126100</td>\n",
" <td> 127000</td>\n",
" <td> 128300</td>\n",
" <td> 129400</td>\n",
" <td> 130600</td>\n",
" <td> 132400</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>72 rows \u00d7 216 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
" RegionName 1996-04 1996-05 1996-06 1996-07 1996-08 \\\n",
"1307 Redwood Heights 185500 185400 184000 182400 181300 \n",
"1481 Adams Point 113800 110900 108300 106300 105300 \n",
"1531 Clinton 125200 125400 125400 125100 125300 \n",
"1582 Cleveland Heights 209900 211300 209100 206200 205500 \n",
"1677 Havenscourt 94300 96100 98200 100100 101300 \n",
"1740 Bushrod 169100 168900 168800 167700 166000 \n",
"1760 Piedmont Avenue 144700 142600 142500 142200 142400 \n",
"1820 Upper Dimond 170600 172900 173100 173600 175200 \n",
"1826 St. Elizabeth 106400 104500 103700 104300 104900 \n",
"1972 Longfellow 134800 130900 128800 127700 126200 \n",
"1990 Upper Rockridge 350300 353500 358300 360900 362100 \n",
"2000 Fremont 104800 106800 108100 108900 110000 \n",
"2024 Meadow Brook 114800 113800 112400 112200 113400 \n",
"2030 Webster 90200 93000 95800 97700 98700 \n",
"2054 Seminary 90300 92400 93600 94700 96200 \n",
"2062 Rancho San Antonio 109800 109700 109400 109800 111100 \n",
"2085 Maxwell Park 129200 130900 133300 134600 135100 \n",
"2099 Glenview 201300 207400 211500 213600 216300 \n",
"2199 Arroyo Viejo 88200 91300 95000 97400 98500 \n",
"2298 Allendale 104800 105400 105700 106000 105900 \n",
"2377 Grand Lake 117300 116900 117500 117700 116600 \n",
"2381 Prescott 136600 134800 133500 131600 129300 \n",
"2394 Lakeshore 268900 277300 283900 286400 287400 \n",
"2579 Temescal 166200 168600 168900 168000 167900 \n",
"2611 Highland Terrace 113900 112600 112200 112900 113700 \n",
"2670 Sequoyah 223100 222700 223800 226100 228500 \n",
"2682 Montclair 326000 325000 322100 319000 318800 \n",
"2734 North Stonehurst 91800 94500 96400 97200 97300 \n",
"2749 Harrington 107900 107200 107100 107400 107700 \n",
"2786 Jefferson 114300 114000 114700 115600 116000 \n",
"2891 Iveywood 97600 98400 100200 101700 102300 \n",
"2903 Fairview Park 0 0 0 0 0 \n",
"2906 Chabot Park 238600 240500 241100 242200 243700 \n",
"2963 Merriwood 304500 300100 296000 293200 291200 \n",
"2988 Eastmont Hills 142500 141600 141000 141000 141300 \n",
"2991 Shafter 0 0 0 0 0 \n",
"3035 Eastmont 93800 93700 94800 96000 97600 \n",
"3043 Castlemont 111300 109900 110200 110300 110400 \n",
"3062 Rockridge 287800 297700 304000 307300 308900 \n",
"3084 Cox 100700 101300 101900 102200 102400 \n",
"3097 Frick 130200 131100 131700 131800 131600 \n",
"3107 Trestle Glen 274300 280600 287000 291400 294100 \n",
"3119 Sobrante Park 0 0 0 0 0 \n",
"3129 Santa Fe 152800 148900 146000 142700 140500 \n",
"3202 Brookfield Village 83600 82600 82300 82600 82800 \n",
"3258 Coliseum 83500 83800 84900 86000 86400 \n",
"3266 Lakewide 147100 144800 149000 152600 154600 \n",
"3271 Chinatown 132400 128100 126200 125900 125300 \n",
"3286 Clawson 0 0 0 0 0 \n",
"3433 Piedmont Pines 332400 330200 328100 325700 324600 \n",
"3504 Upper Peralta Creek 120300 119500 117900 116100 115300 \n",
"3629 Las Palmas 110800 111400 113400 114100 114400 \n",
"3645 Caballo Hills 244000 242600 242100 244600 247900 \n",
"3781 Produce & Waterfront 0 0 0 0 0 \n",
"3863 Oakmore 278700 279100 281100 284300 285800 \n",
"3921 Fairfax 111300 112100 114100 115900 117100 \n",
"3942 Toler Heights 141700 140400 138000 134900 132600 \n",
"3970 Forestland 335100 333200 333000 333300 332300 \n",
"3994 Golden Gate 160500 155300 153100 152100 150000 \n",
"4015 Durant Manor 126900 127000 125900 124100 123400 \n",
" ... ... ... ... ... ... \n",
"\n",
" 1996-09 1996-10 1996-11 1996-12 1997-01 1997-02 1997-03 1997-04 \\\n",
"1307 180700 180600 181100 182000 183700 185500 186300 186700 \n",
"1481 104500 103400 102600 103800 105500 106900 108600 109900 \n",
"1531 125200 125200 125500 125800 125300 125200 125500 125500 \n",
"1582 204900 203400 200400 196400 194200 194000 193000 191800 \n",
"1677 101300 101000 100800 100900 102300 104000 104700 105100 \n",
"1740 164500 163300 163000 162400 162100 162600 162400 160800 \n",
"1760 143400 145200 147500 150000 150400 150200 151600 153300 \n",
"1820 176300 177700 179000 179800 181500 184500 186800 187600 \n",
"1826 104800 105200 106300 107000 107600 108800 110500 111800 \n",
"1972 125000 124500 124800 125700 126800 127500 128600 130200 \n",
"1990 364100 365800 367100 369300 374000 379300 382900 386200 \n",
"2000 111300 112600 112800 112300 112600 113600 114000 113800 \n",
"2024 114000 114400 115100 116600 118200 119600 119200 117700 \n",
"2030 99200 99500 99800 99900 100400 101300 101500 101400 \n",
"2054 96400 95900 95600 95100 95000 96200 97300 97800 \n",
"2062 112000 112100 112700 113900 114900 115200 114500 114000 \n",
"2085 136000 137500 138600 139600 140800 141900 142900 143800 \n",
"2099 219300 221500 221700 221300 222400 225800 228400 228700 \n",
"2199 98700 99300 100300 100600 100800 102000 103700 104300 \n",
"2298 106000 107000 108600 109900 111600 113400 114400 114300 \n",
"2377 115300 114500 114800 116000 116900 118600 120800 122700 \n",
"2381 128300 127200 124700 123200 123200 123900 126000 129300 \n",
"2394 288000 289000 290700 292800 296000 298900 299900 298500 \n",
"2579 168200 168500 168800 169500 170600 172400 173600 173000 \n",
"2611 114600 114600 114000 113800 114100 114200 113700 113200 \n",
"2670 229900 230600 231500 232700 235000 237300 239000 241300 \n",
"2682 319200 318500 318500 320000 322000 324400 325600 325900 \n",
"2734 97600 98100 98700 99400 100400 101400 102200 102800 \n",
"2749 107700 108800 110000 110500 111000 112100 113100 113600 \n",
"2786 116500 117700 119300 120700 121800 122600 122800 123400 \n",
"2891 102700 102700 103000 103700 105200 106200 106600 106700 \n",
"2903 0 0 0 0 0 242200 241800 241900 \n",
"2906 243800 243200 243100 242700 243300 245600 246700 246500 \n",
"2963 291100 290300 289000 289000 291500 293300 293300 293000 \n",
"2988 141300 141000 140900 141200 142000 142800 142400 141700 \n",
"2991 0 0 0 0 0 245800 245200 241500 \n",
"3035 99500 100500 101100 101900 102900 104300 106200 108300 \n",
"3043 111300 112000 112200 111900 111800 111600 111000 110600 \n",
"3062 311400 314400 316000 315600 317300 320700 322400 322700 \n",
"3084 102700 103000 102900 102500 103200 104100 104100 103900 \n",
"3097 131800 132700 133500 133700 134400 134800 134400 133700 \n",
"3107 296400 298900 301800 303700 306400 309600 310800 310600 \n",
"3119 0 0 0 0 0 106900 106800 106400 \n",
"3129 139200 138300 137500 135800 135000 134800 133800 132100 \n",
"3202 83200 83700 84400 85300 86500 87600 88500 89200 \n",
"3258 86200 85600 85000 85200 85900 86800 87600 88300 \n",
"3266 157700 160000 160500 160700 160300 159800 161100 168200 \n",
"3271 125600 126900 128000 128900 129400 130100 131600 132900 \n",
"3286 0 0 0 0 0 0 0 0 \n",
"3433 325800 328000 329900 332500 336400 339600 341600 344500 \n",
"3504 115800 116400 116700 116900 117200 117400 118000 118900 \n",
"3629 115500 115600 114700 114300 114800 115200 115400 115400 \n",
"3645 249000 247500 248000 251000 255400 260300 263900 265800 \n",
"3781 0 0 0 0 0 0 0 0 \n",
"3863 286100 287100 289300 291700 295100 298100 300400 302500 \n",
"3921 118400 120500 122200 122900 124100 125700 126500 126900 \n",
"3942 131400 130900 131300 131600 131300 130800 130500 131400 \n",
"3970 331100 330200 329200 327100 327500 327700 327900 328500 \n",
"3994 147000 145400 145100 144500 143900 143600 143700 144600 \n",
"4015 123500 123400 123400 123700 124600 125500 125800 125800 \n",
" ... ... ... ... ... ... ... ... \n",
"\n",
" 1997-05 1997-06 1997-07 1997-08 1997-09 1997-10 \n",
"1307 187600 188600 190700 194000 196200 196900 ... \n",
"1481 109900 110700 112300 114700 118500 123000 ... \n",
"1531 125200 125200 125700 125900 125900 126200 ... \n",
"1582 191600 191900 192300 194500 197200 199200 ... \n",
"1677 106300 107600 108400 108400 108900 109800 ... \n",
"1740 159500 159100 158900 158800 159900 161100 ... \n",
"1760 156000 158700 162800 167900 170900 171700 ... \n",
"1820 188500 189900 192000 194000 195700 196800 ... \n",
"1826 112200 112000 111800 111800 111900 111800 ... \n",
"1972 130400 129800 129100 129900 131800 134000 ... \n",
"1990 389800 390600 392500 398000 403100 406000 ... \n",
"2000 114300 114900 115200 115000 114600 113900 ... \n",
"2024 117300 118900 120300 120300 119500 118600 ... \n",
"2030 100900 100600 100400 100200 100300 101100 ... \n",
"2054 98600 99300 98700 97800 98200 99300 ... \n",
"2062 115100 116700 116600 115200 113700 113800 ... \n",
"2085 144500 144900 145900 147200 148400 149200 ... \n",
"2099 228000 227500 228200 229800 231300 232200 ... \n",
"2199 103800 102800 102100 101900 102900 104600 ... \n",
"2298 114000 114200 114700 115000 115000 115100 ... \n",
"2377 125200 128400 129600 130200 131400 134100 ... \n",
"2381 131900 134400 137100 140300 143300 144500 ... \n",
"2394 297100 297500 300800 304900 308300 309900 ... \n",
"2579 173000 174500 176800 178500 179700 180700 ... \n",
"2611 113600 114200 114300 114500 114500 115300 ... \n",
"2670 244900 248100 250800 253000 254100 254700 ... \n",
"2682 326700 328200 329300 329700 331400 335000 ... \n",
"2734 102800 102300 102200 102000 101900 102800 ... \n",
"2749 113600 114000 115100 116000 116200 116100 ... \n",
"2786 124700 125700 126600 128100 128600 127200 ... \n",
"2891 106600 106600 107100 107800 108700 110300 ... \n",
"2903 239700 236400 236600 239500 242300 243900 ... \n",
"2906 247400 248500 249200 249900 252200 255400 ... \n",
"2963 294200 296900 301200 305400 307700 310600 ... \n",
"2988 141700 142000 141900 141500 141700 142300 ... \n",
"2991 238100 236900 239700 244800 247200 247100 ... \n",
"3035 109800 110900 110900 108900 106800 106700 ... \n",
