Big race changes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# X-shaped race graphs\n",
    "\n",
    "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",
    "\n",
    "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",
    "\n",
    "## To begin...\n",
    "\n",
    "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",
    "\n",
    "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",
    "\n",
    "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",
    "\n",
    "This assumes you created a database named `census` already.\n",
    "\n",
    "## Download the data\n",
    "\n",
    "\n",
    "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",
    "\n",
    "To download the data, do something like this:\n",
    "\n",
    "```bash\n",
    "curl -O http://census-backup.s3.amazonaws.com/acs/2007/acs2007_1yr/acs2007_1yr_backup.sql.gz\n",
    "curl -O http://census-backup.s3.amazonaws.com/acs/2014/acs2014_1yr/acs2014_1yr_backup.sql.gz    \n",
    "```\n",
    "\n",
    "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",
    "\n",
    "```bash\n",
    "gzcat acs2007_1yr_backup.sql.gz | psql census\n",
    "gzcat acs2014_1yr_backup.sql.gz | psql census\n",
    "```\n",
    "\n",
    "These take a few minutes, so be patient, and probably best to paste the lines one-at-a-time.\n",
    "\n",
    "But now we're ready to code. \n",
    "\n",
    "### connect to the database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from psycopg2 import connect\n",
    "con = connect(database='census')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### execute queries\n",
    "\n",
    "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",
    "\n",
    "\n",
    "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",
    "\n",
    "Here's how to read in that table from the two different Census years:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We leave out summary level 500 -- congressional districts -- because they changed considerably between\n",
    "# 2007 and 2014.\n",
    "sql = \"\"\"\n",
    "select g.name, d.* \n",
    "from {schema}.geoheader g, {schema}.b03002 d\n",
    "where g.geoid = d.geoid\n",
    "and g.sumlevel != 500\n",
    "\"\"\"\n",
    "\n",
    "b03002_14 = pd.read_sql_query(sql.format(schema='acs2014_1yr'), con, index_col='geoid')\n",
    "b03002_07 = pd.read_sql_query(sql.format(schema='acs2007_1yr'), con, index_col='geoid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### simplify the data\n",
    "\n",
    "When talking about race in the United States, the set of \"options\" is often boiled down to:\n",
    "\n",
    "* White\n",
    "* Black\n",
    "* Asian\n",
    "* Hispanic\n",
    "\n",
    "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",
    "\n",
    "<ul>\n",
    "    <li><em>b03002001:</em> Total:<ul>\n",
    "        <li><em>b03002002:</em> Not Hispanic or Latino:<ul>\n",
    "            <li><em>b03002003:</em> White alone</li>\n",
    "            <li><em>b03002004:</em> Black or African American alone</li>\n",
    "            <li><em>b03002005:</em> American Indian and Alaska Native alone</li>\n",
    "            <li><em>b03002006:</em> Asian alone</li>\n",
    "            <li><em>b03002007:</em> Native Hawaiian and Other Pacific Islander alone</li>\n",
    "            <li><em>b03002008:</em> Some other race alone</li>\n",
    "            <li><em>b03002009:</em> Two or more races:<ul>\n",
    "                <li><em>b03002010:</em> Two races including Some other race</li>\n",
    "                <li><em>b03002011:</em> Two races excluding Some other race, and three or more races</li>\n",
    "            </ul></li>\n",
    "        </ul></li>\n",
    "        <li><em>b03002012:</em> Hispanic or Latino:<ul>\n",
    "            <li><em>b03002013:</em> White alone</li>\n",
    "            <li><em>b03002014:</em> Black or African American alone</li>\n",
    "            <li><em>b03002015:</em> American Indian and Alaska Native alone</li>\n",
    "            <li><em>b03002016:</em> Asian alone</li>\n",
    "            <li><em>b03002017:</em> Native Hawaiian and Other Pacific Islander alone</li>\n",
    "            <li><em>b03002018:</em> Some other race alone</li>\n",
    "            <li><em>b03002019:</em> Two or more races:<ul>\n",
    "                <li><em>b03002020:</em> Two races including Some other race</li>\n",
    "                <li><em>b03002021:</em> Two races excluding Some other race, and three or more races</li>\n",
    "            </ul></li>\n",
    "        </ul></li>\n",
    "    </ul></li>\n",
    "</ul>\n",
    "\n",
    "(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",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def simplifier(b03002,percentify=True):\n",
    "    \"\"\"Given a dataframe with B03002 schema, reduce it to a simpler dataframe according to the rules described above\"\"\"\n",
    "    newcols = {\n",
    "        'total': b03002['b03002001'],\n",
    "        'white': b03002['b03002003'],\n",
    "        'black': b03002['b03002004'],\n",
    "        'amerind': b03002['b03002005'],\n",
