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Created April 29, 2014 18:45
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Oakland_Gentrification_Presentation
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gistfile1.txt
{
"metadata": {
"name": "",
"signature": "sha256:3f4c556834523bf703fa68a0c02f05bf2a0654fac00463bd2cc0c29631580cc3"
},
"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": "heading",
"level": 1,
"metadata": {},
"source": [
"Property Values in Oakland\n",
"* History data could be retrieved via Zillow Research Data http://www.zillow.com/research/data/\n",
"* Current data could be retrieved via Zillow API http://www.zillow.com/howto/api/APIOverview.htm"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": "from pandas import DataFrame, Series\nimport pandas as pd\n%matplotlib inline\nfrom numpy.random import randn\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom pylab import figure, show\ntry:\n import seaborn as sns\nexcept Exception as e:\n print \"Attempt to import and enable seaborn failed\", e",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": "csv_import_path = 'C:\\Users\\User\\Documents\\IPython Notebooks\\clean_crime_zillow.csv'\ndf_complete = pd.read_csv(csv_import_path)\ndel df_complete[\"Unnamed: 0\"]",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": "df_complete",
"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>RD</th>\n <th>DEPARTMENT</th>\n <th>REPORTED</th>\n <th>OCCURRED</th>\n <th>YEAROCC</th>\n <th>DAY</th>\n <th>MONTH</th>\n <th>QUARTER</th>\n <th>LONG</th>\n <th>LAT</th>\n <th>NEIGHBORHOOD</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0 </th>\n <td> 10-070714</td>\n <td> MISDEMEANOR WARRANT</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.218806</td>\n <td> 37.774598</td>\n <td> Saint Elizabeth</td>\n </tr>\n <tr>\n <th>1 </th>\n <td> 10-070716</td>\n <td> MISDEMEANOR WARRANT</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.183007</td>\n <td> 37.749861</td>\n <td> Highland</td>\n </tr>\n <tr>\n <th>2 </th>\n <td> 10-070728</td>\n <td> MISDEMEANOR WARRANT</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.182956</td>\n <td> 37.764526</td>\n <td> Hegenberger</td>\n </tr>\n <tr>\n <th>3 </th>\n <td> 10-070734</td>\n <td> MISDEMEANOR WARRANT</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.193989</td>\n <td> 37.766940</td>\n <td> Seminary</td>\n </tr>\n <tr>\n <th>4 </th>\n <td> 10-070794</td>\n <td> MISDEMEANOR WARRANT</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.204981</td>\n <td> 37.791133</td>\n <td> Allendale</td>\n </tr>\n <tr>\n <th>5 </th>\n <td> 10-070802</td>\n <td> MISDEMEANOR WARRANT</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.296525</td>\n <td> 37.812649</td>\n <td> Prescott</td>\n </tr>\n <tr>\n <th>6 </th>\n <td> 10-070790</td>\n <td> OTHER</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.257272</td>\n <td> 37.798217</td>\n <td> Merritt</td>\n </tr>\n <tr>\n <th>7 </th>\n <td> 10-070786</td>\n <td> NARCOTICS</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.174073</td>\n <td> 37.763944</td>\n <td> Eastmont</td>\n </tr>\n <tr>\n <th>8 </th>\n <td> 11-000139</td>\n <td> MISSING</td>\n <td> 1/1/2011</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.276766</td>\n <td> 37.812503</td>\n <td> Ralph Bunche</td>\n </tr>\n <tr>\n <th>9 </th>\n <td> 10-070685</td>\n <td> ROBBERY</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.262447</td>\n <td> 37.836894</td>\n <td> Temescal</td>\n </tr>\n <tr>\n <th>10</th>\n <td> 10-070708</td>\n <td> MISDEMEANOR ASSAULT</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.273002</td>\n <td> 37.806654</td>\n <td> Downtown</td>\n </tr>\n <tr>\n <th>11</th>\n <td> 10-070703</td>\n <td> MISDEMEANOR ASSAULT</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.206761</td>\n <td> 37.786901</td>\n <td> Allendale</td>\n </tr>\n <tr>\n <th>12</th>\n <td> 10-070688</td>\n <td> VANDALISM</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.221372</td>\n <td> 37.771733</td>\n <td> Fruitvale Station</td>\n </tr>\n <tr>\n <th>13</th>\n <td> 11-000052</td>\n <td> RUNAWAY</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.143034</td>\n <td> 37.758854</td>\n <td> Sequoyah</td>\n </tr>\n <tr>\n <th>14</th>\n <td> 11-000001</td>\n <td> missing</td>\n <td> 1/1/2011</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.197101</td>\n <td> 37.766479</td>\n <td> Seminary</td>\n </tr>\n <tr>\n <th>15</th>\n <td> 10-070697</td>\n <td> NARCOTICS</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.164586</td>\n <td> 37.757001</td>\n <td> Castlemont</td>\n </tr>\n <tr>\n <th>16</th>\n <td> 10-070757</td>\n <td> OTHER</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.179337</td>\n <td> 37.804971</td>\n <td> Joaquin Miller Park</td>\n </tr>\n <tr>\n <th>17</th>\n <td> 11-900254</td>\n <td> OTHER</td>\n <td> 1/3/2011</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.195885</td>\n <td> 37.745952</td>\n <td> Coliseum Industrial</td>\n </tr>\n <tr>\n <th>18</th>\n <td> 11-900265</td>\n <td> OTHER</td>\n <td> 1/3/2011</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.137348</td>\n <td> 37.749625</td>\n <td> Chabot Park</td>\n </tr>\n <tr>\n <th>19</th>\n <td> 11-001511</td>\n <td> MISSING</td>\n <td> 1/9/2011</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.285314</td>\n <td> 37.810791</td>\n <td> Oak Center</td>\n </tr>\n <tr>\n <th>20</th>\n <td> 10-070747</td>\n <td> FELONY ASSAULT</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.176025</td>\n <td> 37.771347</td>\n <td> Millsmont</td>\n </tr>\n <tr>\n <th>21</th>\n <td> 11-001455</td>\n <td> FELONY ASSAULT</td>\n <td> 1/9/2011</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.162959</td>\n <td> 37.750513</td>\n <td> Castlemont</td>\n </tr>\n <tr>\n <th>22</th>\n <td> 11-000751</td>\n <td> BURGLARY: RESIDENTIAL</td>\n <td> 1/5/2011</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.294639</td>\n <td> 37.810976</td>\n <td> Prescott</td>\n </tr>\n <tr>\n <th>23</th>\n <td> 11-900198</td>\n <td> BURG - AUTO</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.193932</td>\n <td> 37.787778</td>\n <td> Upper Laurel</td>\n </tr>\n <tr>\n <th>24</th>\n <td> 11-900251</td>\n <td> BURG - AUTO</td>\n <td> 1/3/2011</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.265511</td>\n <td> 37.813393</td>\n <td> Waverly</td>\n </tr>\n <tr>\n <th>25</th>\n <td> 11-900355</td>\n <td> BURG - AUTO</td>\n <td> 1/5/2011</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.206347</td>\n <td> 37.741576</td>\n <td> Oakland Airport</td>\n </tr>\n <tr>\n <th>26</th>\n <td> 11-900354</td>\n <td> FORGERY &amp; COUNTERFEITING</td>\n <td> 1/4/2011</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.214634</td>\n <td> 37.788049</td>\n <td> Peralta-Hacienda</td>\n </tr>\n <tr>\n <th>27</th>\n <td> 11-900185</td>\n <td> GRAND THEFT</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.274879</td>\n <td> 37.803209</td>\n <td> Downtown</td>\n </tr>\n <tr>\n <th>28</th>\n <td> 11-900191</td>\n <td> GRAND THEFT</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.268843</td>\n <td> 37.846684</td>\n <td> Bushrod</td>\n </tr>\n <tr>\n <th>29</th>\n <td> 11-900196</td>\n <td> GRAND THEFT</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.248753</td>\n <td> 37.810139</td>\n <td> Adams Point</td>\n </tr>\n <tr>\n <th>30</th>\n <td> 11-900359</td>\n <td> GRAND THEFT</td>\n <td> 1/5/2011</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.280195</td>\n <td> 37.824929</td>\n <td> Clawson</td>\n </tr>\n <tr>\n <th>31</th>\n <td> 11-900259</td>\n <td> OTHER</td>\n <td> 1/3/2011</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.190818</td>\n <td> 37.733059</td>\n <td> Brookfield Village</td>\n </tr>\n <tr>\n <th>32</th>\n <td> 11-900190</td>\n <td> VANDALISM</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.202415</td>\n <td> 37.797759</td>\n <td> Upper Dimond</td>\n </tr>\n <tr>\n <th>33</th>\n <td> 11-900194</td>\n <td> VANDALISM</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.261736</td>\n <td> 37.824547</td>\n <td> Mosswood</td>\n </tr>\n <tr>\n <th>34</th>\n <td> 11-900201</td>\n <td> VANDALISM</td>\n <td> 1/1/2011</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.257620</td>\n <td> 37.812580</td>\n <td> Adams Point</td>\n </tr>\n <tr>\n <th>35</th>\n <td> 11-900202</td>\n <td> VANDALISM</td>\n <td> 1/1/2011</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.267515</td>\n <td> 37.826922</td>\n <td> Mosswood</td>\n </tr>\n <tr>\n <th>36</th>\n <td> 11-900206</td>\n <td> VANDALISM</td>\n <td> 1/1/2011</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.279062</td>\n <td> 37.798838</td>\n <td> Old City-Produce And Waterfront</td>\n </tr>\n <tr>\n <th>37</th>\n <td> 11-900370</td>\n <td> VANDALISM</td>\n <td> 1/5/2011</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.276413</td>\n <td> 37.796519</td>\n <td> Old City-Produce And Waterfront</td>\n </tr>\n <tr>\n <th>38</th>\n <td> 11-000466</td>\n <td> VANDALISM</td>\n <td> 1/3/2011</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.293263</td>\n <td> 37.806351</td>\n <td> Prescott</td>\n </tr>\n <tr>\n <th>39</th>\n <td> 11-000878</td>\n <td> STOLEN VEHICLE</td>\n <td> 1/5/2011</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.221911</td>\n <td> 37.777838</td>\n <td> Saint Elizabeth</td>\n </tr>\n <tr>\n <th>40</th>\n <td> 11-900197</td>\n <td> MISCELLANEOUS TRAFFIC CRIME</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.177683</td>\n <td> 37.766523</td>\n <td> Bancroft Business-Havenscourt</td>\n </tr>\n <tr>\n <th>41</th>\n <td> 11-900230</td>\n <td> MISCELLANEOUS TRAFFIC CRIME</td>\n <td> 1/2/2011</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.216032</td>\n <td> 37.789641</td>\n <td> Peralta-Hacienda</td>\n </tr>\n <tr>\n <th>42</th>\n <td> 11-900278</td>\n <td> MISCELLANEOUS TRAFFIC CRIME</td>\n <td> 1/3/2011</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.252978</td>\n <td> 37.793392</td>\n <td> East Peralta</td>\n </tr>\n <tr>\n <th>43</th>\n <td> 10-070712</td>\n <td> OTHER</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.262916</td>\n <td> 37.820805</td>\n <td> Pill Hill</td>\n </tr>\n <tr>\n <th>44</th>\n <td> 11-900561</td>\n <td> OTHER</td>\n <td> 1/13/2011</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.260288</td>\n <td> 37.847086</td>\n <td> Fairview Park</td>\n </tr>\n <tr>\n <th>45</th>\n <td> 11-002161</td>\n <td> STOLEN VEHICLE</td>\n <td> 1/12/2011</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.277688</td>\n <td> 37.825465</td>\n <td> Hoover-Foster</td>\n </tr>\n <tr>\n <th>46</th>\n <td> 11-002331</td>\n <td> STOLEN VEHICLE</td>\n <td> 1/13/2011</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.274266</td>\n <td> 37.841606</td>\n <td> Santa Fe</td>\n </tr>\n <tr>\n <th>47</th>\n <td> 10-070722</td>\n <td> MISDEMEANOR ASSAULT</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.214186</td>\n <td> 37.792470</td>\n <td> Upper Peralta Creek-Bartlett</td>\n </tr>\n <tr>\n <th>48</th>\n <td> 11-003536</td>\n <td> VANDALISM</td>\n <td> 1/20/2011</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.267698</td>\n <td> 37.800398</td>\n <td> Civic Center</td>\n </tr>\n <tr>\n <th>49</th>\n <td> 11-000148</td>\n <td> STOLEN VEHICLE</td>\n <td> 1/1/2011</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.181929</td>\n <td> 37.751799</td>\n <td> Woodland</td>\n </tr>\n <tr>\n <th>50</th>\n <td> 11-007017</td>\n <td> MISDEMEANOR ASSAULT</td>\n <td> 2/8/2011</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.183872</td>\n <td> 37.751493</td>\n <td> Woodland</td>\n </tr>\n <tr>\n <th>51</th>\n <td> 11-010384</td>\n <td> OTHER</td>\n <td> 2/28/2011</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.156506</td>\n <td> 37.752310</td>\n <td> Toler Heights</td>\n </tr>\n <tr>\n <th>52</th>\n <td> 10-070679</td>\n <td> DRUNKENNESS</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.177662</td>\n <td> 37.765798</td>\n <td> Bancroft Business-Havenscourt</td>\n </tr>\n <tr>\n <th>53</th>\n <td> 10-070713</td>\n <td> FELONY ASSAULT</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.171585</td>\n <td> 37.769135</td>\n <td> Eastmont Hills</td>\n </tr>\n <tr>\n <th>54</th>\n <td> 10-070731</td>\n <td> MISDEMEANOR WARRANT</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.265492</td>\n <td> 37.848243</td>\n <td> Bushrod</td>\n </tr>\n <tr>\n <th>55</th>\n <td> 10-070739</td>\n <td> MISDEMEANOR ASSAULT</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.171318</td>\n <td> 37.743047</td>\n <td> North Stonehurst</td>\n </tr>\n <tr>\n <th>56</th>\n <td> 10-070742</td>\n <td> ROBBERY</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.277801</td>\n <td> 37.825877</td>\n <td> Hoover-Foster</td>\n </tr>\n <tr>\n <th>57</th>\n <td> 10-070769</td>\n <td> DRUNKENNESS</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.257198</td>\n <td> 37.797625</td>\n <td> Merritt</td>\n </tr>\n <tr>\n <th>58</th>\n <td> 10-070778</td>\n <td> RUNAWAY</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.222702</td>\n <td> 37.792288</td>\n <td> Reservoir Hill-Meadow Brook</td>\n </tr>\n <tr>\n <th>59</th>\n <td> 10-070780</td>\n <td> OTHER</td>\n <td> 12/31/2010</td>\n <td> 12/31/2010</td>\n <td> 2010</td>\n <td> 6</td>\n <td> 12</td>\n <td> 4</td>\n <td>-122.276924</td>\n <td> 37.815517</td>\n <td> McClymonds</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 </tr>\n </tbody>\n</table>\n<p>284603 rows \u00d7 11 columns</p>\n</div>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": " RD DEPARTMENT REPORTED OCCURRED YEAROCC \\\n0 10-070714 MISDEMEANOR WARRANT 12/31/2010 12/31/2010 2010 \n1 10-070716 MISDEMEANOR WARRANT 12/31/2010 12/31/2010 2010 \n2 10-070728 MISDEMEANOR WARRANT 12/31/2010 12/31/2010 2010 \n3 10-070734 MISDEMEANOR WARRANT 12/31/2010 12/31/2010 2010 \n4 10-070794 MISDEMEANOR WARRANT 12/31/2010 12/31/2010 2010 \n5 10-070802 MISDEMEANOR WARRANT 12/31/2010 12/31/2010 2010 \n6 10-070790 OTHER 12/31/2010 12/31/2010 2010 \n7 10-070786 NARCOTICS 12/31/2010 12/31/2010 2010 \n8 11-000139 MISSING 1/1/2011 12/31/2010 2010 \n9 10-070685 ROBBERY 12/31/2010 12/31/2010 2010 \n10 10-070708 MISDEMEANOR ASSAULT 12/31/2010 12/31/2010 2010 \n11 10-070703 MISDEMEANOR ASSAULT 12/31/2010 12/31/2010 2010 \n12 10-070688 VANDALISM 12/31/2010 12/31/2010 2010 \n13 11-000052 RUNAWAY 12/31/2010 12/31/2010 2010 \n14 11-000001 missing 1/1/2011 12/31/2010 2010 \n15 10-070697 NARCOTICS 12/31/2010 12/31/2010 2010 \n16 10-070757 OTHER 12/31/2010 12/31/2010 2010 \n17 11-900254 OTHER 1/3/2011 12/31/2010 2010 \n18 11-900265 OTHER 1/3/2011 12/31/2010 2010 \n19 11-001511 MISSING 1/9/2011 12/31/2010 2010 \n20 10-070747 FELONY ASSAULT 12/31/2010 12/31/2010 2010 \n21 11-001455 FELONY ASSAULT 1/9/2011 12/31/2010 2010 \n22 11-000751 BURGLARY: RESIDENTIAL 1/5/2011 12/31/2010 2010 \n23 11-900198 BURG - AUTO 12/31/2010 12/31/2010 2010 \n24 11-900251 BURG - AUTO 1/3/2011 12/31/2010 2010 \n25 11-900355 BURG - AUTO 1/5/2011 12/31/2010 2010 \n26 11-900354 FORGERY & COUNTERFEITING 1/4/2011 12/31/2010 2010 \n27 11-900185 GRAND THEFT 12/31/2010 12/31/2010 2010 \n28 11-900191 GRAND THEFT 12/31/2010 12/31/2010 2010 \n29 11-900196 GRAND THEFT 12/31/2010 12/31/2010 2010 \n30 11-900359 GRAND THEFT 1/5/2011 12/31/2010 2010 \n31 11-900259 OTHER 1/3/2011 12/31/2010 2010 \n32 11-900190 VANDALISM 12/31/2010 12/31/2010 2010 \n33 11-900194 VANDALISM 12/31/2010 12/31/2010 2010 \n34 11-900201 VANDALISM 1/1/2011 12/31/2010 2010 \n35 11-900202 VANDALISM 1/1/2011 12/31/2010 2010 \n36 11-900206 VANDALISM 1/1/2011 12/31/2010 2010 \n37 11-900370 VANDALISM 1/5/2011 12/31/2010 2010 \n38 11-000466 VANDALISM 1/3/2011 12/31/2010 2010 \n39 11-000878 STOLEN VEHICLE 1/5/2011 12/31/2010 2010 \n40 11-900197 MISCELLANEOUS TRAFFIC CRIME 12/31/2010 12/31/2010 2010 \n41 11-900230 MISCELLANEOUS TRAFFIC CRIME 1/2/2011 12/31/2010 2010 \n42 11-900278 MISCELLANEOUS TRAFFIC CRIME 1/3/2011 12/31/2010 2010 \n43 10-070712 OTHER 12/31/2010 12/31/2010 2010 \n44 11-900561 OTHER 1/13/2011 