"3043 110700 111000 111500 112000 112400 113600 ... \n",
"3062 324900 328600 331800 334100 336800 339800 ... \n",
"3084 103900 104100 104900 105700 106300 107500 ... \n",
"3097 133900 134700 135900 137300 138700 139400 ... \n",
"3107 311900 314300 318600 323300 326400 328800 ... \n",
"3119 107000 108600 109900 110800 111100 111000 ... \n",
"3129 131300 131300 131500 131200 131500 132300 ... \n",
"3202 89900 89800 88800 88200 88800 90100 ... \n",
"3258 89100 89800 90400 91400 93300 95300 ... \n",
"3266 173800 172800 173600 179300 184300 186900 ... \n",
"3271 133600 134500 134500 135300 136300 136300 ... \n",
"3286 0 0 0 0 0 0 ... \n",
"3433 348600 352100 354700 357000 358700 360400 ... \n",
"3504 119900 121200 122400 122700 122700 122900 ... \n",
"3629 115000 114800 115600 116400 117000 118300 ... \n",
"3645 266900 268700 270300 272000 273500 275300 ... \n",
"3781 0 0 0 0 0 0 ... \n",
"3863 304600 306700 309200 312700 316600 321400 ... \n",
"3921 127800 128600 128600 128900 129600 129000 ... \n",
"3942 132800 134100 135700 137000 138400 140300 ... \n",
"3970 330600 330000 329700 331000 335300 341600 ... \n",
"3994 146500 150100 154000 156500 157300 157700 ... \n",
"4015 126100 127000 128300 129400 130600 132400 ... \n",
" ... ... ... ... ... ... \n",
"\n",
"[72 rows x 216 columns]"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"oak_0214 = oak_homevalue[[\"RegionName\", \"2014-02\"]]\n"
],
"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>RegionName</th>\n",
" <th>2014-02</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1307</th>\n",
" <td> Redwood Heights</td>\n",
" <td> 562700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1481</th>\n",
" <td> Adams Point</td>\n",
" <td> 358600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1531</th>\n",
" <td> Clinton</td>\n",
" <td> 364800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1582</th>\n",
" <td> Cleveland Heights</td>\n",
" <td> 631000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1677</th>\n",
" <td> Havenscourt</td>\n",
" <td> 220200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1740</th>\n",
" <td> Bushrod</td>\n",
" <td> 616300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1760</th>\n",
" <td> Piedmont Avenue</td>\n",
" <td> 476900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1820</th>\n",
" <td> Upper Dimond</td>\n",
" <td> 568200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1826</th>\n",
" <td> St. Elizabeth</td>\n",
" <td> 262000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1972</th>\n",
" <td> Longfellow</td>\n",
" <td> 465400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1990</th>\n",
" <td> Upper Rockridge</td>\n",
" <td> 1072100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000</th>\n",
" <td> Fremont</td>\n",
" <td> 270900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024</th>\n",
" <td> Meadow Brook</td>\n",
" <td> 284400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2030</th>\n",
" <td> Webster</td>\n",
" <td> 195100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2054</th>\n",
" <td> Seminary</td>\n",
" <td> 221100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2062</th>\n",
" <td> Rancho San Antonio</td>\n",
" <td> 274700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2085</th>\n",
" <td> Maxwell Park</td>\n",
" <td> 406200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2099</th>\n",
" <td> Glenview</td>\n",
" <td> 717600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2199</th>\n",
" <td> Arroyo Viejo</td>\n",
" <td> 195200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2298</th>\n",
" <td> Allendale</td>\n",
" <td> 299300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2377</th>\n",
" <td> Grand Lake</td>\n",
" <td> 336500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2381</th>\n",
" <td> Prescott</td>\n",
" <td> 354400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2394</th>\n",
" <td> Lakeshore</td>\n",
" <td> 886900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2579</th>\n",
" <td> Temescal</td>\n",
" <td> 679800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2611</th>\n",
" <td> Highland Terrace</td>\n",
" <td> 307100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2670</th>\n",
" <td> Sequoyah</td>\n",
" <td> 574900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2682</th>\n",
" <td> Montclair</td>\n",
" <td> 849500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2734</th>\n",
" <td> North Stonehurst</td>\n",
" <td> 207200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2749</th>\n",
" <td> Harrington</td>\n",
" <td> 272500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2786</th>\n",
" <td> Jefferson</td>\n",
" <td> 293000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2891</th>\n",
" <td> Iveywood</td>\n",
" <td> 211600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2903</th>\n",
" <td> Fairview Park</td>\n",
" <td> 867400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2906</th>\n",
" <td> Chabot Park</td>\n",
" <td> 598300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2963</th>\n",
" <td> Merriwood</td>\n",
" <td> 763100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2988</th>\n",
" <td> Eastmont Hills</td>\n",
" <td> 379000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2991</th>\n",
" <td> Shafter</td>\n",
" <td> 853100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3035</th>\n",
" <td> Eastmont</td>\n",
" <td> 215000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3043</th>\n",
" <td> Castlemont</td>\n",
" <td> 229000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3062</th>\n",
" <td> Rockridge</td>\n",
" <td> 1001800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3084</th>\n",
" <td> Cox</td>\n",
" <td> 203400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3097</th>\n",
" <td> Frick</td>\n",
" <td> 349000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3107</th>\n",
" <td> Trestle Glen</td>\n",
" <td> 899600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3119</th>\n",
" <td> Sobrante Park</td>\n",
" <td> 208300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3129</th>\n",
" <td> Santa Fe</td>\n",
" <td> 477500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3202</th>\n",
" <td> Brookfield Village</td>\n",
" <td> 201300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3258</th>\n",
" <td> Coliseum</td>\n",
" <td> 186800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3266</th>\n",
" <td> Lakewide</td>\n",
" <td> 403800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3271</th>\n",
" <td> Chinatown</td>\n",
" <td> 321100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3286</th>\n",
" <td> Clawson</td>\n",
" <td> 377900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3433</th>\n",
" <td> Piedmont Pines</td>\n",
" <td> 847600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3504</th>\n",
" <td> Upper Peralta Creek</td>\n",
" <td> 347300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3629</th>\n",
" <td> Las Palmas</td>\n",
" <td> 245300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3645</th>\n",
" <td> Caballo Hills</td>\n",
" <td> 643000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3781</th>\n",
" <td> Produce &amp; Waterfront</td>\n",
" <td> 476200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3863</th>\n",
" <td> Oakmore</td>\n",
" <td> 843500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3921</th>\n",
" <td> Fairfax</td>\n",
" <td> 319800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3942</th>\n",
" <td> Toler Heights</td>\n",
" <td> 303900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3970</th>\n",
" <td> Forestland</td>\n",
" <td> 826000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3994</th>\n",
" <td> Golden Gate</td>\n",
" <td> 529100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4015</th>\n",
" <td> Durant Manor</td>\n",
" <td> 279700</td>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>72 rows \u00d7 2 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 32,
"text": [
" RegionName 2014-02\n",
"1307 Redwood Heights 562700\n",
"1481 Adams Point 358600\n",
"1531 Clinton 364800\n",
"1582 Cleveland Heights 631000\n",
"1677 Havenscourt 220200\n",
"1740 Bushrod 616300\n",
"1760 Piedmont Avenue 476900\n",
"1820 Upper Dimond 568200\n",
"1826 St. Elizabeth 262000\n",
"1972 Longfellow 465400\n",
"1990 Upper Rockridge 1072100\n",
"2000 Fremont 270900\n",
"2024 Meadow Brook 284400\n",
"2030 Webster 195100\n",
"2054 Seminary 221100\n",
"2062 Rancho San Antonio 274700\n",
"2085 Maxwell Park 406200\n",
"2099 Glenview 717600\n",
"2199 Arroyo Viejo 195200\n",
"2298 Allendale 299300\n",
"2377 Grand Lake 336500\n",
"2381 Prescott 354400\n",
"2394 Lakeshore 886900\n",
"2579 Temescal 679800\n",
"2611 Highland Terrace 307100\n",
"2670 Sequoyah 574900\n",
"2682 Montclair 849500\n",
"2734 North Stonehurst 207200\n",
"2749 Harrington 272500\n",
"2786 Jefferson 293000\n",
"2891 Iveywood 211600\n",
"2903 Fairview Park 867400\n",
"2906 Chabot Park 598300\n",
"2963 Merriwood 763100\n",
"2988 Eastmont Hills 379000\n",
"2991 Shafter 853100\n",
"3035 Eastmont 215000\n",
"3043 Castlemont 229000\n",
"3062 Rockridge 1001800\n",
"3084 Cox 203400\n",
"3097 Frick 349000\n",
"3107 Trestle Glen 899600\n",
"3119 Sobrante Park 208300\n",
"3129 Santa Fe 477500\n",
"3202 Brookfield Village 201300\n",
"3258 Coliseum 186800\n",
"3266 Lakewide 403800\n",
"3271 Chinatown 321100\n",
"3286 Clawson 377900\n",
"3433 Piedmont Pines 847600\n",
"3504 Upper Peralta Creek 347300\n",
"3629 Las Palmas 245300\n",
"3645 Caballo Hills 643000\n",
"3781 Produce & Waterfront 476200\n",
"3863 Oakmore 843500\n",
"3921 Fairfax 319800\n",