    "        'asian': b03002['b03002006'],\n",
    "        'pacific': b03002['b03002007'],\n",
    "        'other': b03002['b03002008'] + b03002['b03002009'],\n",
    "        'hisp': b03002['b03002012']\n",
    "    }\n",
    "    if percentify:\n",
    "        total = newcols.pop('total')\n",
    "        for col in newcols:\n",
    "            newcols[col] = newcols[col]/total\n",
    "            \n",
    "    return pd.DataFrame.from_dict(newcols)\n",
    "\n",
    "race07 = simplifier(b03002_07)\n",
    "race14 = simplifier(b03002_14)\n",
    "\n",
    "race_chg = race14 - race07\n",
    "race_chg = race_chg.dropna()\n",
    "\n",
    "# it's nice to have the 'name' column at the front when spitting it out in the notebook. \n",
    "# Since we use the geoid for the dataframe index, we can comfortably mix and match data from different queries.\n",
    "race_chg.insert(0,'name', b03002_07['name'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identify the places with most change\n",
    "\n",
    "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col</th>\n",
       "      <th>name</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geoid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>79500US3500200</th>\n",
       "      <td>amerind</td>\n",
       "      <td>PUMA5 00200, New Mexico</td>\n",
       "      <td>0.164586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0608101</th>\n",
       "      <td>asian</td>\n",
       "      <td>PUMA5 08101, California</td>\n",
       "      <td>0.428660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0605500</th>\n",
       "      <td>black</td>\n",
       "      <td>PUMA5 05500, California</td>\n",
       "      <td>-0.392138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0400122</th>\n",
       "      <td>hisp</td>\n",
       "      <td>PUMA5 00122, Arizona</td>\n",
       "      <td>0.607988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US4000100</th>\n",
       "      <td>other</td>\n",
       "      <td>PUMA5 00100, Oklahoma</td>\n",
       "      <td>0.093469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40000US43669</th>\n",
       "      <td>pacific</td>\n",
       "      <td>Kailua (Honolulu County)--Kaneohe, HI Urbanize...</td>\n",
       "      <td>-0.055974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0400122</th>\n",
       "      <td>white</td>\n",
       "      <td>PUMA5 00122, Arizona</td>\n",
       "      <td>-0.649215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                    col                                               name  \\\n",
       "geoid                                                                        \n",
       "79500US3500200  amerind                            PUMA5 00200, New Mexico   \n",
       "79500US0608101    asian                            PUMA5 08101, California   \n",
       "79500US0605500    black                            PUMA5 05500, California   \n",
       "79500US0400122     hisp                               PUMA5 00122, Arizona   \n",
       "79500US4000100    other                              PUMA5 00100, Oklahoma   \n",
       "40000US43669    pacific  Kailua (Honolulu County)--Kaneohe, HI Urbanize...   \n",
       "79500US0400122    white                               PUMA5 00122, Arizona   \n",
       "\n",
       "                   value  \n",
       "geoid                     \n",
       "79500US3500200  0.164586  \n",
       "79500US0608101  0.428660  \n",
       "79500US0605500 -0.392138  \n",
       "79500US0400122  0.607988  \n",
       "79500US4000100  0.093469  \n",
       "40000US43669   -0.055974  \n",
       "79500US0400122 -0.649215  "
      ]
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   "source": [
    "index=[]\n",
    "summary = {'col':[],'value': [], 'name': []}\n",
    "for col in race_chg.columns:\n",
    "    if col != 'name':\n",
    "        top = race_chg[col].max()\n",
    "        bot = race_chg[col].min()\n",
    "        if abs(top) > abs(bot):\n",
    "            val = top\n",
    "        else:\n",
    "            val = bot\n",
    "        row = race_chg[race_chg[col] == val]\n",
    "        summary['col'].append(col)\n",
    "        summary['value'].append(val)\n",
    "        summary['name'].append(row['name'].item()) # we don't want the index label so use item()\n",
    "        index.append(row.index.tolist()[0]) # here's where we want the index/geoid\n",
    "\n",
    "summary = pd.DataFrame(summary,index=pd.Series(index,name='geoid'))\n",
    "summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## a diversion\n",
    "\n",
    "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",
    "\n",
    "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>amerind_chg</th>\n",
       "      <th>asian_chg</th>\n",
       "      <th>black_chg</th>\n",
       "      <th>hisp_chg</th>\n",
       "      <th>other_chg</th>\n",
       "      <th>pacific_chg</th>\n",
       "      <th>white_chg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2080.000000</td>\n",
       "      <td>2080.000000</td>\n",
       "      <td>2080.000000</td>\n",
