├── cuyahoga_2012_votes.xls
├── cuyahoga_2016_primary.xls
├── requirements.txt
├── .gitignore
├── README.md
├── censusreporter_api.py
├── Basic Census Reporter API with Pandas.ipynb
├── 538_race_income_gap.ipynb
├── Cuyahoga Voting.ipynb
└── Big race changes.ipynb


/cuyahoga_2012_votes.xls:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/censusreporter/notebooks/HEAD/cuyahoga_2012_votes.xls


--------------------------------------------------------------------------------
/cuyahoga_2016_primary.xls:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/censusreporter/notebooks/HEAD/cuyahoga_2016_primary.xls


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/requirements.txt:
--------------------------------------------------------------------------------
 1 | pandas==0.18.1
 2 | jsonschema==2.4.0
 3 | psycopg2==2.6
 4 | SQLAlchemy==1.0.4
 5 | bokeh==0.11.1
 6 | matplotlib==1.5.1
 7 | jupyter==1.0.0
 8 | jupyter-client==5.1.0
 9 | jupyter-console==5.1.0
10 | jupyter-core==4.3.0
11 | six==1.10.0
12 | 


--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
 1 | # Byte-compiled / optimized / DLL files
 2 | __pycache__/
 3 | *.py[cod]
 4 | 
 5 | # C extensions
 6 | *.so
 7 | 
 8 | # Distribution / packaging
 9 | .Python
10 | env/
11 | build/
12 | develop-eggs/
13 | dist/
14 | downloads/
15 | eggs/
16 | .eggs/
17 | lib/
18 | lib64/
19 | parts/
20 | sdist/
21 | var/
22 | *.egg-info/
23 | .installed.cfg
24 | *.egg
25 | 
26 | # PyInstaller
27 | #  Usually these files are written by a python script from a template
28 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
29 | *.manifest
30 | *.spec
31 | 
32 | # Installer logs
33 | pip-log.txt
34 | pip-delete-this-directory.txt
35 | 
36 | # Unit test / coverage reports
37 | htmlcov/
38 | .tox/
39 | .coverage
40 | .coverage.*
41 | .cache
42 | nosetests.xml
43 | coverage.xml
44 | *,cover
45 | 
46 | # Translations
47 | *.mo
48 | *.pot
49 | 
50 | # Django stuff:
51 | *.log
52 | 
53 | # Sphinx documentation
54 | docs/_build/
55 | 
56 | # PyBuilder
57 | target/
58 | 
59 | # IPython
60 | .ipynb_checkpoints
61 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # notebooks
 2 | 
 3 | With the recent announcement that [Github would directly render Jupyter notebooks](http://blog.jupyter.org/2015/05/07/rendering-notebooks-on-github/), 
 4 | it seems interesting to try an experiment.
 5 | 
 6 | Census Reporter invites you to make pull requests for notebooks demonstrating methods for analyzing Census data. 
 7 | What that looks like remains to be seen! Join in if you want to help figure it out.
 8 | 
 9 | # index
10 | 
11 | * [Basic Census Reporter API with Pandas](Basic Census Reporter API with Pandas.ipynb)
12 | * [Looking at Black/White income gap in cities / FiveThirtyEight article](538_race_income_gap.ipynb)
13 | 
14 | 
15 | <hr>
16 | <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
17 | 


--------------------------------------------------------------------------------
/censusreporter_api.py:
--------------------------------------------------------------------------------
  1 | # set up some utility methods. If we ever make a python API wrapper, these belong there.
  2 | import requests
  3 | import pandas as pd
  4 | from six import string_types
  5 | 
  6 | API_URL="http://api.censusreporter.org/1.0/data/show/{release}?table_ids={table_ids}&geo_ids={geoids}"
  7 | SEARCH_API_URL="http://api.censusreporter.org/2.1/full-text/search?type=profile&q={query}"
  8 | 
  9 | def _clean_list_arg(arg,default):
 10 |     if arg is None:
 11 |         arg = default
 12 |     if isinstance(arg,string_types):
 13 |         arg = [arg]
 14 |     return arg
 15 | 
 16 | def json_data(tables=None, geoids=None, release='latest'):
 17 |     """Make a basic API request for data for a given table, geoid, and/or release.
 18 | 
 19 |     tables -- An ACS table ID as a string, or a list of such IDs. Default: 'B01001'
 20 |     geoids -- A Census geoID as a string, or a list of such IDs. Default: '040|01000US' ('all states in the US')
 21 |     release -- The ACS release from which to retrieve data. Should be one of:
 22 |         latest - (default) the ACS release which has data for all of the requested geographies
 23 |         acs2013_1yr - the 2013 1-year ACS data. Only includes geographies with population >65,000
 24 |         acs2013_3yr - the 2011-13 3-year ACS data. Only includes geographies with population >20,000
 25 |         acs2013_5yr - the 2009-13 5-year ACS data. Includes all geographies covered in the ACS.
 26 |     """
 27 |     geoids = _clean_list_arg(geoids,'040|01000US')
 28 |     tables = _clean_list_arg(tables,'B01001')
 29 | 
 30 |     url = API_URL.format(table_ids=','.join(tables).upper(),
 31 |                          geoids=','.join(geoids),
 32 |                          release=release)
 33 | 
 34 |     response = requests.get(url)
 35 |     return response.json()
 36 | 
 37 | def _prep_data_for_pandas(json_data,include_moe=False):
 38 |     """Given a dict of dicts as they come from a Census Reporter API call, set it up to be amenable to pandas.DataFrame.from_dict"""
 39 |     result = {}
 40 |     for geoid, tables in json_data['data'].items():
 41 |         flat = {}
 42 |         for table,values in tables.items():
 43 |             for kind, columns in values.items():
 44 |                 if kind == 'estimate':
 45 |                     flat.update(columns)
 46 |                 elif kind == 'error' and include_moe:
 47 |                     renamed = dict((k+"_moe",v) for k,v in columns.items())
 48 |                     flat.update(renamed)
 49 |         result[geoid] = flat
 50 |     return result
 51 | 
 52 | def _prep_headers_for_pandas(json_data,separator=":", level=None, include_moe=False):
 53 |     headers = {}
 54 |     for table in json_data['tables']:
 55 |         stack = [ None ] * 10 # pretty sure no columns are nested deeper than this.
 56 |         for column in sorted(json_data['tables'][table]['columns']):
 57 |             col_md = json_data['tables'][table]['columns'][column]
 58 |             indent = col_md['indent']
 59 |             name = col_md['name'].strip(separator)
 60 |             stack[indent] = name
 61 |             parts = []
 62 |             if indent > 0:
 63 |                 for i in range(1,indent+1):
 64 |                     if stack[i] is not None:
 65 |                         parts.append(stack[i].strip(separator))
 66 |                 name = separator.join(parts)
 67 |             if level is None or indent <= level:
 68 |                 headers[column] = name
 69 |                 if include_moe:
 70 |                     moe_col = '{}_moe'.format(column)
 71 |                     headers[moe_col] = "{} (error)".format(name)
 72 |     return headers
 73 | 
 74 | def search_places(q,sumlevel=None,sumlevels=None):
 75 |     url = SEARCH_API_URL.format(query=q)
 76 |     resp = requests.get(url)
 77 |     j = resp.json()
 78 |     if sumlevel is not None:
 79 |         sumlevels = [ sumlevel ]
 80 |     if sumlevels is not None:
 81 |         sumlevels = map(str,sumlevels)
 82 |         return map(lambda x: x['sumlevel'] in sumlevels, j['results'])
 83 |     else:
 84 |         return j['results']
 85 | 
 86 | def get_dataframe(tables=None, geoids=None, release='latest',level=None,place_names=True,column_names=True, include_moe=False):
 87 |     """Return a pandas DataFrame object for the given tables and geoids.
 88 | 
 89 |     Keyword arguments (all optional):
 90 |     tables -- An ACS table ID as a string, or a list of such IDs. Default: 'B01001'
 91 |     geoids -- A Census geoID as a string, or a list of such IDs. Default: '040|01000US' ('all states in the US')
 92 |     release -- The ACS release from which to retrieve data. Should be one of:
 93 |         latest - (default) the ACS release which has data for all of the requested geographies
 94 |         acs2013_1yr - the 2013 1-year ACS data. Only includes geographies with population >65,000
 95 |         acs2013_3yr - the 2011-13 3-year ACS data. Only includes geographies with population >20,000
 96 |         acs2013_5yr - the 2009-13 5-year ACS data. Includes all geographies covered in the ACS.
 97 |     level -- if provided, should be an integer representing the maximum "indent level" of columns to be returned. Generally, '0' is the total column.
 98 |     place_names -- specify False to omit a 'name' column for each geography row
 99 |     column_names -- specify False to preserve the coded column names instead of using verbal labels
100 |     include_moe -- specify True to include error columns. Defaults to false.
101 | 
102 |     """
103 |     response = json_data(tables, geoids, release)
104 |     if 'error' in response:
105 |         raise Exception(response['error'])
106 |     df = pd.DataFrame.from_dict(_prep_data_for_pandas(response, include_moe=include_moe),orient='index')
107 |     df = df.reindex_axis(sorted(df.columns), axis=1)
108 |     if column_names or level is not None:
109 |         headers = _prep_headers_for_pandas(response, level=level, include_moe=include_moe)
110 |         if level is not None:
111 |             df = df.select(lambda x: x in headers,axis=1)
112 |         if column_names:
113 |             df = df.rename(columns=headers)
114 |     if place_names:
115 |         name_frame = pd.DataFrame.from_dict(response['geography'],orient='index')
116 |         df.insert(0, 'name', name_frame.name)
117 |     return df
118 | 


--------------------------------------------------------------------------------
/Basic Census Reporter API with Pandas.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Basic Census Reporter API with Pandas"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": 1,
 13 |    "metadata": {
 14 |     "collapsed": true
 15 |    },
 16 |    "outputs": [],
 17 |    "source": [
 18 |     "from censusreporter_api import * # a few convenience methods elsewhere in this repo. Not a full pypi project at the moment"
 19 |    ]
 20 |   },
 21 |   {
 22 |    "cell_type": "markdown",
 23 |    "metadata": {},
 24 |    "source": [
 25 |     "# Top 5 States by Total Population"
 26 |    ]
 27 |   },
 28 |   {
 29 |    "cell_type": "code",
 30 |    "execution_count": 2,
 31 |    "metadata": {},
 32 |    "outputs": [
 33 |     {
 34 |      "data": {
 35 |       "text/html": [
 36 |        "<div>\n",
 37 |        "<table border=\"1\" class=\"dataframe\">\n",
 38 |        "  <thead>\n",
 39 |        "    <tr style=\"text-align: right;\">\n",
 40 |        "      <th></th>\n",
 41 |        "      <th>name</th>\n",
 42 |        "      <th>Total</th>\n",
 43 |        "      <th>Male</th>\n",
 44 |        "      <th>Female</th>\n",
 45 |        "    </tr>\n",
 46 |        "  </thead>\n",
 47 |        "  <tbody>\n",
 48 |        "    <tr>\n",
 49 |        "      <th>04000US06</th>\n",
 50 |        "      <td>California</td>\n",
 51 |        "      <td>39144818.0</td>\n",
 52 |        "      <td>19440558.0</td>\n",
 53 |        "      <td>19704260.0</td>\n",
 54 |        "    </tr>\n",
 55 |        "    <tr>\n",
 56 |        "      <th>04000US48</th>\n",
 57 |        "      <td>Texas</td>\n",
 58 |        "      <td>27469114.0</td>\n",
 59 |        "      <td>13625413.0</td>\n",
 60 |        "      <td>13843701.0</td>\n",
 61 |        "    </tr>\n",
 62 |        "    <tr>\n",
 63 |        "      <th>04000US12</th>\n",
 64 |        "      <td>Florida</td>\n",
 65 |        "      <td>20271272.0</td>\n",
 66 |        "      <td>9891238.0</td>\n",
 67 |        "      <td>10380034.0</td>\n",
 68 |        "    </tr>\n",
 69 |        "    <tr>\n",
 70 |        "      <th>04000US36</th>\n",
 71 |        "      <td>New York</td>\n",
 72 |        "      <td>19795791.0</td>\n",
 73 |        "      <td>9616592.0</td>\n",
 74 |        "      <td>10179199.0</td>\n",
 75 |        "    </tr>\n",
 76 |        "    <tr>\n",
 77 |        "      <th>04000US17</th>\n",
 78 |        "      <td>Illinois</td>\n",
 79 |        "      <td>12859995.0</td>\n",
 80 |        "      <td>6310435.0</td>\n",
 81 |        "      <td>6549560.0</td>\n",
 82 |        "    </tr>\n",
 83 |        "  </tbody>\n",
 84 |        "</table>\n",
 85 |        "</div>"
 86 |       ],
 87 |       "text/plain": [
 88 |        "                 name       Total        Male      Female\n",
 89 |        "04000US06  California  39144818.0  19440558.0  19704260.0\n",
 90 |        "04000US48       Texas  27469114.0  13625413.0  13843701.0\n",
 91 |        "04000US12     Florida  20271272.0   9891238.0  10380034.0\n",
 92 |        "04000US36    New York  19795791.0   9616592.0  10179199.0\n",
 93 |        "04000US17    Illinois  12859995.0   6310435.0   6549560.0"
 94 |       ]
 95 |      },
 96 |      "execution_count": 2,
 97 |      "metadata": {},
 98 |      "output_type": "execute_result"
 99 |     }
100 |    ],
101 |    "source": [
102 |     "df = get_dataframe(tables='B01001',geoids='040|01000US',column_names=True,level=1)\n",
103 |     "df.sort_values('Total', ascending=False).head(5)"
104 |    ]
105 |   },
106 |   {
107 |    "cell_type": "markdown",
108 |    "metadata": {},
109 |    "source": [
110 |     "## Top 5 States in Midwest Region by % Female Population"
111 |    ]
112 |   },
113 |   {
114 |    "cell_type": "code",
115 |    "execution_count": 3,
116 |    "metadata": {},
117 |    "outputs": [
118 |     {
119 |      "data": {
120 |       "text/html": [
121 |        "<div>\n",
122 |        "<table border=\"1\" class=\"dataframe\">\n",
123 |        "  <thead>\n",
124 |        "    <tr style=\"text-align: right;\">\n",
125 |        "      <th></th>\n",
126 |        "      <th>name</th>\n",
127 |        "      <th>Total</th>\n",
128 |        "      <th>Male</th>\n",
129 |        "      <th>Female</th>\n",
130 |        "      <th>pct_female</th>\n",
131 |        "    </tr>\n",
132 |        "  </thead>\n",
133 |        "  <tbody>\n",
134 |        "    <tr>\n",
135 |        "      <th>04000US47</th>\n",
136 |        "      <td>Tennessee</td>\n",
137 |        "      <td>6600299.0</td>\n",
138 |        "      <td>3214689.0</td>\n",
139 |        "      <td>3385610.0</td>\n",
140 |        "      <td>0.512948</td>\n",
141 |        "    </tr>\n",
142 |        "    <tr>\n",
143 |        "      <th>04000US42</th>\n",
144 |        "      <td>Pennsylvania</td>\n",
145 |        "      <td>12802503.0</td>\n",
146 |        "      <td>6262246.0</td>\n",
147 |        "      <td>6540257.0</td>\n",
148 |        "      <td>0.510858</td>\n",
149 |        "    </tr>\n",
150 |        "    <tr>\n",
151 |        "      <th>04000US39</th>\n",
152 |        "      <td>Ohio</td>\n",
153 |        "      <td>11613423.0</td>\n",
154 |        "      <td>5683951.0</td>\n",
155 |        "      <td>5929472.0</td>\n",
156 |        "      <td>0.510571</td>\n",
157 |        "    </tr>\n",
158 |        "    <tr>\n",
159 |        "      <th>04000US29</th>\n",
160 |        "      <td>Missouri</td>\n",
161 |        "      <td>6083672.0</td>\n",
162 |        "      <td>2983645.0</td>\n",
163 |        "      <td>3100027.0</td>\n",
164 |        "      <td>0.509565</td>\n",
165 |        "    </tr>\n",
166 |        "    <tr>\n",
167 |        "      <th>04000US17</th>\n",
168 |        "      <td>Illinois</td>\n",
169 |        "      <td>12859995.0</td>\n",
170 |        "      <td>6310435.0</td>\n",
171 |        "      <td>6549560.0</td>\n",
172 |        "      <td>0.509297</td>\n",
173 |        "    </tr>\n",
174 |        "  </tbody>\n",
175 |        "</table>\n",
176 |        "</div>"
177 |       ],
178 |       "text/plain": [
179 |        "                   name       Total       Male     Female  pct_female\n",
180 |        "04000US47     Tennessee   6600299.0  3214689.0  3385610.0    0.512948\n",
181 |        "04000US42  Pennsylvania  12802503.0  6262246.0  6540257.0    0.510858\n",
182 |        "04000US39          Ohio  11613423.0  5683951.0  5929472.0    0.510571\n",
183 |        "04000US29      Missouri   6083672.0  2983645.0  3100027.0    0.509565\n",
184 |        "04000US17      Illinois  12859995.0  6310435.0  6549560.0    0.509297"
185 |       ]
186 |      },
187 |      "execution_count": 3,
188 |      "metadata": {},
189 |      "output_type": "execute_result"
190 |     }
191 |    ],
192 |    "source": [
193 |     "df = get_dataframe(tables='B01001',geoids='040|02000US2',column_names=True,level=1)\n",
194 |     "df['pct_female'] = df['Female'] / df['Total']\n",
195 |     "df.sort_values('pct_female',ascending=False).head(5)"
196 |    ]
197 |   },
198 |   {
199 |    "cell_type": "markdown",
200 |    "metadata": {},
201 |    "source": [
202 |     "## Top 5 Tennessee Counties by % Male Population"
203 |    ]
204 |   },
205 |   {
206 |    "cell_type": "code",
207 |    "execution_count": 4,
208 |    "metadata": {},
209 |    "outputs": [
210 |     {
211 |      "data": {
212 |       "text/html": [
213 |        "<div>\n",
214 |        "<table border=\"1\" class=\"dataframe\">\n",
215 |        "  <thead>\n",
216 |        "    <tr style=\"text-align: right;\">\n",
217 |        "      <th></th>\n",
218 |        "      <th>name</th>\n",
219 |        "      <th>Total</th>\n",
220 |        "      <th>Male</th>\n",
221 |        "      <th>Female</th>\n",
222 |        "      <th>pct_male</th>\n",
223 |        "    </tr>\n",
224 |        "  </thead>\n",
225 |        "  <tbody>\n",
226 |        "    <tr>\n",
227 |        "      <th>05000US47095</th>\n",
228 |        "      <td>Lake County, TN</td>\n",
229 |        "      <td>7687.0</td>\n",
230 |        "      <td>4937.0</td>\n",
231 |        "      <td>2750.0</td>\n",
232 |        "      <td>0.642253</td>\n",
233 |        "    </tr>\n",
234 |        "    <tr>\n",
235 |        "      <th>05000US47007</th>\n",
236 |        "      <td>Bledsoe County, TN</td>\n",
237 |        "      <td>13686.0</td>\n",
238 |        "      <td>7677.0</td>\n",
239 |        "      <td>6009.0</td>\n",
240 |        "      <td>0.560938</td>\n",
241 |        "    </tr>\n",
242 |        "    <tr>\n",
243 |        "      <th>05000US47129</th>\n",
244 |        "      <td>Morgan County, TN</td>\n",
245 |        "      <td>21794.0</td>\n",
246 |        "      <td>12150.0</td>\n",
247 |        "      <td>9644.0</td>\n",
248 |        "      <td>0.557493</td>\n",
249 |        "    </tr>\n",
250 |        "    <tr>\n",
251 |        "      <th>05000US47181</th>\n",
252 |        "      <td>Wayne County, TN</td>\n",
253 |        "      <td>16897.0</td>\n",
254 |        "      <td>9295.0</td>\n",
255 |        "      <td>7602.0</td>\n",
256 |        "      <td>0.550098</td>\n",
257 |        "    </tr>\n",
258 |        "    <tr>\n",
259 |        "      <th>05000US47069</th>\n",
260 |        "      <td>Hardeman County, TN</td>\n",
261 |        "      <td>26253.0</td>\n",
262 |        "      <td>14297.0</td>\n",
263 |        "      <td>11956.0</td>\n",
264 |        "      <td>0.544585</td>\n",
265 |        "    </tr>\n",
266 |        "  </tbody>\n",
267 |        "</table>\n",
268 |        "</div>"
269 |       ],
270 |       "text/plain": [
271 |        "                             name    Total     Male   Female  pct_male\n",
272 |        "05000US47095      Lake County, TN   7687.0   4937.0   2750.0  0.642253\n",
273 |        "05000US47007   Bledsoe County, TN  13686.0   7677.0   6009.0  0.560938\n",
274 |        "05000US47129    Morgan County, TN  21794.0  12150.0   9644.0  0.557493\n",
275 |        "05000US47181     Wayne County, TN  16897.0   9295.0   7602.0  0.550098\n",
276 |        "05000US47069  Hardeman County, TN  26253.0  14297.0  11956.0  0.544585"
277 |       ]
278 |      },
279 |      "execution_count": 4,
280 |      "metadata": {},
281 |      "output_type": "execute_result"
282 |     }
283 |    ],
284 |    "source": [
285 |     "df = get_dataframe(tables='B01001',geoids='050|04000US47',column_names=True,level=1)\n",
286 |     "df['pct_male'] = df['Male'] / df['Total']\n",
287 |     "df.sort_values('pct_male',ascending=False).head(5)"
288 |    ]
289 |   }
290 |  ],
291 |  "metadata": {
292 |   "kernelspec": {
293 |    "display_name": "Python 3",
294 |    "language": "python",
295 |    "name": "python3"
296 |   },
297 |   "language_info": {
298 |    "codemirror_mode": {
299 |     "name": "ipython",
300 |     "version": 3
301 |    },
302 |    "file_extension": ".py",
303 |    "mimetype": "text/x-python",
304 |    "name": "python",
305 |    "nbconvert_exporter": "python",
306 |    "pygments_lexer": "ipython3",
307 |    "version": "3.4.3"
308 |   }
309 |  },
310 |  "nbformat": 4,
311 |  "nbformat_minor": 1
312 | }
313 | 


--------------------------------------------------------------------------------
/538_race_income_gap.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Black/White income gap in cities"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "markdown",
 12 |    "metadata": {},
 13 |    "source": [
 14 |     "I was intrigued by [a FiveThirtyEight.com story](http://fivethirtyeight.com/datalab/how-baltimores-young-black-men-are-boxed-in/) that included this image:\n",
 15 |     "\n",
 16 |     "![this image](https://espnfivethirtyeight.files.wordpress.com/2015/04/casselman-datalab-baltimore-1.png?w=610&h=521)\n",
 17 |     "\n",
 18 |     "I wanted to know which were the places towards the bottom right quadrant, where black median income outpaced white so dramatically. I am also always looking for chances to practice Pandas and iPython notebook and see how well our [Census Reporter SQL data](http://censusreporter.tumblr.com/post/73727555158/easier-access-to-acs-data) can be applied to this kind of work, so I made this notebook."