"3942 Toler Heights 303900\n",
"3970 Forestland 826000\n",
"3994 Golden Gate 529100\n",
"4015 Durant Manor 279700\n",
" ... ...\n",
"\n",
"[72 rows x 2 columns]"
]
}
],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Clinton_df =Clinton[Clinton.columns[2:]].transpose()\n",
"print df\n",
"plt.plot(df)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1531\n",
"1996-04 125200\n",
"1996-05 125400\n",
"1996-06 125400\n",
"1996-07 125100\n",
"1996-08 125300\n",
"1996-09 125200\n",
"1996-10 125200\n",
"1996-11 125500\n",
"1996-12 125800\n",
"1997-01 125300\n",
"1997-02 125200\n",
"1997-03 125500\n",
"1997-04 125500\n",
"1997-05 125200\n",
"1997-06 125200\n",
"1997-07 125700\n",
"1997-08 125900\n",
"1997-09 125900\n",
"1997-10 126200\n",
"1997-11 126400\n",
"1997-12 126000\n",
"1998-01 125900\n",
"1998-02 126600\n",
"1998-03 127700\n",
"1998-04 128100\n",
"1998-05 128300\n",
"1998-06 128500\n",
"1998-07 129200\n",
"1998-08 129900\n",
"1998-09 130300\n",
"1998-10 130000\n",
"1998-11 129500\n",
"1998-12 129600\n",
"1999-01 130500\n",
"1999-02 131000\n",
"1999-03 131500\n",
"1999-04 132300\n",
"1999-05 134100\n",
"1999-06 137000\n",
"1999-07 140400\n",
"1999-08 143400\n",
"1999-09 147100\n",
"1999-10 151000\n",
"1999-11 154000\n",
"1999-12 156600\n",
"2000-01 160400\n",
"2000-02 164600\n",
"2000-03 168400\n",
"2000-04 173900\n",
"2000-05 180600\n",
"2000-06 187600\n",
"2000-07 194800\n",
"2000-08 202400\n",
"2000-09 209200\n",
"2000-10 214500\n",
"2000-11 219000\n",
"2000-12 222700\n",
"2001-01 225800\n",
"2001-02 227200\n",
"2001-03 228300\n",
" ...\n",
"\n",
"[215 rows x 1 columns]\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 26,
"text": [
"[<matplotlib.lines.Line2D at 0xb93cfd0>]"
]
},
{
"metadata": {},
"output_type": "display_data",
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OT6IGrZYWGDMGfvMbI4H0Z//8pxHrX/9qTF2iVHfp9CRKXaWtW+G66+CrXzU7ko4NHw4z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\nNHkopZTqMk0eSimlukyTh1JKqS77/7V5kXyOpoAoAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0xb91ef28>"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fc_rate= generateDFfromFilename(\"HomesSoldAsForeclosures-Ratio_AllHomes\")\n",
"fc_rate.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>RegionName</th>\n",
" <th>City</th>\n",
" <th>State</th>\n",
" <th>Metro</th>\n",
" <th>CountyName</th>\n",
" <th>1998-01</th>\n",
" <th>1998-02</th>\n",
" <th>1998-03</th>\n",
" <th>1998-04</th>\n",
" <th>1998-05</th>\n",
" <th>1998-06</th>\n",
" <th>1998-07</th>\n",
" <th>1998-08</th>\n",
" <th>1998-09</th>\n",
" <th>1998-10</th>\n",
" <th>1998-11</th>\n",
" <th>1998-12</th>\n",
" <th>1999-01</th>\n",
" <th>1999-02</th>\n",
" <th>1999-03</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> Northeast Dallas</td>\n",
" <td> Dallas</td>\n",
" <td> TX</td>\n",
" <td> Dallas-Fort Worth</td>\n",
" <td> Dallas</td>\n",
" <td> 1.1969</td>\n",
" <td> 0.9788</td>\n",
" <td> 0.5575</td>\n",
" <td> 1.2353</td>\n",
" <td> 1.3822</td>\n",
" <td> 1.5509</td>\n",
" <td> 1.2593</td>\n",
" <td> 1.3224</td>\n",
" <td> 0.8679</td>\n",
" <td> 1.1162</td>\n",
" <td> 0.9120</td>\n",
" <td> 0.6609</td>\n",
" <td> 1.1097</td>\n",
" <td> 1.0987</td>\n",
" <td> 0.7659</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> Paradise</td>\n",
" <td> Las Vegas</td>\n",
" <td> NV</td>\n",
" <td> Las Vegas</td>\n",
" <td> Clark</td>\n",
" <td> 3.6928</td>\n",
" <td> 4.2873</td>\n",
" <td> 5.4308</td>\n",
" <td> 5.1075</td>\n",
" <td> 3.8920</td>\n",
" <td> 3.5950</td>\n",
" <td> 4.1949</td>\n",
" <td> 4.4891</td>\n",
" <td> 3.9920</td>\n",
" <td> 3.3169</td>\n",
" <td> 3.0653</td>\n",
" <td> 3.5325</td>\n",
" <td> 4.3873</td>\n",
" <td> 4.2174</td>\n",
" <td> 4.6353</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> Maryvale</td>\n",
" <td> Phoenix</td>\n",
" <td> AZ</td>\n",
" <td> Phoenix</td>\n",
" <td> Maricopa</td>\n",
" <td> 6.9338</td>\n",
" <td> 6.1868</td>\n",
" <td> 5.9987</td>\n",
" <td> 6.5031</td>\n",
" <td> 7.0995</td>\n",
" <td> 7.7198</td>\n",
" <td> 7.9067</td>\n",
" <td> 9.1496</td>\n",
" <td> 8.4973</td>\n",
" <td> 7.5373</td>\n",
" <td> 7.8191</td>\n",
" <td> 7.2100</td>\n",
" <td> 7.1826</td>\n",
" <td> 6.6506</td>\n",
" <td> 6.3429</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> Upper West Side</td>\n",
" <td> New York</td>\n",
" <td> NY</td>\n",
" <td> New York</td>\n",
" <td> New York</td>\n",
" <td> 0.0000</td>\n",
" <td> 0.0000</td>\n",
" <td> 1.2679</td>\n",
" <td> 0.8875</td>\n",
" <td> 0.3804</td>\n",
" <td> 0.3057</td>\n",
" <td> 0.8312</td>\n",
" <td> 0.8265</td>\n",
" <td> 0.3971</td>\n",
" <td> 0.4082</td>\n",
" <td> 0.5397</td>\n",
" <td> 0.9441</td>\n",
" <td> 0.5339</td>\n",
" <td> 0.0000</td>\n",
" <td> 0.0000</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> South Los Angeles</td>\n",
" <td> Los Angeles</td>\n",
" <td> CA</td>\n",
" <td> Los Angeles</td>\n",
" <td> Los Angeles</td>\n",
" <td> 19.7234</td>\n",
" <td> 19.8267</td>\n",
" <td> 22.3834</td>\n",
" <td> 22.0444</td>\n",
" <td> 22.0070</td>\n",
" <td> 20.6364</td>\n",
" <td> 22.2185</td>\n",
" <td> 21.4862</td>\n",
" <td> 20.8686</td>\n",
" <td> 18.5885</td>\n",
" <td> 19.7988</td>\n",
" <td> 21.2616</td>\n",
" <td> 18.6664</td>\n",
" <td> 15.4790</td>\n",
" <td> 18.7420</td>\n",
" <td>...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 199 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
" RegionName City State Metro CountyName \\\n",
"0 Northeast Dallas Dallas TX Dallas-Fort Worth Dallas \n",
"1 Paradise Las Vegas NV Las Vegas Clark \n",
"2 Maryvale Phoenix AZ Phoenix Maricopa \n",
"3 Upper West Side New York NY New York New York \n",
"4 South Los Angeles Los Angeles CA Los Angeles Los Angeles \n",
"\n",
" 1998-01 1998-02 1998-03 1998-04 1998-05 1998-06 1998-07 1998-08 \\\n",
"0 1.1969 0.9788 0.5575 1.2353 1.3822 1.5509 1.2593 1.3224 \n",
"1 3.6928 4.2873 5.4308 5.1075 3.8920 3.5950 4.1949 4.4891 \n",
"2 6.9338 6.1868 5.9987 6.5031 7.0995 7.7198 7.9067 9.1496 \n",
"3 0.0000 0.0000 1.2679 0.8875 0.3804 0.3057 0.8312 0.8265 \n",
"4 19.7234 19.8267 22.3834 22.0444 22.0070 20.6364 22.2185 21.4862 \n",
"\n",
" 1998-09 1998-10 1998-11 1998-12 1999-01 1999-02 1999-03 \n",
"0 0.8679 1.1162 0.9120 0.6609 1.1097 1.0987 0.7659 ... \n",
"1 3.9920 3.3169 3.0653 3.5325 4.3873 4.2174 4.6353 ... \n",
"2 8.4973 7.5373 7.8191 7.2100 7.1826 6.6506 6.3429 ... \n",
"3 0.3971 0.4082 0.5397 0.9441 0.5339 0.0000 0.0000 ... \n",
"4 20.8686 18.5885 19.7988 21.2616 18.6664 15.4790 18.7420 ... \n",
"\n",
"[5 rows x 199 columns]"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Zhvi_sum = generateDFfromFilename(\"Zhvi_Summary_AllHomes\")\n",
"oak_Zhvi_sum = cleanedOakland(Zhvi_sum, [\"City\", \"State\", \"Metro\"])\n",
"oak_Zhvi_sum.sort([\"YoY\"], ascending = False)"
],
"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>Date</th>\n",
" <th>RegionName</th>\n",
" <th>County</th>\n",
" <th>SizeRank</th>\n",
" <th>Zhvi</th>\n",
" <th>MoM</th>\n",
" <th>QoQ</th>\n",
" <th>YoY</th>\n",
" <th>5Year</th>\n",
" <th>10Year</th>\n",
" <th>ZhviRecordCnt</th>\n",
" <th>PeakMonth</th>\n",
" <th>PeakQuarter</th>\n",
" <th>PeakZHVI</th>\n",
" <th>PctFallFromPeak</th>\n",
" <th>LastTimeAtCurrZHVI</th>\n",
" <th>BottomMonth</th>\n",
" <th>BottomQuarter</th>\n",
" <th>BottomZHVI</th>\n",
" <th>PctFallFromPeakToBottom</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1481</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Adams Point</td>\n",
" <td> Alameda</td>\n",
" <td> 1481</td>\n",
" <td> 358600</td>\n",
" <td> 0.013567</td>\n",
" <td> 0.055948</td>\n",
" <td> 0.457131</td>\n",
" <td> 0.044173</td>\n",
" <td> 0.012407</td>\n",
" <td> 980</td>\n",
" <td> 2005-10</td>\n",
" <td> 2005-Q4</td>\n",
" <td> 429900</td>\n",
" <td>-0.165853</td>\n",
" <td> 2004-11</td>\n",
" <td> 2011-09</td>\n",
" <td> 2011-Q3</td>\n",
" <td> 206000</td>\n",
" <td>-0.520819</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2748</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Harrington</td>\n",
" <td> Alameda</td>\n",
" <td> 2748</td>\n",
" <td> 272500</td>\n",
" <td> 0.010757</td>\n",
" <td> 0.040871</td>\n",
" <td> 0.446391</td>\n",
" <td>-0.014537</td>\n",
" <td>-0.013981</td>\n",
" <td> 471</td>\n",
" <td> 2006-04</td>\n",
" <td> 2006-Q2</td>\n",
" <td> 473800</td>\n",
" <td>-0.424863</td>\n",
" <td> 2002-12</td>\n",
" <td> 2012-04</td>\n",
" <td> 2012-Q2</td>\n",
" <td> 167700</td>\n",
" <td>-0.646053</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3504</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Upper Peralta Creek</td>\n",
" <td> Alameda</td>\n",
" <td> 3504</td>\n",
" <td> 347300</td>\n",
" <td> 0.005501</td>\n",
" <td> 0.018774</td>\n",
" <td> 0.417551</td>\n",
" <td> 0.036220</td>\n",
" <td> 0.006689</td>\n",
" <td> 427</td>\n",
" <td> 2006-01</td>\n",
" <td> 2006-Q1</td>\n",
" <td> 497400</td>\n",
" <td>-0.301769</td>\n",
" <td> 2004-06</td>\n",
" <td> 2011-03</td>\n",
" <td> 2011-Q1</td>\n",
" <td> 215000</td>\n",
" <td>-0.567752</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1826</th>\n",
" <td> 2014-02-28</td>\n",
" <td> St. Elizabeth</td>\n",
" <td> Alameda</td>\n",
" <td> 1826</td>\n",
" <td> 262000</td>\n",
" <td> 0.003447</td>\n",
" <td> 0.027854</td>\n",
" <td> 0.409360</td>\n",
" <td>-0.025878</td>\n",
" <td>-0.014434</td>\n",
" <td> 534</td>\n",
" <td> 2006-07</td>\n",
" <td> 2006-Q3</td>\n",
" <td> 494500</td>\n",
" <td>-0.470172</td>\n",
" <td> 2003-01</td>\n",
" <td> 2012-02</td>\n",
" <td> 2012-Q1</td>\n",
" <td> 160600</td>\n",
" <td>-0.675228</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3286</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Clawson</td>\n",
" <td> Alameda</td>\n",
" <td> 3286</td>\n",
" <td> 377900</td>\n",
" <td> 0.010428</td>\n",