       "      <td>2080.000000</td>\n",
       "      <td>2080.000000</td>\n",
       "      <td>2080.000000</td>\n",
       "      <td>2080.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.000148</td>\n",
       "      <td>0.008763</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.024247</td>\n",
       "      <td>0.006655</td>\n",
       "      <td>0.000357</td>\n",
       "      <td>-0.040226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.008454</td>\n",
       "      <td>0.021830</td>\n",
       "      <td>0.028130</td>\n",
       "      <td>0.048114</td>\n",
       "      <td>0.009439</td>\n",
       "      <td>0.003903</td>\n",
       "      <td>0.054326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.094654</td>\n",
       "      <td>-0.120585</td>\n",
       "      <td>-0.392138</td>\n",
       "      <td>-0.452918</td>\n",
       "      <td>-0.047881</td>\n",
       "      <td>-0.055974</td>\n",
       "      <td>-0.649215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.001342</td>\n",
       "      <td>0.000975</td>\n",
       "      <td>-0.006942</td>\n",
       "      <td>0.011746</td>\n",
       "      <td>0.002171</td>\n",
       "      <td>-0.000352</td>\n",
       "      <td>-0.055862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.000196</td>\n",
       "      <td>0.005339</td>\n",
       "      <td>0.000780</td>\n",
       "      <td>0.022289</td>\n",
       "      <td>0.005954</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.039391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000879</td>\n",
       "      <td>0.012810</td>\n",
       "      <td>0.007866</td>\n",
       "      <td>0.036563</td>\n",
       "      <td>0.010856</td>\n",
       "      <td>0.000583</td>\n",
       "      <td>-0.024016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.164586</td>\n",
       "      <td>0.428660</td>\n",
       "      <td>0.370986</td>\n",
       "      <td>0.607988</td>\n",
       "      <td>0.093469</td>\n",
       "      <td>0.047832</td>\n",
       "      <td>0.512265</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       amerind_chg    asian_chg    black_chg     hisp_chg    other_chg  \\\n",
       "count  2080.000000  2080.000000  2080.000000  2080.000000  2080.000000   \n",
       "mean      0.000148     0.008763     0.000057     0.024247     0.006655   \n",
       "std       0.008454     0.021830     0.028130     0.048114     0.009439   \n",
       "min      -0.094654    -0.120585    -0.392138    -0.452918    -0.047881   \n",
       "25%      -0.001342     0.000975    -0.006942     0.011746     0.002171   \n",
       "50%      -0.000196     0.005339     0.000780     0.022289     0.005954   \n",
       "75%       0.000879     0.012810     0.007866     0.036563     0.010856   \n",
       "max       0.164586     0.428660     0.370986     0.607988     0.093469   \n",
       "\n",
       "       pacific_chg    white_chg  \n",
       "count  2080.000000  2080.000000  \n",
       "mean      0.000357    -0.040226  \n",
       "std       0.003903     0.054326  \n",
       "min      -0.055974    -0.649215  \n",
       "25%      -0.000352    -0.055862  \n",
       "50%       0.000000    -0.039391  \n",
       "75%       0.000583    -0.024016  \n",
       "max       0.047832     0.512265  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "race = pd.merge(race07,race14,left_index=True,right_index=True,suffixes=('_07','_14'))\n",
    "race = race.merge(race_chg,left_index=True,right_index=True,suffixes=('','_chg'))\n",
    "race.insert(0,'name',race.pop('name'))\n",
    "race['total_07'] = b03002_07['b03002001']\n",
    "race['total_14'] = b03002_14['b03002001']\n",
    "race = race.merge(race_chg,left_index=True,right_index=True,suffixes=('','_chg'))\n",
    "race[[x for x in race.columns if x.endswith('_chg')]].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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",
    "\n",
    "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. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/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",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n",
      "/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",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10f392390>"
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     "execution_count": 6,
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     "output_type": "execute_result"
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c06bd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "race_chg.plot(kind='box')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using a visualization to see the extremes\n",
    "\n",
    "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",
    "\n",
    "Most to the point, it seems like you can't add tools like the hover tool when you use the `Scatter` class.\n",
    "\n",
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"94c29914-45fe-4f4e-90e0-f3c7b47bd454\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '94c29914-45fe-4f4e-90e0-f3c7b47bd454' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        Bokeh.$(function() {\n",