 19 |    ]
 20 |   },
 21 |   {
 22 |    "cell_type": "code",
 23 |    "execution_count": 1,
 24 |    "metadata": {},
 25 |    "outputs": [],
 26 |    "source": [
 27 |     "# basic setup. You'd do this every time you set out to use pandas with Census Reporter's SQL\n",
 28 |     "import pandas as pd\n",
 29 |     "from sqlalchemy import create_engine \n",
 30 |     "# for below to work, you must set the PGPASSWORD env variable or have no-password login enabled\n",
 31 |     "engine = create_engine('postgresql://census@localhost:5432/census')"
 32 |    ]
 33 |   },
 34 |   {
 35 |    "cell_type": "markdown",
 36 |    "metadata": {},
 37 |    "source": [
 38 |     "After I published this, [Ben Casselman clarified](https://twitter.com/bencasselman/status/595276073077776385) that they used [B19013H Median Household Income (White alone, not Hispanic)](http://censusreporter.org/tables/B19013H/) and [B19013B Median Household Income (Black alone)](http://censusreporter.org/tables/B19013B/) tables from the ACS2013-3 year release."
 39 |    ]
 40 |   },
 41 |   {
 42 |    "cell_type": "code",
 43 |    "execution_count": 2,
 44 |    "metadata": {},
 45 |    "outputs": [],
 46 |    "source": [
 47 |     "# load in white and black median income for Census places (sumlevel = 160)\n",
 48 |     "white = pd.read_sql_query(\"select g.geoid, g.name, d.b19013h001 as white \\\n",
 49 |     "                           from acs2013_3yr.geoheader g, \\\n",
 50 |     "                                acs2013_3yr.b19013h d \\\n",
 51 |     "                                where d.geoid = g.geoid \\\n",
 52 |     "                                and g.sumlevel = 160\",engine, index_col='geoid')\n",
 53 |     "black = pd.read_sql_query(\"select g.geoid, d.b19013b001 as black \\\n",
 54 |     "                           from acs2013_3yr.geoheader g, \\\n",
 55 |     "                                acs2013_3yr.b19013b d \\\n",
 56 |     "                                where d.geoid = g.geoid \\\n",
 57 |     "                                and g.sumlevel = 160\",engine, index_col='geoid')\n"
 58 |    ]
 59 |   },
 60 |   {
 61 |    "cell_type": "code",
 62 |    "execution_count": 3,
 63 |    "metadata": {},
 64 |    "outputs": [],
 65 |    "source": [
 66 |     "# put the parts together and compute the gap\n",
 67 |     "df = white.join(black)\n",
 68 |     "df = df.dropna()\n",
 69 |     "df['gap'] = df.white - df.black\n",
 70 |     "df.sort_values(by='gap',ascending=True,inplace=True)\n"
 71 |    ]
 72 |   },
 73 |   {
 74 |    "cell_type": "markdown",
 75 |    "metadata": {},
 76 |    "source": [
 77 |     "The 538 chart specifies places where blacks make up 10% or more of the population. So, add population of each group. Since the median income above specifies White non-hispanic, we need to use  [B03002 Hispanic or Latino Origin by Race](http://censusreporter.org/tables/B03002/)."
 78 |    ]
 79 |   },
 80 |   {
 81 |    "cell_type": "code",
 82 |    "execution_count": 4,
 83 |    "metadata": {},
 84 |    "outputs": [],
 85 |    "source": [
 86 |     "df.rename(columns={'white': 'white_income', 'black': 'black_income'}, inplace=True)\n",
 87 |     "population = pd.read_sql_query(\"select geoid, b03002001 as total_pop, b03002003 as white_pop, \\\n",
 88 |     "                                b03002004+b03002014 as black_pop from acs2013_3yr.b03002 \\\n",
 89 |     "                                where geoid like '16000US%%'\",\n",
 90 |     "                               engine, index_col='geoid')\n",
 91 |     "df = df.join(population)"
 92 |    ]
 93 |   },
 94 |   {
 95 |    "cell_type": "code",
 96 |    "execution_count": 5,
 97 |    "metadata": {
 98 |     "collapsed": true
 99 |    },
100 |    "outputs": [],
101 |    "source": [
102 |     "df.dropna(inplace=True)\n",
103 |     "df['black_pop_pct'] = df.black_pop / df.total_pop\n",
104 |     "# I'm running out of creative names for my variables\n",
105 |     "df2 = df[(df.black_pop_pct >=.1) & (df.gap < 0)]\n"
106 |    ]
107 |   },
108 |   {
109 |    "cell_type": "code",
110 |    "execution_count": 6,
111 |    "metadata": {},
112 |    "outputs": [
113 |     {
114 |      "data": {
115 |       "text/html": [
116 |        "<div>\n",
117 |        "<table border=\"1\" class=\"dataframe\">\n",
118 |        "  <thead>\n",
119 |        "    <tr style=\"text-align: right;\">\n",
120 |        "      <th></th>\n",
121 |        "      <th>name</th>\n",
122 |        "      <th>white_income</th>\n",
123 |        "      <th>black_income</th>\n",
124 |        "      <th>gap</th>\n",
125 |        "      <th>total_pop</th>\n",
126 |        "      <th>white_pop</th>\n",
127 |        "      <th>black_pop</th>\n",
128 |        "      <th>black_pop_pct</th>\n",
129 |        "    </tr>\n",
130 |        "    <tr>\n",
131 |        "      <th>geoid</th>\n",
132 |        "      <th></th>\n",
133 |        "      <th></th>\n",
134 |        "      <th></th>\n",
135 |        "      <th></th>\n",
136 |        "      <th></th>\n",
137 |        "      <th></th>\n",
138 |        "      <th></th>\n",
139 |        "      <th></th>\n",
140 |        "    </tr>\n",
141 |        "  </thead>\n",
142 |        "  <tbody>\n",
143 |        "    <tr>\n",
144 |        "      <th>16000US0672520</th>\n",
145 |        "      <td>Soledad city, California</td>\n",
146 |        "      <td>60441.0</td>\n",
147 |        "      <td>147632.0</td>\n",
148 |        "      <td>-87191.0</td>\n",
149 |        "      <td>26251.0</td>\n",
150 |        "      <td>3391.0</td>\n",
151 |        "      <td>3312.0</td>\n",
152 |        "      <td>0.126167</td>\n",
153 |        "    </tr>\n",
154 |        "    <tr>\n",
155 |        "      <th>16000US3676089</th>\n",
156 |        "      <td>Uniondale CDP, New York</td>\n",
157 |        "      <td>36991.0</td>\n",
158 |        "      <td>98148.0</td>\n",
159 |        "      <td>-61157.0</td>\n",
160 |        "      <td>25905.0</td>\n",
161 |        "      <td>2416.0</td>\n",
162 |        "      <td>11635.0</td>\n",
163 |        "      <td>0.449141</td>\n",
164 |        "    </tr>\n",
165 |        "    <tr>\n",
166 |        "      <th>16000US3676705</th>\n",
167 |        "      <td>Valley Stream village, New York</td>\n",
168 |        "      <td>78736.0</td>\n",
169 |        "      <td>122880.0</td>\n",
170 |        "      <td>-44144.0</td>\n",
171 |        "      <td>37618.0</td>\n",
172 |        "      <td>13727.0</td>\n",
173 |        "      <td>6763.0</td>\n",
174 |        "      <td>0.179781</td>\n",
175 |        "    </tr>\n",
176 |        "    <tr>\n",
177 |        "      <th>16000US3624273</th>\n",
178 |        "      <td>Elmont CDP, New York</td>\n",
179 |        "      <td>63913.0</td>\n",
180 |        "      <td>96223.0</td>\n",
181 |        "      <td>-32310.0</td>\n",
182 |        "      <td>39761.0</td>\n",
183 |        "      <td>6260.0</td>\n",
184 |        "      <td>19265.0</td>\n",
185 |        "      <td>0.484520</td>\n",
186 |        "    </tr>\n",
187 |        "    <tr>\n",
188 |        "      <th>16000US0611530</th>\n",
189 |        "      <td>Carson city, California</td>\n",
190 |        "      <td>56728.0</td>\n",
191 |        "      <td>81520.0</td>\n",
192 |        "      <td>-24792.0</td>\n",
193 |        "      <td>92277.0</td>\n",
194 |        "      <td>6527.0</td>\n",
195 |        "      <td>17385.0</td>\n",
196 |        "      <td>0.188400</td>\n",
197 |        "    </tr>\n",
198 |        "    <tr>\n",
199 |        "      <th>16000US4816468</th>\n",
200 |        "      <td>Converse city, Texas</td>\n",
201 |        "      <td>54346.0</td>\n",
202 |        "      <td>74891.0</td>\n",
203 |        "      <td>-20545.0</td>\n",
204 |        "      <td>19705.0</td>\n",
205 |        "      <td>6999.0</td>\n",
206 |        "      <td>3545.0</td>\n",
207 |        "      <td>0.179904</td>\n",
208 |        "    </tr>\n",
209 |        "    <tr>\n",
210 |        "      <th>16000US3627485</th>\n",
211 |        "      <td>Freeport village, New York</td>\n",
212 |        "      <td>70816.0</td>\n",
213 |        "      <td>85997.0</td>\n",
214 |        "      <td>-15181.0</td>\n",
215 |        "      <td>43095.0</td>\n",
216 |        "      <td>10098.0</td>\n",
217 |        "      <td>13717.0</td>\n",
218 |        "      <td>0.318297</td>\n",
219 |        "    </tr>\n",
220 |        "    <tr>\n",
221 |        "      <th>16000US0684144</th>\n",
222 |        "      <td>West Carson CDP, California</td>\n",
223 |        "      <td>53826.0</td>\n",
224 |        "      <td>68750.0</td>\n",
225 |        "      <td>-14924.0</td>\n",
226 |        "      <td>21310.0</td>\n",
227 |        "      <td>4097.0</td>\n",
228 |        "      <td>2438.0</td>\n",
229 |        "      <td>0.114406</td>\n",
230 |        "    </tr>\n",
231 |        "    <tr>\n",
232 |        "      <th>16000US3613552</th>\n",
233 |        "      <td>Central Islip CDP, New York</td>\n",
234 |        "      <td>61287.0</td>\n",
235 |        "      <td>75091.0</td>\n",
236 |        "      <td>-13804.0</td>\n",
237 |        "      <td>36457.0</td>\n",
238 |        "      <td>7244.0</td>\n",
239 |        "      <td>9076.0</td>\n",
240 |        "      <td>0.248951</td>\n",
241 |        "    </tr>\n",
242 |        "    <tr>\n",
243 |        "      <th>16000US0615044</th>\n",
244 |        "      <td>Compton city, California</td>\n",
245 |        "      <td>27885.0</td>\n",
246 |        "      <td>40189.0</td>\n",
247 |        "      <td>-12304.0</td>\n",
248 |        "      <td>97495.0</td>\n",
249 |        "      <td>1294.0</td>\n",
250 |        "      <td>30568.0</td>\n",
251 |        "      <td>0.313534</td>\n",
252 |        "    </tr>\n",
253 |        "  </tbody>\n",
254 |        "</table>\n",
255 |        "</div>"
256 |       ],
257 |       "text/plain": [
258 |        "                                           name  white_income  black_income  \\\n",
259 |        "geoid                                                                         \n",
260 |        "16000US0672520         Soledad city, California       60441.0      147632.0   \n",
261 |        "16000US3676089          Uniondale CDP, New York       36991.0       98148.0   \n",
262 |        "16000US3676705  Valley Stream village, New York       78736.0      122880.0   \n",
263 |        "16000US3624273             Elmont CDP, New York       63913.0       96223.0   \n",
264 |        "16000US0611530          Carson city, California       56728.0       81520.0   \n",
265 |        "16000US4816468             Converse city, Texas       54346.0       74891.0   \n",
266 |        "16000US3627485       Freeport village, New York       70816.0       85997.0   \n",
267 |        "16000US0684144      West Carson CDP, California       53826.0       68750.0   \n",
268 |        "16000US3613552      Central Islip CDP, New York       61287.0       75091.0   \n",
269 |        "16000US0615044         Compton city, California       27885.0       40189.0   \n",
270 |        "\n",
271 |        "                    gap  total_pop  white_pop  black_pop  black_pop_pct  \n",
272 |        "geoid                                                                    \n",
273 |        "16000US0672520 -87191.0    26251.0     3391.0     3312.0       0.126167  \n",
274 |        "16000US3676089 -61157.0    25905.0     2416.0    11635.0       0.449141  \n",
275 |        "16000US3676705 -44144.0    37618.0    13727.0     6763.0       0.179781  \n",
276 |        "16000US3624273 -32310.0    39761.0     6260.0    19265.0       0.484520  \n",
277 |        "16000US0611530 -24792.0    92277.0     6527.0    17385.0       0.188400  \n",
278 |        "16000US4816468 -20545.0    19705.0     6999.0     3545.0       0.179904  \n",
279 |        "16000US3627485 -15181.0    43095.0    10098.0    13717.0       0.318297  \n",
280 |        "16000US0684144 -14924.0    21310.0     4097.0     2438.0       0.114406  \n",
281 |        "16000US3613552 -13804.0    36457.0     7244.0     9076.0       0.248951  \n",
282 |        "16000US0615044 -12304.0    97495.0     1294.0    30568.0       0.313534  "
283 |       ]
284 |      },
285 |      "execution_count": 6,
286 |      "metadata": {},
287 |      "output_type": "execute_result"
288 |     }
289 |    ],
290 |    "source": [
291 |     "df2.head(10)"
292 |    ]
293 |   },
294 |   {
295 |    "cell_type": "markdown",
296 |    "metadata": {},
297 |    "source": [
298 |     "_The paragraphs below refer to places which are no longer on the top 10 list after I updated to align with the tables FiveThirtyEight used for their chart. They are places with a large gap in favor of the black population, but they are too small to be included in the ACS 3-year estimates. I don't have time at the moment to investigate the ones which made this top ten list._\n",
299 |     "\n",
300 |     "I don't know much about Maryland, so I don't know what the deal is with [Kingstown](http://censusreporter.org/profiles/16000US2444325). It's only about [30 mi. from Baltimore](http://www.distance-cities.com/search?from=Baltimore%2C+MD&to=Kingstown%2C+MD%2C+United+States) \"as the crow flies,\" but more than 75 by roads because of the Chesapeake Bay.\n",
301 |     "\n",
302 |     "I'm from Ohio, but near Cleveland, so I never knew about [Wilberforce](http://censusreporter.org/profiles/16000US3985092-wilberforce-oh/). As the home of two historically black colleges, I am guessing that the administration and top faculty help account for the gap.\n",
303 |     "\n"
304 |    ]
305 |   },
306 |   {
307 |    "cell_type": "code",
308 |    "execution_count": 7,
309 |    "metadata": {},
310 |    "outputs": [
311 |     {
312 |      "data": {
313 |       "text/html": [
314 |        "<div>\n",
315 |        "<table border=\"1\" class=\"dataframe\">\n",
316 |        "  <thead>\n",
317 |        "    <tr style=\"text-align: right;\">\n",
318 |        "      <th></th>\n",
319 |        "      <th>name</th>\n",
320 |        "      <th>white_income</th>\n",
321 |        "      <th>black_income</th>\n",
322 |        "      <th>gap</th>\n",
323 |        "      <th>total_pop</th>\n",
324 |        "      <th>white_pop</th>\n",
325 |        "      <th>black_pop</th>\n",
326 |        "      <th>black_pop_pct</th>\n",
327 |        "    </tr>\n",
328 |        "    <tr>\n",
329 |        "      <th>geoid</th>\n",
330 |        "      <th></th>\n",
331 |        "      <th></th>\n",
332 |        "      <th></th>\n",
333 |        "      <th></th>\n",
334 |        "      <th></th>\n",
335 |        "      <th></th>\n",
336 |        "      <th></th>\n",
337 |        "      <th></th>\n",
338 |        "    </tr>\n",
339 |        "  </thead>\n",
340 |        "  <tbody>\n",
341 |        "    <tr>\n",
342 |        "      <th>16000US0624680</th>\n",
343 |        "      <td>Fontana city, California</td>\n",
344 |        "      <td>76469.0</td>\n",
345 |        "      <td>79154.0</td>\n",
346 |        "      <td>-2685.0</td>\n",
347 |        "      <td>201293.0</td>\n",
348 |        "      <td>29249.0</td>\n",
349 |        "      <td>20638.0</td>\n",
350 |        "      <td>0.102527</td>\n",
351 |        "    </tr>\n",
352 |        "    <tr>\n",
353 |        "      <th>16000US0660466</th>\n",
354 |        "      <td>Rialto city, California</td>\n",
355 |        "      <td>46141.0</td>\n",
356 |        "      <td>49056.0</td>\n",
357 |        "      <td>-2915.0</td>\n",
358 |        "      <td>101434.0</td>\n",
359 |        "      <td>11722.0</td>\n",
360 |        "      <td>14618.0</td>\n",
361 |        "      <td>0.144113</td>\n",
362 |        "    </tr>\n",
363 |        "    <tr>\n",
364 |        "      <th>16000US4856348</th>\n",
365 |        "      <td>Pearland city, Texas</td>\n",
366 |        "      <td>86944.0</td>\n",
367 |        "      <td>96335.0</td>\n",
368 |        "      <td>-9391.0</td>\n",
369 |        "      <td>98123.0</td>\n",
370 |        "      <td>45309.0</td>\n",
371 |        "      <td>17236.0</td>\n",
372 |        "      <td>0.175657</td>\n",
373 |        "    </tr>\n",
374 |        "  </tbody>\n",
375 |        "</table>\n",
376 |        "</div>"
377 |       ],
378 |       "text/plain": [
379 |        "                                    name  white_income  black_income     gap  \\\n",
380 |        "geoid                                                                          \n",
381 |        "16000US0624680  Fontana city, California       76469.0       79154.0 -2685.0   \n",
382 |        "16000US0660466   Rialto city, California       46141.0       49056.0 -2915.0   \n",
383 |        "16000US4856348      Pearland city, Texas       86944.0       96335.0 -9391.0   \n",
384 |        "\n",
385 |        "                total_pop  white_pop  black_pop  black_pop_pct  \n",
386 |        "geoid                                                           \n",
387 |        "16000US0624680   201293.0    29249.0    20638.0       0.102527  \n",
388 |        "16000US0660466   101434.0    11722.0    14618.0       0.144113  \n",
389 |        "16000US4856348    98123.0    45309.0    17236.0       0.175657  "
390 |       ]
391 |      },
392 |      "execution_count": 7,
393 |      "metadata": {},
394 |      "output_type": "execute_result"
395 |     }
396 |    ],
397 |    "source": [
398 |     "df2.sort_values(by='total_pop',ascending=False).head(3)"
399 |    ]
400 |   },
401 |   {
402 |    "cell_type": "markdown",
403 |    "metadata": {},
404 |    "source": [
405 |     "It turns out that the gap for the biggest places is not too big. Maybe we should filter by some factor."