12/31/2010 2010 \n45 11-002161 STOLEN VEHICLE 1/12/2011 12/31/2010 2010 \n46 11-002331 STOLEN VEHICLE 1/13/2011 12/31/2010 2010 \n47 10-070722 MISDEMEANOR ASSAULT 12/31/2010 12/31/2010 2010 \n48 11-003536 VANDALISM 1/20/2011 12/31/2010 2010 \n49 11-000148 STOLEN VEHICLE 1/1/2011 12/31/2010 2010 \n50 11-007017 MISDEMEANOR ASSAULT 2/8/2011 12/31/2010 2010 \n51 11-010384 OTHER 2/28/2011 12/31/2010 2010 \n52 10-070679 DRUNKENNESS 12/31/2010 12/31/2010 2010 \n53 10-070713 FELONY ASSAULT 12/31/2010 12/31/2010 2010 \n54 10-070731 MISDEMEANOR WARRANT 12/31/2010 12/31/2010 2010 \n55 10-070739 MISDEMEANOR ASSAULT 12/31/2010 12/31/2010 2010 \n56 10-070742 ROBBERY 12/31/2010 12/31/2010 2010 \n57 10-070769 DRUNKENNESS 12/31/2010 12/31/2010 2010 \n58 10-070778 RUNAWAY 12/31/2010 12/31/2010 2010 \n59 10-070780 OTHER 12/31/2010 12/31/2010 2010 \n ... ... ... ... ... \n\n DAY MONTH QUARTER LONG LAT NEIGHBORHOOD \n0 6 12 4 -122.218806 37.774598 Saint Elizabeth \n1 6 12 4 -122.183007 37.749861 Highland \n2 6 12 4 -122.182956 37.764526 Hegenberger \n3 6 12 4 -122.193989 37.766940 Seminary \n4 6 12 4 -122.204981 37.791133 Allendale \n5 6 12 4 -122.296525 37.812649 Prescott \n6 6 12 4 -122.257272 37.798217 Merritt \n7 6 12 4 -122.174073 37.763944 Eastmont \n8 6 12 4 -122.276766 37.812503 Ralph Bunche \n9 6 12 4 -122.262447 37.836894 Temescal \n10 6 12 4 -122.273002 37.806654 Downtown \n11 6 12 4 -122.206761 37.786901 Allendale \n12 6 12 4 -122.221372 37.771733 Fruitvale Station \n13 6 12 4 -122.143034 37.758854 Sequoyah \n14 6 12 4 -122.197101 37.766479 Seminary \n15 6 12 4 -122.164586 37.757001 Castlemont \n16 6 12 4 -122.179337 37.804971 Joaquin Miller Park \n17 6 12 4 -122.195885 37.745952 Coliseum Industrial \n18 6 12 4 -122.137348 37.749625 Chabot Park \n19 6 12 4 -122.285314 37.810791 Oak Center \n20 6 12 4 -122.176025 37.771347 Millsmont \n21 6 12 4 -122.162959 37.750513 Castlemont \n22 6 12 4 -122.294639 37.810976 Prescott \n23 6 12 4 -122.193932 37.787778 Upper Laurel \n24 6 12 4 -122.265511 37.813393 Waverly \n25 6 12 4 -122.206347 37.741576 Oakland Airport \n26 6 12 4 -122.214634 37.788049 Peralta-Hacienda \n27 6 12 4 -122.274879 37.803209 Downtown \n28 6 12 4 -122.268843 37.846684 Bushrod \n29 6 12 4 -122.248753 37.810139 Adams Point \n30 6 12 4 -122.280195 37.824929 Clawson \n31 6 12 4 -122.190818 37.733059 Brookfield Village \n32 6 12 4 -122.202415 37.797759 Upper Dimond \n33 6 12 4 -122.261736 37.824547 Mosswood \n34 6 12 4 -122.257620 37.812580 Adams Point \n35 6 12 4 -122.267515 37.826922 Mosswood \n36 6 12 4 -122.279062 37.798838 Old City-Produce And Waterfront \n37 6 12 4 -122.276413 37.796519 Old City-Produce And Waterfront \n38 6 12 4 -122.293263 37.806351 Prescott \n39 6 12 4 -122.221911 37.777838 Saint Elizabeth \n40 6 12 4 -122.177683 37.766523 Bancroft Business-Havenscourt \n41 6 12 4 -122.216032 37.789641 Peralta-Hacienda \n42 6 12 4 -122.252978 37.793392 East Peralta \n43 6 12 4 -122.262916 37.820805 Pill Hill \n44 6 12 4 -122.260288 37.847086 Fairview Park \n45 6 12 4 -122.277688 37.825465 Hoover-Foster \n46 6 12 4 -122.274266 37.841606 Santa Fe \n47 6 12 4 -122.214186 37.792470 Upper Peralta Creek-Bartlett \n48 6 12 4 -122.267698 37.800398 Civic Center \n49 6 12 4 -122.181929 37.751799 Woodland \n50 6 12 4 -122.183872 37.751493 Woodland \n51 6 12 4 -122.156506 37.752310 Toler Heights \n52 6 12 4 -122.177662 37.765798 Bancroft Business-Havenscourt \n53 6 12 4 -122.171585 37.769135 Eastmont Hills \n54 6 12 4 -122.265492 37.848243 Bushrod \n55 6 12 4 -122.171318 37.743047 North Stonehurst \n56 6 12 4 -122.277801 37.825877 Hoover-Foster \n57 6 12 4 -122.257198 37.797625 Merritt \n58 6 12 4 -122.222702 37.792288 Reservoir Hill-Meadow Brook \n59 6 12 4 -122.276924 37.815517 McClymonds \n ... ... ... ... ... ... \n\n[284603 rows x 11 columns]"
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": "length = len(df_complete)\nprint 'dataframe has {0} rows left after it is cleaned.'.format(length)",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "dataframe has 284603 rows left after it is cleaned.\n"
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": "# ********************** creating a dataframes to plot different visualizations *********************",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": "# ************************ creating a dataframe to plot crime-by-year 2003-2010 ***************************",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": "df_year_plot = df_complete.copy()\n# removing unnecessary columns, keeping only relevant data\ndel df_year_plot[\"REPORTED\"]\ndel df_year_plot[\"DEPARTMENT\"]\ndel df_year_plot[\"NEIGHBORHOOD\"]\ndel df_year_plot[\"RD\"]\ndel df_year_plot[\"OCCURRED\"]\ndel df_year_plot[\"DAY\"]\ndel df_year_plot[\"MONTH\"]\ndel df_year_plot[\"QUARTER\"]\ndel df_year_plot[\"LONG\"]\ndel df_year_plot[\"LAT\"]\nyear_group = df_year_plot.groupby('YEAROCC')\ncrimes = year_group.count()[\"YEAROCC\"].tolist()\nyear = year_group.indices.keys()\nyear = [ int(x) for x in year ]\ncolumns = {'year','crimes'}\ndf = pd.DataFrame(data=zip(crimes,year), columns=columns)#for some reason a number is used as first column\nby_year = df.groupby('year').sum()\nby_year_sorted = by_year.sort('crimes', ascending=True)\n#by_year_sorted['crimes'][0:20].plot(kind='barh')\nplot1 = by_year_sorted['crimes'][0:20]",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": "plot1.plot(kind='barh')",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": "<matplotlib.axes.AxesSubplot at 0x1a81c5f8>"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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ZokWLFmnUqA9PFwwGlZKSouTkZIVCIfN4KBRSSkqK6uvr9cMf/lDFxcXq6OjQ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\nl9qxr/VTAAAAAElFTkSuQmCC\n",
"text": "<matplotlib.figure.Figure at 0x1a81c278>"
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": "N = 8\ncrime_count = (24833, 26671, 31821, 37112, 31018, 30270, 46685, 56193)\n\nind = np.arange(N) # the x locations for the groups\nwidth = 0.35 # the width of the bars\n\nfig, ax = plt.subplots()\nrect = ax.bar(ind, crime_count, width, color='r')\n\nax.set_ylabel('Crimes Committed')\nax.set_title('Total Crimes by Year in Oakland')\nax.set_xticks(ind+width)\nax.set_xticklabels( ('2003','2004','2005','2006','2007','2008','2009','2010') )\n\ndef 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., 1.05*height, '%d'%int(height),\n ha='center', va='bottom')\n\nautolabel(rect)\n\nplt.show()",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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Xr07v3r1xdnZm8eLFfPPNN4VWoIiIiNhOgcF///3XLgnau3cvH3zwgWW8X79+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\nnzHGGMshvPEzxhhjOYQ3fsYYYyyH/AIDtN9YyCHA1QAAAABJRU5ErkJggg==\n",
"text": "<matplotlib.figure.Figure at 0x19d56710>"
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": "# *************** creating a dataframe to plot crime-by-year 2003-2010, by neighborhood ***************************",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": "df_year_hood_plot = df_complete.copy()\n# removing unnecessary columns, keeping only relevant data\ndel df_year_hood_plot[\"REPORTED\"]\ndel df_year_hood_plot[\"DEPARTMENT\"]\ndel df_year_hood_plot[\"RD\"]\ndel df_year_hood_plot[\"OCCURRED\"]\ndel df_year_hood_plot[\"DAY\"]\ndel df_year_hood_plot[\"MONTH\"]\ndel df_year_hood_plot[\"QUARTER\"]\ndel df_year_hood_plot[\"LONG\"]\ndel df_year_hood_plot[\"LAT\"]\nhood_group = df_year_hood_plot.groupby('NEIGHBORHOOD').count()\nhood_group_sorted = hood_group.sort(column='YEAROCC',ascending=False)\ndel hood_group_sorted['YEAROCC']\nhood_group_sorted[:10].plot(kind='barh')",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": "<matplotlib.axes.AxesSubplot at 0x19b82240>"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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E7IrVQq9Nmza88sor+Y7l5OQwdOhQ2rdvX+yBiYiIiMidsXrr9p133iEmJoaA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\njLNHGds/hICAwGLrozSMeIFj51cUI3q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"text": "<matplotlib.figure.Figure at 0x19b94f60>"