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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",
       "            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",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "        });\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }\n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<p><code>&lt;Bokeh Notebook handle for <strong>In[7]</strong>&gt;</code></p>"
      ],
      "text/plain": [
       "<bokeh.io._CommsHandle at 0x10f89dbe0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from bokeh.charts import Scatter, show, output_notebook\n",
    "from bokeh.plotting import figure\n",
    "from bokeh.models import HoverTool, PolyAnnotation\n",
    "from bokeh.models.sources import ColumnDataSource\n",
    "output_notebook()\n",
    "\n",
    "#scatterplot = Scatter(race, x='white_07', y='white_14', title=\"Pct white 2007 vs 2014\",\n",
    "#            xlabel=\"2007 pct white\")\n",
    "# http://blog.rtwilson.com/bokeh-plots-with-dataframe-based-tooltips/\n",
    "hover = HoverTool(tooltips=[('name','@name'),\n",
    "                            ('geoid', '@geoid'),\n",
    "                            ('white', '@white_chg{1.11}'), \n",
    "                            ('black', '@black_chg{1.11}'),\n",
    "                            ('hisp', '@hisp_chg{1.11}'),\n",
    "                            ('asian', '@asian_chg{1.11}')])\n",
    "\n",
    "p = figure(plot_width=750, plot_height=750,\n",
    "           title=\"larger swings\")\n",
    "p.add_tools(hover)\n",
    "\n",
    "_plot_configs = [\n",
    "    ('white', 'red', 'circle'),\n",
    "    ('black', 'blue', 'square'),\n",
    "    ('hisp', 'green', 'triangle'),\n",
    "    ('asian', 'brown', 'inverted_triangle')\n",
    "]\n",
    "\n",
    "for r, c, m in _plot_configs:\n",
    "    x_col = (r+'_07')\n",
    "    y_col = (r+'_14')\n",
    "    chg_col = (r+'_chg')\n",
    "    race_for_plot = race[(race[chg_col].abs() > 0.2)]\n",
    "    p.scatter(x=x_col, y=y_col, size=10, color=c, marker=m, source=ColumnDataSource(race_for_plot), legend=chg_col)\n",
    "\n",
    "\n",
    "p.xaxis.axis_label = '2007 pct of population'\n",
    "p.yaxis.axis_label = '2014 pct'\n",
    "\n",
    "# these just don't work!\n",
    "# drop_anno = PolyAnnotation(plot=p, xs=[.2, .4, .4, .2], ys=[.2, .2, .4, .4], fill_alpha=0.1, fill_color='red')\n",
    "#growth_anno = PolyAnnotation(plot=p, bottom=180, fill_alpha=0.1, fill_color='green')\n",
    "\n",
    "#p.renderers.extend([drop_anno, ])\n",
    "\n",
    "show(p)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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_."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lists instead of Charts\n",
    "\n",
    "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\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>black_07</th>\n",
       "      <th>black_14</th>\n",
       "      <th>black_chg</th>\n",
       "      <th>total_07</th>\n",
       "      <th>total_14</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geoid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>79500US0605500</th>\n",
       "      <td>PUMA5 05500, California</td>\n",
       "      <td>0.414126</td>\n",
       "      <td>0.021988</td>\n",
       "      <td>-0.392138</td>\n",
       "      <td>106581.0</td>\n",
       "      <td>141667.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US2503303</th>\n",
       "      <td>PUMA5 03303, Massachusetts</td>\n",
       "      <td>0.601772</td>\n",
       "      <td>0.258776</td>\n",
       "      <td>-0.342995</td>\n",
       "      <td>115710.0</td>\n",
       "      <td>112634.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US2503304</th>\n",
       "      <td>PUMA5 03304, Massachusetts</td>\n",
       "      <td>0.181944</td>\n",
       "      <td>0.552930</td>\n",
       "      <td>0.370986</td>\n",
       "      <td>117844.0</td>\n",
       "      <td>137728.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US4802308</th>\n",
       "      <td>PUMA5 02308, Texas</td>\n",
       "      <td>0.419891</td>\n",
       "      <td>0.106989</td>\n",
       "      <td>-0.312901</td>\n",
       "      <td>179311.0</td>\n",
       "      <td>105889.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US4802504</th>\n",
       "      <td>PUMA5 02504, Texas</td>\n",
       "      <td>0.054091</td>\n",
       "      <td>0.321509</td>\n",
       "      <td>0.267418</td>\n",
       "      <td>226857.0</td>\n",
       "      <td>104588.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US4803501</th>\n",
       "      <td>PUMA5 03501, Texas</td>\n",
       "      <td>0.034760</td>\n",
       "      <td>0.301364</td>\n",
       "      <td>0.266604</td>\n",
       "      <td>115420.0</td>\n",
       "      <td>166390.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US4804625</th>\n",
       "      <td>PUMA5 04625, Texas</td>\n",
       "      <td>0.008153</td>\n",
       "      <td>0.221798</td>\n",