406 |    ]
407 |   },
408 |   {
409 |    "cell_type": "code",
410 |    "execution_count": 8,
411 |    "metadata": {},
412 |    "outputs": [
413 |     {
414 |      "data": {
415 |       "text/html": [
416 |        "<div>\n",
417 |        "<table border=\"1\" class=\"dataframe\">\n",
418 |        "  <thead>\n",
419 |        "    <tr style=\"text-align: right;\">\n",
420 |        "      <th></th>\n",
421 |        "      <th>name</th>\n",
422 |        "      <th>white_income</th>\n",
423 |        "      <th>black_income</th>\n",
424 |        "      <th>gap</th>\n",
425 |        "      <th>total_pop</th>\n",
426 |        "      <th>white_pop</th>\n",
427 |        "      <th>black_pop</th>\n",
428 |        "      <th>black_pop_pct</th>\n",
429 |        "    </tr>\n",
430 |        "    <tr>\n",
431 |        "      <th>geoid</th>\n",
432 |        "      <th></th>\n",
433 |        "      <th></th>\n",
434 |        "      <th></th>\n",
435 |        "      <th></th>\n",
436 |        "      <th></th>\n",
437 |        "      <th></th>\n",
438 |        "      <th></th>\n",
439 |        "      <th></th>\n",
440 |        "    </tr>\n",
441 |        "  </thead>\n",
442 |        "  <tbody>\n",
443 |        "    <tr>\n",
444 |        "      <th>16000US3624273</th>\n",
445 |        "      <td>Elmont CDP, New York</td>\n",
446 |        "      <td>63913.0</td>\n",
447 |        "      <td>96223.0</td>\n",
448 |        "      <td>-32310.0</td>\n",
449 |        "      <td>39761.0</td>\n",
450 |        "      <td>6260.0</td>\n",
451 |        "      <td>19265.0</td>\n",
452 |        "      <td>0.484520</td>\n",
453 |        "    </tr>\n",
454 |        "    <tr>\n",
455 |        "      <th>16000US3676705</th>\n",
456 |        "      <td>Valley Stream village, New York</td>\n",
457 |        "      <td>78736.0</td>\n",
458 |        "      <td>122880.0</td>\n",
459 |        "      <td>-44144.0</td>\n",
460 |        "      <td>37618.0</td>\n",
461 |        "      <td>13727.0</td>\n",
462 |        "      <td>6763.0</td>\n",
463 |        "      <td>0.179781</td>\n",
464 |        "    </tr>\n",
465 |        "    <tr>\n",
466 |        "      <th>16000US0672520</th>\n",
467 |        "      <td>Soledad city, California</td>\n",
468 |        "      <td>60441.0</td>\n",
469 |        "      <td>147632.0</td>\n",
470 |        "      <td>-87191.0</td>\n",
471 |        "      <td>26251.0</td>\n",
472 |        "      <td>3391.0</td>\n",
473 |        "      <td>3312.0</td>\n",
474 |        "      <td>0.126167</td>\n",
475 |        "    </tr>\n",
476 |        "    <tr>\n",
477 |        "      <th>16000US3676089</th>\n",
478 |        "      <td>Uniondale CDP, New York</td>\n",
479 |        "      <td>36991.0</td>\n",
480 |        "      <td>98148.0</td>\n",
481 |        "      <td>-61157.0</td>\n",
482 |        "      <td>25905.0</td>\n",
483 |        "      <td>2416.0</td>\n",
484 |        "      <td>11635.0</td>\n",
485 |        "      <td>0.449141</td>\n",
486 |        "    </tr>\n",
487 |        "  </tbody>\n",
488 |        "</table>\n",
489 |        "</div>"
490 |       ],
491 |       "text/plain": [
492 |        "                                           name  white_income  black_income  \\\n",
493 |        "geoid                                                                         \n",
494 |        "16000US3624273             Elmont CDP, New York       63913.0       96223.0   \n",
495 |        "16000US3676705  Valley Stream village, New York       78736.0      122880.0   \n",
496 |        "16000US0672520         Soledad city, California       60441.0      147632.0   \n",
497 |        "16000US3676089          Uniondale CDP, New York       36991.0       98148.0   \n",
498 |        "\n",
499 |        "                    gap  total_pop  white_pop  black_pop  black_pop_pct  \n",
500 |        "geoid                                                                    \n",
501 |        "16000US3624273 -32310.0    39761.0     6260.0    19265.0       0.484520  \n",
502 |        "16000US3676705 -44144.0    37618.0    13727.0     6763.0       0.179781  \n",
503 |        "16000US0672520 -87191.0    26251.0     3391.0     3312.0       0.126167  \n",
504 |        "16000US3676089 -61157.0    25905.0     2416.0    11635.0       0.449141  "
505 |       ]
506 |      },
507 |      "execution_count": 8,
508 |      "metadata": {},
509 |      "output_type": "execute_result"
510 |     }
511 |    ],
512 |    "source": [
513 |     "df3 = df2[df2.gap*-1 > df2.white_income/2]\n",
514 |     "df3.sort_values(by='total_pop',ascending=False).head() "
515 |    ]
516 |   },
517 |   {
518 |    "cell_type": "code",
519 |    "execution_count": 9,
520 |    "metadata": {
521 |     "collapsed": true
522 |    },
523 |    "outputs": [],
524 |    "source": [
525 |     "# Always clean up your database\n",
526 |     "engine.dispose()"
527 |    ]
528 |   }
529 |  ],
530 |  "metadata": {
531 |   "kernelspec": {
532 |    "display_name": "Python 3",
533 |    "language": "python",
534 |    "name": "python3"
535 |   },
536 |   "language_info": {
537 |    "codemirror_mode": {
538 |     "name": "ipython",
539 |     "version": 3
540 |    },
541 |    "file_extension": ".py",
542 |    "mimetype": "text/x-python",
543 |    "name": "python",
544 |    "nbconvert_exporter": "python",
545 |    "pygments_lexer": "ipython3",
546 |    "version": "3.4.3"
547 |   }
548 |  },
549 |  "nbformat": 4,
550 |  "nbformat_minor": 1
551 | }
552 | 


--------------------------------------------------------------------------------
/Cuyahoga Voting.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "Starting point:\n",
  8 |     "\n",
  9 |     "<blockquote class=\"twitter-tweet\" data-lang=\"en\"><p lang=\"en\" dir=\"ltr\">Q: In which Cuyahoga County community did Trump win the most votes?<br>A: Cleveland, followed by Parma &amp; Strongsville. <a href=\"https://t.co/awUAUFFAHt\">pic.twitter.com/awUAUFFAHt</a></p>&mdash; Nick Castele (@NickCastele) <a href=\"https://twitter.com/NickCastele/status/769243584654696448\">August 26, 2016</a></blockquote>\n",
 10 |     "<script async src=\"//platform.twitter.com/widgets.js\" charset=\"utf-8\"></script>\n",
 11 |     "<img src=\"https://pbs.twimg.com/media/Cqzl1SNWEAAJoRV.jpg\">\n",
 12 |     "\n",
 13 |     "I was curious about the *rate* of support, so I put together this notebook to work that out."
 14 |    ]
 15 |   },
 16 |   {
 17 |    "cell_type": "code",
 18 |    "execution_count": 1,
 19 |    "metadata": {},
 20 |    "outputs": [],
 21 |    "source": [
 22 |     "from censusreporter_api import get_dataframe, search_places\n",
 23 |     "import pandas as pd"
 24 |    ]
 25 |   },
 26 |   {
 27 |    "cell_type": "code",
 28 |    "execution_count": 2,
 29 |    "metadata": {},
 30 |    "outputs": [],
 31 |    "source": [
 32 |     "df = get_dataframe('b01003','160|05000US39035', include_moe=True)"
 33 |    ]
 34 |   },
 35 |   {
 36 |    "cell_type": "code",
 37 |    "execution_count": 3,
 38 |    "metadata": {},
 39 |    "outputs": [
 40 |     {
 41 |      "data": {
 42 |       "text/html": [
 43 |        "<div>\n",
 44 |        "<table border=\"1\" class=\"dataframe\">\n",
 45 |        "  <thead>\n",
 46 |        "    <tr style=\"text-align: right;\">\n",
 47 |        "      <th></th>\n",
 48 |        "      <th>name</th>\n",
 49 |        "      <th>Total</th>\n",
 50 |        "      <th>Total (error)</th>\n",
 51 |        "    </tr>\n",
 52 |        "  </thead>\n",
 53 |        "  <tbody>\n",
 54 |        "    <tr>\n",
 55 |        "      <th>16000US3916000</th>\n",
 56 |        "      <td>Cleveland, OH</td>\n",
 57 |        "      <td>390584.0</td>\n",
 58 |        "      <td>47.0</td>\n",
 59 |        "    </tr>\n",
 60 |        "    <tr>\n",
 61 |        "      <th>16000US3961000</th>\n",
 62 |        "      <td>Parma, OH</td>\n",
 63 |        "      <td>80380.0</td>\n",
 64 |        "      <td>50.0</td>\n",
 65 |        "    </tr>\n",
 66 |        "    <tr>\n",
 67 |        "      <th>16000US3941664</th>\n",
 68 |        "      <td>Lakewood, OH</td>\n",
 69 |        "      <td>51155.0</td>\n",
 70 |        "      <td>28.0</td>\n",
 71 |        "    </tr>\n",
 72 |        "    <tr>\n",
 73 |        "      <th>16000US3925704</th>\n",
 74 |        "      <td>Euclid, OH</td>\n",
 75 |        "      <td>48105.0</td>\n",
 76 |        "      <td>39.0</td>\n",
 77 |        "    </tr>\n",
 78 |        "    <tr>\n",
 79 |        "      <th>16000US3916014</th>\n",
 80 |        "      <td>Cleveland Heights, OH</td>\n",
 81 |        "      <td>45388.0</td>\n",
 82 |        "      <td>46.0</td>\n",
 83 |        "    </tr>\n",
 84 |        "    <tr>\n",
 85 |        "      <th>16000US3975098</th>\n",
 86 |        "      <td>Strongsville, OH</td>\n",
 87 |        "      <td>44649.0</td>\n",
 88 |        "      <td>40.0</td>\n",
 89 |        "    </tr>\n",
 90 |        "    <tr>\n",
 91 |        "      <th>16000US3983622</th>\n",
 92 |        "      <td>Westlake, OH</td>\n",
 93 |        "      <td>32469.0</td>\n",
 94 |        "      <td>39.0</td>\n",
 95 |        "    </tr>\n",
 96 |        "    <tr>\n",
 97 |        "      <th>16000US3956882</th>\n",
 98 |        "      <td>North Olmsted, OH</td>\n",
 99 |        "      <td>32248.0</td>\n",
100 |        "      <td>28.0</td>\n",
101 |        "    </tr>\n",
102 |        "    <tr>\n",
103 |        "      <th>16000US3957008</th>\n",
104 |        "      <td>North Royalton, OH</td>\n",
105 |        "      <td>30321.0</td>\n",
106 |        "      <td>27.0</td>\n",
107 |        "    </tr>\n",
108 |        "    <tr>\n",
109 |        "      <th>16000US3929428</th>\n",
110 |        "      <td>Garfield Heights, OH</td>\n",
111 |        "      <td>28365.0</td>\n",
112 |        "      <td>35.0</td>\n",
113 |        "    </tr>\n",
114 |        "  </tbody>\n",
115 |        "</table>\n",
116 |        "</div>"
117 |       ],
118 |       "text/plain": [
119 |        "                                 name     Total  Total (error)\n",
120 |        "16000US3916000          Cleveland, OH  390584.0           47.0\n",
121 |        "16000US3961000              Parma, OH   80380.0           50.0\n",
122 |        "16000US3941664           Lakewood, OH   51155.0           28.0\n",
123 |        "16000US3925704             Euclid, OH   48105.0           39.0\n",
124 |        "16000US3916014  Cleveland Heights, OH   45388.0           46.0\n",
125 |        "16000US3975098       Strongsville, OH   44649.0           40.0\n",
126 |        "16000US3983622           Westlake, OH   32469.0           39.0\n",
127 |        "16000US3956882      North Olmsted, OH   32248.0           28.0\n",
128 |        "16000US3957008     North Royalton, OH   30321.0           27.0\n",
129 |        "16000US3929428   Garfield Heights, OH   28365.0           35.0"
130 |       ]
131 |      },
132 |      "execution_count": 3,
133 |      "metadata": {},
134 |      "output_type": "execute_result"
135 |     }
136 |    ],
137 |    "source": [
138 |     "df.sort_values(by='Total',ascending=False).head(10)"
139 |    ]
140 |   },
141 |   {
142 |    "cell_type": "code",
143 |    "execution_count": 4,
144 |    "metadata": {},
145 |    "outputs": [],
146 |    "source": [
147 |     "votes = pd.read_excel('cuyahoga_2012_votes.xls',sheetname=1)"
148 |    ]
149 |   },
150 |   {
151 |    "cell_type": "code",
152 |    "execution_count": 5,
153 |    "metadata": {},
154 |    "outputs": [],
155 |    "source": [
156 |     "sum_cols = list(votes)[2:9]\n",
157 |     "votes['total'] = votes[sum_cols].sum(axis=1)"
158 |    ]
159 |   },
160 |   {
161 |    "cell_type": "code",
162 |    "execution_count": 6,
163 |    "metadata": {},
164 |    "outputs": [],
165 |    "source": [
166 |     "votes['place'] = votes['Precinct'].apply(lambda x: x.split('-')[0])"
167 |    ]
168 |   },
169 |   {
170 |    "cell_type": "code",
171 |    "execution_count": 7,
172 |    "metadata": {},
173 |    "outputs": [],
174 |    "source": [
175 |     "new_cols = ['unity','precinct','soc','ind','con','lib','dem','rep','gre','write-in','over','under','total','place']\n",
176 |     "votes = votes.rename(columns=dict(zip(votes.columns,new_cols)))"
177 |    ]
178 |   },
179 |   {
180 |    "cell_type": "code",
181 |    "execution_count": 8,
182 |    "metadata": {},
183 |    "outputs": [],
184 |    "source": [
185 |     "by_place = votes[['place','dem','rep','total']].groupby(by='place').sum()"
186 |    ]
187 |   },
188 |   {
189 |    "cell_type": "code",
190 |    "execution_count": 9,
191 |    "metadata": {},
192 |    "outputs": [],
193 |    "source": [
194 |     "by_place['dem_pct'] = by_place['dem']/by_place['total']"
195 |    ]
196 |   },
197 |   {
198 |    "cell_type": "code",
199 |    "execution_count": 10,
200 |    "metadata": {},
201 |    "outputs": [
202 |     {
203 |      "data": {
204 |       "text/html": [
205 |        "<div>\n",
206 |        "<table border=\"1\" class=\"dataframe\">\n",
207 |        "  <thead>\n",
208 |        "    <tr style=\"text-align: right;\">\n",
209 |        "      <th></th>\n",
210 |        "      <th>dem</th>\n",
211 |        "      <th>rep</th>\n",
212 |        "      <th>total</th>\n",
213 |        "      <th>dem_pct</th>\n",
214 |        "    </tr>\n",
215 |        "    <tr>\n",
216 |        "      <th>place</th>\n",
217 |        "      <th></th>\n",
218 |        "      <th></th>\n",
219 |        "      <th></th>\n",
220 |        "      <th></th>\n",
221 |        "    </tr>\n",
222 |        "  </thead>\n",
223 |        "  <tbody>\n",
224 |        "    <tr>\n",
225 |        "      <th>HIGHLAND HILLS</th>\n",
226 |        "      <td>412</td>\n",
227 |        "      <td>2</td>\n",
228 |        "      <td>416</td>\n",
229 |        "      <td>0.990385</td>\n",
230 |        "    </tr>\n",
231 |        "    <tr>\n",
232 |        "      <th>EAST CLEVELAND</th>\n",
233 |        "      <td>8814</td>\n",
234 |        "      <td>124</td>\n",
235 |        "      <td>8959</td>\n",
236 |        "      <td>0.983815</td>\n",
237 |        "    </tr>\n",
238 |        "    <tr>\n",
239 |        "      <th>WARRENSVILLE HTS</th>\n",
240 |        "      <td>7545</td>\n",
241 |        "      <td>145</td>\n",
242 |        "      <td>7705</td>\n",
243 |        "      <td>0.979234</td>\n",
244 |        "    </tr>\n",
245 |        "    <tr>\n",
246 |        "      <th>NORTH RANDALL</th>\n",
247 |        "      <td>503</td>\n",
248 |        "      <td>32</td>\n",
249 |        "      <td>537</td>\n",
250 |        "      <td>0.936685</td>\n",
251 |        "    </tr>\n",
252 |        "    <tr>\n",
253 |        "      <th>LINNDALE</th>\n",
254 |        "      <td>29</td>\n",
255 |        "      <td>2</td>\n",
256 |        "      <td>31</td>\n",
257 |        "      <td>0.935484</td>\n",
258 |        "    </tr>\n",
259 |        "    <tr>\n",
260 |        "      <th>BEDFORD HEIGHTS</th>\n",
261 |        "      <td>5671</td>\n",
262 |        "      <td>421</td>\n",
263 |        "      <td>6114</td>\n",
264 |        "      <td>0.927543</td>\n",
265 |        "    </tr>\n",
266 |        "    <tr>\n",
267 |        "      <th>WOODMERE</th>\n",
268 |        "      <td>354</td>\n",
269 |        "      <td>38</td>\n",
270 |        "      <td>395</td>\n",
271 |        "      <td>0.896203</td>\n",
272 |        "    </tr>\n",
273 |        "    <tr>\n",
274 |        "      <th>MAPLE HEIGHTS</th>\n",
275 |        "      <td>11023</td>\n",
276 |        "      <td>1323</td>\n",
277 |        "      <td>12404</td>\n",
278 |        "      <td>0.888665</td>\n",
279 |        "    </tr>\n",
280 |        "    <tr>\n",
281 |        "      <th>CLEVELAND</th>\n",
282 |        "      <td>152262</td>\n",
283 |        "      <td>18643</td>\n",
284 |        "      <td>172240</td>\n",
285 |        "      <td>0.884011</td>\n",
286 |        "    </tr>\n",
287 |        "    <tr>\n",
288 |        "      <th>OAKWOOD</th>\n",
289 |        "      <td>1882</td>\n",
290 |        "      <td>244</td>\n",
291 |        "      <td>2138</td>\n",
292 |        "      <td>0.880262</td>\n",
293 |        "    </tr>\n",
294 |        "    <tr>\n",
295 |        "      <th>CLEVELAND HEIGHTS</th>\n",
296 |        "      <td>22116</td>\n",
297 |        "      <td>4012</td>\n",
298 |        "      <td>26379</td>\n",
299 |        "      <td>0.838394</td>\n",
300 |        "    </tr>\n",
301 |        "    <tr>\n",
302 |        "      <th>EUCLID</th>\n",
303 |        "      <td>20064</td>\n",
304 |        "      <td>4577</td>\n",
305 |        "      <td>24865</td>\n",
306 |        "      <td>0.806917</td>\n",
307 |        "    </tr>\n",
308 |        "    <tr>\n",
309 |        "      <th>SHAKER HEIGHTS</th>\n",
310 |        "      <td>14282</td>\n",
311 |        "      <td>3354</td>\n",
312 |        "      <td>17762</td>\n",
313 |        "      <td>0.804076</td>\n",
314 |        "    </tr>\n",
315 |        "    <tr>\n",
316 |        "      <th>SOUTH EUCLID</th>\n",
317 |        "      <td>9513</td>\n",
318 |        "      <td>2631</td>\n",
319 |        "      <td>12233</td>\n",
320 |        "      <td>0.777651</td>\n",
321 |        "    </tr>\n",
322 |        "    <tr>\n",
323 |        "      <th>GARFIELD HEIGHTS</th>\n",
324 |        "      <td>10266</td>\n",
325 |        "      <td>3134</td>\n",
326 |        "      <td>13551</td>\n",
327 |        "      <td>0.757582</td>\n",
328 |        "    </tr>\n",
329 |        "    <tr>\n",
330 |        "      <th>BEDFORD</th>\n",
331 |        "      <td>5030</td>\n",
332 |        "      <td>1556</td>\n",
333 |        "      <td>6656</td>\n",
334 |        "      <td>0.755709</td>\n",
335 |        "    </tr>\n",
336 |        "    <tr>\n",
337 |        "      <th>RICHMOND HEIGHTS</th>\n",
338 |        "      <td>4496</td>\n",
339 |        "      <td>1461</td>\n",
340 |        "      <td>5991</td>\n",
341 |        "      <td>0.750459</td>\n",
342 |        "    </tr>\n",
343 |        "    <tr>\n",
344 |        "      <th>GLENWILLOW</th>\n",
345 |        "      <td>285</td>\n",
346 |        "      <td>117</td>\n",
347 |        "      <td>404</td>\n",
348 |        "      <td>0.705446</td>\n",
349 |        "    </tr>\n",
350 |        "    <tr>\n",
351 |        "      <th>LAKEWOOD</th>\n",
352 |        "      <td>17356</td>\n",
353 |        "      <td>7119</td>\n",
354 |        "      <td>24928</td>\n",
355 |        "      <td>0.696245</td>\n",
356 |        "    </tr>\n",
357 |        "    <tr>\n",
358 |        "      <th>NEWBURGH HEIGHTS</th>\n",
359 |        "      <td>507</td>\n",
360 |        "      <td>219</td>\n",
361 |        "      <td>735</td>\n",
362 |        "      <td>0.689796</td>\n",
363 |        "    </tr>\n",
364 |        "    <tr>\n",
365 |        "      <th>BEACHWOOD</th>\n",
366 |        "      <td>4873</td>\n",
367 |        "      <td>2315</td>\n",
368 |        "      <td>7226</td>\n",
369 |        "      <td>0.674370</td>\n",
370 |        "    </tr>\n",
371 |        "    <tr>\n",
372 |        "      <th>UNIVERSITY HEIGHTS</th>\n",
373 |        "      <td>4561</td>\n",
374 |        "      <td>2169</td>\n",
375 |        "      <td>6777</td>\n",
376 |        "      <td>0.673012</td>\n",
377 |        "    </tr>\n",
378 |        "    <tr>\n",
379 |        "      <th>ORANGE</th>\n",
380 |        "      <td>1391</td>\n",
381 |        "      <td>764</td>\n",
382 |        "      <td>2163</td>\n",
383 |        "      <td>0.643088</td>\n",
384 |        "    </tr>\n",
385 |        "    <tr>\n",
386 |        "      <th>BROOKLYN</th>\n",
387 |        "      <td>3316</td>\n",
388 |        "      <td>1787</td>\n",
389 |        "      <td>5177</td>\n",
390 |        "      <td>0.640525</td>\n",
391 |        "    </tr>\n",
392 |        "    <tr>\n",
393 |        "      <th>BROOK PARK</th>\n",
394 |        "      <td>5611</td>\n",
395 |        "      <td>3138</td>\n",
396 |        "      <td>8883</td>\n",
397 |        "      <td>0.631656</td>\n",
398 |        "    </tr>\n",
399 |        "    <tr>\n",
400 |        "      <th>BEREA</th>\n",
401 |        "      <td>5826</td>\n",
402 |        "      <td>3530</td>\n",
403 |        "      <td>9505</td>\n",
404 |        "      <td>0.612941</td>\n",
405 |        "    </tr>\n",
406 |        "    <tr>\n",
407 |        "      <th>MAYFIELD HEIGHTS</th>\n",
408 |        "      <td>5631</td>\n",
409 |        "      <td>3471</td>\n",
410 |        "      <td>9188</td>\n",
411 |        "      <td>0.612865</td>\n",
412 |        "    </tr>\n",
413 |        "    <tr>\n",
414 |        "      <th>BRATENAHL</th>\n",
415 |        "      <td>582</td>\n",
416 |        "      <td>371</td>\n",
417 |        "      <td>961</td>\n",
418 |        "      <td>0.605619</td>\n",
419 |        "    </tr>\n",
420 |        "    <tr>\n",
421 |        "      <th>PARMA HEIGHTS</th>\n",
422 |        "      <td>5482</td>\n",
423 |        "      <td>3638</td>\n",
424 |        "      <td>9250</td>\n",
425 |        "      <td>0.592649</td>\n",
426 |        "    </tr>\n",
427 |        "    <tr>\n",
428 |        "      <th>CUYAHOGA HEIGHTS</th>\n",
429 |        "      <td>196</td>\n",
430 |        "      <td>146</td>\n",
431 |        "      <td>345</td>\n",
432 |        "      <td>0.568116</td>\n",
433 |        "    </tr>\n",
434 |        "    <tr>\n",
435 |        "      <th>PARMA</th>\n",
436 |        "      <td>20942</td>\n",
437 |        "      <td>15483</td>\n",
438 |        "      <td>37001</td>\n",
439 |        "      <td>0.565985</td>\n",
440 |        "    </tr>\n",
441 |        "    <tr>\n",
442 |        "      <th>LYNDHURST</th>\n",
443 |        "      <td>4795</td>\n",
444 |        "      <td>3593</td>\n",
445 |        "      <td>8489</td>\n",
446 |        "      <td>0.564849</td>\n",
447 |        "    </tr>\n",
448 |        "    <tr>\n",
449 |        "      <th>FAIRVIEW PARK</th>\n",
450 |        "      <td>5291</td>\n",
451 |        "      <td>4037</td>\n",
452 |        "      <td>9470</td>\n",
453 |        "      <td>0.558712</td>\n",
454 |        "    </tr>\n",
455 |        "    <tr>\n",
456 |        "      <th>NORTH OLMSTED</th>\n",
457 |        "      <td>9251</td>\n",
458 |        "      <td>7613</td>\n",
459 |        "      <td>17059</td>\n",
460 |        "      <td>0.542294</td>\n",
461 |        "    </tr>\n",
462 |        "    <tr>\n",
463 |        "      <th>SOLON</th>\n",
464 |        "      <td>7203</td>\n",
465 |        "      <td>6094</td>\n",
466 |        "      <td>13405</td>\n",
467 |        "      <td>0.537337</td>\n",
468 |        "    </tr>\n",