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": "hood_group_sorted.tail(10).plot(kind='barh')",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": "<matplotlib.axes.AxesSubplot at 0x18ed4fd0>"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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YRIiXL1++pfiBG4t/6i2woum991bTu3d/fH39HB1KodL/BJQ/uG7+IpI3dscA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\nxbhx43jwwQf5/fffSUpKshtb9+7d8fP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"text": "<matplotlib.figure.Figure at 0x18ed8c18>"
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": "hood_group_sorted[::10].plot(kind='barh')",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 19,
"text": "<matplotlib.axes.AxesSubplot at 0x19b872e8>"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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OZsqUKTz00EN0796d9957j5IlS7J8+fK/c1vuWkpKGklJV2xy7etd+8ZTGGLJb46cGyg/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\nSUVORK5CYII=\n",
"text": "<matplotlib.figure.Figure at 0x19b875f8>"
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": "hood_year_group = df_year_hood_plot.groupby('NEIGHBORHOOD').apply(lambda x: x.groupby('YEAROCC').agg('count'))",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": "hood_year_group[: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></th>\n <th>YEAROCC</th>\n <th>NEIGHBORHOOD</th>\n </tr>\n <tr>\n <th>NEIGHBORHOOD</th>\n <th>YEAROCC</th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>Acorn-Acorn Industrial</th>\n <th>2003</th>\n <td> 375</td>\n <td> 375</td>\n </tr>\n </tbody>\n</table>\n<p>1 rows \u00d7 2 columns</p>\n</div>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 21,
"text": " YEAROCC NEIGHBORHOOD\nNEIGHBORHOOD YEAROCC \nAcorn-Acorn Industrial 2003 375 375\n\n[1 rows x 2 columns]"
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": "hood_year_group",
"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>YEAROCC</th>\n <th>NEIGHBORHOOD</th>\n </tr>\n <tr>\n <th>NEIGHBORHOOD</th>\n <th>YEAROCC</th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th rowspan=\"8\" valign=\"top\">Acorn-Acorn Industrial</th>\n <th>2003</th>\n <td> 375</td>\n <td> 375</td>\n </tr>\n <tr>\n <th>2004</th>\n <td> 359</td>\n <td> 359</td>\n </tr>\n <tr>\n <th>2005</th>\n <td> 395</td>\n <td> 395</td>\n </tr>\n <tr>\n <th>2006</th>\n <td> 481</td>\n <td> 481</td>\n </tr>\n <tr>\n <th>2007</th>\n <td> 402</td>\n <td> 402</td>\n </tr>\n <tr>\n <th>2008</th>\n <td> 375</td>\n <td> 375</td>\n </tr>\n <tr>\n <th>2009</th>\n <td> 563</td>\n <td> 563</td>\n </tr>\n <tr>\n <th>2010</th>\n <td> 679</td>\n <td> 679</td>\n </tr>\n <tr>\n <th rowspan=\"8\" valign=\"top\">Adams Point</th>\n <th>2003</th>\n <td> 430</td>\n <td> 430</td>\n </tr>\n <tr>\n <th>2004</th>\n <td> 431</td>\n <td> 431</td>\n </tr>\n <tr>\n <th>2005</th>\n <td> 513</td>\n <td> 513</td>\n </tr>\n <tr>\n <th>2006</th>\n <td> 488</td>\n <td> 488</td>\n </tr>\n <tr>\n <th>2007</th>\n <td> 351</td>\n <td> 351</td>\n </tr>\n <tr>\n <th>2008</th>\n <td> 356</td>\n <td> 356</td>\n </tr>\n <tr>\n <th>2009</th>\n <td> 648</td>\n <td> 648</td>\n </tr>\n <tr>\n <th>2010</th>\n <td> 727</td>\n <td> 727</td>\n </tr>\n <tr>\n <th rowspan=\"8\" valign=\"top\">Allendale</th>\n <th>2003</th>\n <td> 296</td>\n <td> 296</td>\n </tr>\n <tr>\n <th>2004</th>\n <td> 311</td>\n <td> 311</td>\n </tr>\n <tr>\n <th>2005</th>\n <td> 306</td>\n <td> 306</td>\n </tr>\n <tr>\n <th>2006</th>\n <td> 419</td>\n <td> 419</td>\n </tr>\n <tr>\n <th>2007</th>\n <td> 294</td>\n <td> 294</td>\n </tr>\n <tr>\n <th>2008</th>\n <td> 311</td>\n <td> 311</td>\n </tr>\n <tr>\n <th>2009</th>\n <td> 442</td>\n <td> 442</td>\n </tr>\n <tr>\n <th>2010</th>\n <td> 585</td>\n <td> 585</td>\n </tr>\n <tr>\n <th rowspan=\"8\" valign=\"top\">Arrowhead Marsh</th>\n <th>2003</th>\n <td> 16</td>\n <td> 16</td>\n </tr>\n <tr>\n <th>2004</th>\n <td> 31</td>\n <td> 31</td>\n </tr>\n <tr>\n <th>2005</th>\n <td> 32</td>\n <td> 32</td>\n </tr>\n <tr>\n <th>2006</th>\n <td> 37</td>\n <td> 37</td>\n </tr>\n <tr>\n <th>2007</th>\n <td> 18</td>\n <td> 18</td>\n </tr>\n <tr>\n <th>2008</th>\n <td> 25</td>\n <td> 25</td>\n </tr>\n <tr>\n <th>2009</th>\n <td> 47</td>\n <td> 47</td>\n </tr>\n <tr>\n <th>2010</th>\n <td> 47</td>\n <td> 47</td>\n </tr>\n <tr>\n <th rowspan=\"8\" valign=\"top\">Arroyo Viejo</th>\n <th>2003</th>\n <td> 361</td>\n <td> 361</td>\n </tr>\n <tr>\n <th>2004</th>\n <td> 333</td>\n <td> 333</td>\n </tr>\n <tr>\n <th>2005</th>\n <td> 461</td>\n <td> 461</td>\n </tr>\n <tr>\n <th>2006</th>\n <td> 673</td>\n <td> 673</td>\n </tr>\n <tr>\n <th>2007</th>\n <td> 511</td>\n <td> 511</td>\n </tr>\n <tr>\n <th>2008</th>\n <td> 516</td>\n <td> 516</td>\n </tr>\n <tr>\n <th>2009</th>\n <td> 845</td>\n <td> 845</td>\n </tr>\n <tr>\n <th>2010</th>\n <td> 1088</td>\n <td> 1088</td>\n </tr>\n <tr>\n <th rowspan=\"8\" valign=\"top\">Bancroft Business-Havenscourt</th>\n <th>2003</th>\n <td> 346</td>\n <td> 346</td>\n </tr>\n <tr>\n <th>2004</th>\n <td> 395</td>\n <td> 395</td>\n </tr>\n <tr>\n <th>2005</th>\n <td> 569</td>\n <td> 569</td>\n </tr>\n <tr>\n <th>2006</th>\n <td> 558</td>\n <td> 558</td>\n </tr>\n <tr>\n <th>2007</th>\n <td> 478</td>\n <td> 478</td>\n </tr>\n <tr>\n <th>2008</th>\n <td> 527</td>\n <td> 527</td>\n </tr>\n <tr>\n <th>2009</th>\n <td> 945</td>\n <td> 945</td>\n </tr>\n <tr>\n <th>2010</th>\n <td> 1237</td>\n <td> 1237</td>\n </tr>\n <tr>\n <th rowspan=\"8\" valign=\"top\">Bartlett</th>\n <th>2003</th>\n <td> 46</td>\n <td> 46</td>\n </tr>\n <tr>\n <th>2004</th>\n <td> 34</td>\n <td> 34</td>\n </tr>\n <tr>\n <th>2005</th>\n <td> 37</td>\n <td> 37</td>\n </tr>\n <tr>\n <th>2006</th>\n <td> 57</td>\n <td> 57</td>\n </tr>\n <tr>\n <th>2007</th>\n <td> 46</td>\n <td> 46</td>\n </tr>\n <tr>\n <th>2008</th>\n <td> 37</td>\n <td> 37</td>\n </tr>\n <tr>\n <th>2009</th>\n <td> 63</td>\n <td> 63</td>\n </tr>\n <tr>\n <th>2010</th>\n <td> 90</td>\n <td> 90</td>\n </tr>\n <tr>\n <th rowspan=\"4\" valign=\"top\">Bella Vista</th>\n <th>2003</th>\n <td> 123</td>\n <td> 123</td>\n </tr>\n <tr>\n <th>2004</th>\n <td> 124</td>\n <td> 124</td>\n </tr>\n <tr>\n <th>2005</th>\n <td> 129</td>\n <td> 129</td>\n </tr>\n <tr>\n <th>2006</th>\n <td> 215</td>\n <td> 215</td>\n </tr>\n <tr>\n <td></td>\n <td></td>\n <td>...</td>\n <td>...</td>\n </tr>\n </tbody>\n</table>\n<p>1048 rows \u00d7 2 columns</p>\n</div>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 22,
"text": " YEAROCC NEIGHBORHOOD\nNEIGHBORHOOD YEAROCC \nAcorn-Acorn Industrial 2003 375 375\n 2004 359 359\n 2005 395 395\n 2006 481 481\n 2007 402 402\n 2008 375 375\n 2009 563 563\n 2010 679 679\nAdams Point 2003 430 430\n 2004 431 431\n 2005 513 513\n 2006 488 488\n 2007 351 351\n 2008 356 356\n 2009 648 648\n 2010 727 727\nAllendale 2003 296 296\n 2004 311 311\n 2005 306 306\n 2006 419 419\n 2007 294 294\n 2008 311 311\n 2009 442 442\n 2010 585 585\nArrowhead Marsh 2003 16 16\n 2004 31 31\n 2005 32 32\n 2006 37 37\n 2007 18 18\n 2008 25 25\n 2009 47 47\n 2010 47 47\nArroyo Viejo 2003 361 361\n 2004 333 333\n 2005 461 461\n 2006 673 673\n 2007 511 511\n 2008 516 516\n 2009 845 845\n 2010 1088 1088\nBancroft Business-Havenscourt 2003 346 346\n 2004 395 395\n 2005 569 569\n 2006 558 558\n 2007 478 478\n 2008 527 527\n 2009 945 945\n 2010 1237 1237\nBartlett 2003 46 46\n 2004 34 34\n 2005 37 37\n 2006 57 57\n 2007 46 46\n 2008 37 37\n 2009 63 63\n 2010 90 90\nBella Vista 2003 123 123\n 2004 124 124\n 2005 129 129\n 2006 215 215\n ... ...\n\n[1048 rows x 2 columns]"
}
],
"prompt_number": 22
},
{
"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)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"1. Plot overall Oakland history homevalue"
]
},
{
"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.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>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",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 1 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 100,