       "      <td>0.213644</td>\n",
       "      <td>153925.0</td>\n",
       "      <td>114140.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      name  black_07  black_14  black_chg  \\\n",
       "geoid                                                                       \n",
       "79500US0605500     PUMA5 05500, California  0.414126  0.021988  -0.392138   \n",
       "79500US2503303  PUMA5 03303, Massachusetts  0.601772  0.258776  -0.342995   \n",
       "79500US2503304  PUMA5 03304, Massachusetts  0.181944  0.552930   0.370986   \n",
       "79500US4802308          PUMA5 02308, Texas  0.419891  0.106989  -0.312901   \n",
       "79500US4802504          PUMA5 02504, Texas  0.054091  0.321509   0.267418   \n",
       "79500US4803501          PUMA5 03501, Texas  0.034760  0.301364   0.266604   \n",
       "79500US4804625          PUMA5 04625, Texas  0.008153  0.221798   0.213644   \n",
       "\n",
       "                total_07  total_14  \n",
       "geoid                               \n",
       "79500US0605500  106581.0  141667.0  \n",
       "79500US2503303  115710.0  112634.0  \n",
       "79500US2503304  117844.0  137728.0  \n",
       "79500US4802308  179311.0  105889.0  \n",
       "79500US4802504  226857.0  104588.0  \n",
       "79500US4803501  115420.0  166390.0  \n",
       "79500US4804625  153925.0  114140.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "race[race['black_chg'].abs() > 0.2][['name','black_07','black_14','black_chg','total_07', 'total_14']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>asian_07</th>\n",
       "      <th>asian_14</th>\n",
       "      <th>asian_chg</th>\n",
       "      <th>total_07</th>\n",
       "      <th>total_14</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geoid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>79500US0608101</th>\n",
       "      <td>PUMA5 08101, California</td>\n",
       "      <td>0.041138</td>\n",
       "      <td>0.469799</td>\n",
       "      <td>0.428660</td>\n",
       "      <td>240360.0</td>\n",
       "      <td>149579.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0608102</th>\n",
       "      <td>PUMA5 08102, California</td>\n",
       "      <td>0.082052</td>\n",
       "      <td>0.353831</td>\n",
       "      <td>0.271779</td>\n",
       "      <td>130844.0</td>\n",
       "      <td>122683.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0608103</th>\n",
       "      <td>PUMA5 08103, California</td>\n",
       "      <td>0.011072</td>\n",
       "      <td>0.260809</td>\n",
       "      <td>0.249737</td>\n",
       "      <td>148930.0</td>\n",
       "      <td>104728.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US2701303</th>\n",
       "      <td>PUMA5 01303, Minnesota</td>\n",
       "      <td>0.030409</td>\n",
       "      <td>0.241126</td>\n",
       "      <td>0.210717</td>\n",
       "      <td>115230.0</td>\n",
       "      <td>135506.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   name  asian_07  asian_14  asian_chg  \\\n",
       "geoid                                                                    \n",
       "79500US0608101  PUMA5 08101, California  0.041138  0.469799   0.428660   \n",
       "79500US0608102  PUMA5 08102, California  0.082052  0.353831   0.271779   \n",
       "79500US0608103  PUMA5 08103, California  0.011072  0.260809   0.249737   \n",
       "79500US2701303   PUMA5 01303, Minnesota  0.030409  0.241126   0.210717   \n",
       "\n",
       "                total_07  total_14  \n",
       "geoid                               \n",
       "79500US0608101  240360.0  149579.0  \n",
       "79500US0608102  130844.0  122683.0  \n",
       "79500US0608103  148930.0  104728.0  \n",
       "79500US2701303  115230.0  135506.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "race[race['asian_chg'].abs() > 0.2][['name','asian_07','asian_14','asian_chg','total_07', 'total_14']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>hisp_07</th>\n",
       "      <th>hisp_14</th>\n",
       "      <th>hisp_chg</th>\n",
       "      <th>total_07</th>\n",
       "      <th>total_14</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geoid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>79500US0400103</th>\n",
       "      <td>PUMA5 00103, Arizona</td>\n",
       "      <td>0.186270</td>\n",
       "      <td>0.479699</td>\n",
       "      <td>0.293429</td>\n",
       "      <td>239088.0</td>\n",
       "      <td>112433.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0400109</th>\n",
       "      <td>PUMA5 00109, Arizona</td>\n",
       "      <td>0.512736</td>\n",
       "      <td>0.197885</td>\n",
       "      <td>-0.314852</td>\n",
       "      <td>174813.0</td>\n",
       "      <td>111939.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0400115</th>\n",
       "      <td>PUMA5 00115, Arizona</td>\n",
       "      <td>0.461815</td>\n",
       "      <td>0.245164</td>\n",
       "      <td>-0.216651</td>\n",
       "      <td>161229.0</td>\n",
       "      <td>104583.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0400117</th>\n",