469 |        "    <tr>\n",
470 |        "      <th>BROOKLYN HEIGHTS</th>\n",
471 |        "      <td>467</td>\n",
472 |        "      <td>413</td>\n",
473 |        "      <td>894</td>\n",
474 |        "      <td>0.522371</td>\n",
475 |        "    </tr>\n",
476 |        "    <tr>\n",
477 |        "      <th>PEPPER PIKE</th>\n",
478 |        "      <td>2084</td>\n",
479 |        "      <td>1908</td>\n",
480 |        "      <td>4011</td>\n",
481 |        "      <td>0.519571</td>\n",
482 |        "    </tr>\n",
483 |        "    <tr>\n",
484 |        "      <th>OLMSTED FALLS</th>\n",
485 |        "      <td>2386</td>\n",
486 |        "      <td>2156</td>\n",
487 |        "      <td>4610</td>\n",
488 |        "      <td>0.517570</td>\n",
489 |        "    </tr>\n",
490 |        "    <tr>\n",
491 |        "      <th>MIDDLEBURG HEIGHTS</th>\n",
492 |        "      <td>4209</td>\n",
493 |        "      <td>3943</td>\n",
494 |        "      <td>8253</td>\n",
495 |        "      <td>0.509996</td>\n",
496 |        "    </tr>\n",
497 |        "    <tr>\n",
498 |        "      <th>OLMSTED TOWNSHIP</th>\n",
499 |        "      <td>3467</td>\n",
500 |        "      <td>3248</td>\n",
501 |        "      <td>6814</td>\n",
502 |        "      <td>0.508805</td>\n",
503 |        "    </tr>\n",
504 |        "    <tr>\n",
505 |        "      <th>MORELAND HILLS</th>\n",
506 |        "      <td>1090</td>\n",
507 |        "      <td>1107</td>\n",
508 |        "      <td>2217</td>\n",
509 |        "      <td>0.491655</td>\n",
510 |        "    </tr>\n",
511 |        "    <tr>\n",
512 |        "      <th>ROCKY RIVER</th>\n",
513 |        "      <td>6005</td>\n",
514 |        "      <td>6377</td>\n",
515 |        "      <td>12499</td>\n",
516 |        "      <td>0.480438</td>\n",
517 |        "    </tr>\n",
518 |        "    <tr>\n",
519 |        "      <th>SEVEN HILLS</th>\n",
520 |        "      <td>3306</td>\n",
521 |        "      <td>3584</td>\n",
522 |        "      <td>6987</td>\n",
523 |        "      <td>0.473164</td>\n",
524 |        "    </tr>\n",
525 |        "    <tr>\n",
526 |        "      <th>BAY VILLAGE</th>\n",
527 |        "      <td>4596</td>\n",
528 |        "      <td>5190</td>\n",
529 |        "      <td>9905</td>\n",
530 |        "      <td>0.464008</td>\n",
531 |        "    </tr>\n",
532 |        "    <tr>\n",
533 |        "      <th>WALTON HILLS</th>\n",
534 |        "      <td>674</td>\n",
535 |        "      <td>790</td>\n",
536 |        "      <td>1480</td>\n",
537 |        "      <td>0.455405</td>\n",
538 |        "    </tr>\n",
539 |        "    <tr>\n",
540 |        "      <th>CHAGRIN FALLS</th>\n",
541 |        "      <td>1143</td>\n",
542 |        "      <td>1363</td>\n",
543 |        "      <td>2524</td>\n",
544 |        "      <td>0.452853</td>\n",
545 |        "    </tr>\n",
546 |        "    <tr>\n",
547 |        "      <th>VALLEY VIEW</th>\n",
548 |        "      <td>593</td>\n",
549 |        "      <td>715</td>\n",
550 |        "      <td>1325</td>\n",
551 |        "      <td>0.447547</td>\n",
552 |        "    </tr>\n",
553 |        "    <tr>\n",
554 |        "      <th>STRONGSVILLE</th>\n",
555 |        "      <td>11208</td>\n",
556 |        "      <td>13700</td>\n",
557 |        "      <td>25192</td>\n",
558 |        "      <td>0.444903</td>\n",
559 |        "    </tr>\n",
560 |        "    <tr>\n",
561 |        "      <th>WESTLAKE</th>\n",
562 |        "      <td>8030</td>\n",
563 |        "      <td>10068</td>\n",
564 |        "      <td>18255</td>\n",
565 |        "      <td>0.439879</td>\n",
566 |        "    </tr>\n",
567 |        "    <tr>\n",
568 |        "      <th>MAYFIELD VILLAGE</th>\n",
569 |        "      <td>871</td>\n",
570 |        "      <td>1107</td>\n",
571 |        "      <td>1999</td>\n",
572 |        "      <td>0.435718</td>\n",
573 |        "    </tr>\n",
574 |        "    <tr>\n",
575 |        "      <th>NORTH ROYALTON</th>\n",
576 |        "      <td>6974</td>\n",
577 |        "      <td>9041</td>\n",
578 |        "      <td>16227</td>\n",
579 |        "      <td>0.429778</td>\n",
580 |        "    </tr>\n",
581 |        "    <tr>\n",
582 |        "      <th>BROADVIEW HEIGHTS</th>\n",
583 |        "      <td>4545</td>\n",
584 |        "      <td>6008</td>\n",
585 |        "      <td>10662</td>\n",
586 |        "      <td>0.426280</td>\n",
587 |        "    </tr>\n",
588 |        "    <tr>\n",
589 |        "      <th>HIGHLAND HEIGHTS</th>\n",
590 |        "      <td>2199</td>\n",
591 |        "      <td>2989</td>\n",
592 |        "      <td>5224</td>\n",
593 |        "      <td>0.420942</td>\n",
594 |        "    </tr>\n",
595 |        "    <tr>\n",
596 |        "      <th>INDEPENDENCE</th>\n",
597 |        "      <td>1809</td>\n",
598 |        "      <td>2639</td>\n",
599 |        "      <td>4509</td>\n",
600 |        "      <td>0.401198</td>\n",
601 |        "    </tr>\n",
602 |        "    <tr>\n",
603 |        "      <th>BRECKSVILLE</th>\n",
604 |        "      <td>3378</td>\n",
605 |        "      <td>5142</td>\n",
606 |        "      <td>8625</td>\n",
607 |        "      <td>0.391652</td>\n",
608 |        "    </tr>\n",
609 |        "    <tr>\n",
610 |        "      <th>GATES MILLS</th>\n",
611 |        "      <td>546</td>\n",
612 |        "      <td>1066</td>\n",
613 |        "      <td>1627</td>\n",
614 |        "      <td>0.335587</td>\n",
615 |        "    </tr>\n",
616 |        "    <tr>\n",
617 |        "      <th>BENTLEYVILLE</th>\n",
618 |        "      <td>202</td>\n",
619 |        "      <td>428</td>\n",
620 |        "      <td>631</td>\n",
621 |        "      <td>0.320127</td>\n",
622 |        "    </tr>\n",
623 |        "    <tr>\n",
624 |        "      <th>CHAGRIN FALLS TWP</th>\n",
625 |        "      <td>29</td>\n",
626 |        "      <td>63</td>\n",
627 |        "      <td>92</td>\n",
628 |        "      <td>0.315217</td>\n",
629 |        "    </tr>\n",
630 |        "    <tr>\n",
631 |        "      <th>HUNTING VALLEY</th>\n",
632 |        "      <td>101</td>\n",
633 |        "      <td>312</td>\n",
634 |        "      <td>416</td>\n",
635 |        "      <td>0.242788</td>\n",
636 |        "    </tr>\n",
637 |        "  </tbody>\n",
638 |        "</table>\n",
639 |        "</div>"
640 |       ],
641 |       "text/plain": [
642 |        "                        dem    rep   total   dem_pct\n",
643 |        "place                                               \n",
644 |        "HIGHLAND HILLS          412      2     416  0.990385\n",
645 |        "EAST CLEVELAND         8814    124    8959  0.983815\n",
646 |        "WARRENSVILLE HTS       7545    145    7705  0.979234\n",
647 |        "NORTH RANDALL           503     32     537  0.936685\n",
648 |        "LINNDALE                 29      2      31  0.935484\n",
649 |        "BEDFORD HEIGHTS        5671    421    6114  0.927543\n",
650 |        "WOODMERE                354     38     395  0.896203\n",
651 |        "MAPLE HEIGHTS         11023   1323   12404  0.888665\n",
652 |        "CLEVELAND            152262  18643  172240  0.884011\n",
653 |        "OAKWOOD                1882    244    2138  0.880262\n",
654 |        "CLEVELAND HEIGHTS     22116   4012   26379  0.838394\n",
655 |        "EUCLID                20064   4577   24865  0.806917\n",
656 |        "SHAKER HEIGHTS        14282   3354   17762  0.804076\n",
657 |        "SOUTH EUCLID           9513   2631   12233  0.777651\n",
658 |        "GARFIELD HEIGHTS      10266   3134   13551  0.757582\n",
659 |        "BEDFORD                5030   1556    6656  0.755709\n",
660 |        "RICHMOND HEIGHTS       4496   1461    5991  0.750459\n",
661 |        "GLENWILLOW              285    117     404  0.705446\n",
662 |        "LAKEWOOD              17356   7119   24928  0.696245\n",
663 |        "NEWBURGH HEIGHTS        507    219     735  0.689796\n",
664 |        "BEACHWOOD              4873   2315    7226  0.674370\n",
665 |        "UNIVERSITY HEIGHTS     4561   2169    6777  0.673012\n",
666 |        "ORANGE                 1391    764    2163  0.643088\n",
667 |        "BROOKLYN               3316   1787    5177  0.640525\n",
668 |        "BROOK PARK             5611   3138    8883  0.631656\n",
669 |        "BEREA                  5826   3530    9505  0.612941\n",
670 |        "MAYFIELD HEIGHTS       5631   3471    9188  0.612865\n",
671 |        "BRATENAHL               582    371     961  0.605619\n",
672 |        "PARMA HEIGHTS          5482   3638    9250  0.592649\n",
673 |        "CUYAHOGA HEIGHTS        196    146     345  0.568116\n",
674 |        "PARMA                 20942  15483   37001  0.565985\n",
675 |        "LYNDHURST              4795   3593    8489  0.564849\n",
676 |        "FAIRVIEW PARK          5291   4037    9470  0.558712\n",
677 |        "NORTH OLMSTED          9251   7613   17059  0.542294\n",
678 |        "SOLON                  7203   6094   13405  0.537337\n",
679 |        "BROOKLYN HEIGHTS        467    413     894  0.522371\n",
680 |        "PEPPER PIKE            2084   1908    4011  0.519571\n",
681 |        "OLMSTED FALLS          2386   2156    4610  0.517570\n",
682 |        "MIDDLEBURG HEIGHTS     4209   3943    8253  0.509996\n",
683 |        "OLMSTED TOWNSHIP       3467   3248    6814  0.508805\n",
684 |        "MORELAND HILLS         1090   1107    2217  0.491655\n",
685 |        "ROCKY RIVER            6005   6377   12499  0.480438\n",
686 |        "SEVEN HILLS            3306   3584    6987  0.473164\n",
687 |        "BAY VILLAGE            4596   5190    9905  0.464008\n",
688 |        "WALTON HILLS            674    790    1480  0.455405\n",
689 |        "CHAGRIN FALLS          1143   1363    2524  0.452853\n",
690 |        "VALLEY VIEW             593    715    1325  0.447547\n",
691 |        "STRONGSVILLE          11208  13700   25192  0.444903\n",
692 |        "WESTLAKE               8030  10068   18255  0.439879\n",
693 |        "MAYFIELD VILLAGE        871   1107    1999  0.435718\n",
694 |        "NORTH ROYALTON         6974   9041   16227  0.429778\n",
695 |        "BROADVIEW HEIGHTS      4545   6008   10662  0.426280\n",
696 |        "HIGHLAND HEIGHTS       2199   2989    5224  0.420942\n",
697 |        "INDEPENDENCE           1809   2639    4509  0.401198\n",
698 |        "BRECKSVILLE            3378   5142    8625  0.391652\n",
699 |        "GATES MILLS             546   1066    1627  0.335587\n",
700 |        "BENTLEYVILLE            202    428     631  0.320127\n",
701 |        "CHAGRIN FALLS TWP        29     63      92  0.315217\n",
702 |        "HUNTING VALLEY          101    312     416  0.242788"
703 |       ]
704 |      },
705 |      "execution_count": 10,
706 |      "metadata": {},
707 |      "output_type": "execute_result"
708 |     }
709 |    ],
710 |    "source": [
711 |     "by_place.sort_values(by='dem_pct',ascending=False)"
712 |    ]
713 |   },
714 |   {
715 |    "cell_type": "markdown",
716 |    "metadata": {
717 |     "collapsed": true
718 |    },
719 |    "source": [
720 |     "**Note (July 27, 2017)** I left this kind of half-done and now I'm not really motivated to finish the analysis, which I guess would be comparing pct democratic support change between 2012 and 2016. If someone wants to finish it up and make a pull request, go for it!\n"
721 |    ]
722 |   }
723 |  ],
724 |  "metadata": {
725 |   "kernelspec": {
726 |    "display_name": "Python 3",
727 |    "language": "python",
728 |    "name": "python3"
729 |   },
730 |   "language_info": {
731 |    "codemirror_mode": {
732 |     "name": "ipython",
733 |     "version": 3
734 |    },
735 |    "file_extension": ".py",
736 |    "mimetype": "text/x-python",
737 |    "name": "python",
738 |    "nbconvert_exporter": "python",
739 |    "pygments_lexer": "ipython3",
740 |    "version": "3.4.3"
741 |   }
742 |  },
743 |  "nbformat": 4,
744 |  "nbformat_minor": 1
745 | }
746 | 


--------------------------------------------------------------------------------
/Big race changes.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "# X-shaped race graphs\n",
   8 |     "\n",
   9 |     "Reading, I saw the statistic that a South-side Chicago neighborhood went from 90% white to 95% black in 30 years. If you graphed black and white population on a line, they'd cross in the middle like an X.\n",
  10 |     "\n",
  11 |     "I wondered if there were any interesting recent cases of that kind of shift, and decided to see how easy that is with [Census Reporter](https://censusreporter.org). This is a mostly through-composed notebook, with a little cleanup. Maybe it's useful to other folks, or at least to me when I'm trying to remember how I did something...\n",
  12 |     "\n",
  13 |     "## To begin...\n",
  14 |     "\n",
  15 |     "Census Reporter has [an API](https://api.censusreporter.org) but it only serves the most recent ACS. Since we're talking historical comparison, that isn't going to do it for this.\n",
  16 |     "\n",
  17 |     "However, we've created [postgres database dumps for all ACS data released so far](http://censusreporter.tumblr.com/post/73727555158/easier-access-to-acs-data) so that other people can use the data with less setup.\n",
  18 |     "\n",
  19 |     "This is not what most people would call easy, but it is straightforward once you have the tools installed. Instructions about installing postgres is out of scope here, but if you created a database on a running postgres server, the rest really is pretty easy.\n",
  20 |     "\n",
  21 |     "This assumes you created a database named `census` already.\n",
  22 |     "\n",
  23 |     "## Download the data\n",
  24 |     "\n",
  25 |     "\n",
  26 |     "The ACS 1-year database dumps are less than 500 MB, so dealing with them is pretty fast. The ACS 5-year database dumps are very large, and you should only mess with them if you need them for more than one project.\n",
  27 |     "\n",
  28 |     "To download the data, do something like this:\n",
  29 |     "\n",
  30 |     "```bash\n",
  31 |     "curl -O http://census-backup.s3.amazonaws.com/acs/2007/acs2007_1yr/acs2007_1yr_backup.sql.gz\n",
  32 |     "curl -O http://census-backup.s3.amazonaws.com/acs/2014/acs2014_1yr/acs2014_1yr_backup.sql.gz    \n",
  33 |     "```\n",
  34 |     "\n",
  35 |     "Then, assuming you have created a database named `census` and that you don't need to specify any username, password or other connection details, do this:\n",
  36 |     "\n",
  37 |     "```bash\n",
  38 |     "gzcat acs2007_1yr_backup.sql.gz | psql census\n",
  39 |     "gzcat acs2014_1yr_backup.sql.gz | psql census\n",
  40 |     "```\n",
  41 |     "\n",
  42 |     "These take a few minutes, so be patient, and probably best to paste the lines one-at-a-time.\n",
  43 |     "\n",
  44 |     "But now we're ready to code. \n",
  45 |     "\n",
  46 |     "### connect to the database"
  47 |    ]
  48 |   },
  49 |   {
  50 |    "cell_type": "code",
  51 |    "execution_count": 1,
  52 |    "metadata": {
  53 |     "collapsed": true
  54 |    },
  55 |    "outputs": [],
  56 |    "source": [
  57 |     "import pandas as pd\n",
  58 |     "from psycopg2 import connect\n",
  59 |     "con = connect(database='census')"
  60 |    ]
  61 |   },
  62 |   {
  63 |    "cell_type": "markdown",
  64 |    "metadata": {},
  65 |    "source": [
  66 |     "### execute queries\n",
  67 |     "\n",
  68 |     "We want to consider race, which is a complicated subject, including in how the Census deals with it. At least for the span of the ACS, the questions are the same -- comparing back even just in the last few decades may mean a detour to brush up on how to compare when the questions were asked differently.\n",
  69 |     "\n",
  70 |     "\n",
  71 |     "You might start looking for the data we need in the Census Bureau's tables in the \"race\" topic. However, latino is counted as an \"ethnicity,\" and there's a separate topic for the tables which report that data. The best table for our analysis is [B03002: Hispanic or Latino Origin by Race](http://censusreporter.org/tables/B03002/)\n",
  72 |     "\n",
  73 |     "Here's how to read in that table from the two different Census years:"
  74 |    ]
  75 |   },
  76 |   {
  77 |    "cell_type": "code",
  78 |    "execution_count": 2,
  79 |    "metadata": {},
  80 |    "outputs": [],
  81 |    "source": [
  82 |     "# We leave out summary level 500 -- congressional districts -- because they changed considerably between\n",
  83 |     "# 2007 and 2014.\n",
  84 |     "sql = \"\"\"\n",
  85 |     "select g.name, d.* \n",
  86 |     "from {schema}.geoheader g, {schema}.b03002 d\n",
  87 |     "where g.geoid = d.geoid\n",
  88 |     "and g.sumlevel != 500\n",
  89 |     "\"\"\"\n",
  90 |     "\n",
  91 |     "b03002_14 = pd.read_sql_query(sql.format(schema='acs2014_1yr'), con, index_col='geoid')\n",
  92 |     "b03002_07 = pd.read_sql_query(sql.format(schema='acs2007_1yr'), con, index_col='geoid')"
  93 |    ]
  94 |   },
  95 |   {
  96 |    "cell_type": "markdown",
  97 |    "metadata": {},
  98 |    "source": [
  99 |     "### simplify the data\n",
 100 |     "\n",
 101 |     "When talking about race in the United States, the set of \"options\" is often boiled down to:\n",
 102 |     "\n",
 103 |     "* White\n",
 104 |     "* Black\n",
 105 |     "* Asian\n",
 106 |     "* Hispanic\n",
 107 |     "\n",
 108 |     "On a national level, this covers more than 95% of the population. Since we're looking at sub-national data, we shouldn't assume that the other categories aren't relevant, but we still, there are 22 columns in B03002, and that's a lot to deal with. \n",
 109 |     "\n",
 110 |     "<ul>\n",
 111 |     "    <li><em>b03002001:</em> Total:<ul>\n",
 112 |     "        <li><em>b03002002:</em> Not Hispanic or Latino:<ul>\n",
 113 |     "            <li><em>b03002003:</em> White alone</li>\n",
 114 |     "            <li><em>b03002004:</em> Black or African American alone</li>\n",
 115 |     "            <li><em>b03002005:</em> American Indian and Alaska Native alone</li>\n",
 116 |     "            <li><em>b03002006:</em> Asian alone</li>\n",
 117 |     "            <li><em>b03002007:</em> Native Hawaiian and Other Pacific Islander alone</li>\n",
 118 |     "            <li><em>b03002008:</em> Some other race alone</li>\n",
 119 |     "            <li><em>b03002009:</em> Two or more races:<ul>\n",
 120 |     "                <li><em>b03002010:</em> Two races including Some other race</li>\n",
 121 |     "                <li><em>b03002011:</em> Two races excluding Some other race, and three or more races</li>\n",
 122 |     "            </ul></li>\n",
 123 |     "        </ul></li>\n",
 124 |     "        <li><em>b03002012:</em> Hispanic or Latino:<ul>\n",
 125 |     "            <li><em>b03002013:</em> White alone</li>\n",
 126 |     "            <li><em>b03002014:</em> Black or African American alone</li>\n",
 127 |     "            <li><em>b03002015:</em> American Indian and Alaska Native alone</li>\n",
 128 |     "            <li><em>b03002016:</em> Asian alone</li>\n",
 129 |     "            <li><em>b03002017:</em> Native Hawaiian and Other Pacific Islander alone</li>\n",
 130 |     "            <li><em>b03002018:</em> Some other race alone</li>\n",
 131 |     "            <li><em>b03002019:</em> Two or more races:<ul>\n",
 132 |     "                <li><em>b03002020:</em> Two races including Some other race</li>\n",
 133 |     "                <li><em>b03002021:</em> Two races excluding Some other race, and three or more races</li>\n",
 134 |     "            </ul></li>\n",
 135 |     "        </ul></li>\n",
 136 |     "    </ul></li>\n",
 137 |     "</ul>\n",
 138 |     "\n",
 139 |     "(If you want to learn a little more about how race is handled in the ACS, check out [Census Reporter's \"Race and Hispanic Origin\" topic page](http://censusreporter.org/topics/race-hispanic/).)\n",
 140 |     "\n"
 141 |    ]
 142 |   },
 143 |   {
 144 |    "cell_type": "markdown",
 145 |    "metadata": {},
 146 |    "source": [
 147 |     "For our purposes, we're going to count all Hispanic as a single group. For \"non-hispanic\", we'll collapse \"some other race\" and \"two or more races\" into \"other\". \n"
 148 |    ]
 149 |   },
 150 |   {
 151 |    "cell_type": "code",
 152 |    "execution_count": 3,
 153 |    "metadata": {
 154 |     "collapsed": true
 155 |    },
 156 |    "outputs": [],
 157 |    "source": [
 158 |     "def simplifier(b03002,percentify=True):\n",
 159 |     "    \"\"\"Given a dataframe with B03002 schema, reduce it to a simpler dataframe according to the rules described above\"\"\"\n",
 160 |     "    newcols = {\n",
 161 |     "        'total': b03002['b03002001'],\n",
 162 |     "        'white': b03002['b03002003'],\n",
 163 |     "        'black': b03002['b03002004'],\n",
 164 |     "        'amerind': b03002['b03002005'],\n",
 165 |     "        'asian': b03002['b03002006'],\n",
 166 |     "        'pacific': b03002['b03002007'],\n",
 167 |     "        'other': b03002['b03002008'] + b03002['b03002009'],\n",
 168 |     "        'hisp': b03002['b03002012']\n",
 169 |     "    }\n",
 170 |     "    if percentify:\n",
 171 |     "        total = newcols.pop('total')\n",
 172 |     "        for col in newcols:\n",
 173 |     "            newcols[col] = newcols[col]/total\n",
 174 |     "            \n",
 175 |     "    return pd.DataFrame.from_dict(newcols)\n",
 176 |     "\n",
 177 |     "race07 = simplifier(b03002_07)\n",
 178 |     "race14 = simplifier(b03002_14)\n",
 179 |     "\n",
 180 |     "race_chg = race14 - race07\n",
 181 |     "race_chg = race_chg.dropna()\n",
 182 |     "\n",
 183 |     "# it's nice to have the 'name' column at the front when spitting it out in the notebook. \n",
 184 |     "# Since we use the geoid for the dataframe index, we can comfortably mix and match data from different queries.\n",
 185 |     "race_chg.insert(0,'name', b03002_07['name'])\n"
 186 |    ]
 187 |   },
 188 |   {
 189 |    "cell_type": "markdown",
 190 |    "metadata": {},
 191 |    "source": [
 192 |     "### Identify the places with most change\n",
 193 |     "\n",
 194 |     "So, which places had the biggest change in any given race? This is probably not a particularly savvy way to get that, but it gave me a chance to practice the `pandas` syntax for building up data for a `DataFrame` which isn't already assembled in a CSV or database table."