" <td> 0.033926</td>\n",
" <td> 0.400148</td>\n",
" <td> 0.021843</td>\n",
" <td> 0.010506</td>\n",
" <td> 596</td>\n",
" <td> 2007-03</td>\n",
" <td> 2007-Q1</td>\n",
" <td> 503400</td>\n",
" <td>-0.249305</td>\n",
" <td> 2004-10</td>\n",
" <td> 2012-03</td>\n",
" <td> 2012-Q1</td>\n",
" <td> 224300</td>\n",
" <td>-0.554430</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2786</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Jefferson</td>\n",
" <td> Alameda</td>\n",
" <td> 2786</td>\n",
" <td> 293000</td>\n",
" <td> 0.008953</td>\n",
" <td> 0.025551</td>\n",
" <td> 0.366604</td>\n",
" <td>-0.012424</td>\n",
" <td>-0.012270</td>\n",
" <td> 746</td>\n",
" <td> 2006-04</td>\n",
" <td> 2006-Q2</td>\n",
" <td> 494800</td>\n",
" <td>-0.407842</td>\n",
" <td> 2003-01</td>\n",
" <td> 2012-05</td>\n",
" <td> 2012-Q2</td>\n",
" <td> 185500</td>\n",
" <td>-0.625101</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3202</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Brookfield Village</td>\n",
" <td> Alameda</td>\n",
" <td> 3202</td>\n",
" <td> 201300</td>\n",
" <td> 0.019241</td>\n",
" <td> 0.051175</td>\n",
" <td> 0.365672</td>\n",
" <td>-0.015723</td>\n",
" <td>-0.030716</td>\n",
" <td> 961</td>\n",
" <td> 2006-08</td>\n",
" <td> 2006-Q3</td>\n",
" <td> 409500</td>\n",
" <td>-0.508425</td>\n",
" <td> 2002-05</td>\n",
" <td> 2011-10</td>\n",
" <td> 2011-Q4</td>\n",
" <td> 121900</td>\n",
" <td>-0.702320</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2298</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Allendale</td>\n",
" <td> Alameda</td>\n",
" <td> 2298</td>\n",
" <td> 299300</td>\n",
" <td> 0.014576</td>\n",
" <td> 0.029584</td>\n",
" <td> 0.364175</td>\n",
" <td> 0.029664</td>\n",
" <td>-0.006944</td>\n",
" <td> 845</td>\n",
" <td> 2006-06</td>\n",
" <td> 2006-Q2</td>\n",
" <td> 482000</td>\n",
" <td>-0.379046</td>\n",
" <td> 2003-11</td>\n",
" <td> 2012-02</td>\n",
" <td> 2012-Q1</td>\n",
" <td> 182800</td>\n",
" <td>-0.620747</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4243</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Oak Knoll-Golf Links</td>\n",
" <td> Alameda</td>\n",
" <td> 4243</td>\n",
" <td> 403800</td>\n",
" <td> 0.024093</td>\n",
" <td> 0.086068</td>\n",
" <td> 0.353217</td>\n",
" <td> 0.027255</td>\n",
" <td> 0.003355</td>\n",
" <td> 566</td>\n",
" <td> 2006-11</td>\n",
" <td> 2006-Q4</td>\n",
" <td> 582700</td>\n",
" <td>-0.307019</td>\n",
" <td> 2004-04</td>\n",
" <td> 2012-02</td>\n",
" <td> 2012-Q1</td>\n",
" <td> 231200</td>\n",
" <td>-0.603226</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3781</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Produce &amp; Waterfront</td>\n",
" <td> Alameda</td>\n",
" <td> 3781</td>\n",
" <td> 476200</td>\n",
" <td> 0.011470</td>\n",
" <td> 0.053773</td>\n",
" <td> 0.351305</td>\n",
" <td> 0.052846</td>\n",
" <td> 0.027941</td>\n",
" <td> 992</td>\n",
" <td> 2005-12</td>\n",
" <td> 2005-Q4</td>\n",
" <td> 489300</td>\n",
" <td>-0.026773</td>\n",
" <td> 2005-07</td>\n",
" <td> 2010-08</td>\n",
" <td> 2010-Q3</td>\n",
" <td> 286300</td>\n",
" <td>-0.414878</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2054</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Seminary</td>\n",
" <td> Alameda</td>\n",
" <td> 2054</td>\n",
" <td> 221100</td>\n",
" <td> 0.017019</td>\n",
" <td> 0.052356</td>\n",
" <td> 0.319212</td>\n",
" <td>-0.003314</td>\n",
" <td>-0.022640</td>\n",
" <td> 789</td>\n",
" <td> 2007-01</td>\n",
" <td> 2007-Q1</td>\n",
" <td> 433100</td>\n",
" <td>-0.489494</td>\n",
" <td> 2002-08</td>\n",
" <td> 2012-04</td>\n",
" <td> 2012-Q2</td>\n",
" <td> 132800</td>\n",
" <td>-0.693373</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4015</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Durant Manor</td>\n",
" <td> Alameda</td>\n",
" <td> 4015</td>\n",
" <td> 279700</td>\n",
" <td> 0.002150</td>\n",
" <td> 0.016352</td>\n",
" <td> 0.318096</td>\n",
" <td>-0.009451</td>\n",
" <td>-0.020080</td>\n",
" <td> 642</td>\n",
" <td> 2006-08</td>\n",
" <td> 2006-Q3</td>\n",
" <td> 490900</td>\n",
" <td>-0.430230</td>\n",
" <td> 2002-12</td>\n",
" <td> 2012-04</td>\n",
" <td> 2012-Q2</td>\n",
" <td> 177500</td>\n",
" <td>-0.638419</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2989</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Eastmont Hills</td>\n",
" <td> Alameda</td>\n",
" <td> 2989</td>\n",
" <td> 379000</td>\n",
" <td> 0.007979</td>\n",
" <td> 0.027100</td>\n",
" <td> 0.317344</td>\n",
" <td> 0.023708</td>\n",
" <td> 0.002650</td>\n",
" <td> 1235</td>\n",
" <td> 2006-03</td>\n",
" <td> 2006-Q1</td>\n",
" <td> 562700</td>\n",
" <td>-0.326462</td>\n",
" <td> 2004-04</td>\n",
" <td> 2012-03</td>\n",
" <td> 2012-Q1</td>\n",
" <td> 250800</td>\n",
" <td>-0.554292</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4843</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Foothill Square</td>\n",
" <td> Alameda</td>\n",
" <td> 4843</td>\n",
" <td> 265100</td>\n",
" <td>-0.005626</td>\n",
" <td> 0.009520</td>\n",
" <td> 0.311727</td>\n",
" <td> 0.005055</td>\n",
" <td>-0.018952</td>\n",
" <td> 423</td>\n",
" <td> 2006-12</td>\n",
" <td> 2006-Q4</td>\n",
" <td> 497700</td>\n",
" <td>-0.467350</td>\n",
" <td> 2002-05</td>\n",
" <td> 2011-07</td>\n",
" <td> 2011-Q3</td>\n",
" <td> 163700</td>\n",
" <td>-0.671087</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2377</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Grand Lake</td>\n",
" <td> Alameda</td>\n",
" <td> 2377</td>\n",
" <td> 336500</td>\n",
" <td> 0.008089</td>\n",
" <td> 0.029997</td>\n",
" <td> 0.310869</td>\n",
" <td> 0.025910</td>\n",
" <td> 0.000986</td>\n",
" <td> 1133</td>\n",
" <td> 2005-11</td>\n",
" <td> 2005-Q4</td>\n",
" <td> 453800</td>\n",
" <td>-0.258484</td>\n",
" <td> 2004-03</td>\n",
" <td> 2012-06</td>\n",
" <td> 2012-Q2</td>\n",
" <td> 245200</td>\n",
" <td>-0.459674</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3942</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Toler Heights</td>\n",
" <td> Alameda</td>\n",
" <td> 3942</td>\n",
" <td> 303900</td>\n",
" <td> 0.014691</td>\n",
" <td> 0.062959</td>\n",
" <td> 0.304292</td>\n",
" <td> 0.005078</td>\n",
" <td>-0.011452</td>\n",
" <td> 620</td>\n",
" <td> 2006-11</td>\n",
" <td> 2006-Q4</td>\n",
" <td> 520900</td>\n",
" <td>-0.416587</td>\n",
" <td> 2003-03</td>\n",
" <td> 2011-10</td>\n",
" <td> 2011-Q4</td>\n",
" <td> 179500</td>\n",
" <td>-0.655404</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Fremont</td>\n",
" <td> Alameda</td>\n",
" <td> 2000</td>\n",
" <td> 270900</td>\n",
" <td> 0.001479</td>\n",
" <td> 0.008939</td>\n",
" <td> 0.303030</td>\n",
" <td>-0.002200</td>\n",
" <td>-0.014058</td>\n",
" <td> 572</td>\n",
" <td> 2006-11</td>\n",
" <td> 2006-Q4</td>\n",
" <td> 461600</td>\n",
" <td>-0.413128</td>\n",
" <td> 2003-03</td>\n",
" <td> 2011-09</td>\n",
" <td> 2011-Q3</td>\n",
" <td> 170200</td>\n",
" <td>-0.631282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2611</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Highland Terrace</td>\n",
" <td> Alameda</td>\n",
" <td> 2611</td>\n",
" <td> 307100</td>\n",
" <td> 0.016551</td>\n",
" <td> 0.029501</td>\n",
" <td> 0.297423</td>\n",
" <td> 0.006914</td>\n",
" <td>-0.004541</td>\n",
" <td> 570</td>\n",
" <td> 2006-08</td>\n",
" <td> 2006-Q3</td>\n",
" <td> 485800</td>\n",
" <td>-0.367847</td>\n",
" <td> 2003-12</td>\n",
" <td> 2011-10</td>\n",
" <td> 2011-Q4</td>\n",
" <td> 198000</td>\n",
" <td>-0.592425</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3629</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Las Palmas</td>\n",
" <td> Alameda</td>\n",
" <td> 3629</td>\n",
" <td> 245300</td>\n",
" <td> 0.009881</td>\n",
" <td> 0.046948</td>\n",
" <td> 0.297197</td>\n",
" <td>-0.002026</td>\n",
" <td>-0.024668</td>\n",
" <td> 661</td>\n",
" <td> 2006-08</td>\n",
" <td> 2006-Q3</td>\n",
" <td> 476300</td>\n",
" <td>-0.484988</td>\n",
" <td> 2002-06</td>\n",
" <td> 2012-02</td>\n",
" <td> 2012-Q1</td>\n",
" <td> 158900</td>\n",
" <td>-0.666387</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3994</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Golden Gate</td>\n",
" <td> Alameda</td>\n",
" <td> 3994</td>\n",
" <td> 529100</td>\n",
" <td> 0.011470</td>\n",
" <td> 0.036029</td>\n",
" <td> 0.286722</td>\n",
" <td> 0.069005</td>\n",
" <td> 0.028701</td>\n",
" <td> 424</td>\n",
" <td> 2006-06</td>\n",
" <td> 2006-Q2</td>\n",
" <td> 553000</td>\n",
" <td>-0.043219</td>\n",
" <td> 2005-07</td>\n",
" <td> 2011-07</td>\n",
" <td> 2011-Q3</td>\n",
" <td> 293400</td>\n",
" <td>-0.469439</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Meadow Brook</td>\n",
" <td> Alameda</td>\n",
" <td> 2024</td>\n",
" <td> 284400</td>\n",
" <td> 0.002821</td>\n",
" <td> 0.010661</td>\n",
" <td> 0.286296</td>\n",
" <td> 0.003985</td>\n",
" <td>-0.013316</td>\n",
" <td> 646</td>\n",
" <td> 2006-07</td>\n",
" <td> 2006-Q3</td>\n",
" <td> 494400</td>\n",
" <td>-0.424757</td>\n",
" <td> 2003-07</td>\n",
" <td> 2012-03</td>\n",
" <td> 2012-Q1</td>\n",
" <td> 174300</td>\n",
" <td>-0.647451</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2735</th>\n",
" <td> 2014-02-28</td>\n",
" <td> North Stonehurst</td>\n",
" <td> Alameda</td>\n",
" <td> 2735</td>\n",
" <td> 207200</td>\n",
" <td> 0.020187</td>\n",
" <td> 0.071355</td>\n",
" <td> 0.285360</td>\n",
" <td>-0.008743</td>\n",
" <td>-0.035462</td>\n",
" <td> 653</td>\n",
" <td> 2006-12</td>\n",
" <td> 2006-Q4</td>\n",
" <td> 441900</td>\n",
" <td>-0.531116</td>\n",
" <td> 2002-04</td>\n",
" <td> 2011-09</td>\n",
" <td> 2011-Q3</td>\n",
" <td> 133700</td>\n",
" <td>-0.697443</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3035</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Eastmont</td>\n",