"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",
"\n",
"[5 rows x 1 columns]"
]
}
],
"prompt_number": 100
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"intersection = oak_history['20000101':'20101201']\n",
"oak_history_2000_2010=list(intersection.index)\n",
"plt.plot(oak_history_2000_2010,intersection['Oakland'].tolist(), marker=\"o\", color=\"red\")\n",
"plt.xlabel(\"Year\")\n",
"plt.ylabel(\"Home Value in USD\")\n",
"plt.title(\"Overall Oakland Home Value (2000/01 - 2010/12)\")\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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N/SKRCCKRyNDN0EsulwuPAwMDERgYaLS2sMFB/T4pqiVY4nBvTsoTg2xOSmfw\nMi7GlZWVhaysrE4da/Cg4ujoCAAYNWoUnn76aZw4cQL29va4evUqHBwcUF5ejtGjRwNo6YEUFxcL\nzy0pKYGzszPEYjFKSkrabFc958qVK3ByckJjYyOqqqpgZ2cHsVis8SYUFxdj5syZbdqnHlQY6w08\nJ+X+TAsJwbSQEMTKZMKKxup4yNBwtD9wb9q0Se+xBh3+unPnDmpqagAAt2/fRkZGBry9vTF37lwk\nJSUBaKnQmj9/PgBg7ty52LdvHxoaGlBYWIiLFy8iICAADg4OsLKyQm5uLogIe/fuxbx584TnqF7r\ns88+w6xZswAAwcHByMjIQGVlJRQKBY4cOQKZTGbI02WsU7TnX6jwRbFz1IfCVHNXIszNUXH9OudV\n+gJDVghcunSJfHx8yMfHhyZMmECvvvoqEbVUZs2aNYvc3d0pKChIoypr69atJJFIaNy4cZSWliZs\nP3nyJHl5eZFEIqHVq1cL25VKJYWGhpJUKqUpU6ZQYWGhsO/DDz8kqVRKUqmUEhMT27TPwKfPWBvZ\nKSkU5edHK01NNaqZ1kskXMHUBcL7aGam8T5u4PexV7R37eS1vwbv6bNepr1o5BEARebmsPT0RNgr\nr3AepYu010tT4bXBDI/X/mKsD9BO0E8DAKUScaNGcUC5D9q5KRVO2BsXr/3FWC/hi2DP4txU38RB\nhbFewhfBnqWesFfhOT7Gx8NfjBlYTmoqMuLjcaOoCCsBvKe2b7AvGtkd6nNXhlRVoenkSTzx2ms8\nlGhknKgfvKfPeoF6ch5oKYFNGD4cjhIJLMXiQXfzLUPKmTIFGbdvw3TkSDQOG4bgNWv4vTUQTtQz\nZiTqyXmgNUFfV4c4sZgrlHpQTmoq0q9cwdarV4VtvDS+cXBOhTED4uR878iIj9cIKACvB2YsHFQY\nMyBOzvcODt59BwcVxgwkJzUVV2/cQLSJ5p8ZVyj1PA7efQcHFcYMQJWg/+D0aYQ3NyMOLetTvfDw\nw4P+jo6GwOXFfQcn6hkzAJ4937s0yovPn0eTSMTB20i4p8KYAfAYf++bFhKCzWlpmLluHejqVXyz\nfTtiZTJeubiXcU+FMQPgMX7jyElNRfqbb2JrfT2QkwOAS4t7G/dUGOtBqnvP3ygsxEqtfTzGb3ja\n84IALi3ubdxTYayH6Jo9H6Y2e55vE2x4POxofBxUGOshPHve+HjY0fh4+IuxHsKfko2PS4uNj3sq\njPUQ/pStLsaXAAAgAElEQVRsfBqlxXfuoOTYMQw1N8c3b7yBjPh4XmSyF3BQYawHqM+ef7e5WdjO\nS9v3vmkhIZgWEtKS41q8GFvz84V9XAlmeLz0/eA9fdZD+N7zfRPfw95weOl7xgyIZ8/3TZzjMg5O\n1DPWTXzx6ps4x2UcHFQYu0+qiY6/njmjcz9fvIyLK8GMw+BBpampCX5+fpgzZw4AQC6Xw9nZGX5+\nfvDz88Phw4eFY7dt2wZ3d3eMHz8eGWpjoadOnYK3tzfc3d2xdu1aYXt9fT3CwsLg7u6OqVOnoqio\nSNiXlJQEDw8PeHh4YM+ePYY+TTbIqPIoWzIysKqqCjFa+/niZXzTQkIg27kTcTIZ/jxmDMJMTHDH\n3BwZ8fG8HpghkYHt2LGDFi9eTHPmzCEiIrlcTjt27GhzXH5+Pvn4+FBDQwMVFhaSRCKh5uZmIiLy\n9/en3NxcIiKaPXs2HT58mIiIEhISKDo6moiI9u3bR2FhYUREVFFRQWPHjiWFQkEKhUJ4rK0XTp8N\nUDHBwUSA8C8boFiAImxtKVYmo+yUFGM3kbXKTkmhDRKJxs9rg0TCP6NuaO/aadCeSklJCQ4dOoQV\nK1YIlQJEpLNqIDk5GeHh4TAzM4OrqyukUilyc3NRXl6OmpoaBAQEAAAiIiJw4MABAMDBgwcRGRkJ\nAFiwYAEyMzMBAOnp6QgODoaNjQ1sbGwQFBSENK72YD1IO48yDcBmAG4TJ2JzWhon6PsQXg+sdxk0\nqLz44ot44403YKJ25zuRSIRdu3bBx8cHUVFRqKysBACUlZXB2dlZOM7Z2RmlpaVttovFYpSWlgIA\nSktL4eLiAgAwNTWFtbU1Kioq9L4WY90l5FHOntW5n/MofQ8XUvQug5UUp6SkYPTo0fDz80NWVpaw\nPTo6Gi+//DIAIC4uDi+99BJ2795tqGZ0SC6XC48DAwMRGBhotLawvk17PkoMgK1q+3miY9+kXQWW\nAyADQPHZs4iVyXiWfSdkZWVpXMfb02FQqaysxMWLFwEAHh4esLa27tQLf//99zh48CAOHToEpVKJ\n6upqREREaCTNV6xYISTwxWIxiouLhX0lJSVwdnaGWCxGSUlJm+2q51y5cgVOTk5obGxEVVUV7Ozs\nIBaLNd6A4uJizJw5U2c71YMKY+3Rno8CAHEArtjaYkxAAK9C3EcFr1mDmIIC4cNAOlo/DCgUQEYG\nz7LvBO0P3Js2bdJ/sL5ki1KppMjISLK2tiZfX1/y8fEha2trWrZsGdXX13cpqZOVlUVPPfUUERGV\nlZUJ2998800KDw8nonuJ+vr6erp06RKNHTtWSNQHBATQ8ePHqbm5uU2ifuXKlURE9Mknn2gk6t3c\n3EihUNCtW7eEx9raOX3G2tg4fbpGslf1b+P06cZuGutAdkoKxcpkFGZrq/NnGCuTGbuJ/Up71069\nPZUtW7bg7t27KC4uhqWlJQCgpqYGq1atwubNm7F58+ZORzkigkgkAgD8v//3//Dzzz9DJBLBzc0N\n77//PgDA09MTCxcuhKenJ0xNTfHOO+8Iz3nnnXewbNky1NXV4cknn8QTTzwBAIiKisLSpUvh7u4O\nOzs77Nu3DwDwhz/8AXFxcfD39wcAbNy4ETY2Np1uL2PaclJTcT4vT+c+zqP0far1wOSBgUB2dpv9\nnF/pOXrX/powYQJOnDiBESNGaGyvra3FlClTkK+2SFt/xWt/sc5Q5VJkBQX3hk5abZBI8MTOnTx0\n0k/wemA9o71rp97qryFDhrQJKABgYWGhUc3F2ECnyqVMAyBDSx5FDmCRnR0HlH5GfZZ9DoBYABHm\n5qi4fp0nRPaQdhP1t27darNNfSiLscFAvSRVWDASgNzLiwNKP6P6ea2Ii4PZuXN4t74eUCqB06cR\n07paB/9Mu0dvUKmursakSZN6sy2M9Um8MOHAMi0kBBnx8diiNX9la0EB4nbt4qDSTXqDyuXLl3ux\nGYz1PTmpqciIj8eNwkKsBPCe2j6ek9K/8YRIw9EbVIqKimBtbS1UTX3zzTc4cOAAXF1d8Ze//AVD\nhw7ttUYy1tvUJzoCLePvYcOHw1EigaVYzHNS+jnufRqO3ox7aGgo7ty5AwA4c+YMQkND8eCDD+LM\nmTNYtWpVrzWQMWPQXi9qGoD9dXWwFIt5ba8BQHtZ/BwAYebmqCktRaxMxkn7btDbU1EqlXBycgIA\nfPTRR4iKisJLL72E5uZm+Pj49FoDGTMGHh4Z2FQfCuJ27cL1khKIfv0V+5VKIC8PyMvjWfbdoLen\nol6DnJmZKSxzwuXEbKDjiY6Dw7SQEGxOS8MosRjvNTVp7ONVjO+f3p7KjBkzEBoaCkdHR1RWVgpB\npaysDMP0jEcy1t+pcikvVFTwgpGDBPdKe5beoPL2229j//79uHr1Kr777jshMX/t2jVs3bpV39MY\n69e0cylxAIYA+NXODqt4ouOAxEn7nqU3qJiYmCA8PLzNdj8/P4M2iDFj4omOg4/6KsYq3Cu9f3qD\nioWFhcbMeZFIhJEjR2LmzJl4/fXXYWdn1ysNZKw38afWwUc9aT+krAwlFy9iqJUVvnnjDWTEx/P9\nVrpI74KSuty6dQuJiYn44Ycf8OmnnxqyXb2CF5RkKuoTHUUXL7ad6MhDX4NCTkoK0p95Blvv3hW2\nxUgkkPHPX0N7184uBRUVPz8/nD59utsNMzYOKgzQPdExQW2iYxBPdBw0eBXjzmnv2tnl2wnfvXsX\nTVrld4z1Z7omOk6rq0Nc60RHNnhwJVj36Q0qn3/+eZtopFAosH//fjz77LO90jjGegNfSJgK59S6\nT29Q+eqrr9ok6u3s7PDXv/4VITwUwAYInujI1HElWPfdV05loOCcyuDGd3RkuuSkpuLIrl0YcuoU\nmkaORNA//sG/B1p6PFE/UHBQGdzUk7I5AI5AbaJjUhJfSAa5nH/8AxkvvwxTf380mptzabGaHk3U\nMzZQ8ERHpk9OairS33sPW+vqgJwcAOBFJjuJV4dkgxYnZZk+2hWBAC8y2Vkd9lSUSiU+//xzXL58\nGY2NjQBauj4vv/yywRvHmCEIEx2vXOE7OjKduCLw/nUYVObNmwcbGxtMmjQJ5vwJjvVzfEdH1hnc\ni+0G6sCECRM6OqRdjY2N5OvrS0899RQREVVUVNDjjz9O7u7uFBQURAqFQjj21VdfJalUSuPGjaP0\n9HRh+8mTJ8nLy4ukUimtWbNG2K5UKmnhwoUklUppypQpdPnyZWFfYmIiubu7k7u7OyUlJelsWydO\nnw0wMcHBRECbf7EymbGbxvqQ7JQU2iCRCL8f2QAtNDentV5eFBMcTNkpKcZuolG1d+3sMKfy6KOP\n4uzZs/cdtHbu3AlPT09hzstrr72GoKAgXLhwAbNmzcJrr70GADh37hz279+Pc+fOIS0tDatWrRKq\nC6Kjo7F7925cvHgRFy9eRFrrLOfdu3fDzs4OFy9exIsvvoh169YBaFmj7JVXXsGJEydw4sQJbNq0\nCZWVlfd9Dmzg4GEN1hnTQkIg27kTcTIZ/jxhAj4eMgT7lUq8nZeHLRkZSF+7lm85rEeHQeXo0aOY\nNGkSPDw84O3tDW9vb0ycOLFTL15SUoJDhw5hxYoVQoA4ePAgIiMjAQCRkZE4cOAAACA5ORnh4eEw\nMzODq6srpFIpcnNzUV5ejpqaGgQEBAAAIiIihOeov9aCBQuQmZkJAEhPT0dwcDBsbGxgY2ODoKAg\nIRCxwY2HNVhn8Z0h70+HOZXDhw/f94u/+OKLeOONN1BdXS1su3btGuzt7QEA9vb2uHbtGoCWO0pO\nnTpVOM7Z2RmlpaUwMzODs7OzsF0sFqO0tBQAUFpaChcXl5YTMTWFtbU1KioqUFZWpvEc1WuxwS0n\nNRVXb9xAtEiEd9Vq7Dk5z9rDvduu0RtUqqurYWVlBSsrq/t64ZSUFIwePRp+fn7IysrSeYxIJNJY\nCsYY5HK58DgwMBCBgYFGawszHFWC/oOCAuSg5Y6ORebmsPT0RNgrr3BynunFvVsgKytL73Vcm96g\nEh4ejtTUVDz88MNtLvwikQiXLl1q94W///57HDx4EIcOHYJSqUR1dTWWLl0Ke3t7XL16FQ4ODigv\nL8fo0aMBtPRAiouLheeXlJTA2dkZYrEYJSUlbbarnnPlyhU4OTmhsbERVVVVsLOzg1gs1ngDiouL\nMXPmTJ3tVA8qbOBSn3cgTHRUKhE3ahQHFNYu9fXAcgBkALhibg6L69eRk5o6KH5/tD9wb9q0Sf/B\nvVEpkJWVJVR//e1vf6PXXnuNiIi2bdtG69atIyKi/Px88vHxofr6erp06RKNHTuWmpubiYgoICCA\njh8/Ts3NzTR79mw6fPgwERElJCTQypUriYjok08+obCwMCJqqTBzc3MjhUJBt27dEh5r66XTZ33A\nxunTdVZ9bZw+3dhNY/1AdkoKRfn50cqhQzV+fzZIJIOyEqy9a2evBZU5c+YQUcsFf9asWTpLirdu\n3UoSiYTGjRtHaWlpwnZVSbFEIqHVq1cL25VKJYWGhgolxYWFhcK+Dz/8kKRSKUmlUkpMTNTZLg4q\nA192SgrFBAdTmIUFlxKzbuFy9Hvau3bygpKD9/QHPPWJjjkAr0TMukUeGAh5drbwtWoorMTaGs5T\npgyqBSd5QUk2KGnnUYCWBP0VW1uMCQjg2fOsS9QT9uofUnKqqpCRkYEPcnKQ4OQEO2trjLayQuOw\nYYMq0Kh0KqgcPXoUv//+O5YvX44bN26gtrYWbm5uhm4bY/dFtbZXSW6uxnZVgl4+cSLkPG+JdZF6\nwj4DrQEFasFFqUT6pUsaveHBuLJxh5Mf5XI5tm/fjm3btgEAGhoa8Nxzzxm8YYzdD9WQ15aMDDhX\nVek8ZjCVgrKeoz7LvsTaGgCE4KL9WGUwTpLsMKh8+eWXSE5OxogRIwC0lPHW1NQYvGGM3Q/1Ia9g\nADFa+zdIJAjiiY7sPqlm2TtPmQJAc6hH/XEOgFgAcgAXT5wYVEu6dDj8NWzYMJiY3Is9t2/fNmiD\nGOsO7RtvAS15lGJra7hMncp5FNYjVENhIrV7rjS2/t+mKEShQMzatQAGxzBYhz2V0NBQ/PnPf0Zl\nZSX++c9/YtasWVixYkVvtI2xLslJTcX5vDyNbdMAbAbgMnUqNqelDYo/amZ4qqGwq35+iG4dTlX1\njAf7MFinSoozMjKQ0Xovb5lMhqCgIIM3rDdwSfHAocqlyAoKuHSY9aqc1FQc2bULQ5RKlFRX486F\nC/hYx4iOfPp0yDu51Elf1961k+epDN7TH1BiZTJsaf3gkwPgCIAhAH61s8OqpCQOKKzXqP8uAvfm\nsxTb2sLF339AlBl3a56KhYWFsPZXQ0MD7t69CwsLC42VhxkzFl3lw8LaXgDkXl79/g+Y9S/aa4UJ\nPWeFAsjIGPBlxh0GldraWuFxc3MzDh48iOPHjxu0UYx1hvqM+Vg9x3D5MOttqmARt2sXLp44gX0K\nBYB7PRazggIkREYCA7QHfV/DX76+vjhz5owh2tOrePir/8pJTUVCZCT2V1S0fA1ehoX1PaqlXXT9\nfsZIJJD109/Pbg1/ff7558Lj5uZmnDp1CsOHD++51jHWRaoeykOtAQXg8mHWN6mWdtGuCMsBICoo\nwO6lS5ExQPIsKh0Gla+++krIqZiamsLV1RXJyckGbxhj+qgmOGoPealyKXGt5cOMGZsqv2KmNp9l\noOdZuPpr8J5+v6OelE+squIhL9YvaA/VxgLYor4f/a867L5Kile3s5SFSCRCfHx8z7TOiDio9B/a\nSXnVHyWXD7P+QP33V46W5VsArQUpoXZXyYcewqLNm/vs7/J9BZXExERh2Ev7EJFIhMjIyB5uZu/j\noNL3qXonv//4o0YVDfdQWH+jmiSpXhGm+oCk/TudAyBh+HA4SiSwcHLqc70XnvyoBweVvk3fpzvg\nXg9FlZQP4qQ86yd0/V5r9777eu+lW9Vf169fx/bt23Hu3DnU1dUJL/jNN9/0bCsZU6M9Dt2otZ+T\n8qy/0p7HAoVC40Ks614tUCqRc/o0EkJD8UUf7b2odLig5JIlSzB+/HhcunQJcrkcrq6umDx5cm+0\njQ0SOampiJXJ8GcvL4SNHIklrq74ODRUo2SYl7FnA4lqCf1Ve/ciRiLR+NCkCjDqZciqALO/rg7P\n5OUBGRn44Nlnserhh/vesvod3eDez8+PiIi8vb2FbZMmTeroaf1CJ06fGVh2SgptkEgoG6ANABFA\nMVr/q/5lAxQLUIStLcXKZJSdkmLs5jPWbdkpKRTl50crzc01fu83qv3ux6j9DWzQ+rvYIJH0+t9C\ne9fODnsqQ4cOBQA4ODggJSUFP/30ExStSSbG7peqd/LO0qUat2cF7n1S0+6dTAPQJJEgau9eXsae\nDRjTQkLwwU8/IfyzzxAnk+HGhAlYOXx4h70XQHMSZaxM1jd6LfqiTUNDAxERffXVV6RQKOjs2bM0\nffp08vPzo+Tk5J4PfUbQzukzA1L1TtQ/jen6VKbeO9kIUJidHfdO2KDQmd6Ldq8lG6CFw4fTWi8v\nigkONujfSnvXTr3VX6NHj8bcuXMRHh6OmTNnCuXFAwlXf/U+fRPB9FW/qHDJMBuMVGXI10tKILp0\nCSPr6oS/E2NWjLV77dQXbW7cuEHvvvsuBQYGkpOTE61Zs4Z++OGHTkeyuro6CggIIB8fH3rooYfo\n73//OxERbdy4kcRiMfn6+pKvry8dOnRIeM6rr75KUqmUxo0bR+np6cL2kydPkpeXF0mlUlqzZo2w\nXalU0sKFC0kqldKUKVPo8uXLwr7ExERyd3cnd3d3SkpK0tnGdk6fGYCqh6Lr01Z7n7o4f8JY295L\nZ3Iuhuq9tHft7NRVtbS0lN566y2aOnUqjR07ltavX9+pb3z79m0iIrp79y5NmTKFjh49SnK5nHbs\n2NHm2Pz8fPLx8aGGhgYqLCwkiURCzc3NRETk7+9Pubm5REQ0e/ZsOnz4MBERJSQkUHR0NBER7du3\nj8LCwoiIqKKigsaOHUsKhYIUCoXwuM3Jc1DpFdkpKRQTHExhtrbtJuDnjhhBYXZ2HEgYa0d2SgrF\nymTC35N6gNEeOjZUUr+9a2eHiXoAcHJyQlRUFFauXAkLCwt88MEHneoiPfDAAwBabu7V1NQEW1tb\nVe+ozbHJyckIDw+HmZkZXF1dIZVKkZubi/LyctTU1CAgIAAAEBERgQMHDgAADh48KMzsX7BgATIz\nMwEA6enpCA4Oho2NDWxsbBAUFIQ0nstgFKqJXlsyMjC+tcBDXwL+pf37se/mTbz9yy+ciGdMD+1y\nZODePC5d812AliGxWNy7l4shE/rtBpW6ujr85z//wTPPPAOpVIpvvvkGr7/+OsrKyjr14s3NzfD1\n9YW9vT1mzJiBCRMmAAB27doFHx8fREVFobKyEgBQVlYGZ2dn4bnOzs4oLS1ts10sFqO0tBQAUFpa\nChcXFwAtKyhbW1ujoqJC72ux3qXKn2xtXYFV9Ys/DYAMLcvUywEssrPjfAljXTQtJASynTs7rBhT\n5Vu2oOXv7YWKCiSEhuKv3t4GqRjTO6N+8eLFOHLkCKZPn44lS5bg3//+d5fvo2JiYoIzZ86gqqoK\nMpkMWVlZiI6OxssvvwwAiIuLw0svvYTdu3d37yy6QS6XC48DAwMRGBhotLYMJLrueaLqoWzFvRnx\nGyQSrOKAwth9mRYSIvzt5KSmYk9cHKLPn8e7SqUQYPRNoszJy0NGXh4+yMnBvg4S+llZWcjKyupU\nm/QGFZlMhvfffx+WlpadeqH2WFtbIyQkBCdPntS4aK9YsQJz5swB0NIDKS4uFvaVlJTA2dkZYrEY\nJSUlbbarnnPlyhU4OTmhsbERVVVVsLOzg1gs1ngDiouLMXPmTJ1tUw8qrOfouueJ+o20rtjaYkxA\nAN9Ii7EeogowOampiNu1CzdKSrDy0iU4tC6vBdz/EjDaH7g3bdqkvyHdztjocePGDSE5fufOHXrs\nscfo66+/pvLycuGYN998k8LDw4noXqK+vr6eLl26RGPHjhUS9QEBAXT8+HFqbm5uk6hfuXIlERF9\n8sknGol6Nzc3UigUdOvWLeGxNgOe/qClSspHWlvrTRauN8IMYMYGo+yUFFpoZ9fphH52676l5uYU\n7een9++0vWunwa6qZ8+eJT8/P/Lx8SFvb2/avn07EREtXbqUvL29aeLEiTRv3jy6evWq8JytW7eS\nRCKhcePGUVpamrBdVVIskUho9erVwnalUkmhoaFCSXFhYaGw78MPPySpVEpSqZQSExN1tpGDSs9S\nn9TIExgZ6xt0/V12txy5vWsnL30/eE+/x8XKZNiSkQGAJzAy1pe0N4lSjvaX31eJkUgga/377dbS\n97dv38abb76JK1eu4F//+hcuXryI3377DU899dR9nh4bqEzr64XH6vkT1T1POH/CmHF0JqGvrxwZ\n0FxjLMPfv93v1eE8leXLl2Po0KH4/vvvAbTMWYmJ0V6EnA12OampOJ+Xp7FtGoDNAFxa73nCAYUx\n4+vKApaAZklylEIBtI5G6NNhUCkoKMC6deuE1YpHjBjR5ZNgA5uqfPiFigq+5wlj/YRqEuX7eXlY\n/OmnuOrnh2hzcwCaN8XTrhjb0valNHQ4/DVs2DDhjo9AS5AZNmxYV9vPBjBV+bBKHIAhAH61s+M5\nKIz1A/rKkd+rq9O77L4+HQYVuVyOJ554AiUlJVi8eDGOHTuGxMTE7rSfDRA5qanIiI9HyfHjwjbV\npEYAkHt5cUBhrB/Rzr3ou+VxezpV/XXz5k0cb71wTJ06FSNHjrzvRvclXP11/1RDXqoJjrq6xHEy\nGd8/nrF+TvW3LiooEP7ORdC9hiPQiZwK0LLGVlNTE+rr65GTk4Mvvviih5rL+iv1IS++fzxjA5dq\njTH1nEt7OuypLF++HL/88gsmTJgAE5N7Mej//u//ut9aI+OeStcJQ165uUisqrq3HcAR3CsfDuLy\nYcYGHNV8ly3p6XqvnR0GFU9PT+Tn5/OdHxkPeTHGALR/7exw+Mvf3x/nzp3r8Uax/oeHvBhjHekw\nob98+XI88sgjcHBwEEqJRSIRzp49a/DGsb4jJzUVxSdOCF/zjHnGmC4dBpWoqCh89NFH8PLy0sip\nsMFDNezl0npDNRVV+XBc64x5xhjrMKiMHj0ac+fO7Y22sD5KNeyVg3s32VLZIJHgCR7yYoy16jBR\nv2rVKlRWVmLOnDnCUi0ikQjPPPNMrzTQkDhR3z5dlV6qKq8hAH6ztUX03r085MXYINOtVYrv3LmD\noUOHIkNrEbGBEFSYftqVXirqM+bjAgI4oDDGNPD9VAbv6beL743CGNOnWyXFxcXFePrppzFq1CiM\nGjUKCxYs0LhnPBt4dFV6ydBS6bXM2hpxMhkHFMaYTp26n8rcuXNRVlaGsrIyzJkzB8uXL++NtjEj\naK/Si++NwhjrSIdB5caNG1i+fDnMzMxgZmaGZcuW4fr1673RNmYEqkovntzIGLsfHQYVOzs77N27\nF01NTWhsbMRHH300YFYpZvfkpKYiViZDSW4uAM0hLzmAcFtbHvJijHWow0T95cuXsXr1amHp+0cf\nfRS7du3CmDFjeqWBhsSJ+ha8phdjrCvau3Zy9dfgPX0BV3oxxrrivuaprFYbO9d+AZFIhPj4+B5s\nIjMWXtOLMdaT9OZUJk2ahMmTJ2PSpElITk4WHqv+dUSpVGLKlCnw9fWFp6cn1q9fDwC4desWgoKC\n4OHhgeDgYFSqVRlt27YN7u7uGD9+vMZky1OnTsHb2xvu7u5Yu3atsL2+vh5hYWFwd3fH1KlTUVRU\nJOxLSkqCh4cHPDw8sGfPnq69K4MEV3oxxnocdYKvr29nDmvj9u3bRER09+5dmjJlCh09epT+9re/\n0euvv05ERK+99hqtW7eOiIjy8/PJx8eHGhoaqLCwkCQSCTU3NxMRkb+/P+Xm5hIR0ezZs+nw4cNE\nRJSQkEDR0dFERLRv3z4KCwsjIqKKigoaO3YsKRQKUigUwmNtnTz9ASc7JYVigoMpzNaWCKBsgDYA\nRGr/1ksklJ2SYuymMsb6oPaunQZddviBBx4AADQ0NKCpqQm2trY4ePAgIiMjAQCRkZE4cOAAACA5\nORnh4eEwMzODq6srpFIpcnNzUV5ejpqaGgQEBAAAIiIihOeov9aCBQuQmZkJAEhPT0dwcDBsbGxg\nY2ODoKAgpHGSGcC93smWjAyMVygAcKUXY6znGDSoNDc3w9fXF/b29pgxYwYmTJiAa9euwd7eHgBg\nb2+Pa9euAQDKysrg7OwsPNfZ2RmlpaVttovFYpSWlgIASktL4eLiAgAwNTWFtbU1Kioq9L7WYJeT\nmoqEyEjhRluNavtUQ15yAFJe04sxdp/0JuotLCyEWwjX1dXB0tJS2CcSiVBdXd3hi5uYmODMmTOo\nqqqCTCbDt99+q7FfJBIZ/TbFcrlceBwYGIjAwECjtcUQVCsN3ygthejSJTxUVyfsU01w5KXsGWPt\nycrKQlZWVqeO1RtUamtre6o9sLa2RkhICE6dOgV7e3tcvXoVDg4OKC8vx+jRowG09ECKi4uF55SU\nlMDZ2RlisVhjrTHVdtVzrly5AicnJzQ2NqKqqgp2dnYQi8Uab0BxcTFmzpyps23qQWUgyUlNxZ64\nOJidP493lUph/on2isNAy7DXFVtbjAkI4Eovxlgb2h+4N23apPdYgw1/3bx5U6jsqqurw5EjR+Dn\n54e5c+ciKSkJQEuF1vz58wEAc+fOxb59+9DQ0IDCwkJcvHgRAQEBcHBwgJWVFXJzc0FE2Lt3L+bN\nmyc8R/Van332GWbNmgUACA4ORkZGBiorK6FQKHDkyBHIZDJDnWqfo8qbOJw+jXeVSgD3Pj1oL78y\nDUCTRIKovXu50osx1n2Gqg44e/Ys+fn5kY+PD3l7e9P27duJqKUya9asWeTu7k5BQUEaVVlbt24l\niYHH9FYAABPsSURBVERC48aNo7S0NGH7yZMnycvLiyQSCa1evVrYrlQqKTQ0lKRSKU2ZMoUKCwuF\nfR9++CFJpVKSSqWUmJios40GPH2jigkOJgJoo1o1V4za42yAYlv3h9nZcZUXY6xL2rt28oz6AXT6\n2ndqVF9yhWfKM8Z6Srfu/Mj6B113alRPxKvyJ2HDh8NRIoGlWMz5E8ZYj+OeSj8/fVXv5Pcff8S+\n1nkn6r0S1T3li8zNYenpibBXXuFAwhjrFu6pDEDa1V1ytX261u9awb0Sxlgv4KDSDwnVXQUFQs6k\nUeuYaa3/4lrX72KMsd7AQaUfUJ/AWHn1Kprq6/FZba1G74QnMjLG+gIOKn2U9kz4xXV1SAfwPiAE\nE+1lVgCeyMgYMy4OKn1MezPhVb0QVTDR7p1MA5AmkSCKy4QZY0bCQaUP0O6VONTVCbkSU63/gbbB\nJA5c3cUY6xs4qBiZrvvDy9X2N2r9D+ge6uLqLsZYX8BBxYhUS9Hvr6gAcO+HoR5AVL0SGXioizHW\n93FQMRJVD+Wh1oAC6M6VqHolCcOHw3T0aCyqrYWDoyPPiGeM9UkcVIwkIz5eY0kVoP1cyQucK2GM\n9QMcVHqZsOjj8eMAOl6fi3MljLH+hINKL9K16KN60n0IgF/t7PBCUhIHEsZYv8QLSvbC6Xe06KMK\nL0XPGOsPeEFJI1LvncjVtuta9JET74yx/o6DioGpEvIAL/rIGBv4DHaPetbCtL5eeKx9f3igZcgr\niBd9ZIwNENxTMaCc1FScz8sTvuZFHxljAx0n6g10+qpciqyggBPyjLEBhRP1RqCeSwE0S4ZXcUBh\njA1QHFR6mDC5MTdX2KZKyAOA3MuLAwpjbMDioNKDdE1u1NZkbt6rbWKMsd5k0Oqv4uJizJgxAxMm\nTICXlxfi4+MBAHK5HM7OzvDz84Ofnx8OHz4sPGfbtm1wd3fH+PHjkZGRIWw/deoUvL294e7ujrVr\n1wrb6+vrERYWBnd3d0ydOhVFRUXCvqSkJHh4eMDDwwN79uwx5KkKKw6rhry40osxNiiRAZWXl9Pp\n06eJiKimpoY8PDzo3LlzJJfLaceOHW2Oz8/PJx8fH2poaKDCwkKSSCTU3NxMRET+/v6Um5tLRESz\nZ8+mw4cPExFRQkICRUdHExHRvn37KCwsjIiIKioqaOzYsaRQKEihUAiP1fXU6WenpNAGiYQ2AkRq\n/7IBigUo0tqaYmUyyk5J6ZHvxxhjxtTetdOgPRUHBwf4+voCACwsLPDQQw+htLRUFczaHJ+cnIzw\n8HCYmZnB1dUVUqkUubm5KC8vR01NDQICAgAAEREROHDgAADg4MGDiIyMBAAsWLAAmZmZAID09HQE\nBwfDxsYGNjY2CAoKQpqBJhiqkvK6JjduBuDSOrmRcymMsYGu1yY/Xr58GadPn8bUqVMBALt27YKP\njw+ioqJQWVkJACgrK4Ozs7PwHGdnZ5SWlrbZLhaLheBUWloKFxcXAICpqSmsra1RUVGh97V6Uk5q\nKmJlMiEpz0NejLHBrlcS9bW1tXj22Wexc+dOWFhYIDo6Gi+//DIAIC4uDi+99BJ2797dG01pQy6X\nC48DAwMRGBjYqed1dsVhLh9mjPV3WVlZyMrK6tSxBg8qd+/exYIFC/Dcc89h/vz5AIDRo0cL+1es\nWIE5c+YAaOmBFBcXC/tKSkrg7OwMsViMkpKSNttVz7ly5QqcnJzQ2NiIqqoq2NnZQSwWa7wJxcXF\nmDlzZpv2qQeVrlCfh6J9T5RpaOmhcEBhjA0E2h+4N23apPdYgw5/ERGioqLg6emJv/71r8L28vJy\n4fGXX34Jb29vAMDcuXOxb98+NDQ0oLCwEBcvXkRAQAAcHBxgZWWF3NxcEBH27t2LefPmCc9JSkoC\nAHz22WeYNWsWACA4OBgZGRmorKyEQqHAkSNHIJPJeuS8clJTUXzihPD1NLTcQz4OwDJra8TJZDxj\nnjE2OBmyQuDo0aMkEonIx8eHfH19ydfXlw4dOkRLly4lb29vmjhxIs2bN4+uXr0qPGfr1q0kkUho\n3LhxlJaWJmw/efIkeXl5kUQiodWrVwvblUolhYaGklQqpSlTplBhYaGw78MPPySpVEpSqZQSExPb\ntO9+Tl9V6RWjVeml+hcrk3X5NRljrD9p79rJa3918fRjZTJsycjgm2wxxgYtXvurB6mWstdOyv9m\na4toDiiMsUGOg0onCbcE/vlnYZv6ml5xAQEcUBhjgx4HlU5QLx/Owb1KL5UNEgme4LkojDHGQaUz\n1MuH+UZbjDGmHweVTlC/JTBwb9hLPnEi5HxvecYYE/A96juhcdgwndt5GXvGGNPEQaUdqrW9bpSU\nYKXWPl7TizHG2uLhLz3Uk/MAkAMgbPhwOEoksBSLOY/CGGM6cFDRQ/se89MATKurQ5xYjM2cR2GM\nMZ14+EsP7eS8yhClspdbwhhj/QcHFR1yUlNxPi9P5z5OzjPGmH4cVLSocikvVFTwDbcYY6yLOKei\nRTuXwjfcYoyxzuOgokU9l6K+tpfcy4sDCmOMdYCHv1qp5qT8evaszv2cS2GMsY5xTwW8YCRjjPUU\nDirgBSMZY6yncFABLxjJGGM9ZdDnVHhOCmOM9ZxBH1R4TgpjjPUcEem7e/0gIBKJoDr5HABHoDYn\nJSmJ8yiMMaaDSCT6/+3df0xVdR8H8PcFTaNr8tAPMC7qBe69wL2XHzIBVxhG/FqILSKCpjzLZuRU\naIPMpyjaKmxrc7qabf0wYAXUchZuSo7kGc4uCt3YRCQ2r+xy75XGr4FQcIHP8wfjBKTIj3PO7dHP\nazsbnMs57/M5uPPhnvP9XnGr1sFN5Sbrix9/HMV1dXIfDmOM/V+Yq6lIevvLarViy5Yt0Ov1MBgM\nOHLkCACgt7cXCQkJ0Gq1SExMRH9/v7BNSUkJNBoNgoKC8OOPPwrrm5qaYDQaodFokJeXJ6wfGRlB\nZmYmNBoNYmJi0NHRIbxWWloKrVYLrVaLsrKyeR83P0thjLFFIgk5HA4ym81ERDQ4OEharZYuX75M\nhYWF9MEHHxAR0cGDB2n//v1ERNTS0kJhYWE0OjpKFouFAgICaGJigoiINm7cSA0NDURElJKSQqdO\nnSIioo8//pheeeUVIiKqrKykzMxMIiLq6ekhf39/6uvro76+PuHr6QDQfwCiacuBgAD678mTUp4W\nIiI6e/as5Bn/tOy7sWZXZt+NNbsy+26qea7WIek7FR8fH4SHhwMAlEolgoODYbPZ8MMPPyAnJwcA\nkJOTgxMnTgAAvv/+e2RlZWH58uVYv349AgMD0dDQAIfDgcHBQURFRQEAduzYIWwzfV/p6emora0F\nANTU1CAxMRGenp7w9PREQkICTt9keHASJuek/Hv1ahQlJSFZps/3qnPh7TVXZd+NNbsy+26s2ZXZ\nd2PNNyPbPJVr167BbDYjOjoaXV1d8Pb2BgB4e3ujq6sLAGC32xETEyNso1KpYLPZsHz5cqhUKmG9\nr68vbDYbAMBms8HPz2+ymGXLsHr1avT09MBut8/YZmpfs03NSSmKieH/fIsxxpZIliHFN27cQHp6\nOg4fPoxVq1bNeE2hUEChUMhxGLfEw4cZY0wkUt97Gx0dpcTERDp06JCwTqfTkcPhICIiu91OOp2O\niIhKSkqopKRE+LmkpCQymUzkcDgoKChIWP/1119Tbm6u8DM///wzERE5nU568MEHiYiooqKCXn75\nZWGbXbt2UWVl5Yxj+9fKlQSAF1544YWXBSxhYWG3vOZL2lQmJiZo+/btlJ+fP2N9YWEhHTx4kIgm\nG8nsB/UjIyN09epV8vf3Fx7UR0VFkclkoomJib89qJ9qMBUVFTMe1KvVaurr66Pe3l7ha8YYY9KR\ntKnU19eTQqGgsLAwCg8Pp/DwcDp16hT19PRQfHw8aTQaSkhImHGxf++99yggIIB0Oh2dPn1aWN/Y\n2EgGg4ECAgJo7969wvo///yTMjIyKDAwkKKjo8lisQivffHFFxQYGEiBgYH05ZdfSlkqY4wxIrqr\nJz8yxhgT1x312V9yTLaUI/uNN97A2rVr/zaoQersP/74A0899RSCg4NhMBhw4MAB2WpOTk5GeHg4\n9Ho9du7cCafTKVv2lLS0NBiNxjlzxc6Oi4tDUFAQIiIiEBERge7ubllyR0dHsWvXLuh0OgQHB+P4\n8eOy1Dw4OCjUGhERgYceegivvvqqbOf72LFjMBqNCAsLQ0pKCnp6emTJraqqQlhYGAwGA15//fU5\n611Mdm9vL7Zs2YJVq1Zh76xBRwu9li2Zq98qiUmOyZZyZDc0NJDD4SClUilr3cPDw1RXV0dEkwMs\nYmNj56xbzJoHBweF/aanp1N5ebnkNY+Pjwv7++677yg7O5uMRqNs55uIKC4ujpqamm6bKXbuW2+9\nRUVFRcK+u7u7Jc+efr6nREZGUn19vSx1j4yMkJeXF/X09BAR0WuvvUbFxcWS53Z3d9PatWuFc5yT\nk0O1tbWi1jw0NETnzp2jTz75hPbs2TNjXwu9li3VHdVUZtu2bRudOXOGdDodXb9+nYgmf1lTo83e\nf/99YcAA0V8jyex2+4zRZrNHkkmZPd18m4oU2UREeXl59Nlnn8maOzo6Slu3bl3wP/ylZA8ODtJj\njz1Gly9fJoPBsKDcpWbHxcVRY2PjgjMXm2symYiIyM/Pj4aHhxeVu9js2b/rtrY28vPzkyXbZDLR\n+Pg4BQQEUEdHB01MTFBubi59+umnktd84cIFio+PF9aXlZXR7t27Ra15yrFjx2Y0FTGuZQt1R93+\nmm6+ky1vNkFy9vrpky2lzl4qsbL7+/tRXV2N+Ph42XKTkpLg7e2Ne++9F8nJyZLXbLfbAQBFRUUo\nKCiAh4fHvDPFygYmP1UiIiIC7777ruS5NptNuGXy5ptvIjIyEs899xx+//13WbKnq6ysxPPPPz/v\n3KVkd3Z2ws3NDYcPH4bBYICvry9aW1vx4osvSpprt9uh0WjQ1taGjo4OjI2N4cSJE7BaraLWPGX2\nnD+bzbaka9li3JFNxZWTLZeSvdTjEit7bGwMWVlZyMvLw/r162XLrampgcPhwMjICEpLS2+bu9Rs\nIsKvv/6Kq1evYtu2bbf81FUpsqd89dVXuHTpEurr61FfX4/y8nLJc8fGxtDZ2YlHH30UTU1N2LRp\nEwoKCm6bu9Ts2a9VVVUhKytrXrliZA8MDGDfvn1obm6G3W6H0WhESUmJpLkA4OnpiaNHjyIzMxOb\nN2+GWq2Gu7v7bXPFyHaFO66pOJ1OpKenY/v27Xj66acBTHb069evAwAcDgcefvhhAJNde/pfDJ2d\nnVCpVPD19UVnZ+eM9b6+vpJnzydDjuypB7j79u2TveYVK1YgPT0dFy9elDxbpVLBZDKhsbERarUa\nsbGx+O233/DEE0/IVvcjjzwCYPKz8bKzs3HhwgXJcx944AF4eHjgmWeeAQA8++yz+OWXX2SrGQCa\nm5sxNjaGiIiI2+aKld3a2gq1Wg21Wg0AyMjIwPnz52WpOTU1FSaTCefPn4dWq4VOpxO15ltZ7LVs\nKe6opkJE2LlzJ0JCQpCfny+sT0tLE/7yLS0tFX5BaWlpqKysxOjoKCwWC9rb2xEVFQUfHx/cf//9\naGhoABGhvLxc2EbqbFfWDUzeEhkYGMChQ4dkyx0aGoLD4QAw+Vf0yZMnb3uxESs7NzcXNpsNFosF\n586dg1arxU8//SRL9vj4uDDay+l0orq6es7RZ2LlKhQKbN26FWfPngUA1NbWQq/Xy1LzlIqKCmRn\nZ8+ZKXa2v78/rly5IpzzM2fOICQkRJaap24v9vX14ejRo3jppZdErXn6dtOtWbNmwdeyJZP0iY3M\n5JhsKUd2YWEhqVQqcnd3J5VKRe+8844s2VarlRQKBYWEhAj7+fzzzyXP7erqoo0bN1JoaCgZjUYq\nKCgQRinJcb6nWCyWeY3+Eiv7xo0bFBkZSaGhoaTX6yk/P3/OusWsuaOjgzZv3kyhoaH05JNPktVq\nlfV8+/v7U1tb223PtdjZpaWlZDAYKDQ0lNLS0qi3t1eW3KysLAoJCaGQkBCqqqqSpOZ169aRl5cX\nKZVKUqlU1NraSkQLv5YtFU9+ZIwxJpo76vYXY4wx1+KmwhhjTDTcVBhjjImGmwpjjDHRcFNhjDEm\nGm4qjDHGRMNNhTEZERFiY2Nx+vRpYd23336LlJQUFx4VY+LheSqMyaylpQUZGRkwm81wOp3YsGED\nampqhI8PWYixsTEsW7ZMgqNkbHG4qTDmAvv374eHhweGhoagVCrR0dGBS5cuwel0ori4GGlpabh2\n7Rp27NiBoaEhAMBHH32ETZs2oa6uDkVFRfDy8sKVK1fQ1tbm4moY+ws3FcZcYHh4GBs2bMA999yD\n1NRU6PV6vPDCC+jv70d0dDTMZjMUCgXc3NywYsUKtLe3Izs7GxcvXkRdXR1SU1PR0tKCdevWuboU\nxmbg982MuYCHhwcyMzOhVCrxzTffoLq6Gh9++CEAYGRkBFarFT4+PtizZw+am5vh7u6O9vZ2Yfuo\nqChuKOwfiZsKYy7i5uYGNzc3EBGOHz8OjUYz4/Xi4mKsWbMG5eXlGB8fx8qVK4XX7rvvPrkPl7F5\n4dFfjLlYUlISjhw5InxvNpsBAAMDA/Dx8QEAlJWVYXx83CXHx9hCcFNhzIUUCgWKiorgdDoRGhoK\ng8GAt99+GwCwe/dulJaWIjw8HG1tbVAqlTO2Y+yfiB/UM8YYEw2/U2GMMSYabiqMMcZEw02FMcaY\naLipMMYYEw03FcYYY6LhpsIYY0w03FQYY4yJhpsKY4wx0fwPDDPyoC7MtrYAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0xbe847b8>"
]
}
],
"prompt_number": 96
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"oak_history_1996_2014=list(oak_history.index)\n",
"plt.plot(oak_history_1996_2014,oak_history['Oakland'].tolist(), marker=\"o\", color=\"blue\")\n",
"plt.xlabel(\"Year\")\n",
"plt.ylabel(\"Home Value in USD\")\n",
"plt.title(\"Overall Oakland Home Value (1996/04 - 2014/02)\")\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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O5aY75JaVtZ4NuTE4DC4uOH/+PJycnHD//n0EBQVh4MCBWst5PB54PF4jWxse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\nQvuEzc/p16+Nvn37ER4ehtk8iNWrkzXJKKXuad1dFkETDy/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"text": [
"<matplotlib.figure.Figure at 0x17ffb048>"
]
}
],
"prompt_number": 133
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"2. Oakland foreclosure rate"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fc_rate= generateDFfromFilename(\"City_HomesSoldAsForeclosures-Ratio_AllHomes\")\n",
"oak_fc_rate = fc_rate[(fc_rate[\"RegionName\"]==\"Oakland\") & (fc_rate[\"State\"]==\"CA\")].set_index(\"RegionName\").drop([\"State\", \"Metro\", \"CountyName\"], axis=1).transpose()\n",
"oak_fc_rate.index = pd.to_datetime(oak_fc_rate.index)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 166
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fc_intersection = oak_fc_rate['20000101':'20101201']\n",
"oak_fc_2000_2010=list(fc_intersection.index)\n",
"plt.plot(oak_fc_2000_2010,fc_intersection['Oakland'].tolist(), color=\"red\")\n",
"plt.xlabel(\"Year\")\n",
"plt.ylabel(\"%\")\n",
"plt.title(\"Overall Oakland Foreclosure Rate (2000/01 - 2010/12)\")\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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yIsWIHCLAy+vx835+wOrVWgvXVHHPnjFmWNevi/HwV66Im5X07w80bqz5ehQX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\nM8ZYA/D/LUBvXejlO6UAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0xe51a6a0>"
]
}
],
"prompt_number": 165
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"3. Plot top 5 changed neighborhoods"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Zhvi_sum = generateDFfromFilename(\"Neighborhood_Zhvi_Summary_AllHomes\")\n",
"oak_Zhvi_sum = cleanedOakland(Zhvi_sum, [\"City\", \"State\", \"Metro\", \"County\"])\n",
"oak_change = oak_Zhvi_sum.sort([\"PctFallFromPeakToBottom\"], ascending = True)[[\"RegionName\",\"PctFallFromPeakToBottom\"]]\n",
"oak_change_top10=oak_change.head(10).set_index(\"RegionName\")\n",
"oak_change_top10[\"PctFallFromPeakToBottom\"]= oak_change_top10[\"PctFallFromPeakToBottom\"] *(-1)\n",
"oak_change_top10"
],
"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>PctFallFromPeakToBottom</th>\n",
" </tr>\n",
" <tr>\n",
" <th>RegionName</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Webster</th>\n",
" <td> 0.748180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Coliseum</th>\n",
" <td> 0.745098</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Arroyo Viejo</th>\n",
" <td> 0.730192</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Columbia Gardens</th>\n",
" <td> 0.716732</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cox</th>\n",
" <td> 0.706743</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Eastmont</th>\n",
" <td> 0.706636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Brookfield Village</th>\n",
" <td> 0.702320</td>\n",
" </tr>\n",
" <tr>\n",
" <th>North Stonehurst</th>\n",
" <td> 0.697443</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Seminary</th>\n",
" <td> 0.693373</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Havenscourt</th>\n",
" <td> 0.686335</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10 rows \u00d7 1 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 139,
"text": [
" PctFallFromPeakToBottom\n",
"RegionName \n",