 195 |    ]
 196 |   },
 197 |   {
 198 |    "cell_type": "code",
 199 |    "execution_count": 4,
 200 |    "metadata": {},
 201 |    "outputs": [
 202 |     {
 203 |      "data": {
 204 |       "text/html": [
 205 |        "<div>\n",
 206 |        "<table border=\"1\" class=\"dataframe\">\n",
 207 |        "  <thead>\n",
 208 |        "    <tr style=\"text-align: right;\">\n",
 209 |        "      <th></th>\n",
 210 |        "      <th>col</th>\n",
 211 |        "      <th>name</th>\n",
 212 |        "      <th>value</th>\n",
 213 |        "    </tr>\n",
 214 |        "    <tr>\n",
 215 |        "      <th>geoid</th>\n",
 216 |        "      <th></th>\n",
 217 |        "      <th></th>\n",
 218 |        "      <th></th>\n",
 219 |        "    </tr>\n",
 220 |        "  </thead>\n",
 221 |        "  <tbody>\n",
 222 |        "    <tr>\n",
 223 |        "      <th>79500US3500200</th>\n",
 224 |        "      <td>amerind</td>\n",
 225 |        "      <td>PUMA5 00200, New Mexico</td>\n",
 226 |        "      <td>0.164586</td>\n",
 227 |        "    </tr>\n",
 228 |        "    <tr>\n",
 229 |        "      <th>79500US0608101</th>\n",
 230 |        "      <td>asian</td>\n",
 231 |        "      <td>PUMA5 08101, California</td>\n",
 232 |        "      <td>0.428660</td>\n",
 233 |        "    </tr>\n",
 234 |        "    <tr>\n",
 235 |        "      <th>79500US0605500</th>\n",
 236 |        "      <td>black</td>\n",
 237 |        "      <td>PUMA5 05500, California</td>\n",
 238 |        "      <td>-0.392138</td>\n",
 239 |        "    </tr>\n",
 240 |        "    <tr>\n",
 241 |        "      <th>79500US0400122</th>\n",
 242 |        "      <td>hisp</td>\n",
 243 |        "      <td>PUMA5 00122, Arizona</td>\n",
 244 |        "      <td>0.607988</td>\n",
 245 |        "    </tr>\n",
 246 |        "    <tr>\n",
 247 |        "      <th>79500US4000100</th>\n",
 248 |        "      <td>other</td>\n",
 249 |        "      <td>PUMA5 00100, Oklahoma</td>\n",
 250 |        "      <td>0.093469</td>\n",
 251 |        "    </tr>\n",
 252 |        "    <tr>\n",
 253 |        "      <th>40000US43669</th>\n",
 254 |        "      <td>pacific</td>\n",
 255 |        "      <td>Kailua (Honolulu County)--Kaneohe, HI Urbanize...</td>\n",
 256 |        "      <td>-0.055974</td>\n",
 257 |        "    </tr>\n",
 258 |        "    <tr>\n",
 259 |        "      <th>79500US0400122</th>\n",
 260 |        "      <td>white</td>\n",
 261 |        "      <td>PUMA5 00122, Arizona</td>\n",
 262 |        "      <td>-0.649215</td>\n",
 263 |        "    </tr>\n",
 264 |        "  </tbody>\n",
 265 |        "</table>\n",
 266 |        "</div>"
 267 |       ],
 268 |       "text/plain": [
 269 |        "                    col                                               name  \\\n",
 270 |        "geoid                                                                        \n",
 271 |        "79500US3500200  amerind                            PUMA5 00200, New Mexico   \n",
 272 |        "79500US0608101    asian                            PUMA5 08101, California   \n",
 273 |        "79500US0605500    black                            PUMA5 05500, California   \n",
 274 |        "79500US0400122     hisp                               PUMA5 00122, Arizona   \n",
 275 |        "79500US4000100    other                              PUMA5 00100, Oklahoma   \n",
 276 |        "40000US43669    pacific  Kailua (Honolulu County)--Kaneohe, HI Urbanize...   \n",
 277 |        "79500US0400122    white                               PUMA5 00122, Arizona   \n",
 278 |        "\n",
 279 |        "                   value  \n",
 280 |        "geoid                     \n",
 281 |        "79500US3500200  0.164586  \n",
 282 |        "79500US0608101  0.428660  \n",
 283 |        "79500US0605500 -0.392138  \n",
 284 |        "79500US0400122  0.607988  \n",
 285 |        "79500US4000100  0.093469  \n",
 286 |        "40000US43669   -0.055974  \n",
 287 |        "79500US0400122 -0.649215  "
 288 |       ]
 289 |      },
 290 |      "execution_count": 4,
 291 |      "metadata": {},
 292 |      "output_type": "execute_result"
 293 |     }
 294 |    ],
 295 |    "source": [
 296 |     "index=[]\n",
 297 |     "summary = {'col':[],'value': [], 'name': []}\n",
 298 |     "for col in race_chg.columns:\n",
 299 |     "    if col != 'name':\n",
 300 |     "        top = race_chg[col].max()\n",
 301 |     "        bot = race_chg[col].min()\n",
 302 |     "        if abs(top) > abs(bot):\n",
 303 |     "            val = top\n",
 304 |     "        else:\n",
 305 |     "            val = bot\n",
 306 |     "        row = race_chg[race_chg[col] == val]\n",
 307 |     "        summary['col'].append(col)\n",
 308 |     "        summary['value'].append(val)\n",
 309 |     "        summary['name'].append(row['name'].item()) # we don't want the index label so use item()\n",
 310 |     "        index.append(row.index.tolist()[0]) # here's where we want the index/geoid\n",
 311 |     "\n",
 312 |     "summary = pd.DataFrame(summary,index=pd.Series(index,name='geoid'))\n",
 313 |     "summary"
 314 |    ]
 315 |   },
 316 |   {
 317 |    "cell_type": "markdown",
 318 |    "metadata": {},
 319 |    "source": [
 320 |     "## a diversion\n",
 321 |     "\n",
 322 |     "Here I decided to try to get all the bits in a single dataframe, `race`. I don't remember why, but the merging didn't seem to work the way I expected at first. I may have just been making bonehead mistakes because now it looks fairly straightforward.\n",
 323 |     "\n",
 324 |     "Then I was curious about the spread of changes. I could have (more) easily just described the `race_chg` dataframe, but sometimes you choose a step based on where you're stepping from so the last line here is just a trick to select only the `chg` columns from the merged dataframe for a quick look at them."
 325 |    ]
 326 |   },
 327 |   {
 328 |    "cell_type": "code",
 329 |    "execution_count": 5,
 330 |    "metadata": {},
 331 |    "outputs": [
 332 |     {
 333 |      "data": {
 334 |       "text/html": [
 335 |        "<div>\n",
 336 |        "<table border=\"1\" class=\"dataframe\">\n",
 337 |        "  <thead>\n",
 338 |        "    <tr style=\"text-align: right;\">\n",
 339 |        "      <th></th>\n",
 340 |        "      <th>amerind_chg</th>\n",
 341 |        "      <th>asian_chg</th>\n",
 342 |        "      <th>black_chg</th>\n",
 343 |        "      <th>hisp_chg</th>\n",
 344 |        "      <th>other_chg</th>\n",
 345 |        "      <th>pacific_chg</th>\n",
 346 |        "      <th>white_chg</th>\n",
 347 |        "    </tr>\n",
 348 |        "  </thead>\n",
 349 |        "  <tbody>\n",
 350 |        "    <tr>\n",
 351 |        "      <th>count</th>\n",
 352 |        "      <td>2080.000000</td>\n",
 353 |        "      <td>2080.000000</td>\n",
 354 |        "      <td>2080.000000</td>\n",
 355 |        "      <td>2080.000000</td>\n",
 356 |        "      <td>2080.000000</td>\n",
 357 |        "      <td>2080.000000</td>\n",
 358 |        "      <td>2080.000000</td>\n",
 359 |        "    </tr>\n",
 360 |        "    <tr>\n",
 361 |        "      <th>mean</th>\n",
 362 |        "      <td>0.000148</td>\n",
 363 |        "      <td>0.008763</td>\n",
 364 |        "      <td>0.000057</td>\n",
 365 |        "      <td>0.024247</td>\n",
 366 |        "      <td>0.006655</td>\n",
 367 |        "      <td>0.000357</td>\n",
 368 |        "      <td>-0.040226</td>\n",
 369 |        "    </tr>\n",
 370 |        "    <tr>\n",
 371 |        "      <th>std</th>\n",
 372 |        "      <td>0.008454</td>\n",
 373 |        "      <td>0.021830</td>\n",
 374 |        "      <td>0.028130</td>\n",
 375 |        "      <td>0.048114</td>\n",
 376 |        "      <td>0.009439</td>\n",
 377 |        "      <td>0.003903</td>\n",
 378 |        "      <td>0.054326</td>\n",
 379 |        "    </tr>\n",
 380 |        "    <tr>\n",
 381 |        "      <th>min</th>\n",
 382 |        "      <td>-0.094654</td>\n",
 383 |        "      <td>-0.120585</td>\n",
 384 |        "      <td>-0.392138</td>\n",
 385 |        "      <td>-0.452918</td>\n",
 386 |        "      <td>-0.047881</td>\n",
 387 |        "      <td>-0.055974</td>\n",
 388 |        "      <td>-0.649215</td>\n",
 389 |        "    </tr>\n",
 390 |        "    <tr>\n",
 391 |        "      <th>25%</th>\n",
 392 |        "      <td>-0.001342</td>\n",
 393 |        "      <td>0.000975</td>\n",
 394 |        "      <td>-0.006942</td>\n",
 395 |        "      <td>0.011746</td>\n",
 396 |        "      <td>0.002171</td>\n",
 397 |        "      <td>-0.000352</td>\n",
 398 |        "      <td>-0.055862</td>\n",
 399 |        "    </tr>\n",
 400 |        "    <tr>\n",
 401 |        "      <th>50%</th>\n",
 402 |        "      <td>-0.000196</td>\n",
 403 |        "      <td>0.005339</td>\n",
 404 |        "      <td>0.000780</td>\n",
 405 |        "      <td>0.022289</td>\n",
 406 |        "      <td>0.005954</td>\n",
 407 |        "      <td>0.000000</td>\n",
 408 |        "      <td>-0.039391</td>\n",
 409 |        "    </tr>\n",
 410 |        "    <tr>\n",
 411 |        "      <th>75%</th>\n",
 412 |        "      <td>0.000879</td>\n",
 413 |        "      <td>0.012810</td>\n",
 414 |        "      <td>0.007866</td>\n",
 415 |        "      <td>0.036563</td>\n",
 416 |        "      <td>0.010856</td>\n",
 417 |        "      <td>0.000583</td>\n",
 418 |        "      <td>-0.024016</td>\n",
 419 |        "    </tr>\n",
 420 |        "    <tr>\n",
 421 |        "      <th>max</th>\n",
 422 |        "      <td>0.164586</td>\n",
 423 |        "      <td>0.428660</td>\n",
 424 |        "      <td>0.370986</td>\n",
 425 |        "      <td>0.607988</td>\n",
 426 |        "      <td>0.093469</td>\n",
 427 |        "      <td>0.047832</td>\n",
 428 |        "      <td>0.512265</td>\n",
 429 |        "    </tr>\n",
 430 |        "  </tbody>\n",
 431 |        "</table>\n",
 432 |        "</div>"
 433 |       ],
 434 |       "text/plain": [
 435 |        "       amerind_chg    asian_chg    black_chg     hisp_chg    other_chg  \\\n",
 436 |        "count  2080.000000  2080.000000  2080.000000  2080.000000  2080.000000   \n",
 437 |        "mean      0.000148     0.008763     0.000057     0.024247     0.006655   \n",
 438 |        "std       0.008454     0.021830     0.028130     0.048114     0.009439   \n",
 439 |        "min      -0.094654    -0.120585    -0.392138    -0.452918    -0.047881   \n",
 440 |        "25%      -0.001342     0.000975    -0.006942     0.011746     0.002171   \n",
 441 |        "50%      -0.000196     0.005339     0.000780     0.022289     0.005954   \n",
 442 |        "75%       0.000879     0.012810     0.007866     0.036563     0.010856   \n",
 443 |        "max       0.164586     0.428660     0.370986     0.607988     0.093469   \n",
 444 |        "\n",
 445 |        "       pacific_chg    white_chg  \n",
 446 |        "count  2080.000000  2080.000000  \n",
 447 |        "mean      0.000357    -0.040226  \n",
 448 |        "std       0.003903     0.054326  \n",
 449 |        "min      -0.055974    -0.649215  \n",
 450 |        "25%      -0.000352    -0.055862  \n",
 451 |        "50%       0.000000    -0.039391  \n",
 452 |        "75%       0.000583    -0.024016  \n",
 453 |        "max       0.047832     0.512265  "
 454 |       ]
 455 |      },
 456 |      "execution_count": 5,
 457 |      "metadata": {},
 458 |      "output_type": "execute_result"
 459 |     }
 460 |    ],
 461 |    "source": [
 462 |     "race = pd.merge(race07,race14,left_index=True,right_index=True,suffixes=('_07','_14'))\n",
 463 |     "race = race.merge(race_chg,left_index=True,right_index=True,suffixes=('','_chg'))\n",
 464 |     "race.insert(0,'name',race.pop('name'))\n",
 465 |     "race['total_07'] = b03002_07['b03002001']\n",
 466 |     "race['total_14'] = b03002_14['b03002001']\n",
 467 |     "race = race.merge(race_chg,left_index=True,right_index=True,suffixes=('','_chg'))\n",
 468 |     "race[[x for x in race.columns if x.endswith('_chg')]].describe()"
 469 |    ]
 470 |   },
 471 |   {
 472 |    "cell_type": "markdown",
 473 |    "metadata": {},
 474 |    "source": [
 475 |     "I always like boxplots. These are pretty hard to read, but I still think they look cool, and I like how much they data they pack.\n",
 476 |     "\n",
 477 |     "Matplotlib on MacOS with virtualenv is often a headache, but this seems to work consistently. Bokeh doesn't have built-in boxplots, so this is what we've got. "
 478 |    ]
 479 |   },
 480 |   {
 481 |    "cell_type": "code",
 482 |    "execution_count": 6,
 483 |    "metadata": {},
 484 |    "outputs": [
 485 |     {
 486 |      "name": "stderr",
 487 |      "output_type": "stream",
 488 |      "text": [
 489 |       "/Users/germuska/.virtualenvs/notebooks/lib/python3.4/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
 490 |       "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n",
 491 |       "/Users/germuska/.virtualenvs/notebooks/lib/python3.4/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
 492 |       "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
 493 |      ]
 494 |     },
 495 |     {
 496 |      "data": {
 497 |       "text/plain": [
 498 |        "<matplotlib.axes._subplots.AxesSubplot at 0x10f392390>"
 499 |       ]
 500 |      },
 501 |      "execution_count": 6,
 502 |      "metadata": {},
 503 |      "output_type": "execute_result"
 504 |     },
 505 |     {
 506 |      "data": {
 507 |       "image/png": 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 508 |       "text/plain": [
 509 |        "<matplotlib.figure.Figure at 0x10c06bd68>"
 510 |       ]
 511 |      },
 512 |      "metadata": {},
 513 |      "output_type": "display_data"
 514 |     }
 515 |    ],
 516 |    "source": [
 517 |     "%matplotlib inline\n",
 518 |     "from matplotlib import pyplot as plt\n",
 519 |     "race_chg.plot(kind='box')"
 520 |    ]
 521 |   },
 522 |   {
 523 |    "cell_type": "markdown",
 524 |    "metadata": {},
 525 |    "source": [
 526 |     "One conclusion I draw from some of these summaries is that the number of places with dramatic changes in the percent of native american, pacific islander, some other race and multiracial is few, so I'm leaving them out of the rest of the analysis."