       "      <td>PUMA5 00117, Arizona</td>\n",
       "      <td>0.665978</td>\n",
       "      <td>0.368033</td>\n",
       "      <td>-0.297946</td>\n",
       "      <td>153493.0</td>\n",
       "      <td>108417.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0400118</th>\n",
       "      <td>PUMA5 00118, Arizona</td>\n",
       "      <td>0.788194</td>\n",
       "      <td>0.541578</td>\n",
       "      <td>-0.246616</td>\n",
       "      <td>175321.0</td>\n",
       "      <td>114495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0400119</th>\n",
       "      <td>PUMA5 00119, Arizona</td>\n",
       "      <td>0.274150</td>\n",
       "      <td>0.658849</td>\n",
       "      <td>0.384699</td>\n",
       "      <td>148404.0</td>\n",
       "      <td>116957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0400121</th>\n",
       "      <td>PUMA5 00121, Arizona</td>\n",
       "      <td>0.171333</td>\n",
       "      <td>0.667758</td>\n",
       "      <td>0.496425</td>\n",
       "      <td>247273.0</td>\n",
       "      <td>143892.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0400122</th>\n",
       "      <td>PUMA5 00122, Arizona</td>\n",
       "      <td>0.165037</td>\n",
       "      <td>0.773025</td>\n",
       "      <td>0.607988</td>\n",
       "      <td>139399.0</td>\n",
       "      <td>94724.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0601301</th>\n",
       "      <td>PUMA5 01301, California</td>\n",
       "      <td>0.178530</td>\n",
       "      <td>0.493466</td>\n",
       "      <td>0.314936</td>\n",
       "      <td>135266.0</td>\n",
       "      <td>123269.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0601901</th>\n",
       "      <td>PUMA5 01901, California</td>\n",
       "      <td>0.509523</td>\n",
       "      <td>0.777063</td>\n",
       "      <td>0.267541</td>\n",
       "      <td>126485.0</td>\n",
       "      <td>155246.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0602500</th>\n",
       "      <td>PUMA5 02500, California</td>\n",
       "      <td>0.361858</td>\n",
       "      <td>0.822755</td>\n",
       "      <td>0.460897</td>\n",
       "      <td>198473.0</td>\n",
       "      <td>179091.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0606701</th>\n",
       "      <td>PUMA5 06701, California</td>\n",
       "      <td>0.506864</td>\n",
       "      <td>0.158718</td>\n",
       "      <td>-0.348146</td>\n",
       "      <td>205935.0</td>\n",
       "      <td>110101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0608102</th>\n",
       "      <td>PUMA5 08102, California</td>\n",
       "      <td>0.666756</td>\n",
       "      <td>0.327511</td>\n",
       "      <td>-0.339245</td>\n",
       "      <td>130844.0</td>\n",
       "      <td>122683.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0608104</th>\n",
       "      <td>PUMA5 08104, California</td>\n",
       "      <td>0.542739</td>\n",
       "      <td>0.168271</td>\n",
       "      <td>-0.374467</td>\n",
       "      <td>232493.0</td>\n",
       "      <td>126807.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0608105</th>\n",
       "      <td>PUMA5 08105, California</td>\n",
       "      <td>0.537985</td>\n",
       "      <td>0.253777</td>\n",
       "      <td>-0.284208</td>\n",
       "      <td>204539.0</td>\n",
       "      <td>136269.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US3500200</th>\n",
       "      <td>PUMA5 00200, New Mexico</td>\n",
       "      <td>0.696722</td>\n",
       "      <td>0.243803</td>\n",
       "      <td>-0.452918</td>\n",
       "      <td>109167.0</td>\n",
       "      <td>98760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US3500300</th>\n",
       "      <td>PUMA5 00300, New Mexico</td>\n",
       "      <td>0.360262</td>\n",
       "      <td>0.622404</td>\n",
       "      <td>0.262142</td>\n",
       "      <td>115416.0</td>\n",
       "      <td>123068.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US3603102</th>\n",
       "      <td>PUMA5 03102, New York</td>\n",
       "      <td>0.068151</td>\n",
       "      <td>0.277975</td>\n",
       "      <td>0.209823</td>\n",
       "      <td>156857.0</td>\n",
       "      <td>149618.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US4802502</th>\n",
       "      <td>PUMA5 02502, Texas</td>\n",
       "      <td>0.484273</td>\n",
       "      <td>0.187739</td>\n",
       "      <td>-0.296534</td>\n",
       "      <td>116455.0</td>\n",
       "      <td>127022.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US4803200</th>\n",
       "      <td>PUMA5 03200, Texas</td>\n",
       "      <td>0.356576</td>\n",
       "      <td>0.581805</td>\n",
       "      <td>0.225230</td>\n",
       "      <td>126408.0</td>\n",
       "      <td>114956.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US4803400</th>\n",
       "      <td>PUMA5 03400, Texas</td>\n",
       "      <td>0.553636</td>\n",
       "      <td>0.179002</td>\n",
       "      <td>-0.374634</td>\n",
       "      <td>95160.0</td>\n",