"Webster 0.748180\n",
"Coliseum 0.745098\n",
"Arroyo Viejo 0.730192\n",
"Columbia Gardens 0.716732\n",
"Cox 0.706743\n",
"Eastmont 0.706636\n",
"Brookfield Village 0.702320\n",
"North Stonehurst 0.697443\n",
"Seminary 0.693373\n",
"Havenscourt 0.686335\n",
"\n",
"[10 rows x 1 columns]"
]
}
],
"prompt_number": 139
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ind = np.arange(5)\n",
"width = 0.5\n",
"plt.bar(ind, list(oak_change_top10[\"PctFallFromPeakToBottom\"])[:5], width, color = 'green')\n",
"plt.ylabel('Percentage from peak to bottom')\n",
"plt.title('Top 5 Changed Neighborhood in Oakland')\n",
"plt.xticks(ind+width, ('Webster', 'Coliseum', 'Arroyo Viejo', 'Columbia Gardens', 'Cox'))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 143,
"text": [
"([<matplotlib.axis.XTick at 0x15381828>,\n",
" <matplotlib.axis.XTick at 0x15ed97b8>,\n",
" <matplotlib.axis.XTick at 0x161d0d68>,\n",
" <matplotlib.axis.XTick at 0x161d52e8>,\n",
" <matplotlib.axis.XTick at 0x161d5940>],\n",
" <a list of 5 Text xticklabel objects>)"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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TjBs3Dr169YJCoYCHhwd3KxERNXKyyWHbtm1444030L59ewDAmTNnpB4EERE1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\n",
"text": [
"<matplotlib.figure.Figure at 0x15d5c390>"
]
}
],
"prompt_number": 143
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"4. Plot top 5 neighborhood homevalue history"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"homevalue = generateDFfromFilename(\"Neighborhood_Zhvi_AllHomes\")\n",
"oak_homevalue = cleanedOakland(homevalue,[\"City\", \"State\", \"Metro\", \"CountyName\"]).set_index(\"RegionName\").transpose()\n",
"oak_homevalue.index= pd.to_datetime(oak_homevalue.index)\n",
"oak_homevalue.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>RegionName</th>\n",
" <th>Redwood Heights</th>\n",
" <th>Adams Point</th>\n",
" <th>Clinton</th>\n",
" <th>Cleveland Heights</th>\n",
" <th>Havenscourt</th>\n",
" <th>Bushrod</th>\n",
" <th>Piedmont Avenue</th>\n",
" <th>Upper Dimond</th>\n",
" <th>St. Elizabeth</th>\n",
" <th>Longfellow</th>\n",
" <th>Upper Rockridge</th>\n",
" <th>Fremont</th>\n",
" <th>Meadow Brook</th>\n",
" <th>Webster</th>\n",
" <th>Seminary</th>\n",
" <th>Rancho San Antonio</th>\n",
" <th>Maxwell Park</th>\n",
" <th>Glenview</th>\n",
" <th>Arroyo Viejo</th>\n",
" <th>Allendale</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1996-04-01</th>\n",
" <td> 185500</td>\n",
" <td> 113800</td>\n",
" <td> 125200</td>\n",
" <td> 209900</td>\n",
" <td> 94300</td>\n",
" <td> 169100</td>\n",
" <td> 144700</td>\n",
" <td> 170600</td>\n",
" <td> 106400</td>\n",
" <td> 134800</td>\n",
" <td> 350300</td>\n",
" <td> 104800</td>\n",
" <td> 114800</td>\n",
" <td> 90200</td>\n",
" <td> 90300</td>\n",
" <td> 109800</td>\n",
" <td> 129200</td>\n",
" <td> 201300</td>\n",
" <td> 88200</td>\n",
" <td> 104800</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1996-05-01</th>\n",
" <td> 185400</td>\n",
" <td> 110900</td>\n",
" <td> 125400</td>\n",
" <td> 211300</td>\n",
" <td> 96100</td>\n",
" <td> 168900</td>\n",
" <td> 142600</td>\n",
" <td> 172900</td>\n",
" <td> 104500</td>\n",
" <td> 130900</td>\n",
" <td> 353500</td>\n",
" <td> 106800</td>\n",
" <td> 113800</td>\n",
" <td> 93000</td>\n",
" <td> 92400</td>\n",
" <td> 109700</td>\n",
" <td> 130900</td>\n",
" <td> 207400</td>\n",
" <td> 91300</td>\n",
" <td> 105400</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1996-06-01</th>\n",
" <td> 184000</td>\n",
" <td> 108300</td>\n",
" <td> 125400</td>\n",
" <td> 209100</td>\n",
" <td> 98200</td>\n",
" <td> 168800</td>\n",
" <td> 142500</td>\n",
" <td> 173100</td>\n",
" <td> 103700</td>\n",
" <td> 128800</td>\n",
" <td> 358300</td>\n",
" <td> 108100</td>\n",
" <td> 112400</td>\n",
" <td> 95800</td>\n",
" <td> 93600</td>\n",
" <td> 109400</td>\n",
" <td> 133300</td>\n",
" <td> 211500</td>\n",
" <td> 95000</td>\n",
" <td> 105700</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1996-07-01</th>\n",
" <td> 182400</td>\n",
" <td> 106300</td>\n",
" <td> 125100</td>\n",
" <td> 206200</td>\n",
" <td> 100100</td>\n",
" <td> 167700</td>\n",
" <td> 142200</td>\n",
" <td> 173600</td>\n",
" <td> 104300</td>\n",
" <td> 127700</td>\n",
" <td> 360900</td>\n",
" <td> 108900</td>\n",
" <td> 112200</td>\n",
" <td> 97700</td>\n",
" <td> 94700</td>\n",
" <td> 109800</td>\n",
" <td> 134600</td>\n",
" <td> 213600</td>\n",
" <td> 97400</td>\n",
" <td> 106000</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1996-08-01</th>\n",
" <td> 181300</td>\n",
" <td> 105300</td>\n",
" <td> 125300</td>\n",
" <td> 205500</td>\n",
" <td> 101300</td>\n",
" <td> 166000</td>\n",
" <td> 142400</td>\n",
" <td> 175200</td>\n",
" <td> 104900</td>\n",
" <td> 126200</td>\n",
" <td> 362100</td>\n",
" <td> 110000</td>\n",
" <td> 113400</td>\n",
" <td> 98700</td>\n",
" <td> 96200</td>\n",
" <td> 111100</td>\n",
" <td> 135100</td>\n",
" <td> 216300</td>\n",
" <td> 98500</td>\n",
" <td> 105900</td>\n",
" <td>...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 72 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 148,
"text": [
"RegionName Redwood Heights Adams Point Clinton Cleveland Heights \\\n",
"1996-04-01 185500 113800 125200 209900 \n",
"1996-05-01 185400 110900 125400 211300 \n",
"1996-06-01 184000 108300 125400 209100 \n",
"1996-07-01 182400 106300 125100 206200 \n",
"1996-08-01 181300 105300 125300 205500 \n",
"\n",
"RegionName Havenscourt Bushrod Piedmont Avenue Upper Dimond \\\n",
"1996-04-01 94300 169100 144700 170600 \n",
"1996-05-01 96100 168900 142600 172900 \n",
"1996-06-01 98200 168800 142500 173100 \n",
"1996-07-01 100100 167700 142200 173600 \n",
"1996-08-01 101300 166000 142400 175200 \n",
"\n",
"RegionName St. Elizabeth Longfellow Upper Rockridge Fremont Meadow Brook \\\n",
"1996-04-01 106400 134800 350300 104800 114800 \n",
"1996-05-01 104500 130900 353500 106800 113800 \n",
"1996-06-01 103700 128800 358300 108100 112400 \n",
"1996-07-01 104300 127700 360900 108900 112200 \n",
"1996-08-01 104900 126200 362100 110000 113400 \n",
"\n",
"RegionName Webster Seminary Rancho San Antonio Maxwell Park Glenview \\\n",
"1996-04-01 90200 90300 109800 129200 201300 \n",
"1996-05-01 93000 92400 109700 130900 207400 \n",
"1996-06-01 95800 93600 109400 133300 211500 \n",
"1996-07-01 97700 94700 109800 134600 213600 \n",
"1996-08-01 98700 96200 111100 135100 216300 \n",
"\n",
"RegionName Arroyo Viejo Allendale \n",
"1996-04-01 88200 104800 ... \n",
"1996-05-01 91300 105400 ... \n",
"1996-06-01 95000 105700 ... \n",
"1996-07-01 97400 106000 ... \n",
"1996-08-01 98500 105900 ... \n",
"\n",
"[5 rows x 72 columns]"
]
}
],
"prompt_number": 148
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"neighborhoods_top5_changed = ['Webster', 'Coliseum', 'Arroyo Viejo', 'Columbia Gardens', 'Cox']\n",
"year_index = list(oak_homevalue.index)\n",
"fig = plt.figure()\n",
"\n",
"ax1 = fig.add_subplot(2, 3, 1)\n",
"ax2 = fig.add_subplot(2, 3, 2)\n",
"ax3 = fig.add_subplot(2, 3, 3)\n",
"ax4 = fig.add_subplot(2, 3, 4)\n",
"ax5 = fig.add_subplot(2, 3, 5)\n",
"\n",
"ax1.plot(year_index, oak_homevalue['Webster'].tolist())\n",
"ax2.plot(year_index, oak_homevalue['Coliseum'].tolist())\n",
"ax3.plot(year_index, oak_homevalue['Arroyo Viejo'].tolist())\n",
"ax4.plot(year_index, oak_homevalue['Columbia Gardens'].tolist())\n",
"ax5.plot(year_index, oak_homevalue['Cox'].tolist())\n",
"\n",
"fig.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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7uzsFBwdX+bJYugFjRQUzTFOSbdTk6NWL6Ndfa24XS9fyciIvrwxq185yDBiX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\nXGByD6dPa3sk6kcgAFasAEQiYNgwlqH85pvAgwdM+besDFi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s7HDz5k0AgEQigbm5udSv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\n",
"text": [
"<matplotlib.figure.Figure at 0xe49ed30>"
]
}
],
"prompt_number": 152
},
{
"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
}
],
"metadata": {}
}
]
}
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