" <td> Alameda</td>\n",
" <td> 3035</td>\n",
" <td> 215000</td>\n",
" <td> 0.022349</td>\n",
" <td> 0.039149</td>\n",
" <td> 0.284349</td>\n",
" <td>-0.013226</td>\n",
" <td>-0.029848</td>\n",
" <td> 826</td>\n",
" <td> 2006-12</td>\n",
" <td> 2006-Q4</td>\n",
" <td> 429500</td>\n",
" <td>-0.499418</td>\n",
" <td> 2002-05</td>\n",
" <td> 2011-10</td>\n",
" <td> 2011-Q4</td>\n",
" <td> 126000</td>\n",
" <td>-0.706636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1677</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Havenscourt</td>\n",
" <td> Alameda</td>\n",
" <td> 1677</td>\n",
" <td> 220200</td>\n",
" <td> 0.022284</td>\n",
" <td> 0.052581</td>\n",
" <td> 0.262615</td>\n",
" <td>-0.012589</td>\n",
" <td>-0.027594</td>\n",
" <td> 1527</td>\n",
" <td> 2007-02</td>\n",
" <td> 2007-Q1</td>\n",
" <td> 450800</td>\n",
" <td>-0.511535</td>\n",
" <td> 2002-05</td>\n",
" <td> 2011-10</td>\n",
" <td> 2011-Q4</td>\n",
" <td> 141400</td>\n",
" <td>-0.686335</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2085</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Maxwell Park</td>\n",
" <td> Alameda</td>\n",
" <td> 2085</td>\n",
" <td> 406200</td>\n",
" <td> 0.001479</td>\n",
" <td> 0.010951</td>\n",
" <td> 0.259926</td>\n",
" <td> 0.018624</td>\n",
" <td> 0.000444</td>\n",
" <td> 1982</td>\n",
" <td> 2006-08</td>\n",
" <td> 2006-Q3</td>\n",
" <td> 545800</td>\n",
" <td>-0.255771</td>\n",
" <td> 2004-03</td>\n",
" <td> 2011-11</td>\n",
" <td> 2011-Q4</td>\n",
" <td> 279000</td>\n",
" <td>-0.488824</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3266</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Lakewide</td>\n",
" <td> Alameda</td>\n",
" <td> 3266</td>\n",
" <td> 403800</td>\n",
" <td> 0.007988</td>\n",
" <td> 0.032209</td>\n",
" <td> 0.253648</td>\n",
" <td> 0.018630</td>\n",
" <td> 0.023022</td>\n",
" <td> 539</td>\n",
" <td> 2005-08</td>\n",
" <td> 2005-Q3</td>\n",
" <td> 490700</td>\n",
" <td>-0.177094</td>\n",
" <td> 2004-11</td>\n",
" <td> 2012-01</td>\n",
" <td> 2012-Q1</td>\n",
" <td> 270800</td>\n",
" <td>-0.448135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3119</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Sobrante Park</td>\n",
" <td> Alameda</td>\n",
" <td> 3119</td>\n",
" <td> 208300</td>\n",
" <td> 0.023084</td>\n",
" <td> 0.061131</td>\n",
" <td> 0.250300</td>\n",
" <td>-0.021826</td>\n",
" <td>-0.029459</td>\n",
" <td> 765</td>\n",
" <td> 2007-03</td>\n",
" <td> 2007-Q1</td>\n",
" <td> 438100</td>\n",
" <td>-0.524538</td>\n",
" <td> 2002-03</td>\n",
" <td> 2011-07</td>\n",
" <td> 2011-Q3</td>\n",
" <td> 148500</td>\n",
" <td>-0.661036</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3258</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Coliseum</td>\n",
" <td> Alameda</td>\n",
" <td> 3258</td>\n",
" <td> 186800</td>\n",
" <td> 0.013565</td>\n",
" <td> 0.054771</td>\n",
" <td> 0.246996</td>\n",
" <td>-0.018039</td>\n",
" <td>-0.030985</td>\n",
" <td> 410</td>\n",
" <td> 2007-02</td>\n",
" <td> 2007-Q1</td>\n",
" <td> 418200</td>\n",
" <td>-0.553324</td>\n",
" <td> 2002-01</td>\n",
" <td> 2011-11</td>\n",
" <td> 2011-Q4</td>\n",
" <td> 106600</td>\n",
" <td>-0.745098</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1972</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Longfellow</td>\n",
" <td> Alameda</td>\n",
" <td> 1972</td>\n",
" <td> 465400</td>\n",
" <td> 0.001506</td>\n",
" <td> 0.013723</td>\n",
" <td> 0.244718</td>\n",
" <td> 0.078333</td>\n",
" <td> 0.029407</td>\n",
" <td> 1116</td>\n",
" <td> 2006-11</td>\n",
" <td> 2006-Q4</td>\n",
" <td> 531800</td>\n",
" <td>-0.124859</td>\n",
" <td> 2005-08</td>\n",
" <td> 2009-07</td>\n",
" <td> 2009-Q3</td>\n",
" <td> 284600</td>\n",
" <td>-0.464836</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3043</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Castlemont</td>\n",
" <td> Alameda</td>\n",
" <td> 3043</td>\n",
" <td> 229000</td>\n",
" <td> 0.005268</td>\n",
" <td> 0.034327</td>\n",
" <td> 0.241192</td>\n",
" <td>-0.012285</td>\n",
" <td>-0.023036</td>\n",
" <td> 552</td>\n",
" <td> 2006-09</td>\n",
" <td> 2006-Q3</td>\n",
" <td> 433100</td>\n",
" <td>-0.471254</td>\n",
" <td> 2002-04</td>\n",
" <td> 2011-03</td>\n",
" <td> 2011-Q1</td>\n",
" <td> 145800</td>\n",
" <td>-0.663357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2029</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Webster</td>\n",
" <td> Alameda</td>\n",
" <td> 2029</td>\n",
" <td> 195100</td>\n",
" <td> 0.022537</td>\n",
" <td> 0.065538</td>\n",
" <td> 0.235592</td>\n",
" <td>-0.017587</td>\n",
" <td>-0.037492</td>\n",
" <td> 901</td>\n",
" <td> 2007-01</td>\n",
" <td> 2007-Q1</td>\n",
" <td> 439600</td>\n",
" <td>-0.556187</td>\n",
" <td> 2002-02</td>\n",
" <td> 2011-05</td>\n",
" <td> 2011-Q2</td>\n",
" <td> 110700</td>\n",
" <td>-0.748180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3921</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Fairfax</td>\n",
" <td> Alameda</td>\n",
" <td> 3921</td>\n",
" <td> 319800</td>\n",
" <td>-0.004359</td>\n",
" <td> 0.000939</td>\n",
" <td> 0.231421</td>\n",
" <td> 0.019896</td>\n",
" <td>-0.009997</td>\n",
" <td> 449</td>\n",
" <td> 2007-03</td>\n",
" <td> 2007-Q1</td>\n",
" <td> 504500</td>\n",
" <td>-0.366105</td>\n",
" <td> 2003-09</td>\n",
" <td> 2011-11</td>\n",
" <td> 2011-Q4</td>\n",
" <td> 211100</td>\n",
" <td>-0.581566</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2907</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Chabot Park</td>\n",
" <td> Alameda</td>\n",
" <td> 2907</td>\n",
" <td> 598300</td>\n",
" <td> 0.000167</td>\n",
" <td>-0.003166</td>\n",
" <td> 0.229552</td>\n",
" <td> 0.017533</td>\n",
" <td> 0.011132</td>\n",
" <td> 1097</td>\n",
" <td> 2006-09</td>\n",
" <td> 2006-Q3</td>\n",
" <td> 734500</td>\n",
" <td>-0.185432</td>\n",
" <td> 2004-09</td>\n",
" <td> 2012-03</td>\n",
" <td> 2012-Q1</td>\n",
" <td> 438000</td>\n",
" <td>-0.403676</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2199</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Arroyo Viejo</td>\n",
" <td> Alameda</td>\n",
" <td> 2199</td>\n",
" <td> 195200</td>\n",
" <td> 0.012448</td>\n",
" <td> 0.036093</td>\n",
" <td> 0.227673</td>\n",
" <td>-0.021762</td>\n",
" <td>-0.036256</td>\n",
" <td> 1043</td>\n",
" <td> 2007-03</td>\n",
" <td> 2007-Q1</td>\n",
" <td> 432900</td>\n",
" <td>-0.549088</td>\n",
" <td> 2002-01</td>\n",
" <td> 2011-09</td>\n",
" <td> 2011-Q3</td>\n",
" <td> 116800</td>\n",
" <td>-0.730192</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5144</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Columbia Gardens</td>\n",
" <td> Alameda</td>\n",
" <td> 5144</td>\n",
" <td> 183000</td>\n",
" <td> 0.013289</td>\n",
" <td> 0.046911</td>\n",
" <td> 0.221629</td>\n",
" <td>-0.006010</td>\n",
" <td>-0.037647</td>\n",
" <td> 415</td>\n",
" <td> 2006-12</td>\n",
" <td> 2006-Q4</td>\n",
" <td> 432100</td>\n",
" <td>-0.576487</td>\n",
" <td> 2001-10</td>\n",
" <td> 2011-10</td>\n",
" <td> 2011-Q4</td>\n",
" <td> 122400</td>\n",
" <td>-0.716732</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2062</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Rancho San Antonio</td>\n",
" <td> Alameda</td>\n",
" <td> 2062</td>\n",
" <td> 274700</td>\n",
" <td> 0.005490</td>\n",
" <td> 0.004388</td>\n",
" <td> 0.214412</td>\n",
" <td>-0.018316</td>\n",
" <td>-0.016067</td>\n",
" <td> 485</td>\n",
" <td> 2006-03</td>\n",
" <td> 2006-Q1</td>\n",
" <td> 479500</td>\n",
" <td>-0.427112</td>\n",
" <td> 2003-05</td>\n",
" <td> 2011-11</td>\n",
" <td> 2011-Q4</td>\n",
" <td> 184000</td>\n",
" <td>-0.616267</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3098</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Frick</td>\n",
" <td> Alameda</td>\n",
" <td> 3098</td>\n",
" <td> 349000</td>\n",
" <td> 0.004895</td>\n",
" <td> 0.007797</td>\n",
" <td> 0.206360</td>\n",
" <td> 0.032100</td>\n",
" <td>-0.007450</td>\n",
" <td> 833</td>\n",
" <td> 2006-11</td>\n",
" <td> 2006-Q4</td>\n",
" <td> 491100</td>\n",
" <td>-0.289350</td>\n",
" <td> 2003-10</td>\n",
" <td> 2012-03</td>\n",
" <td> 2012-Q1</td>\n",
" <td> 231300</td>\n",
" <td>-0.529016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1741</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Bushrod</td>\n",
" <td> Alameda</td>\n",
" <td> 1741</td>\n",
" <td> 616300</td>\n",
" <td> 0.001788</td>\n",
" <td> 0.006533</td>\n",
" <td> 0.199261</td>\n",
" <td> 0.061403</td>\n",
" <td> 0.032270</td>\n",
" <td> 1254</td>\n",
" <td> 2014-02</td>\n",
" <td> 2014-Q1</td>\n",
" <td> 616300</td>\n",
" <td> 0.000000</td>\n",
" <td> 2014-02</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2891</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Iveywood</td>\n",
" <td> Alameda</td>\n",
" <td> 2891</td>\n",
" <td> 211600</td>\n",
" <td> 0.018777</td>\n",
" <td> 0.059059</td>\n",
" <td> 0.189432</td>\n",
" <td>-0.019504</td>\n",
" <td>-0.033009</td>\n",
" <td> 747</td>\n",
" <td> 2006-08</td>\n",
" <td> 2006-Q3</td>\n",
" <td> 450300</td>\n",
" <td>-0.530091</td>\n",
" <td> 2002-02</td>\n",
" <td> 2011-02</td>\n",
" <td> 2011-Q1</td>\n",
" <td> 150400</td>\n",
" <td>-0.666000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2381</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Prescott</td>\n",
" <td> Alameda</td>\n",
" <td> 2381</td>\n",
" <td> 354400</td>\n",
" <td> 0.009112</td>\n",
" <td> 0.023390</td>\n",
" <td> 0.180546</td>\n",
" <td> 0.012292</td>\n",
" <td> 0.008946</td>\n",
" <td> 819</td>\n",
" <td> 2007-03</td>\n",
" <td> 2007-Q1</td>\n",
" <td> 496100</td>\n",
" <td>-0.285628</td>\n",
" <td> 2004-08</td>\n",
" <td> 2011-10</td>\n",
" <td> 2011-Q4</td>\n",
" <td> 259700</td>\n",