       "      <td>179294.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US4803501</th>\n",
       "      <td>PUMA5 03501, Texas</td>\n",
       "      <td>0.659825</td>\n",
       "      <td>0.229695</td>\n",
       "      <td>-0.430130</td>\n",
       "      <td>115420.0</td>\n",
       "      <td>166390.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   name   hisp_07   hisp_14  hisp_chg  \\\n",
       "geoid                                                                   \n",
       "79500US0400103     PUMA5 00103, Arizona  0.186270  0.479699  0.293429   \n",
       "79500US0400109     PUMA5 00109, Arizona  0.512736  0.197885 -0.314852   \n",
       "79500US0400115     PUMA5 00115, Arizona  0.461815  0.245164 -0.216651   \n",
       "79500US0400117     PUMA5 00117, Arizona  0.665978  0.368033 -0.297946   \n",
       "79500US0400118     PUMA5 00118, Arizona  0.788194  0.541578 -0.246616   \n",
       "79500US0400119     PUMA5 00119, Arizona  0.274150  0.658849  0.384699   \n",
       "79500US0400121     PUMA5 00121, Arizona  0.171333  0.667758  0.496425   \n",
       "79500US0400122     PUMA5 00122, Arizona  0.165037  0.773025  0.607988   \n",
       "79500US0601301  PUMA5 01301, California  0.178530  0.493466  0.314936   \n",
       "79500US0601901  PUMA5 01901, California  0.509523  0.777063  0.267541   \n",
       "79500US0602500  PUMA5 02500, California  0.361858  0.822755  0.460897   \n",
       "79500US0606701  PUMA5 06701, California  0.506864  0.158718 -0.348146   \n",
       "79500US0608102  PUMA5 08102, California  0.666756  0.327511 -0.339245   \n",
       "79500US0608104  PUMA5 08104, California  0.542739  0.168271 -0.374467   \n",
       "79500US0608105  PUMA5 08105, California  0.537985  0.253777 -0.284208   \n",
       "79500US3500200  PUMA5 00200, New Mexico  0.696722  0.243803 -0.452918   \n",
       "79500US3500300  PUMA5 00300, New Mexico  0.360262  0.622404  0.262142   \n",
       "79500US3603102    PUMA5 03102, New York  0.068151  0.277975  0.209823   \n",
       "79500US4802502       PUMA5 02502, Texas  0.484273  0.187739 -0.296534   \n",
       "79500US4803200       PUMA5 03200, Texas  0.356576  0.581805  0.225230   \n",
       "79500US4803400       PUMA5 03400, Texas  0.553636  0.179002 -0.374634   \n",
       "79500US4803501       PUMA5 03501, Texas  0.659825  0.229695 -0.430130   \n",
       "\n",
       "                total_07  total_14  \n",
       "geoid                               \n",
       "79500US0400103  239088.0  112433.0  \n",
       "79500US0400109  174813.0  111939.0  \n",
       "79500US0400115  161229.0  104583.0  \n",
       "79500US0400117  153493.0  108417.0  \n",
       "79500US0400118  175321.0  114495.0  \n",
       "79500US0400119  148404.0  116957.0  \n",
       "79500US0400121  247273.0  143892.0  \n",
       "79500US0400122  139399.0   94724.0  \n",
       "79500US0601301  135266.0  123269.0  \n",
       "79500US0601901  126485.0  155246.0  \n",
       "79500US0602500  198473.0  179091.0  \n",
       "79500US0606701  205935.0  110101.0  \n",
       "79500US0608102  130844.0  122683.0  \n",
       "79500US0608104  232493.0  126807.0  \n",
       "79500US0608105  204539.0  136269.0  \n",
       "79500US3500200  109167.0   98760.0  \n",
       "79500US3500300  115416.0  123068.0  \n",
       "79500US3603102  156857.0  149618.0  \n",
       "79500US4802502  116455.0  127022.0  \n",
       "79500US4803200  126408.0  114956.0  \n",
       "79500US4803400   95160.0  179294.0  \n",
       "79500US4803501  115420.0  166390.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "race[race['hisp_chg'].abs() > 0.2][['name','hisp_07','hisp_14','hisp_chg','total_07', 'total_14']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>white_07</th>\n",
       "      <th>white_14</th>\n",
       "      <th>white_chg</th>\n",
       "      <th>total_07</th>\n",
       "      <th>total_14</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geoid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16000US4847892</th>\n",
       "      <td>Mesquite city, Texas</td>\n",
       "      <td>0.528739</td>\n",
       "      <td>0.307210</td>\n",
       "      <td>-0.221530</td>\n",
       "      <td>126081.0</td>\n",
       "      <td>144289.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31400US3798037964</th>\n",
       "      <td>Philadelphia, PA Metropolitan Division, Philad...</td>\n",
       "      <td>0.662279</td>\n",
       "      <td>0.443912</td>\n",
       "      <td>-0.218367</td>\n",
       "      <td>3887694.0</td>\n",
       "      <td>2123257.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0400103</th>\n",
       "      <td>PUMA5 00103, Arizona</td>\n",
       "      <td>0.728518</td>\n",
       "      <td>0.406740</td>\n",
       "      <td>-0.321778</td>\n",
       "      <td>239088.0</td>\n",
       "      <td>112433.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0400106</th>\n",
       "      <td>PUMA5 00106, Arizona</td>\n",
       "      <td>0.755835</td>\n",
       "      <td>0.486296</td>\n",
       "      <td>-0.269539</td>\n",
       "      <td>95112.0</td>\n",