 527 |    ]
 528 |   },
 529 |   {
 530 |    "cell_type": "markdown",
 531 |    "metadata": {},
 532 |    "source": [
 533 |     "## Using a visualization to see the extremes\n",
 534 |     "\n",
 535 |     "It took me a bunch of time to get the tooltips going -- again, it seems simple now, but I was piecing bits together from online examples while translating, and it took some fiddling.\n",
 536 |     "\n",
 537 |     "Most to the point, it seems like you can't add tools like the hover tool when you use the `Scatter` class.\n",
 538 |     "\n",
 539 |     "I wanted to reduce the number of points plotted, so I decided to eliminate those which would cluster closest to the center line, which is essentially-no-change.  I omitted from the chart places in the data where the net change in population for the given group was less than 20 percentage points, either more or less. \n"
 540 |    ]
 541 |   },
 542 |   {
 543 |    "cell_type": "code",
 544 |    "execution_count": 7,
 545 |    "metadata": {},
 546 |    "outputs": [
 547 |     {
 548 |      "data": {
 549 |       "text/html": [
 550 |        "\n",
 551 |        "    <div class=\"bk-banner\">\n",
 552 |        "        <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
 553 |        "        <span id=\"195707d2-e7a8-4a23-8c25-b28b21e886fa\">Loading BokehJS ...</span>\n",
 554 |        "    </div>"
 555 |       ]
 556 |      },
 557 |      "metadata": {},
 558 |      "output_type": "display_data"
 559 |     },
 560 |     {
 561 |      "data": {
 562 |       "application/javascript": [
 563 |        "\n",
 564 |        "(function(global) {\n",
 565 |        "  function now() {\n",
 566 |        "    return new Date();\n",
 567 |        "  }\n",
 568 |        "\n",
 569 |        "  if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n",
 570 |        "    window._bokeh_onload_callbacks = [];\n",
 571 |        "  }\n",
 572 |        "\n",
 573 |        "  function run_callbacks() {\n",
 574 |        "    window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
 575 |        "    delete window._bokeh_onload_callbacks\n",
 576 |        "    console.info(\"Bokeh: all callbacks have finished\");\n",
 577 |        "  }\n",
 578 |        "\n",
 579 |        "  function load_libs(js_urls, callback) {\n",
 580 |        "    window._bokeh_onload_callbacks.push(callback);\n",
 581 |        "    if (window._bokeh_is_loading > 0) {\n",
 582 |        "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
 583 |        "      return null;\n",
 584 |        "    }\n",
 585 |        "    if (js_urls == null || js_urls.length === 0) {\n",
 586 |        "      run_callbacks();\n",
 587 |        "      return null;\n",
 588 |        "    }\n",
 589 |        "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
 590 |        "    window._bokeh_is_loading = js_urls.length;\n",
 591 |        "    for (var i = 0; i < js_urls.length; i++) {\n",
 592 |        "      var url = js_urls[i];\n",
 593 |        "      var s = document.createElement('script');\n",
 594 |        "      s.src = url;\n",
 595 |        "      s.async = false;\n",
 596 |        "      s.onreadystatechange = s.onload = function() {\n",
 597 |        "        window._bokeh_is_loading--;\n",
 598 |        "        if (window._bokeh_is_loading === 0) {\n",
 599 |        "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
 600 |        "          run_callbacks()\n",
 601 |        "        }\n",
 602 |        "      };\n",
 603 |        "      s.onerror = function() {\n",
 604 |        "        console.warn(\"failed to load library \" + url);\n",
 605 |        "      };\n",
 606 |        "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
 607 |        "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
 608 |        "    }\n",
 609 |        "  };\n",
 610 |        "\n",
 611 |        "  var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-compiler-0.11.1.min.js'];\n",
 612 |        "\n",
 613 |        "  var inline_js = [\n",
 614 |        "    function(Bokeh) {\n",
 615 |        "      Bokeh.set_log_level(\"info\");\n",
 616 |        "    },\n",
 617 |        "    \n",
 618 |        "    function(Bokeh) {\n",
 619 |        "      Bokeh.$(\"#195707d2-e7a8-4a23-8c25-b28b21e886fa\").text(\"BokehJS successfully loaded\");\n",
 620 |        "    },\n",
 621 |        "    function(Bokeh) {\n",
 622 |        "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n",
 623 |        "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n",
 624 |        "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n",
 625 |        "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n",
 626 |        "    }\n",
 627 |        "  ];\n",
 628 |        "\n",
 629 |        "  function run_inline_js() {\n",
 630 |        "    for (var i = 0; i < inline_js.length; i++) {\n",
 631 |        "      inline_js[i](window.Bokeh);\n",
 632 |        "    }\n",
 633 |        "  }\n",
 634 |        "\n",
 635 |        "  if (window._bokeh_is_loading === 0) {\n",
 636 |        "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
 637 |        "    run_inline_js();\n",
 638 |        "  } else {\n",
 639 |        "    load_libs(js_urls, function() {\n",
 640 |        "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
 641 |        "      run_inline_js();\n",
 642 |        "    });\n",
 643 |        "  }\n",
 644 |        "}(this));"
 645 |       ]
 646 |      },
 647 |      "metadata": {},
 648 |      "output_type": "display_data"
 649 |     },
 650 |     {
 651 |      "data": {
 652 |       "text/html": [
 653 |        "\n",
 654 |        "\n",
 655 |        "    <div class=\"plotdiv\" id=\"94c29914-45fe-4f4e-90e0-f3c7b47bd454\"></div>\n",
 656 |        "<script type=\"text/javascript\">\n",
 657 |        "  \n",
 658 |        "  (function(global) {\n",
 659 |        "    function now() {\n",
 660 |        "      return new Date();\n",
 661 |        "    }\n",
 662 |        "  \n",
 663 |        "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n",
 664 |        "      window._bokeh_onload_callbacks = [];\n",
 665 |        "    }\n",
 666 |        "  \n",
 667 |        "    function run_callbacks() {\n",
 668 |        "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
 669 |        "      delete window._bokeh_onload_callbacks\n",
 670 |        "      console.info(\"Bokeh: all callbacks have finished\");\n",
 671 |        "    }\n",
 672 |        "  \n",
 673 |        "    function load_libs(js_urls, callback) {\n",
 674 |        "      window._bokeh_onload_callbacks.push(callback);\n",
 675 |        "      if (window._bokeh_is_loading > 0) {\n",
 676 |        "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
 677 |        "        return null;\n",
 678 |        "      }\n",
 679 |        "      if (js_urls == null || js_urls.length === 0) {\n",
 680 |        "        run_callbacks();\n",
 681 |        "        return null;\n",
 682 |        "      }\n",
 683 |        "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
 684 |        "      window._bokeh_is_loading = js_urls.length;\n",
 685 |        "      for (var i = 0; i < js_urls.length; i++) {\n",
 686 |        "        var url = js_urls[i];\n",
 687 |        "        var s = document.createElement('script');\n",
 688 |        "        s.src = url;\n",
 689 |        "        s.async = false;\n",
 690 |        "        s.onreadystatechange = s.onload = function() {\n",
 691 |        "          window._bokeh_is_loading--;\n",
 692 |        "          if (window._bokeh_is_loading === 0) {\n",
 693 |        "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
 694 |        "            run_callbacks()\n",
 695 |        "          }\n",
 696 |        "        };\n",
 697 |        "        s.onerror = function() {\n",
 698 |        "          console.warn(\"failed to load library \" + url);\n",
 699 |        "        };\n",
 700 |        "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
 701 |        "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
 702 |        "      }\n",
 703 |        "    };var element = document.getElementById(\"94c29914-45fe-4f4e-90e0-f3c7b47bd454\");\n",
 704 |        "    if (element == null) {\n",
 705 |        "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '94c29914-45fe-4f4e-90e0-f3c7b47bd454' but no matching script tag was found. \")\n",
 706 |        "      return false;\n",
 707 |        "    }\n",
 708 |        "  \n",
 709 |        "    var js_urls = [];\n",
 710 |        "  \n",
 711 |        "    var inline_js = [\n",
 712 |        "      function(Bokeh) {\n",
 713 |        "        Bokeh.$(function() {\n",
 714 |        "            var docs_json = 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pct of population\",\"formatter\":{\"id\":\"b2d33375-8418-4912-85f6-f8893252c463\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"e7369278-4dbe-4890-a1b3-1c566be6c6d1\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"86743abe-8541-4a77-8062-70f3a0538164\",\"type\":\"BasicTicker\"}},\"id\":\"e5ae96c1-7aa5-472e-b89a-6ea4bfd2df5f\",\"type\":\"LinearAxis\"},{\"attributes\":{\"data_source\":{\"id\":\"5a1489e6-4ed0-4f9c-9a60-7ac4b555e719\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"33b70d8c-37c4-487f-8612-a6b92a95626e\",\"type\":\"Triangle\"},\"hover_glyph\":null,\"nonselection_glyph\":{\"id\":\"accbede7-af62-43d1-8f3a-b322b8cf8154\",\"type\":\"Triangle\"},\"selection_glyph\":null},\"id\":\"9dd8572c-dffe-44f6-9fd3-b2b40c029c0c\",\"type\":\"GlyphRenderer\"}],\"root_ids\":[\"e7369278-4dbe-4890-a1b3-1c566be6c6d1\"]},\"title\":\"Bokeh Application\",\"version\":\"0.11.1\"}};\n",
 715 |        "            var render_items = [{\"docid\":\"62343614-dc77-4a5d-8a30-83c27059740c\",\"elementid\":\"94c29914-45fe-4f4e-90e0-f3c7b47bd454\",\"modelid\":\"e7369278-4dbe-4890-a1b3-1c566be6c6d1\",\"notebook_comms_target\":\"652fe7eb-c9e5-4e6e-bbe8-c5a3c16f9f9d\"}];\n",
 716 |        "            \n",
 717 |        "            Bokeh.embed.embed_items(docs_json, render_items);\n",
 718 |        "        });\n",
 719 |        "      },\n",
 720 |        "      function(Bokeh) {\n",
 721 |        "      }\n",
 722 |        "    ];\n",
 723 |        "  \n",
 724 |        "    function run_inline_js() {\n",
 725 |        "      for (var i = 0; i < inline_js.length; i++) {\n",
 726 |        "        inline_js[i](window.Bokeh);\n",
 727 |        "      }\n",
 728 |        "    }\n",
 729 |        "  \n",
 730 |        "    if (window._bokeh_is_loading === 0) {\n",
 731 |        "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
 732 |        "      run_inline_js();\n",
 733 |        "    } else {\n",
 734 |        "      load_libs(js_urls, function() {\n",
 735 |        "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
 736 |        "        run_inline_js();\n",
 737 |        "      });\n",
 738 |        "    }\n",
 739 |        "  }(this));\n",
 740 |        "</script>"
 741 |       ]
 742 |      },
 743 |      "metadata": {},
 744 |      "output_type": "display_data"
 745 |     },
 746 |     {
 747 |      "data": {
 748 |       "text/html": [
 749 |        "<p><code>&lt;Bokeh Notebook handle for <strong>In[7]</strong>&gt;</code></p>"
 750 |       ],
 751 |       "text/plain": [
 752 |        "<bokeh.io._CommsHandle at 0x10f89dbe0>"
 753 |       ]
 754 |      },
 755 |      "execution_count": 7,
 756 |      "metadata": {},
 757 |      "output_type": "execute_result"
 758 |     }
 759 |    ],
 760 |    "source": [
 761 |     "from bokeh.charts import Scatter, show, output_notebook\n",
 762 |     "from bokeh.plotting import figure\n",
 763 |     "from bokeh.models import HoverTool, PolyAnnotation\n",
 764 |     "from bokeh.models.sources import ColumnDataSource\n",
 765 |     "output_notebook()\n",
 766 |     "\n",
 767 |     "#scatterplot = Scatter(race, x='white_07', y='white_14', title=\"Pct white 2007 vs 2014\",\n",
 768 |     "#            xlabel=\"2007 pct white\")\n",
 769 |     "# http://blog.rtwilson.com/bokeh-plots-with-dataframe-based-tooltips/\n",
 770 |     "hover = HoverTool(tooltips=[('name','@name'),\n",
 771 |     "                            ('geoid', '@geoid'),\n",
 772 |     "                            ('white', '@white_chg{1.11}'), \n",
 773 |     "                            ('black', '@black_chg{1.11}'),\n",
 774 |     "                            ('hisp', '@hisp_chg{1.11}'),\n",
 775 |     "                            ('asian', '@asian_chg{1.11}')])\n",
 776 |     "\n",
 777 |     "p = figure(plot_width=750, plot_height=750,\n",
 778 |     "           title=\"larger swings\")\n",
 779 |     "p.add_tools(hover)\n",
 780 |     "\n",
 781 |     "_plot_configs = [\n",
 782 |     "    ('white', 'red', 'circle'),\n",
 783 |     "    ('black', 'blue', 'square'),\n",
 784 |     "    ('hisp', 'green', 'triangle'),\n",
 785 |     "    ('asian', 'brown', 'inverted_triangle')\n",
 786 |     "]\n",
 787 |     "\n",
 788 |     "for r, c, m in _plot_configs:\n",
 789 |     "    x_col = (r+'_07')\n",
 790 |     "    y_col = (r+'_14')\n",
 791 |     "    chg_col = (r+'_chg')\n",
 792 |     "    race_for_plot = race[(race[chg_col].abs() > 0.2)]\n",
 793 |     "    p.scatter(x=x_col, y=y_col, size=10, color=c, marker=m, source=ColumnDataSource(race_for_plot), legend=chg_col)\n",
 794 |     "\n",
 795 |     "\n",
 796 |     "p.xaxis.axis_label = '2007 pct of population'\n",
 797 |     "p.yaxis.axis_label = '2014 pct'\n",
 798 |     "\n",
 799 |     "# these just don't work!\n",
 800 |     "# drop_anno = PolyAnnotation(plot=p, xs=[.2, .4, .4, .2], ys=[.2, .2, .4, .4], fill_alpha=0.1, fill_color='red')\n",
 801 |     "#growth_anno = PolyAnnotation(plot=p, bottom=180, fill_alpha=0.1, fill_color='green')\n",
 802 |     "\n",
 803 |     "#p.renderers.extend([drop_anno, ])\n",
 804 |     "\n",
 805 |     "show(p)\n"
 806 |    ]
 807 |   },
 808 |   {
 809 |    "cell_type": "markdown",
 810 |    "metadata": {},
 811 |    "source": [
 812 |     "Details on formatting the tooltips for Bokeh are not super easy to find. I had to hunt around to figure out how to format the change floats to have only two decimal places. I'm still not sure what to do about really long names, like _Philadelphia, PA Metro Division; Philadelphia-Camden-Wilmington, PA-NJ-DE-MD Metro Area_."
 813 |    ]
 814 |   },
 815 |   {
 816 |    "cell_type": "markdown",
 817 |    "metadata": {},
 818 |    "source": [
 819 |     "### Lists instead of Charts\n",
 820 |     "\n",
 821 |     "Below are those places as a list, instead of on the chart. Broken up by race because again, I didn't come up with a clever way to say \"any row where the absolute value of any of these values is greater than 0.2\""
 822 |    ]
 823 |   },
 824 |   {
 825 |    "cell_type": "code",
 826 |    "execution_count": 8,
 827 |    "metadata": {},
 828 |    "outputs": [
 829 |     {
 830 |      "data": {
 831 |       "text/html": [
 832 |        "<div>\n",
 833 |        "<table border=\"1\" class=\"dataframe\">\n",
 834 |        "  <thead>\n",
 835 |        "    <tr style=\"text-align: right;\">\n",
 836 |        "      <th></th>\n",
 837 |        "      <th>name</th>\n",
 838 |        "      <th>black_07</th>\n",
 839 |        "      <th>black_14</th>\n",
 840 |        "      <th>black_chg</th>\n",
 841 |        "      <th>total_07</th>\n",
 842 |        "      <th>total_14</th>\n",
 843 |        "    </tr>\n",
 844 |        "    <tr>\n",
 845 |        "      <th>geoid</th>\n",
 846 |        "      <th></th>\n",
 847 |        "      <th></th>\n",
 848 |        "      <th></th>\n",
 849 |        "      <th></th>\n",
 850 |        "      <th></th>\n",
 851 |        "      <th></th>\n",
 852 |        "    </tr>\n",
 853 |        "  </thead>\n",
 854 |        "  <tbody>\n",
 855 |        "    <tr>\n",
 856 |        "      <th>79500US0605500</th>\n",
 857 |        "      <td>PUMA5 05500, California</td>\n",
 858 |        "      <td>0.414126</td>\n",
 859 |        "      <td>0.021988</td>\n",
 860 |        "      <td>-0.392138</td>\n",
 861 |        "      <td>106581.0</td>\n",
 862 |        "      <td>141667.0</td>\n",
 863 |        "    </tr>\n",
 864 |        "    <tr>\n",
 865 |        "      <th>79500US2503303</th>\n",
 866 |        "      <td>PUMA5 03303, Massachusetts</td>\n",
 867 |        "      <td>0.601772</td>\n",
 868 |        "      <td>0.258776</td>\n",
 869 |        "      <td>-0.342995</td>\n",
 870 |        "      <td>115710.0</td>\n",
 871 |        "      <td>112634.0</td>\n",
 872 |        "    </tr>\n",
 873 |        "    <tr>\n",
 874 |        "      <th>79500US2503304</th>\n",
 875 |        "      <td>PUMA5 03304, Massachusetts</td>\n",
 876 |        "      <td>0.181944</td>\n",
 877 |        "      <td>0.552930</td>\n",
 878 |        "      <td>0.370986</td>\n",
 879 |        "      <td>117844.0</td>\n",
 880 |        "      <td>137728.0</td>\n",
 881 |        "    </tr>\n",
 882 |        "    <tr>\n",
 883 |        "      <th>79500US4802308</th>\n",
 884 |        "      <td>PUMA5 02308, Texas</td>\n",
 885 |        "      <td>0.419891</td>\n",
 886 |        "      <td>0.106989</td>\n",
 887 |        "      <td>-0.312901</td>\n",
 888 |        "      <td>179311.0</td>\n",
 889 |        "      <td>105889.0</td>\n",
 890 |        "    </tr>\n",
 891 |        "    <tr>\n",
 892 |        "      <th>79500US4802504</th>\n",
 893 |        "      <td>PUMA5 02504, Texas</td>\n",
 894 |        "      <td>0.054091</td>\n",
 895 |        "      <td>0.321509</td>\n",
 896 |        "      <td>0.267418</td>\n",
 897 |        "      <td>226857.0</td>\n",
 898 |        "      <td>104588.0</td>\n",
 899 |        "    </tr>\n",
 900 |        "    <tr>\n",
 901 |        "      <th>79500US4803501</th>\n",
 902 |        "      <td>PUMA5 03501, Texas</td>\n",
 903 |        "      <td>0.034760</td>\n",
 904 |        "      <td>0.301364</td>\n",
 905 |        "      <td>0.266604</td>\n",
 906 |        "      <td>115420.0</td>\n",
 907 |        "      <td>166390.0</td>\n",
 908 |        "    </tr>\n",
 909 |        "    <tr>\n",
 910 |        "      <th>79500US4804625</th>\n",
 911 |        "      <td>PUMA5 04625, Texas</td>\n",
 912 |        "      <td>0.008153</td>\n",
 913 |        "      <td>0.221798</td>\n",
 914 |        "      <td>0.213644</td>\n",
 915 |        "      <td>153925.0</td>\n",
 916 |        "      <td>114140.0</td>\n",
 917 |        "    </tr>\n",
 918 |        "  </tbody>\n",
 919 |        "</table>\n",
 920 |        "</div>"
 921 |       ],
 922 |       "text/plain": [
 923 |        "                                      name  black_07  black_14  black_chg  \\\n",
 924 |        "geoid                                                                       \n",
 925 |        "79500US0605500     PUMA5 05500, California  0.414126  0.021988  -0.392138   \n",
 926 |        "79500US2503303  PUMA5 03303, Massachusetts  0.601772  0.258776  -0.342995   \n",
 927 |        "79500US2503304  PUMA5 03304, Massachusetts  0.181944  0.552930   0.370986   \n",
 928 |        "79500US4802308          PUMA5 02308, Texas  0.419891  0.106989  -0.312901   \n",
 929 |        "79500US4802504          PUMA5 02504, Texas  0.054091  0.321509   0.267418   \n",
 930 |        "79500US4803501          PUMA5 03501, Texas  0.034760  0.301364   0.266604   \n",
 931 |        "79500US4804625          PUMA5 04625, Texas  0.008153  0.221798   0.213644   \n",
 932 |        "\n",
 933 |        "                total_07  total_14  \n",
 934 |        "geoid                               \n",
 935 |        "79500US0605500  106581.0  141667.0  \n",
 936 |        "79500US2503303  115710.0  112634.0  \n",
 937 |        "79500US2503304  117844.0  137728.0  \n",
 938 |        "79500US4802308  179311.0  105889.0  \n",
 939 |        "79500US4802504  226857.0  104588.0  \n",
 940 |        "79500US4803501  115420.0  166390.0  \n",
 941 |        "79500US4804625  153925.0  114140.0  "
 942 |       ]
 943 |      },
 944 |      "execution_count": 8,
 945 |      "metadata": {},
 946 |      "output_type": "execute_result"
 947 |     }
 948 |    ],
 949 |    "source": [
 950 |     "race[race['black_chg'].abs() > 0.2][['name','black_07','black_14','black_chg','total_07', 'total_14']]"
 951 |    ]
 952 |   },
 953 |   {
 954 |    "cell_type": "code",
 955 |    "execution_count": 9,
 956 |    "metadata": {},
 957 |    "outputs": [
 958 |     {
 959 |      "data": {
 960 |       "text/html": [
 961 |        "<div>\n",