" <td>-0.476517</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4809</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Leona Heights</td>\n",
" <td> Alameda</td>\n",
" <td> 4809</td>\n",
" <td> 597000</td>\n",
" <td> 0.004881</td>\n",
" <td> 0.014788</td>\n",
" <td> 0.179143</td>\n",
" <td> 0.013422</td>\n",
" <td> 0.009917</td>\n",
" <td> 421</td>\n",
" <td> 2006-02</td>\n",
" <td> 2006-Q1</td>\n",
" <td> 706500</td>\n",
" <td>-0.154989</td>\n",
" <td> 2004-09</td>\n",
" <td> 2012-01</td>\n",
" <td> 2012-Q1</td>\n",
" <td> 454100</td>\n",
" <td>-0.357254</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3084</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Cox</td>\n",
" <td> Alameda</td>\n",
" <td> 3084</td>\n",
" <td> 203400</td>\n",
" <td> 0.015984</td>\n",
" <td> 0.048454</td>\n",
" <td> 0.178447</td>\n",
" <td>-0.019721</td>\n",
" <td>-0.033946</td>\n",
" <td> 425</td>\n",
" <td> 2006-08</td>\n",
" <td> 2006-Q3</td>\n",
" <td> 437500</td>\n",
" <td>-0.535086</td>\n",
" <td> 2002-02</td>\n",
" <td> 2011-03</td>\n",
" <td> 2011-Q1</td>\n",
" <td> 128300</td>\n",
" <td>-0.706743</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1307</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Redwood Heights</td>\n",
" <td> Alameda</td>\n",
" <td> 1307</td>\n",
" <td> 562700</td>\n",
" <td> 0.003925</td>\n",
" <td> 0.017909</td>\n",
" <td> 0.172292</td>\n",
" <td> 0.023255</td>\n",
" <td> 0.012575</td>\n",
" <td> 2839</td>\n",
" <td> 2005-08</td>\n",
" <td> 2005-Q3</td>\n",
" <td> 652400</td>\n",
" <td>-0.137492</td>\n",
" <td> 2004-09</td>\n",
" <td> 2012-02</td>\n",
" <td> 2012-Q1</td>\n",
" <td> 409100</td>\n",
" <td>-0.372931</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2991</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Shafter</td>\n",
" <td> Alameda</td>\n",
" <td> 2991</td>\n",
" <td> 853100</td>\n",
" <td> 0.009825</td>\n",
" <td> 0.019479</td>\n",
" <td> 0.165756</td>\n",
" <td> 0.042807</td>\n",
" <td> 0.034263</td>\n",
" <td> 792</td>\n",
" <td> 2014-02</td>\n",
" <td> 2014-Q1</td>\n",
" <td> 853100</td>\n",
" <td> 0.000000</td>\n",
" <td> 2014-02</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2579</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Temescal</td>\n",
" <td> Alameda</td>\n",
" <td> 2579</td>\n",
" <td> 679800</td>\n",
" <td> 0.000589</td>\n",
" <td>-0.007011</td>\n",
" <td> 0.164839</td>\n",
" <td> 0.053682</td>\n",
" <td> 0.033049</td>\n",
" <td> 759</td>\n",
" <td> 2013-09</td>\n",
" <td> 2013-Q3</td>\n",
" <td> 691100</td>\n",
" <td>-0.016351</td>\n",
" <td> 2013-09</td>\n",
" <td> 2014-01</td>\n",
" <td> 2014-Q1</td>\n",
" <td> 679400</td>\n",
" <td>-0.016930</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2903</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Fairview Park</td>\n",
" <td> Alameda</td>\n",
" <td> 2903</td>\n",
" <td> 867400</td>\n",
" <td> 0.002195</td>\n",
" <td> 0.010249</td>\n",
" <td> 0.157768</td>\n",
" <td> 0.063192</td>\n",
" <td> 0.040033</td>\n",
" <td> 804</td>\n",
" <td> 2014-02</td>\n",
" <td> 2014-Q1</td>\n",
" <td> 867400</td>\n",
" <td> 0.000000</td>\n",
" <td> 2014-02</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4173</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Lincoln Highlands</td>\n",
" <td> Alameda</td>\n",
" <td> 4173</td>\n",
" <td> 681100</td>\n",
" <td> 0.000147</td>\n",
" <td> 0.006502</td>\n",
" <td> 0.157349</td>\n",
" <td> 0.030640</td>\n",
" <td> 0.012725</td>\n",
" <td> 704</td>\n",
" <td> 2005-08</td>\n",
" <td> 2005-Q3</td>\n",
" <td> 735900</td>\n",
" <td>-0.074467</td>\n",
" <td> 2005-02</td>\n",
" <td> 2012-01</td>\n",
" <td> 2012-Q1</td>\n",
" <td> 499900</td>\n",
" <td>-0.320696</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2670</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Sequoyah</td>\n",
" <td> Alameda</td>\n",
" <td> 2670</td>\n",
" <td> 574900</td>\n",
" <td>-0.002083</td>\n",
" <td>-0.011010</td>\n",
" <td> 0.150030</td>\n",
" <td> 0.013168</td>\n",
" <td> 0.004474</td>\n",
" <td> 1911</td>\n",
" <td> 2006-09</td>\n",
" <td> 2006-Q3</td>\n",
" <td> 724500</td>\n",
" <td>-0.206487</td>\n",
" <td> 2004-09</td>\n",
" <td> 2011-08</td>\n",
" <td> 2011-Q3</td>\n",
" <td> 447300</td>\n",
" <td>-0.382609</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1531</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Clinton</td>\n",
" <td> Alameda</td>\n",
" <td> 1531</td>\n",
" <td> 364800</td>\n",
" <td>-0.008157</td>\n",
" <td>-0.032104</td>\n",
" <td> 0.149338</td>\n",
" <td> 0.007570</td>\n",
" <td> 0.002220</td>\n",
" <td> 462</td>\n",
" <td> 2006-10</td>\n",
" <td> 2006-Q4</td>\n",
" <td> 505400</td>\n",
" <td>-0.278195</td>\n",
" <td> 2004-03</td>\n",
" <td> 2011-10</td>\n",
" <td> 2011-Q4</td>\n",
" <td> 251700</td>\n",
" <td>-0.501979</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4924</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Skyline - Hillcrest Estates</td>\n",
" <td> Alameda</td>\n",
" <td> 4924</td>\n",
" <td> 849200</td>\n",
" <td> 0.000236</td>\n",
" <td> 0.006758</td>\n",
" <td> 0.147257</td>\n",
" <td> 0.025813</td>\n",
" <td> 0.012554</td>\n",
" <td> 560</td>\n",
" <td> 2005-12</td>\n",
" <td> 2005-Q4</td>\n",
" <td> 921900</td>\n",
" <td>-0.078859</td>\n",
" <td> 2005-01</td>\n",
" <td> 2012-03</td>\n",
" <td> 2012-Q1</td>\n",
" <td> 607300</td>\n",
" <td>-0.341252</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1820</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Upper Dimond</td>\n",
" <td> Alameda</td>\n",
" <td> 1820</td>\n",
" <td> 568200</td>\n",
" <td> 0.001763</td>\n",
" <td> 0.007090</td>\n",
" <td> 0.144641</td>\n",
" <td> 0.022490</td>\n",
" <td> 0.012970</td>\n",
" <td> 1776</td>\n",
" <td> 2005-05</td>\n",
" <td> 2005-Q2</td>\n",
" <td> 620800</td>\n",
" <td>-0.084729</td>\n",
" <td> 2004-10</td>\n",
" <td> 2011-12</td>\n",
" <td> 2011-Q4</td>\n",
" <td> 418600</td>\n",
" <td>-0.325709</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3644</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Caballo Hills</td>\n",
" <td> Alameda</td>\n",
" <td> 3644</td>\n",
" <td> 643000</td>\n",
" <td> 0.002495</td>\n",
" <td> 0.007837</td>\n",
" <td> 0.130649</td>\n",
" <td> 0.002444</td>\n",
" <td> 0.007484</td>\n",
" <td> 740</td>\n",
" <td> 2007-03</td>\n",
" <td> 2007-Q1</td>\n",
" <td> 765900</td>\n",
" <td>-0.160465</td>\n",
" <td> 2004-09</td>\n",
" <td> 2011-12</td>\n",
" <td> 2011-Q4</td>\n",
" <td> 494600</td>\n",
" <td>-0.354224</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2099</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Glenview</td>\n",
" <td> Alameda</td>\n",
" <td> 2099</td>\n",
" <td> 717600</td>\n",
" <td> 0.002935</td>\n",
" <td> 0.002935</td>\n",
" <td> 0.127416</td>\n",
" <td> 0.029803</td>\n",
" <td> 0.020710</td>\n",
" <td> 1606</td>\n",
" <td> 2013-09</td>\n",
" <td> 2013-Q3</td>\n",
" <td> 722500</td>\n",
" <td>-0.006782</td>\n",
" <td> 2013-09</td>\n",
" <td> 2013-12</td>\n",
" <td> 2013-Q4</td>\n",
" <td> 713900</td>\n",
" <td>-0.011903</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4530</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Woodminster</td>\n",
" <td> Alameda</td>\n",
" <td> 4530</td>\n",
" <td> 813400</td>\n",
" <td> 0.000123</td>\n",
" <td>-0.001105</td>\n",
" <td> 0.126593</td>\n",
" <td> 0.031192</td>\n",
" <td> 0.019553</td>\n",
" <td> 414</td>\n",
" <td> 2007-03</td>\n",
" <td> 2007-Q1</td>\n",
" <td> 861200</td>\n",
" <td>-0.055504</td>\n",
" <td> 2005-06</td>\n",
" <td> 2011-06</td>\n",
" <td> 2011-Q2</td>\n",
" <td> 634800</td>\n",
" <td>-0.262889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4895</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Crestmont</td>\n",
" <td> Alameda</td>\n",
" <td> 4895</td>\n",
" <td> 723900</td>\n",
" <td> 0.001522</td>\n",
" <td> 0.003883</td>\n",
" <td> 0.118856</td>\n",
" <td> 0.024431</td>\n",
" <td> 0.017400</td>\n",
" <td> 549</td>\n",
" <td> 2006-05</td>\n",
" <td> 2006-Q2</td>\n",
" <td> 763500</td>\n",
" <td>-0.051866</td>\n",
" <td> 2005-01</td>\n",
" <td> 2012-01</td>\n",
" <td> 2012-Q1</td>\n",
" <td> 530800</td>\n",
" <td>-0.304781</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2394</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Lakeshore</td>\n",
" <td> Alameda</td>\n",
" <td> 2394</td>\n",
" <td> 886900</td>\n",
" <td> 0.004531</td>\n",
" <td> 0.008643</td>\n",
" <td> 0.112658</td>\n",
" <td> 0.031012</td>\n",
" <td> 0.022151</td>\n",
" <td> 1167</td>\n",
" <td> 2005-08</td>\n",
" <td> 2005-Q3</td>\n",
" <td> 889700</td>\n",
" <td>-0.003147</td>\n",
" <td> 2005-08</td>\n",
" <td> 2011-08</td>\n",
" <td> 2011-Q3</td>\n",
" <td> 664400</td>\n",
" <td>-0.253231</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3064</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Rockridge</td>\n",
" <td> Alameda</td>\n",
" <td> 3064</td>\n",
" <td> 1001800</td>\n",
" <td>-0.002787</td>\n",
" <td>-0.006348</td>\n",
" <td> 0.105740</td>\n",
" <td> 0.045441</td>\n",
" <td> 0.030268</td>\n",
" <td> 927</td>\n",
" <td> 2013-11</td>\n",
" <td> 2013-Q4</td>\n",
" <td> 1008200</td>\n",
" <td>-0.006348</td>\n",
" <td> 2013-10</td>\n",
" <td> 2014-02</td>\n",
" <td> 2014-Q1</td>\n",
" <td> 1001800</td>\n",
" <td>-0.006348</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3107</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Trestle Glen</td>\n",
" <td> Alameda</td>\n",
" <td> 3107</td>\n",
" <td> 899600</td>\n",
" <td> 0.000667</td>\n",
" <td>-0.001221</td>\n",
" <td> 0.105567</td>\n",
" <td> 0.027262</td>\n",
" <td> 0.021531</td>\n",
" <td> 1156</td>\n",
" <td> 2005-06</td>\n",
" <td> 2005-Q2</td>\n",
" <td> 912700</td>\n",
" <td>-0.014353</td>\n",
" <td> 2005-05</td>\n",
" <td> 2011-07</td>\n",
" <td> 2011-Q3</td>\n",