 962 |        "<table border=\"1\" class=\"dataframe\">\n",
 963 |        "  <thead>\n",
 964 |        "    <tr style=\"text-align: right;\">\n",
 965 |        "      <th></th>\n",
 966 |        "      <th>name</th>\n",
 967 |        "      <th>asian_07</th>\n",
 968 |        "      <th>asian_14</th>\n",
 969 |        "      <th>asian_chg</th>\n",
 970 |        "      <th>total_07</th>\n",
 971 |        "      <th>total_14</th>\n",
 972 |        "    </tr>\n",
 973 |        "    <tr>\n",
 974 |        "      <th>geoid</th>\n",
 975 |        "      <th></th>\n",
 976 |        "      <th></th>\n",
 977 |        "      <th></th>\n",
 978 |        "      <th></th>\n",
 979 |        "      <th></th>\n",
 980 |        "      <th></th>\n",
 981 |        "    </tr>\n",
 982 |        "  </thead>\n",
 983 |        "  <tbody>\n",
 984 |        "    <tr>\n",
 985 |        "      <th>79500US0608101</th>\n",
 986 |        "      <td>PUMA5 08101, California</td>\n",
 987 |        "      <td>0.041138</td>\n",
 988 |        "      <td>0.469799</td>\n",
 989 |        "      <td>0.428660</td>\n",
 990 |        "      <td>240360.0</td>\n",
 991 |        "      <td>149579.0</td>\n",
 992 |        "    </tr>\n",
 993 |        "    <tr>\n",
 994 |        "      <th>79500US0608102</th>\n",
 995 |        "      <td>PUMA5 08102, California</td>\n",
 996 |        "      <td>0.082052</td>\n",
 997 |        "      <td>0.353831</td>\n",
 998 |        "      <td>0.271779</td>\n",
 999 |        "      <td>130844.0</td>\n",
1000 |        "      <td>122683.0</td>\n",
1001 |        "    </tr>\n",
1002 |        "    <tr>\n",
1003 |        "      <th>79500US0608103</th>\n",
1004 |        "      <td>PUMA5 08103, California</td>\n",
1005 |        "      <td>0.011072</td>\n",
1006 |        "      <td>0.260809</td>\n",
1007 |        "      <td>0.249737</td>\n",
1008 |        "      <td>148930.0</td>\n",
1009 |        "      <td>104728.0</td>\n",
1010 |        "    </tr>\n",
1011 |        "    <tr>\n",
1012 |        "      <th>79500US2701303</th>\n",
1013 |        "      <td>PUMA5 01303, Minnesota</td>\n",
1014 |        "      <td>0.030409</td>\n",
1015 |        "      <td>0.241126</td>\n",
1016 |        "      <td>0.210717</td>\n",
1017 |        "      <td>115230.0</td>\n",
1018 |        "      <td>135506.0</td>\n",
1019 |        "    </tr>\n",
1020 |        "  </tbody>\n",
1021 |        "</table>\n",
1022 |        "</div>"
1023 |       ],
1024 |       "text/plain": [
1025 |        "                                   name  asian_07  asian_14  asian_chg  \\\n",
1026 |        "geoid                                                                    \n",
1027 |        "79500US0608101  PUMA5 08101, California  0.041138  0.469799   0.428660   \n",
1028 |        "79500US0608102  PUMA5 08102, California  0.082052  0.353831   0.271779   \n",
1029 |        "79500US0608103  PUMA5 08103, California  0.011072  0.260809   0.249737   \n",
1030 |        "79500US2701303   PUMA5 01303, Minnesota  0.030409  0.241126   0.210717   \n",
1031 |        "\n",
1032 |        "                total_07  total_14  \n",
1033 |        "geoid                               \n",
1034 |        "79500US0608101  240360.0  149579.0  \n",
1035 |        "79500US0608102  130844.0  122683.0  \n",
1036 |        "79500US0608103  148930.0  104728.0  \n",
1037 |        "79500US2701303  115230.0  135506.0  "
1038 |       ]
1039 |      },
1040 |      "execution_count": 9,
1041 |      "metadata": {},
1042 |      "output_type": "execute_result"
1043 |     }
1044 |    ],
1045 |    "source": [
1046 |     "race[race['asian_chg'].abs() > 0.2][['name','asian_07','asian_14','asian_chg','total_07', 'total_14']]"
1047 |    ]
1048 |   },
1049 |   {
1050 |    "cell_type": "code",
1051 |    "execution_count": 10,
1052 |    "metadata": {},
1053 |    "outputs": [
1054 |     {
1055 |      "data": {
1056 |       "text/html": [
1057 |        "<div>\n",
1058 |        "<table border=\"1\" class=\"dataframe\">\n",
1059 |        "  <thead>\n",
1060 |        "    <tr style=\"text-align: right;\">\n",
1061 |        "      <th></th>\n",
1062 |        "      <th>name</th>\n",
1063 |        "      <th>hisp_07</th>\n",
1064 |        "      <th>hisp_14</th>\n",
1065 |        "      <th>hisp_chg</th>\n",
1066 |        "      <th>total_07</th>\n",
1067 |        "      <th>total_14</th>\n",
1068 |        "    </tr>\n",
1069 |        "    <tr>\n",
1070 |        "      <th>geoid</th>\n",
1071 |        "      <th></th>\n",
1072 |        "      <th></th>\n",
1073 |        "      <th></th>\n",
1074 |        "      <th></th>\n",
1075 |        "      <th></th>\n",
1076 |        "      <th></th>\n",
1077 |        "    </tr>\n",
1078 |        "  </thead>\n",
1079 |        "  <tbody>\n",
1080 |        "    <tr>\n",
1081 |        "      <th>79500US0400103</th>\n",
1082 |        "      <td>PUMA5 00103, Arizona</td>\n",
1083 |        "      <td>0.186270</td>\n",
1084 |        "      <td>0.479699</td>\n",
1085 |        "      <td>0.293429</td>\n",
1086 |        "      <td>239088.0</td>\n",
1087 |        "      <td>112433.0</td>\n",
1088 |        "    </tr>\n",
1089 |        "    <tr>\n",
1090 |        "      <th>79500US0400109</th>\n",
1091 |        "      <td>PUMA5 00109, Arizona</td>\n",
1092 |        "      <td>0.512736</td>\n",
1093 |        "      <td>0.197885</td>\n",
1094 |        "      <td>-0.314852</td>\n",
1095 |        "      <td>174813.0</td>\n",
1096 |        "      <td>111939.0</td>\n",
1097 |        "    </tr>\n",
1098 |        "    <tr>\n",
1099 |        "      <th>79500US0400115</th>\n",
1100 |        "      <td>PUMA5 00115, Arizona</td>\n",
1101 |        "      <td>0.461815</td>\n",
1102 |        "      <td>0.245164</td>\n",
1103 |        "      <td>-0.216651</td>\n",
1104 |        "      <td>161229.0</td>\n",
1105 |        "      <td>104583.0</td>\n",
1106 |        "    </tr>\n",
1107 |        "    <tr>\n",
1108 |        "      <th>79500US0400117</th>\n",
1109 |        "      <td>PUMA5 00117, Arizona</td>\n",
1110 |        "      <td>0.665978</td>\n",
1111 |        "      <td>0.368033</td>\n",
1112 |        "      <td>-0.297946</td>\n",
1113 |        "      <td>153493.0</td>\n",
1114 |        "      <td>108417.0</td>\n",
1115 |        "    </tr>\n",
1116 |        "    <tr>\n",
1117 |        "      <th>79500US0400118</th>\n",
1118 |        "      <td>PUMA5 00118, Arizona</td>\n",
1119 |        "      <td>0.788194</td>\n",
1120 |        "      <td>0.541578</td>\n",
1121 |        "      <td>-0.246616</td>\n",
1122 |        "      <td>175321.0</td>\n",
1123 |        "      <td>114495.0</td>\n",
1124 |        "    </tr>\n",
1125 |        "    <tr>\n",
1126 |        "      <th>79500US0400119</th>\n",
1127 |        "      <td>PUMA5 00119, Arizona</td>\n",
1128 |        "      <td>0.274150</td>\n",
1129 |        "      <td>0.658849</td>\n",
1130 |        "      <td>0.384699</td>\n",
1131 |        "      <td>148404.0</td>\n",
1132 |        "      <td>116957.0</td>\n",
1133 |        "    </tr>\n",
1134 |        "    <tr>\n",
1135 |        "      <th>79500US0400121</th>\n",
1136 |        "      <td>PUMA5 00121, Arizona</td>\n",
1137 |        "      <td>0.171333</td>\n",
1138 |        "      <td>0.667758</td>\n",
1139 |        "      <td>0.496425</td>\n",
1140 |        "      <td>247273.0</td>\n",
1141 |        "      <td>143892.0</td>\n",
1142 |        "    </tr>\n",
1143 |        "    <tr>\n",
1144 |        "      <th>79500US0400122</th>\n",
1145 |        "      <td>PUMA5 00122, Arizona</td>\n",
1146 |        "      <td>0.165037</td>\n",
1147 |        "      <td>0.773025</td>\n",
1148 |        "      <td>0.607988</td>\n",
1149 |        "      <td>139399.0</td>\n",
1150 |        "      <td>94724.0</td>\n",
1151 |        "    </tr>\n",
1152 |        "    <tr>\n",
1153 |        "      <th>79500US0601301</th>\n",
1154 |        "      <td>PUMA5 01301, California</td>\n",
1155 |        "      <td>0.178530</td>\n",
1156 |        "      <td>0.493466</td>\n",
1157 |        "      <td>0.314936</td>\n",
1158 |        "      <td>135266.0</td>\n",
1159 |        "      <td>123269.0</td>\n",
1160 |        "    </tr>\n",
1161 |        "    <tr>\n",
1162 |        "      <th>79500US0601901</th>\n",
1163 |        "      <td>PUMA5 01901, California</td>\n",
1164 |        "      <td>0.509523</td>\n",
1165 |        "      <td>0.777063</td>\n",
1166 |        "      <td>0.267541</td>\n",
1167 |        "      <td>126485.0</td>\n",
1168 |        "      <td>155246.0</td>\n",
1169 |        "    </tr>\n",
1170 |        "    <tr>\n",
1171 |        "      <th>79500US0602500</th>\n",
1172 |        "      <td>PUMA5 02500, California</td>\n",
1173 |        "      <td>0.361858</td>\n",
1174 |        "      <td>0.822755</td>\n",
1175 |        "      <td>0.460897</td>\n",
1176 |        "      <td>198473.0</td>\n",
1177 |        "      <td>179091.0</td>\n",
1178 |        "    </tr>\n",
1179 |        "    <tr>\n",
1180 |        "      <th>79500US0606701</th>\n",
1181 |        "      <td>PUMA5 06701, California</td>\n",
1182 |        "      <td>0.506864</td>\n",
1183 |        "      <td>0.158718</td>\n",
1184 |        "      <td>-0.348146</td>\n",
1185 |        "      <td>205935.0</td>\n",
1186 |        "      <td>110101.0</td>\n",
1187 |        "    </tr>\n",
1188 |        "    <tr>\n",
1189 |        "      <th>79500US0608102</th>\n",
1190 |        "      <td>PUMA5 08102, California</td>\n",
1191 |        "      <td>0.666756</td>\n",
1192 |        "      <td>0.327511</td>\n",
1193 |        "      <td>-0.339245</td>\n",
1194 |        "      <td>130844.0</td>\n",
1195 |        "      <td>122683.0</td>\n",
1196 |        "    </tr>\n",
1197 |        "    <tr>\n",
1198 |        "      <th>79500US0608104</th>\n",
1199 |        "      <td>PUMA5 08104, California</td>\n",
1200 |        "      <td>0.542739</td>\n",
1201 |        "      <td>0.168271</td>\n",
1202 |        "      <td>-0.374467</td>\n",
1203 |        "      <td>232493.0</td>\n",
1204 |        "      <td>126807.0</td>\n",
1205 |        "    </tr>\n",
1206 |        "    <tr>\n",
1207 |        "      <th>79500US0608105</th>\n",
1208 |        "      <td>PUMA5 08105, California</td>\n",
1209 |        "      <td>0.537985</td>\n",
1210 |        "      <td>0.253777</td>\n",
1211 |        "      <td>-0.284208</td>\n",
1212 |        "      <td>204539.0</td>\n",
1213 |        "      <td>136269.0</td>\n",
1214 |        "    </tr>\n",
1215 |        "    <tr>\n",
1216 |        "      <th>79500US3500200</th>\n",
1217 |        "      <td>PUMA5 00200, New Mexico</td>\n",
1218 |        "      <td>0.696722</td>\n",
1219 |        "      <td>0.243803</td>\n",
1220 |        "      <td>-0.452918</td>\n",
1221 |        "      <td>109167.0</td>\n",
1222 |        "      <td>98760.0</td>\n",
1223 |        "    </tr>\n",
1224 |        "    <tr>\n",
1225 |        "      <th>79500US3500300</th>\n",
1226 |        "      <td>PUMA5 00300, New Mexico</td>\n",
1227 |        "      <td>0.360262</td>\n",
1228 |        "      <td>0.622404</td>\n",
1229 |        "      <td>0.262142</td>\n",
1230 |        "      <td>115416.0</td>\n",
1231 |        "      <td>123068.0</td>\n",
1232 |        "    </tr>\n",
1233 |        "    <tr>\n",
1234 |        "      <th>79500US3603102</th>\n",
1235 |        "      <td>PUMA5 03102, New York</td>\n",
1236 |        "      <td>0.068151</td>\n",
1237 |        "      <td>0.277975</td>\n",
1238 |        "      <td>0.209823</td>\n",
1239 |        "      <td>156857.0</td>\n",
1240 |        "      <td>149618.0</td>\n",
1241 |        "    </tr>\n",
1242 |        "    <tr>\n",
1243 |        "      <th>79500US4802502</th>\n",
1244 |        "      <td>PUMA5 02502, Texas</td>\n",
1245 |        "      <td>0.484273</td>\n",
1246 |        "      <td>0.187739</td>\n",
1247 |        "      <td>-0.296534</td>\n",
1248 |        "      <td>116455.0</td>\n",
1249 |        "      <td>127022.0</td>\n",
1250 |        "    </tr>\n",
1251 |        "    <tr>\n",
1252 |        "      <th>79500US4803200</th>\n",
1253 |        "      <td>PUMA5 03200, Texas</td>\n",
1254 |        "      <td>0.356576</td>\n",
1255 |        "      <td>0.581805</td>\n",
1256 |        "      <td>0.225230</td>\n",
1257 |        "      <td>126408.0</td>\n",
1258 |        "      <td>114956.0</td>\n",
1259 |        "    </tr>\n",
1260 |        "    <tr>\n",
1261 |        "      <th>79500US4803400</th>\n",
1262 |        "      <td>PUMA5 03400, Texas</td>\n",
1263 |        "      <td>0.553636</td>\n",
1264 |        "      <td>0.179002</td>\n",
1265 |        "      <td>-0.374634</td>\n",
1266 |        "      <td>95160.0</td>\n",
1267 |        "      <td>179294.0</td>\n",
1268 |        "    </tr>\n",
1269 |        "    <tr>\n",
1270 |        "      <th>79500US4803501</th>\n",
1271 |        "      <td>PUMA5 03501, Texas</td>\n",
1272 |        "      <td>0.659825</td>\n",
1273 |        "      <td>0.229695</td>\n",
1274 |        "      <td>-0.430130</td>\n",
1275 |        "      <td>115420.0</td>\n",
1276 |        "      <td>166390.0</td>\n",
1277 |        "    </tr>\n",
1278 |        "  </tbody>\n",
1279 |        "</table>\n",
1280 |        "</div>"
1281 |       ],
1282 |       "text/plain": [
1283 |        "                                   name   hisp_07   hisp_14  hisp_chg  \\\n",
1284 |        "geoid                                                                   \n",
1285 |        "79500US0400103     PUMA5 00103, Arizona  0.186270  0.479699  0.293429   \n",
1286 |        "79500US0400109     PUMA5 00109, Arizona  0.512736  0.197885 -0.314852   \n",
1287 |        "79500US0400115     PUMA5 00115, Arizona  0.461815  0.245164 -0.216651   \n",
1288 |        "79500US0400117     PUMA5 00117, Arizona  0.665978  0.368033 -0.297946   \n",
1289 |        "79500US0400118     PUMA5 00118, Arizona  0.788194  0.541578 -0.246616   \n",
1290 |        "79500US0400119     PUMA5 00119, Arizona  0.274150  0.658849  0.384699   \n",
1291 |        "79500US0400121     PUMA5 00121, Arizona  0.171333  0.667758  0.496425   \n",
1292 |        "79500US0400122     PUMA5 00122, Arizona  0.165037  0.773025  0.607988   \n",
1293 |        "79500US0601301  PUMA5 01301, California  0.178530  0.493466  0.314936   \n",
1294 |        "79500US0601901  PUMA5 01901, California  0.509523  0.777063  0.267541   \n",
1295 |        "79500US0602500  PUMA5 02500, California  0.361858  0.822755  0.460897   \n",
1296 |        "79500US0606701  PUMA5 06701, California  0.506864  0.158718 -0.348146   \n",
1297 |        "79500US0608102  PUMA5 08102, California  0.666756  0.327511 -0.339245   \n",
1298 |        "79500US0608104  PUMA5 08104, California  0.542739  0.168271 -0.374467   \n",
1299 |        "79500US0608105  PUMA5 08105, California  0.537985  0.253777 -0.284208   \n",
1300 |        "79500US3500200  PUMA5 00200, New Mexico  0.696722  0.243803 -0.452918   \n",
1301 |        "79500US3500300  PUMA5 00300, New Mexico  0.360262  0.622404  0.262142   \n",
1302 |        "79500US3603102    PUMA5 03102, New York  0.068151  0.277975  0.209823   \n",
1303 |        "79500US4802502       PUMA5 02502, Texas  0.484273  0.187739 -0.296534   \n",
1304 |        "79500US4803200       PUMA5 03200, Texas  0.356576  0.581805  0.225230   \n",
1305 |        "79500US4803400       PUMA5 03400, Texas  0.553636  0.179002 -0.374634   \n",
1306 |        "79500US4803501       PUMA5 03501, Texas  0.659825  0.229695 -0.430130   \n",
1307 |        "\n",
1308 |        "                total_07  total_14  \n",
1309 |        "geoid                               \n",
1310 |        "79500US0400103  239088.0  112433.0  \n",
1311 |        "79500US0400109  174813.0  111939.0  \n",
1312 |        "79500US0400115  161229.0  104583.0  \n",
1313 |        "79500US0400117  153493.0  108417.0  \n",
1314 |        "79500US0400118  175321.0  114495.0  \n",
1315 |        "79500US0400119  148404.0  116957.0  \n",
1316 |        "79500US0400121  247273.0  143892.0  \n",
1317 |        "79500US0400122  139399.0   94724.0  \n",
1318 |        "79500US0601301  135266.0  123269.0  \n",
1319 |        "79500US0601901  126485.0  155246.0  \n",
1320 |        "79500US0602500  198473.0  179091.0  \n",
1321 |        "79500US0606701  205935.0  110101.0  \n",
1322 |        "79500US0608102  130844.0  122683.0  \n",
1323 |        "79500US0608104  232493.0  126807.0  \n",
1324 |        "79500US0608105  204539.0  136269.0  \n",
1325 |        "79500US3500200  109167.0   98760.0  \n",
1326 |        "79500US3500300  115416.0  123068.0  \n",
1327 |        "79500US3603102  156857.0  149618.0  \n",
1328 |        "79500US4802502  116455.0  127022.0  \n",
1329 |        "79500US4803200  126408.0  114956.0  \n",
1330 |        "79500US4803400   95160.0  179294.0  \n",
1331 |        "79500US4803501  115420.0  166390.0  "
1332 |       ]
1333 |      },
1334 |      "execution_count": 10,
1335 |      "metadata": {},
1336 |      "output_type": "execute_result"
1337 |     }
1338 |    ],
1339 |    "source": [
1340 |     "race[race['hisp_chg'].abs() > 0.2][['name','hisp_07','hisp_14','hisp_chg','total_07', 'total_14']]"
1341 |    ]
1342 |   },
1343 |   {
1344 |    "cell_type": "code",
1345 |    "execution_count": 11,
1346 |    "metadata": {
1347 |     "scrolled": false
1348 |    },
1349 |    "outputs": [
1350 |     {
1351 |      "data": {
1352 |       "text/html": [
1353 |        "<div>\n",
1354 |        "<table border=\"1\" class=\"dataframe\">\n",
1355 |        "  <thead>\n",
1356 |        "    <tr style=\"text-align: right;\">\n",
1357 |        "      <th></th>\n",
1358 |        "      <th>name</th>\n",
1359 |        "      <th>white_07</th>\n",
1360 |        "      <th>white_14</th>\n",
1361 |        "      <th>white_chg</th>\n",
1362 |        "      <th>total_07</th>\n",
1363 |        "      <th>total_14</th>\n",
1364 |        "    </tr>\n",
1365 |        "    <tr>\n",
1366 |        "      <th>geoid</th>\n",
1367 |        "      <th></th>\n",
1368 |        "      <th></th>\n",
1369 |        "      <th></th>\n",
1370 |        "      <th></th>\n",
1371 |        "      <th></th>\n",
1372 |        "      <th></th>\n",
1373 |        "    </tr>\n",
1374 |        "  </thead>\n",
1375 |        "  <tbody>\n",
1376 |        "    <tr>\n",
1377 |        "      <th>16000US4847892</th>\n",
1378 |        "      <td>Mesquite city, Texas</td>\n",
1379 |        "      <td>0.528739</td>\n",
1380 |        "      <td>0.307210</td>\n",
1381 |        "      <td>-0.221530</td>\n",
1382 |        "      <td>126081.0</td>\n",
1383 |        "      <td>144289.0</td>\n",
1384 |        "    </tr>\n",
1385 |        "    <tr>\n",
1386 |        "      <th>31400US3798037964</th>\n",
1387 |        "      <td>Philadelphia, PA Metropolitan Division, Philad...</td>\n",
1388 |        "      <td>0.662279</td>\n",
1389 |        "      <td>0.443912</td>\n",
1390 |        "      <td>-0.218367</td>\n",
1391 |        "      <td>3887694.0</td>\n",
1392 |        "      <td>2123257.0</td>\n",
1393 |        "    </tr>\n",
1394 |        "    <tr>\n",
1395 |        "      <th>79500US0400103</th>\n",
1396 |        "      <td>PUMA5 00103, Arizona</td>\n",
1397 |        "      <td>0.728518</td>\n",
1398 |        "      <td>0.406740</td>\n",
1399 |        "      <td>-0.321778</td>\n",
1400 |        "      <td>239088.0</td>\n",
1401 |        "      <td>112433.0</td>\n",
1402 |        "    </tr>\n",
1403 |        "    <tr>\n",
1404 |        "      <th>79500US0400106</th>\n",
1405 |        "      <td>PUMA5 00106, Arizona</td>\n",
1406 |        "      <td>0.755835</td>\n",
1407 |        "      <td>0.486296</td>\n",
1408 |        "      <td>-0.269539</td>\n",
1409 |        "      <td>95112.0</td>\n",
1410 |        "      <td>123398.0</td>\n",
1411 |        "    </tr>\n",
1412 |        "    <tr>\n",
1413 |        "      <th>79500US0400108</th>\n",
1414 |        "      <td>PUMA5 00108, Arizona</td>\n",
1415 |        "      <td>0.755408</td>\n",
1416 |        "      <td>0.552054</td>\n",
1417 |        "      <td>-0.203354</td>\n",
1418 |        "      <td>120850.0</td>\n",
1419 |        "      <td>105593.0</td>\n",
1420 |        "    </tr>\n",
1421 |        "    <tr>\n",
1422 |        "      <th>79500US0400109</th>\n",
1423 |        "      <td>PUMA5 00109, Arizona</td>\n",
1424 |        "      <td>0.354030</td>\n",
1425 |        "      <td>0.621428</td>\n",
1426 |        "      <td>0.267398</td>\n",
1427 |        "      <td>174813.0</td>\n",
1428 |        "      <td>111939.0</td>\n",
1429 |        "    </tr>\n",
1430 |        "    <tr>\n",
1431 |        "      <th>79500US0400115</th>\n",