" <td> 690800</td>\n",
" <td>-0.243125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2963</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Merriwood</td>\n",
" <td> Alameda</td>\n",
" <td> 2963</td>\n",
" <td> 763100</td>\n",
" <td> 0.002101</td>\n",
" <td> 0.006728</td>\n",
" <td> 0.104661</td>\n",
" <td> 0.022098</td>\n",
" <td> 0.015501</td>\n",
" <td> 1288</td>\n",
" <td> 2005-09</td>\n",
" <td> 2005-Q3</td>\n",
" <td> 842800</td>\n",
" <td>-0.094566</td>\n",
" <td> 2004-12</td>\n",
" <td> 2011-12</td>\n",
" <td> 2011-Q4</td>\n",
" <td> 608800</td>\n",
" <td>-0.277646</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3129</th>\n",
" <td> 2014-02-28</td>\n",
" <td> Santa Fe</td>\n",
" <td> Alameda</td>\n",
" <td> 3129</td>\n",
" <td> 477500</td>\n",
" <td> 0.014878</td>\n",
" <td> 0.030205</td>\n",
" <td> 0.104557</td>\n",
" <td> 0.057180</td>\n",
" <td> 0.022806</td>\n",
" <td> 601</td>\n",
" <td> 2005-11</td>\n",
" <td> 2005-Q4</td>\n",
" <td> 531400</td>\n",
" <td>-0.101430</td>\n",
" <td> 2005-04</td>\n",
" <td> 2011-11</td>\n",
" <td> 2011-Q4</td>\n",
" <td> 274600</td>\n",
" <td>-0.483252</td>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>72 rows \u00d7 20 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
" Date RegionName County SizeRank Zhvi \\\n",
"1481 2014-02-28 Adams Point Alameda 1481 358600 \n",
"2748 2014-02-28 Harrington Alameda 2748 272500 \n",
"3504 2014-02-28 Upper Peralta Creek Alameda 3504 347300 \n",
"1826 2014-02-28 St. Elizabeth Alameda 1826 262000 \n",
"3286 2014-02-28 Clawson Alameda 3286 377900 \n",
"2786 2014-02-28 Jefferson Alameda 2786 293000 \n",
"3202 2014-02-28 Brookfield Village Alameda 3202 201300 \n",
"2298 2014-02-28 Allendale Alameda 2298 299300 \n",
"4243 2014-02-28 Oak Knoll-Golf Links Alameda 4243 403800 \n",
"3781 2014-02-28 Produce & Waterfront Alameda 3781 476200 \n",
"2054 2014-02-28 Seminary Alameda 2054 221100 \n",
"4015 2014-02-28 Durant Manor Alameda 4015 279700 \n",
"2989 2014-02-28 Eastmont Hills Alameda 2989 379000 \n",
"4843 2014-02-28 Foothill Square Alameda 4843 265100 \n",
"2377 2014-02-28 Grand Lake Alameda 2377 336500 \n",
"3942 2014-02-28 Toler Heights Alameda 3942 303900 \n",
"2000 2014-02-28 Fremont Alameda 2000 270900 \n",
"2611 2014-02-28 Highland Terrace Alameda 2611 307100 \n",
"3629 2014-02-28 Las Palmas Alameda 3629 245300 \n",
"3994 2014-02-28 Golden Gate Alameda 3994 529100 \n",
"2024 2014-02-28 Meadow Brook Alameda 2024 284400 \n",
"2735 2014-02-28 North Stonehurst Alameda 2735 207200 \n",
"3035 2014-02-28 Eastmont Alameda 3035 215000 \n",
"1677 2014-02-28 Havenscourt Alameda 1677 220200 \n",
"2085 2014-02-28 Maxwell Park Alameda 2085 406200 \n",
"3266 2014-02-28 Lakewide Alameda 3266 403800 \n",
"3119 2014-02-28 Sobrante Park Alameda 3119 208300 \n",
"3258 2014-02-28 Coliseum Alameda 3258 186800 \n",
"1972 2014-02-28 Longfellow Alameda 1972 465400 \n",
"3043 2014-02-28 Castlemont Alameda 3043 229000 \n",
"2029 2014-02-28 Webster Alameda 2029 195100 \n",
"3921 2014-02-28 Fairfax Alameda 3921 319800 \n",
"2907 2014-02-28 Chabot Park Alameda 2907 598300 \n",
"2199 2014-02-28 Arroyo Viejo Alameda 2199 195200 \n",
"5144 2014-02-28 Columbia Gardens Alameda 5144 183000 \n",
"2062 2014-02-28 Rancho San Antonio Alameda 2062 274700 \n",
"3098 2014-02-28 Frick Alameda 3098 349000 \n",
"1741 2014-02-28 Bushrod Alameda 1741 616300 \n",
"2891 2014-02-28 Iveywood Alameda 2891 211600 \n",
"2381 2014-02-28 Prescott Alameda 2381 354400 \n",
"4809 2014-02-28 Leona Heights Alameda 4809 597000 \n",
"3084 2014-02-28 Cox Alameda 3084 203400 \n",
"1307 2014-02-28 Redwood Heights Alameda 1307 562700 \n",
"2991 2014-02-28 Shafter Alameda 2991 853100 \n",
"2579 2014-02-28 Temescal Alameda 2579 679800 \n",
"2903 2014-02-28 Fairview Park Alameda 2903 867400 \n",
"4173 2014-02-28 Lincoln Highlands Alameda 4173 681100 \n",
"2670 2014-02-28 Sequoyah Alameda 2670 574900 \n",
"1531 2014-02-28 Clinton Alameda 1531 364800 \n",
"4924 2014-02-28 Skyline - Hillcrest Estates Alameda 4924 849200 \n",
"1820 2014-02-28 Upper Dimond Alameda 1820 568200 \n",
"3644 2014-02-28 Caballo Hills Alameda 3644 643000 \n",
"2099 2014-02-28 Glenview Alameda 2099 717600 \n",
"4530 2014-02-28 Woodminster Alameda 4530 813400 \n",
"4895 2014-02-28 Crestmont Alameda 4895 723900 \n",
"2394 2014-02-28 Lakeshore Alameda 2394 886900 \n",
"3064 2014-02-28 Rockridge Alameda 3064 1001800 \n",
"3107 2014-02-28 Trestle Glen Alameda 3107 899600 \n",
"2963 2014-02-28 Merriwood Alameda 2963 763100 \n",
"3129 2014-02-28 Santa Fe Alameda 3129 477500 \n",
" ... ... ... ... ... \n",
"\n",
" MoM QoQ YoY 5Year 10Year ZhviRecordCnt \\\n",
"1481 0.013567 0.055948 0.457131 0.044173 0.012407 980 \n",
"2748 0.010757 0.040871 0.446391 -0.014537 -0.013981 471 \n",
"3504 0.005501 0.018774 0.417551 0.036220 0.006689 427 \n",
"1826 0.003447 0.027854 0.409360 -0.025878 -0.014434 534 \n",
"3286 0.010428 0.033926 0.400148 0.021843 0.010506 596 \n",
"2786 0.008953 0.025551 0.366604 -0.012424 -0.012270 746 \n",
"3202 0.019241 0.051175 0.365672 -0.015723 -0.030716 961 \n",
"2298 0.014576 0.029584 0.364175 0.029664 -0.006944 845 \n",
"4243 0.024093 0.086068 0.353217 0.027255 0.003355 566 \n",
"3781 0.011470 0.053773 0.351305 0.052846 0.027941 992 \n",
"2054 0.017019 0.052356 0.319212 -0.003314 -0.022640 789 \n",
"4015 0.002150 0.016352 0.318096 -0.009451 -0.020080 642 \n",
"2989 0.007979 0.027100 0.317344 0.023708 0.002650 1235 \n",
"4843 -0.005626 0.009520 0.311727 0.005055 -0.018952 423 \n",
"2377 0.008089 0.029997 0.310869 0.025910 0.000986 1133 \n",
"3942 0.014691 0.062959 0.304292 0.005078 -0.011452 620 \n",
"2000 0.001479 0.008939 0.303030 -0.002200 -0.014058 572 \n",
"2611 0.016551 0.029501 0.297423 0.006914 -0.004541 570 \n",
"3629 0.009881 0.046948 0.297197 -0.002026 -0.024668 661 \n",
"3994 0.011470 0.036029 0.286722 0.069005 0.028701 424 \n",
"2024 0.002821 0.010661 0.286296 0.003985 -0.013316 646 \n",
"2735 0.020187 0.071355 0.285360 -0.008743 -0.035462 653 \n",
"3035 0.022349 0.039149 0.284349 -0.013226 -0.029848 826 \n",
"1677 0.022284 0.052581 0.262615 -0.012589 -0.027594 1527 \n",
"2085 0.001479 0.010951 0.259926 0.018624 0.000444 1982 \n",
"3266 0.007988 0.032209 0.253648 0.018630 0.023022 539 \n",
"3119 0.023084 0.061131 0.250300 -0.021826 -0.029459 765 \n",
"3258 0.013565 0.054771 0.246996 -0.018039 -0.030985 410 \n",
"1972 0.001506 0.013723 0.244718 0.078333 0.029407 1116 \n",
"3043 0.005268 0.034327 0.241192 -0.012285 -0.023036 552 \n",
"2029 0.022537 0.065538 0.235592 -0.017587 -0.037492 901 \n",
"3921 -0.004359 0.000939 0.231421 0.019896 -0.009997 449 \n",
"2907 0.000167 -0.003166 0.229552 0.017533 0.011132 1097 \n",
"2199 0.012448 0.036093 0.227673 -0.021762 -0.036256 1043 \n",
"5144 0.013289 0.046911 0.221629 -0.006010 -0.037647 415 \n",
"2062 0.005490 0.004388 0.214412 -0.018316 -0.016067 485 \n",
"3098 0.004895 0.007797 0.206360 0.032100 -0.007450 833 \n",
"1741 0.001788 0.006533 0.199261 0.061403 0.032270 1254 \n",
"2891 0.018777 0.059059 0.189432 -0.019504 -0.033009 747 \n",
"2381 0.009112 0.023390 0.180546 0.012292 0.008946 819 \n",
"4809 0.004881 0.014788 0.179143 0.013422 0.009917 421 \n",
"3084 0.015984 0.048454 0.178447 -0.019721 -0.033946 425 \n",
"1307 0.003925 0.017909 0.172292 0.023255 0.012575 2839 \n",
"2991 0.009825 0.019479 0.165756 0.042807 0.034263 792 \n",
"2579 0.000589 -0.007011 0.164839 0.053682 0.033049 759 \n",
"2903 0.002195 0.010249 0.157768 0.063192 0.040033 804 \n",
"4173 0.000147 0.006502 0.157349 0.030640 0.012725 704 \n",
"2670 -0.002083 -0.011010 0.150030 0.013168 0.004474 1911 \n",
"1531 -0.008157 -0.032104 0.149338 0.007570 0.002220 462 \n",
"4924 0.000236 0.006758 0.147257 0.025813 0.012554 560 \n",
"1820 0.001763 0.007090 0.144641 0.022490 0.012970 1776 \n",
"3644 0.002495 0.007837 0.130649 0.002444 0.007484 740 \n",
"2099 0.002935 0.002935 0.127416 0.029803 0.020710 1606 \n",
"4530 0.000123 -0.001105 0.126593 0.031192 0.019553 414 \n",
"4895 0.001522 0.003883 0.118856 0.024431 0.017400 549 \n",
"2394 0.004531 0.008643 0.112658 0.031012 0.022151 1167 \n",
"3064 -0.002787 -0.006348 0.105740 0.045441 0.030268 927 \n",
"3107 0.000667 -0.001221 0.105567 0.027262 0.021531 1156 \n",
"2963 0.002101 0.006728 0.104661 0.022098 0.015501 1288 \n",
"3129 0.014878 0.030205 0.104557 0.057180 0.022806 601 \n",
" ... ... ... ... ... ... \n",
"\n",
" PeakMonth PeakQuarter PeakZHVI PctFallFromPeak LastTimeAtCurrZHVI \\\n",
"1481 2005-10 2005-Q4 429900 -0.165853 2004-11 \n",
"2748 2006-04 2006-Q2 473800 -0.424863 2002-12 \n",
"3504 2006-01 2006-Q1 497400 -0.301769 2004-06 \n",
"1826 2006-07 2006-Q3 494500 -0.470172 2003-01 \n",
"3286 2007-03 2007-Q1 503400 -0.249305 2004-10 \n",
"2786 2006-04 2006-Q2 494800 -0.407842 2003-01 \n",
"3202 2006-08 2006-Q3 409500 -0.508425 2002-05 \n",
"2298 2006-06 2006-Q2 482000 -0.379046 2003-11 \n",
"4243 2006-11 2006-Q4 582700 -0.307019 2004-04 \n",
"3781 2005-12 2005-Q4 489300 -0.026773 2005-07 \n",
"2054 2007-01 2007-Q1 433100 -0.489494 2002-08 \n",
"4015 2006-08 2006-Q3 490900 -0.430230 2002-12 \n",
"2989 2006-03 2006-Q1 562700 -0.326462 2004-04 \n",
"4843 2006-12 2006-Q4 497700 -0.467350 2002-05 \n",
"2377 2005-11 2005-Q4 453800 -0.258484 2004-03 \n",
"3942 2006-11 2006-Q4 520900 -0.416587 2003-03 \n",
"2000 2006-11 2006-Q4 461600 -0.413128 2003-03 \n",
"2611 2006-08 2006-Q3 485800 -0.367847 2003-12 \n",
"3629 2006-08 2006-Q3 476300 -0.484988 2002-06 \n",
"3994 2006-06 2006-Q2 553000 -0.043219 2005-07 \n",
"2024 2006-07 2006-Q3 494400 -0.424757 2003-07 \n",
"2735 2006-12 2006-Q4 441900 -0.531116 2002-04 \n",
"3035 2006-12 2006-Q4 429500 -0.499418 2002-05 \n",
"1677 2007-02 2007-Q1 450800 -0.511535 2002-05 \n",
"2085 2006-08 2006-Q3 545800 -0.255771 2004-03 \n",
"3266 2005-08 2005-Q3 490700 -0.177094 2004-11 \n",