1432 |        "      <td>PUMA5 00115, Arizona</td>\n",
1433 |        "      <td>0.433080</td>\n",
1434 |        "      <td>0.652907</td>\n",
1435 |        "      <td>0.219828</td>\n",
1436 |        "      <td>161229.0</td>\n",
1437 |        "      <td>104583.0</td>\n",
1438 |        "    </tr>\n",
1439 |        "    <tr>\n",
1440 |        "      <th>79500US0400117</th>\n",
1441 |        "      <td>PUMA5 00117, Arizona</td>\n",
1442 |        "      <td>0.178125</td>\n",
1443 |        "      <td>0.520038</td>\n",
1444 |        "      <td>0.341913</td>\n",
1445 |        "      <td>153493.0</td>\n",
1446 |        "      <td>108417.0</td>\n",
1447 |        "    </tr>\n",
1448 |        "    <tr>\n",
1449 |        "      <th>79500US0400119</th>\n",
1450 |        "      <td>PUMA5 00119, Arizona</td>\n",
1451 |        "      <td>0.547667</td>\n",
1452 |        "      <td>0.133314</td>\n",
1453 |        "      <td>-0.414353</td>\n",
1454 |        "      <td>148404.0</td>\n",
1455 |        "      <td>116957.0</td>\n",
1456 |        "    </tr>\n",
1457 |        "    <tr>\n",
1458 |        "      <th>79500US0400121</th>\n",
1459 |        "      <td>PUMA5 00121, Arizona</td>\n",
1460 |        "      <td>0.739846</td>\n",
1461 |        "      <td>0.157160</td>\n",
1462 |        "      <td>-0.582687</td>\n",
1463 |        "      <td>247273.0</td>\n",
1464 |        "      <td>143892.0</td>\n",
1465 |        "    </tr>\n",
1466 |        "    <tr>\n",
1467 |        "      <th>79500US0400122</th>\n",
1468 |        "      <td>PUMA5 00122, Arizona</td>\n",
1469 |        "      <td>0.764159</td>\n",
1470 |        "      <td>0.114944</td>\n",
1471 |        "      <td>-0.649215</td>\n",
1472 |        "      <td>139399.0</td>\n",
1473 |        "      <td>94724.0</td>\n",
1474 |        "    </tr>\n",
1475 |        "    <tr>\n",
1476 |        "      <th>79500US0601301</th>\n",
1477 |        "      <td>PUMA5 01301, California</td>\n",
1478 |        "      <td>0.386017</td>\n",
1479 |        "      <td>0.121231</td>\n",
1480 |        "      <td>-0.264786</td>\n",
1481 |        "      <td>135266.0</td>\n",
1482 |        "      <td>123269.0</td>\n",
1483 |        "    </tr>\n",
1484 |        "    <tr>\n",
1485 |        "      <th>79500US0602500</th>\n",
1486 |        "      <td>PUMA5 02500, California</td>\n",
1487 |        "      <td>0.515743</td>\n",
1488 |        "      <td>0.122027</td>\n",
1489 |        "      <td>-0.393715</td>\n",
1490 |        "      <td>198473.0</td>\n",
1491 |        "      <td>179091.0</td>\n",
1492 |        "    </tr>\n",
1493 |        "    <tr>\n",
1494 |        "      <th>79500US0603701</th>\n",
1495 |        "      <td>PUMA5 03701, California</td>\n",
1496 |        "      <td>0.758281</td>\n",
1497 |        "      <td>0.508031</td>\n",
1498 |        "      <td>-0.250249</td>\n",
1499 |        "      <td>131781.0</td>\n",
1500 |        "      <td>134472.0</td>\n",
1501 |        "    </tr>\n",
1502 |        "    <tr>\n",
1503 |        "      <th>79500US0603702</th>\n",
1504 |        "      <td>PUMA5 03702, California</td>\n",
1505 |        "      <td>0.708622</td>\n",
1506 |        "      <td>0.440702</td>\n",
1507 |        "      <td>-0.267920</td>\n",
1508 |        "      <td>130655.0</td>\n",
1509 |        "      <td>201617.0</td>\n",
1510 |        "    </tr>\n",
1511 |        "    <tr>\n",
1512 |        "      <th>79500US0605500</th>\n",
1513 |        "      <td>PUMA5 05500, California</td>\n",
1514 |        "      <td>0.024310</td>\n",
1515 |        "      <td>0.536575</td>\n",
1516 |        "      <td>0.512265</td>\n",
1517 |        "      <td>106581.0</td>\n",
1518 |        "      <td>141667.0</td>\n",
1519 |        "    </tr>\n",
1520 |        "    <tr>\n",
1521 |        "      <th>79500US0606101</th>\n",
1522 |        "      <td>PUMA5 06101, California</td>\n",
1523 |        "      <td>0.504492</td>\n",
1524 |        "      <td>0.709097</td>\n",
1525 |        "      <td>0.204605</td>\n",
1526 |        "      <td>170510.0</td>\n",
1527 |        "      <td>133835.0</td>\n",
1528 |        "    </tr>\n",
1529 |        "    <tr>\n",
1530 |        "      <th>79500US0606102</th>\n",
1531 |        "      <td>PUMA5 06102, California</td>\n",
1532 |        "      <td>0.510391</td>\n",
1533 |        "      <td>0.735933</td>\n",
1534 |        "      <td>0.225542</td>\n",
1535 |        "      <td>140990.0</td>\n",
1536 |        "      <td>115959.0</td>\n",
1537 |        "    </tr>\n",
1538 |        "    <tr>\n",
1539 |        "      <th>79500US0606701</th>\n",
1540 |        "      <td>PUMA5 06701, California</td>\n",
1541 |        "      <td>0.410061</td>\n",
1542 |        "      <td>0.751601</td>\n",
1543 |        "      <td>0.341539</td>\n",
1544 |        "      <td>205935.0</td>\n",
1545 |        "      <td>110101.0</td>\n",
1546 |        "    </tr>\n",
1547 |        "    <tr>\n",
1548 |        "      <th>79500US0608101</th>\n",
1549 |        "      <td>PUMA5 08101, California</td>\n",
1550 |        "      <td>0.599488</td>\n",
1551 |        "      <td>0.233763</td>\n",
1552 |        "      <td>-0.365726</td>\n",
1553 |        "      <td>240360.0</td>\n",
1554 |        "      <td>149579.0</td>\n",
1555 |        "    </tr>\n",
1556 |        "    <tr>\n",
1557 |        "      <th>79500US0608103</th>\n",
1558 |        "      <td>PUMA5 08103, California</td>\n",
1559 |        "      <td>0.753804</td>\n",
1560 |        "      <td>0.495312</td>\n",
1561 |        "      <td>-0.258492</td>\n",
1562 |        "      <td>148930.0</td>\n",
1563 |        "      <td>104728.0</td>\n",
1564 |        "    </tr>\n",
1565 |        "    <tr>\n",
1566 |        "      <th>79500US0608104</th>\n",
1567 |        "      <td>PUMA5 08104, California</td>\n",
1568 |        "      <td>0.215766</td>\n",
1569 |        "      <td>0.516951</td>\n",
1570 |        "      <td>0.301185</td>\n",
1571 |        "      <td>232493.0</td>\n",
1572 |        "      <td>126807.0</td>\n",
1573 |        "    </tr>\n",
1574 |        "    <tr>\n",
1575 |        "      <th>79500US0608105</th>\n",
1576 |        "      <td>PUMA5 08105, California</td>\n",
1577 |        "      <td>0.116667</td>\n",
1578 |        "      <td>0.528668</td>\n",
1579 |        "      <td>0.412000</td>\n",
1580 |        "      <td>204539.0</td>\n",
1581 |        "      <td>136269.0</td>\n",
1582 |        "    </tr>\n",
1583 |        "    <tr>\n",
1584 |        "      <th>79500US2503303</th>\n",
1585 |        "      <td>PUMA5 03303, Massachusetts</td>\n",
1586 |        "      <td>0.089206</td>\n",
1587 |        "      <td>0.481267</td>\n",
1588 |        "      <td>0.392061</td>\n",
1589 |        "      <td>115710.0</td>\n",
1590 |        "      <td>112634.0</td>\n",
1591 |        "    </tr>\n",
1592 |        "    <tr>\n",
1593 |        "      <th>79500US2503304</th>\n",
1594 |        "      <td>PUMA5 03304, Massachusetts</td>\n",
1595 |        "      <td>0.539569</td>\n",
1596 |        "      <td>0.115997</td>\n",
1597 |        "      <td>-0.423573</td>\n",
1598 |        "      <td>117844.0</td>\n",
1599 |        "      <td>137728.0</td>\n",
1600 |        "    </tr>\n",
1601 |        "    <tr>\n",
1602 |        "      <th>79500US3402301</th>\n",
1603 |        "      <td>PUMA5 02301, New Jersey</td>\n",
1604 |        "      <td>0.511410</td>\n",
1605 |        "      <td>0.258935</td>\n",
1606 |        "      <td>-0.252474</td>\n",
1607 |        "      <td>200753.0</td>\n",
1608 |        "      <td>120590.0</td>\n",
1609 |        "    </tr>\n",
1610 |        "    <tr>\n",
1611 |        "      <th>79500US3500200</th>\n",
1612 |        "      <td>PUMA5 00200, New Mexico</td>\n",
1613 |        "      <td>0.213663</td>\n",
1614 |        "      <td>0.500233</td>\n",
1615 |        "      <td>0.286569</td>\n",
1616 |        "      <td>109167.0</td>\n",
1617 |        "      <td>98760.0</td>\n",
1618 |        "    </tr>\n",
1619 |        "    <tr>\n",
1620 |        "      <th>79500US3500300</th>\n",
1621 |        "      <td>PUMA5 00300, New Mexico</td>\n",
1622 |        "      <td>0.566291</td>\n",
1623 |        "      <td>0.284282</td>\n",
1624 |        "      <td>-0.282009</td>\n",
1625 |        "      <td>115416.0</td>\n",
1626 |        "      <td>123068.0</td>\n",
1627 |        "    </tr>\n",
1628 |        "    <tr>\n",
1629 |        "      <th>79500US3603102</th>\n",
1630 |        "      <td>PUMA5 03102, New York</td>\n",
1631 |        "      <td>0.841614</td>\n",
1632 |        "      <td>0.553282</td>\n",
1633 |        "      <td>-0.288331</td>\n",
1634 |        "      <td>156857.0</td>\n",
1635 |        "      <td>149618.0</td>\n",
1636 |        "    </tr>\n",
1637 |        "    <tr>\n",
1638 |        "      <th>79500US3700400</th>\n",
1639 |        "      <td>PUMA5 00400, North Carolina</td>\n",
1640 |        "      <td>0.818956</td>\n",
1641 |        "      <td>0.609494</td>\n",
1642 |        "      <td>-0.209462</td>\n",
1643 |        "      <td>223526.0</td>\n",
1644 |        "      <td>120185.0</td>\n",
1645 |        "    </tr>\n",
1646 |        "    <tr>\n",
1647 |        "      <th>79500US4802308</th>\n",
1648 |        "      <td>PUMA5 02308, Texas</td>\n",
1649 |        "      <td>0.157603</td>\n",
1650 |        "      <td>0.412300</td>\n",
1651 |        "      <td>0.254696</td>\n",
1652 |        "      <td>179311.0</td>\n",
1653 |        "      <td>105889.0</td>\n",
1654 |        "    </tr>\n",
1655 |        "    <tr>\n",
1656 |        "      <th>79500US4802313</th>\n",
1657 |        "      <td>PUMA5 02313, Texas</td>\n",
1658 |        "      <td>0.342277</td>\n",
1659 |        "      <td>0.564901</td>\n",
1660 |        "      <td>0.222624</td>\n",
1661 |        "      <td>130403.0</td>\n",
1662 |        "      <td>116870.0</td>\n",
1663 |        "    </tr>\n",
1664 |        "    <tr>\n",
1665 |        "      <th>79500US4802502</th>\n",
1666 |        "      <td>PUMA5 02502, Texas</td>\n",
1667 |        "      <td>0.226250</td>\n",
1668 |        "      <td>0.457684</td>\n",
1669 |        "      <td>0.231434</td>\n",
1670 |        "      <td>116455.0</td>\n",
1671 |        "      <td>127022.0</td>\n",
1672 |        "    </tr>\n",
1673 |        "    <tr>\n",
1674 |        "      <th>79500US4802504</th>\n",
1675 |        "      <td>PUMA5 02504, Texas</td>\n",
1676 |        "      <td>0.525767</td>\n",
1677 |        "      <td>0.309175</td>\n",
1678 |        "      <td>-0.216592</td>\n",
1679 |        "      <td>226857.0</td>\n",
1680 |        "      <td>104588.0</td>\n",
1681 |        "    </tr>\n",
1682 |        "    <tr>\n",
1683 |        "      <th>79500US4802507</th>\n",
1684 |        "      <td>PUMA5 02507, Texas</td>\n",
1685 |        "      <td>0.735555</td>\n",
1686 |        "      <td>0.504637</td>\n",
1687 |        "      <td>-0.230918</td>\n",
1688 |        "      <td>156369.0</td>\n",
1689 |        "      <td>125627.0</td>\n",
1690 |        "    </tr>\n",
1691 |        "    <tr>\n",
1692 |        "      <th>79500US4803400</th>\n",
1693 |        "      <td>PUMA5 03400, Texas</td>\n",
1694 |        "      <td>0.386969</td>\n",
1695 |        "      <td>0.699929</td>\n",
1696 |        "      <td>0.312959</td>\n",
1697 |        "      <td>95160.0</td>\n",
1698 |        "      <td>179294.0</td>\n",
1699 |        "    </tr>\n",
1700 |        "    <tr>\n",
1701 |        "      <th>79500US4805303</th>\n",
1702 |        "      <td>PUMA5 05303, Texas</td>\n",
1703 |        "      <td>0.515004</td>\n",
1704 |        "      <td>0.265493</td>\n",
1705 |        "      <td>-0.249511</td>\n",
1706 |        "      <td>198111.0</td>\n",
1707 |        "      <td>144000.0</td>\n",
1708 |        "    </tr>\n",
1709 |        "    <tr>\n",
1710 |        "      <th>97000US4830390</th>\n",
1711 |        "      <td>MESQUITE INDEPENDENT SCHOOL DISTRICT , Texas</td>\n",
1712 |        "      <td>0.511651</td>\n",
1713 |        "      <td>0.298632</td>\n",
1714 |        "      <td>-0.213019</td>\n",
1715 |        "      <td>155778.0</td>\n",
1716 |        "      <td>172483.0</td>\n",
1717 |        "    </tr>\n",
1718 |        "  </tbody>\n",
1719 |        "</table>\n",
1720 |        "</div>"
1721 |       ],
1722 |       "text/plain": [
1723 |        "                                                                name  \\\n",
1724 |        "geoid                                                                  \n",
1725 |        "16000US4847892                                  Mesquite city, Texas   \n",
1726 |        "31400US3798037964  Philadelphia, PA Metropolitan Division, Philad...   \n",
1727 |        "79500US0400103                                  PUMA5 00103, Arizona   \n",
1728 |        "79500US0400106                                  PUMA5 00106, Arizona   \n",
1729 |        "79500US0400108                                  PUMA5 00108, Arizona   \n",
1730 |        "79500US0400109                                  PUMA5 00109, Arizona   \n",
1731 |        "79500US0400115                                  PUMA5 00115, Arizona   \n",
1732 |        "79500US0400117                                  PUMA5 00117, Arizona   \n",
1733 |        "79500US0400119                                  PUMA5 00119, Arizona   \n",
1734 |        "79500US0400121                                  PUMA5 00121, Arizona   \n",
1735 |        "79500US0400122                                  PUMA5 00122, Arizona   \n",
1736 |        "79500US0601301                               PUMA5 01301, California   \n",
1737 |        "79500US0602500                               PUMA5 02500, California   \n",
1738 |        "79500US0603701                               PUMA5 03701, California   \n",
1739 |        "79500US0603702                               PUMA5 03702, California   \n",
1740 |        "79500US0605500                               PUMA5 05500, California   \n",
1741 |        "79500US0606101                               PUMA5 06101, California   \n",
1742 |        "79500US0606102                               PUMA5 06102, California   \n",
1743 |        "79500US0606701                               PUMA5 06701, California   \n",
1744 |        "79500US0608101                               PUMA5 08101, California   \n",
1745 |        "79500US0608103                               PUMA5 08103, California   \n",
1746 |        "79500US0608104                               PUMA5 08104, California   \n",
1747 |        "79500US0608105                               PUMA5 08105, California   \n",
1748 |        "79500US2503303                            PUMA5 03303, Massachusetts   \n",
1749 |        "79500US2503304                            PUMA5 03304, Massachusetts   \n",
1750 |        "79500US3402301                               PUMA5 02301, New Jersey   \n",
1751 |        "79500US3500200                               PUMA5 00200, New Mexico   \n",
1752 |        "79500US3500300                               PUMA5 00300, New Mexico   \n",
1753 |        "79500US3603102                                 PUMA5 03102, New York   \n",
1754 |        "79500US3700400                           PUMA5 00400, North Carolina   \n",
1755 |        "79500US4802308                                    PUMA5 02308, Texas   \n",
1756 |        "79500US4802313                                    PUMA5 02313, Texas   \n",
1757 |        "79500US4802502                                    PUMA5 02502, Texas   \n",
1758 |        "79500US4802504                                    PUMA5 02504, Texas   \n",
1759 |        "79500US4802507                                    PUMA5 02507, Texas   \n",
1760 |        "79500US4803400                                    PUMA5 03400, Texas   \n",
1761 |        "79500US4805303                                    PUMA5 05303, Texas   \n",
1762 |        "97000US4830390          MESQUITE INDEPENDENT SCHOOL DISTRICT , Texas   \n",
1763 |        "\n",
1764 |        "                   white_07  white_14  white_chg   total_07   total_14  \n",
1765 |        "geoid                                                                   \n",
1766 |        "16000US4847892     0.528739  0.307210  -0.221530   126081.0   144289.0  \n",
1767 |        "31400US3798037964  0.662279  0.443912  -0.218367  3887694.0  2123257.0  \n",
1768 |        "79500US0400103     0.728518  0.406740  -0.321778   239088.0   112433.0  \n",
1769 |        "79500US0400106     0.755835  0.486296  -0.269539    95112.0   123398.0  \n",
1770 |        "79500US0400108     0.755408  0.552054  -0.203354   120850.0   105593.0  \n",
1771 |        "79500US0400109     0.354030  0.621428   0.267398   174813.0   111939.0  \n",
1772 |        "79500US0400115     0.433080  0.652907   0.219828   161229.0   104583.0  \n",
1773 |        "79500US0400117     0.178125  0.520038   0.341913   153493.0   108417.0  \n",
1774 |        "79500US0400119     0.547667  0.133314  -0.414353   148404.0   116957.0  \n",
1775 |        "79500US0400121     0.739846  0.157160  -0.582687   247273.0   143892.0  \n",
1776 |        "79500US0400122     0.764159  0.114944  -0.649215   139399.0    94724.0  \n",
1777 |        "79500US0601301     0.386017  0.121231  -0.264786   135266.0   123269.0  \n",
1778 |        "79500US0602500     0.515743  0.122027  -0.393715   198473.0   179091.0  \n",
1779 |        "79500US0603701     0.758281  0.508031  -0.250249   131781.0   134472.0  \n",
1780 |        "79500US0603702     0.708622  0.440702  -0.267920   130655.0   201617.0  \n",
1781 |        "79500US0605500     0.024310  0.536575   0.512265   106581.0   141667.0  \n",
1782 |        "79500US0606101     0.504492  0.709097   0.204605   170510.0   133835.0  \n",
1783 |        "79500US0606102     0.510391  0.735933   0.225542   140990.0   115959.0  \n",
1784 |        "79500US0606701     0.410061  0.751601   0.341539   205935.0   110101.0  \n",
1785 |        "79500US0608101     0.599488  0.233763  -0.365726   240360.0   149579.0  \n",
1786 |        "79500US0608103     0.753804  0.495312  -0.258492   148930.0   104728.0  \n",
1787 |        "79500US0608104     0.215766  0.516951   0.301185   232493.0   126807.0  \n",
1788 |        "79500US0608105     0.116667  0.528668   0.412000   204539.0   136269.0  \n",
1789 |        "79500US2503303     0.089206  0.481267   0.392061   115710.0   112634.0  \n",
1790 |        "79500US2503304     0.539569  0.115997  -0.423573   117844.0   137728.0  \n",
1791 |        "79500US3402301     0.511410  0.258935  -0.252474   200753.0   120590.0  \n",
1792 |        "79500US3500200     0.213663  0.500233   0.286569   109167.0    98760.0  \n",
1793 |        "79500US3500300     0.566291  0.284282  -0.282009   115416.0   123068.0  \n",
1794 |        "79500US3603102     0.841614  0.553282  -0.288331   156857.0   149618.0  \n",
1795 |        "79500US3700400     0.818956  0.609494  -0.209462   223526.0   120185.0  \n",
1796 |        "79500US4802308     0.157603  0.412300   0.254696   179311.0   105889.0  \n",
1797 |        "79500US4802313     0.342277  0.564901   0.222624   130403.0   116870.0  \n",
1798 |        "79500US4802502     0.226250  0.457684   0.231434   116455.0   127022.0  \n",
1799 |        "79500US4802504     0.525767  0.309175  -0.216592   226857.0   104588.0  \n",
1800 |        "79500US4802507     0.735555  0.504637  -0.230918   156369.0   125627.0  \n",
1801 |        "79500US4803400     0.386969  0.699929   0.312959    95160.0   179294.0  \n",
1802 |        "79500US4805303     0.515004  0.265493  -0.249511   198111.0   144000.0  \n",
1803 |        "97000US4830390     0.511651  0.298632  -0.213019   155778.0   172483.0  "
1804 |       ]
1805 |      },
1806 |      "execution_count": 11,
1807 |      "metadata": {},
1808 |      "output_type": "execute_result"
1809 |     }
1810 |    ],
1811 |    "source": [
1812 |     "race[race['white_chg'].abs() > 0.2][['name','white_07','white_14','white_chg','total_07', 'total_14']]"
1813 |    ]
1814 |   }
1815 |  ],
1816 |  "metadata": {
1817 |   "kernelspec": {
1818 |    "display_name": "Python 3",
1819 |    "language": "python",
1820 |    "name": "python3"
1821 |   },
1822 |   "language_info": {
1823 |    "codemirror_mode": {
1824 |     "name": "ipython",
1825 |     "version": 3
1826 |    },
1827 |    "file_extension": ".py",
1828 |    "mimetype": "text/x-python",
1829 |    "name": "python",
1830 |    "nbconvert_exporter": "python",
1831 |    "pygments_lexer": "ipython3",
1832 |    "version": "3.4.3"
1833 |   }
1834 |  },
1835 |  "nbformat": 4,
1836 |  "nbformat_minor": 1
1837 | }
1838 | 


--------------------------------------------------------------------------------