"3119 2007-03 2007-Q1 438100 -0.524538 2002-03 \n",
"3258 2007-02 2007-Q1 418200 -0.553324 2002-01 \n",
"1972 2006-11 2006-Q4 531800 -0.124859 2005-08 \n",
"3043 2006-09 2006-Q3 433100 -0.471254 2002-04 \n",
"2029 2007-01 2007-Q1 439600 -0.556187 2002-02 \n",
"3921 2007-03 2007-Q1 504500 -0.366105 2003-09 \n",
"2907 2006-09 2006-Q3 734500 -0.185432 2004-09 \n",
"2199 2007-03 2007-Q1 432900 -0.549088 2002-01 \n",
"5144 2006-12 2006-Q4 432100 -0.576487 2001-10 \n",
"2062 2006-03 2006-Q1 479500 -0.427112 2003-05 \n",
"3098 2006-11 2006-Q4 491100 -0.289350 2003-10 \n",
"1741 2014-02 2014-Q1 616300 0.000000 2014-02 \n",
"2891 2006-08 2006-Q3 450300 -0.530091 2002-02 \n",
"2381 2007-03 2007-Q1 496100 -0.285628 2004-08 \n",
"4809 2006-02 2006-Q1 706500 -0.154989 2004-09 \n",
"3084 2006-08 2006-Q3 437500 -0.535086 2002-02 \n",
"1307 2005-08 2005-Q3 652400 -0.137492 2004-09 \n",
"2991 2014-02 2014-Q1 853100 0.000000 2014-02 \n",
"2579 2013-09 2013-Q3 691100 -0.016351 2013-09 \n",
"2903 2014-02 2014-Q1 867400 0.000000 2014-02 \n",
"4173 2005-08 2005-Q3 735900 -0.074467 2005-02 \n",
"2670 2006-09 2006-Q3 724500 -0.206487 2004-09 \n",
"1531 2006-10 2006-Q4 505400 -0.278195 2004-03 \n",
"4924 2005-12 2005-Q4 921900 -0.078859 2005-01 \n",
"1820 2005-05 2005-Q2 620800 -0.084729 2004-10 \n",
"3644 2007-03 2007-Q1 765900 -0.160465 2004-09 \n",
"2099 2013-09 2013-Q3 722500 -0.006782 2013-09 \n",
"4530 2007-03 2007-Q1 861200 -0.055504 2005-06 \n",
"4895 2006-05 2006-Q2 763500 -0.051866 2005-01 \n",
"2394 2005-08 2005-Q3 889700 -0.003147 2005-08 \n",
"3064 2013-11 2013-Q4 1008200 -0.006348 2013-10 \n",
"3107 2005-06 2005-Q2 912700 -0.014353 2005-05 \n",
"2963 2005-09 2005-Q3 842800 -0.094566 2004-12 \n",
"3129 2005-11 2005-Q4 531400 -0.101430 2005-04 \n",
" ... ... ... ... ... \n",
"\n",
" BottomMonth BottomQuarter BottomZHVI PctFallFromPeakToBottom \n",
"1481 2011-09 2011-Q3 206000 -0.520819 \n",
"2748 2012-04 2012-Q2 167700 -0.646053 \n",
"3504 2011-03 2011-Q1 215000 -0.567752 \n",
"1826 2012-02 2012-Q1 160600 -0.675228 \n",
"3286 2012-03 2012-Q1 224300 -0.554430 \n",
"2786 2012-05 2012-Q2 185500 -0.625101 \n",
"3202 2011-10 2011-Q4 121900 -0.702320 \n",
"2298 2012-02 2012-Q1 182800 -0.620747 \n",
"4243 2012-02 2012-Q1 231200 -0.603226 \n",
"3781 2010-08 2010-Q3 286300 -0.414878 \n",
"2054 2012-04 2012-Q2 132800 -0.693373 \n",
"4015 2012-04 2012-Q2 177500 -0.638419 \n",
"2989 2012-03 2012-Q1 250800 -0.554292 \n",
"4843 2011-07 2011-Q3 163700 -0.671087 \n",
"2377 2012-06 2012-Q2 245200 -0.459674 \n",
"3942 2011-10 2011-Q4 179500 -0.655404 \n",
"2000 2011-09 2011-Q3 170200 -0.631282 \n",
"2611 2011-10 2011-Q4 198000 -0.592425 \n",
"3629 2012-02 2012-Q1 158900 -0.666387 \n",
"3994 2011-07 2011-Q3 293400 -0.469439 \n",
"2024 2012-03 2012-Q1 174300 -0.647451 \n",
"2735 2011-09 2011-Q3 133700 -0.697443 \n",
"3035 2011-10 2011-Q4 126000 -0.706636 \n",
"1677 2011-10 2011-Q4 141400 -0.686335 \n",
"2085 2011-11 2011-Q4 279000 -0.488824 \n",
"3266 2012-01 2012-Q1 270800 -0.448135 \n",
"3119 2011-07 2011-Q3 148500 -0.661036 \n",
"3258 2011-11 2011-Q4 106600 -0.745098 \n",
"1972 2009-07 2009-Q3 284600 -0.464836 \n",
"3043 2011-03 2011-Q1 145800 -0.663357 \n",
"2029 2011-05 2011-Q2 110700 -0.748180 \n",
"3921 2011-11 2011-Q4 211100 -0.581566 \n",
"2907 2012-03 2012-Q1 438000 -0.403676 \n",
"2199 2011-09 2011-Q3 116800 -0.730192 \n",
"5144 2011-10 2011-Q4 122400 -0.716732 \n",
"2062 2011-11 2011-Q4 184000 -0.616267 \n",
"3098 2012-03 2012-Q1 231300 -0.529016 \n",
"1741 0 0 0 0.000000 \n",
"2891 2011-02 2011-Q1 150400 -0.666000 \n",
"2381 2011-10 2011-Q4 259700 -0.476517 \n",
"4809 2012-01 2012-Q1 454100 -0.357254 \n",
"3084 2011-03 2011-Q1 128300 -0.706743 \n",
"1307 2012-02 2012-Q1 409100 -0.372931 \n",
"2991 0 0 0 0.000000 \n",
"2579 2014-01 2014-Q1 679400 -0.016930 \n",
"2903 0 0 0 0.000000 \n",
"4173 2012-01 2012-Q1 499900 -0.320696 \n",
"2670 2011-08 2011-Q3 447300 -0.382609 \n",
"1531 2011-10 2011-Q4 251700 -0.501979 \n",
"4924 2012-03 2012-Q1 607300 -0.341252 \n",
"1820 2011-12 2011-Q4 418600 -0.325709 \n",
"3644 2011-12 2011-Q4 494600 -0.354224 \n",
"2099 2013-12 2013-Q4 713900 -0.011903 \n",
"4530 2011-06 2011-Q2 634800 -0.262889 \n",
"4895 2012-01 2012-Q1 530800 -0.304781 \n",
"2394 2011-08 2011-Q3 664400 -0.253231 \n",
"3064 2014-02 2014-Q1 1001800 -0.006348 \n",
"3107 2011-07 2011-Q3 690800 -0.243125 \n",
"2963 2011-12 2011-Q4 608800 -0.277646 \n",
"3129 2011-11 2011-Q4 274600 -0.483252 \n",
" ... ... ... ... \n",
"\n",
"[72 rows x 20 columns]"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"oakland_homevalue = cleanedOakland(homevalue) #from 1996-04 to 2014-02, 72 neighborhoods\n",
"neighborhood_list = oakland_homevalue.RegionName.tolist()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"crime_neighborhood = [u'Lakewide',\n",
" u'Eastmont',\n",
" u'Coliseum',\n",
" u'North Kennedy Tract',\n",
" u'Acorn-Acorn Industrial',\n",
" u'North Stonehurst',\n",
" u'Arrowhead Marsh',\n",
" u'Lakeshore',\n",
" u'Grand Lake',\n",
" u'Fairfax',\n",
" u'Fremont',\n",
" u'Upper Dimond',\n",
" u'Maxwell Park',\n",
" u'South Stonehurst',\n",
" u'Oak Tree',\n",
" u'Fruitvale Station',\n",
" u'Chinatown',\n",
" u'Shafter',\n",
" u'Bella Vista',\n",
" u'Rockridge',\n",
" u'Harrington',\n",
" u'Webster',\n",
" u'Montclair',\n",
" u'Oakmore',\n",
" u'Produce And Waterfront',\n",
" u'Golf Links',\n",
" u'Hiller Highlands',\n",
" u'Glenview',\n",
" u'Redwood Heights',\n",
" u'Adams Point',\n",
" u'Jefferson',\n",
" u'Melrose',\n",
" u'Panoramic Hill',\n",
" u'Bancroft Business-Havenscourt',\n",
" u'Piedmont',\n",
" u'Highland Terrace',\n",
" u'Golden Gate',\n",
" u'Merritt',\n",
" u'Claremont',\n",
" u'Hawthorne',\n",
" u'Downtown',\n",
" u'Elmhurst Park',\n",
" u'Joaquin Miller Park',\n",
" u'Shepherd Canyon',\n",
" u'Mountain View Cemetery',\n",
" u'Oakland Airport',\n",
" u'School',\n",
" u'Waverly',\n",
" u'Paradise Park',\n",
" u'Glen Highlands',\n",
" u'Ralph Bunche',\n",
" u'Seminary',\n",
" u'Fitchburg',\n",
" u'Dimond',\n",
" u'Bushrod',\n",
" u'Clinton',\n",
" u'Oakland Ave-Harrison St',\n",
" u'Columbia Gardens',\n",
" u'Upper Peralta Creek-Bartlett',\n",
" u'Coliseum Industrial',\n",
" u'Santa Fe',\n",
" u'Sequoyah',\n",
" u'Ivy Hill',\n",
" u'Lake Merritt',\n",
" u'Crocker Highland',\n",
" u'Piedmont Pines',\n",
" u'Brookfield Village',\n",
" u'Hoover-Foster',\n",
" u'Upper Laurel',\n",
" u'Fairview Park',\n",
" u'Clawson',\n",
" u'Arroyo Viejo',\n",
" u'Peralta-Laney',\n",
" u'Rancho San Antonio',\n",
" u'Piedmont Avenue',\n",
" u'San Pablo Gateway',\n",
" u'Bartlett',\n",
" u'Woodminster',\n",
" u'Mills College',\n",
" u'East Peralta',\n",
" u'Woodland',\n",
" u'Chabot Park',\n",
" u'Highland',\n",
" u'Gaskill',\n",
" u'Las Palmas',\n",
" u'Civic Center',\n",
" u'Peralta-Hacienda',\n",
" u'South Kennedy Tract',\n",
" u'Tuxedo',\n",
" u'Lincoln Highlands',\n",
" u'Laurel',\n",
" u'Toler Heights',\n",
" u'Pill Hill',\n",
" u'Fairfax Business-Wentworth-Holland',\n",
" u'Montclair Business',\n",
" u'Crestmont',\n",
" u'Castlemont',\n",
" u'Lockwood Tevis',\n",
" u'Sheffield Village',\n",
" u'McClymonds',\n",
" u'Reservoir Hill-Meadow Brook',\n",
" u'Allendale',\n",
" u'Durant Manor',\n",
" u'Northgate',\n",
" u'Eastmont Hills',\n",
" u'Iveywood',\n",
" u'Patten',\n",
" u'Sobrante Park',\n",
" u'Merriwood',\n",
" u'Lynn-Highland Park',\n",
" u'Temescal',\n",
" u'Frick',\n",
" u'Cox',\n",
" u'South Prescott',\n",
" u'Mosswood',\n",
" u'Skyline-Hillcrest Estates',\n",
" u'Oak Center',\n",
" u'Old City-Produce And Waterfront',\n",
" u'Prescott',\n",
" u'Saint Elizabeth',\n",
" u'Longfellow',\n",
" u'Millsmont',\n",
" u'Cleveland Heights',\n",
" u'Trestle Glen',\n",
" u'Caballo Hills',\n",
" u'Upper Rockridge',\n",
" u'Forestland',\n",
" u'Sausal Creek',\n",
" u'Leona Heights',\n",
" u'Hegenberger',\n",
" u'Foothill Square']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"not_in_crime =[for name in neighborhood_list if name not in crime_neighborhood]\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Havenscourt\n",
"St. Elizabeth\n",
"Meadow Brook\n",
"Upper Peralta Creek\n",
"Produce & Waterfront\n",
"Oak Knoll-Golf Links\n",
"Skyline - Hillcrest Estates\n"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"intersection = [name for name in neighborhood_list if name in crime_neighborhood]\n",
"len(intersection)\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 22,
"text": [
"65"
]
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"APdf = oakland_homevalue[oakland_homevalue.RegionName==\"Clawson\"]\n",
"APdf"
],
"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>RegionName</th>\n",
" <th>CountyName</th>\n",
" <th>1996-04</th>\n",
" <th>1996-05</th>\n",
" <th>1996-06</th>\n",
" <th>1996-07</th>\n",
" <th>1996-08</th>\n",
" <th>1996-09</th>\n",
" <th>1996-10</th>\n",
" <th>1996-11</th>\n",
" <th>1996-12</th>\n",
" <th>1997-01</th>\n",
" <th>1997-02</th>\n",
" <th>1997-03</th>\n",
" <th>1997-04</th>\n",
" <th>1997-05</th>\n",
" <th>1997-06</th>\n",
" <th>1997-07</th>\n",
" <th>1997-08</th>\n",
" <th>1997-09</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3286</th>\n",
" <td> Clawson</td>\n",
" <td> Alameda</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1 rows \u00d7 217 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
" RegionName CountyName 1996-04 1996-05 1996-06 1996-07 1996-08 \\\n",
"3286 Clawson Alameda 0 0 0 0 0 \n",
"\n",
" 1996-09 1996-10 1996-11 1996-12 1997-01 1997-02 1997-03 1997-04 \\\n",
"3286 0 0 0 0 0 0 0 0 \n",
"\n",
" 1997-05 1997-06 1997-07 1997-08 1997-09 \n",
"3286 0 0 0 0 0 ... \n",
"\n",
"[1 rows x 217 columns]"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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