       "      <td>123398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0400108</th>\n",
       "      <td>PUMA5 00108, Arizona</td>\n",
       "      <td>0.755408</td>\n",
       "      <td>0.552054</td>\n",
       "      <td>-0.203354</td>\n",
       "      <td>120850.0</td>\n",
       "      <td>105593.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0400109</th>\n",
       "      <td>PUMA5 00109, Arizona</td>\n",
       "      <td>0.354030</td>\n",
       "      <td>0.621428</td>\n",
       "      <td>0.267398</td>\n",
       "      <td>174813.0</td>\n",
       "      <td>111939.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0400115</th>\n",
       "      <td>PUMA5 00115, Arizona</td>\n",
       "      <td>0.433080</td>\n",
       "      <td>0.652907</td>\n",
       "      <td>0.219828</td>\n",
       "      <td>161229.0</td>\n",
       "      <td>104583.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0400117</th>\n",
       "      <td>PUMA5 00117, Arizona</td>\n",
       "      <td>0.178125</td>\n",
       "      <td>0.520038</td>\n",
       "      <td>0.341913</td>\n",
       "      <td>153493.0</td>\n",
       "      <td>108417.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0400119</th>\n",
       "      <td>PUMA5 00119, Arizona</td>\n",
       "      <td>0.547667</td>\n",
       "      <td>0.133314</td>\n",
       "      <td>-0.414353</td>\n",
       "      <td>148404.0</td>\n",
       "      <td>116957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0400121</th>\n",
       "      <td>PUMA5 00121, Arizona</td>\n",
       "      <td>0.739846</td>\n",
       "      <td>0.157160</td>\n",
       "      <td>-0.582687</td>\n",
       "      <td>247273.0</td>\n",
       "      <td>143892.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0400122</th>\n",
       "      <td>PUMA5 00122, Arizona</td>\n",
       "      <td>0.764159</td>\n",
       "      <td>0.114944</td>\n",
       "      <td>-0.649215</td>\n",
       "      <td>139399.0</td>\n",
       "      <td>94724.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0601301</th>\n",
       "      <td>PUMA5 01301, California</td>\n",
       "      <td>0.386017</td>\n",
       "      <td>0.121231</td>\n",
       "      <td>-0.264786</td>\n",
       "      <td>135266.0</td>\n",
       "      <td>123269.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0602500</th>\n",
       "      <td>PUMA5 02500, California</td>\n",
       "      <td>0.515743</td>\n",
       "      <td>0.122027</td>\n",
       "      <td>-0.393715</td>\n",
       "      <td>198473.0</td>\n",
       "      <td>179091.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0603701</th>\n",
       "      <td>PUMA5 03701, California</td>\n",
       "      <td>0.758281</td>\n",
       "      <td>0.508031</td>\n",
       "      <td>-0.250249</td>\n",
       "      <td>131781.0</td>\n",
       "      <td>134472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0603702</th>\n",
       "      <td>PUMA5 03702, California</td>\n",
       "      <td>0.708622</td>\n",
       "      <td>0.440702</td>\n",
       "      <td>-0.267920</td>\n",
       "      <td>130655.0</td>\n",
       "      <td>201617.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0605500</th>\n",
       "      <td>PUMA5 05500, California</td>\n",
       "      <td>0.024310</td>\n",
       "      <td>0.536575</td>\n",
       "      <td>0.512265</td>\n",
       "      <td>106581.0</td>\n",
       "      <td>141667.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0606101</th>\n",
       "      <td>PUMA5 06101, California</td>\n",
       "      <td>0.504492</td>\n",
       "      <td>0.709097</td>\n",
       "      <td>0.204605</td>\n",
       "      <td>170510.0</td>\n",
       "      <td>133835.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0606102</th>\n",
       "      <td>PUMA5 06102, California</td>\n",
       "      <td>0.510391</td>\n",
       "      <td>0.735933</td>\n",
       "      <td>0.225542</td>\n",
       "      <td>140990.0</td>\n",
       "      <td>115959.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0606701</th>\n",
       "      <td>PUMA5 06701, California</td>\n",
       "      <td>0.410061</td>\n",
       "      <td>0.751601</td>\n",
       "      <td>0.341539</td>\n",
       "      <td>205935.0</td>\n",
       "      <td>110101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0608101</th>\n",
       "      <td>PUMA5 08101, California</td>\n",
       "      <td>0.599488</td>\n",
       "      <td>0.233763</td>\n",
       "      <td>-0.365726</td>\n",
       "      <td>240360.0</td>\n",
       "      <td>149579.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0608103</th>\n",
       "      <td>PUMA5 08103, California</td>\n",
       "      <td>0.753804</td>\n",
       "      <td>0.495312</td>\n",
       "      <td>-0.258492</td>\n",
       "      <td>148930.0</td>\n",
       "      <td>104728.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0608104</th>\n",
       "      <td>PUMA5 08104, California</td>\n",
       "      <td>0.215766</td>\n",
       "      <td>0.516951</td>\n",
       "      <td>0.301185</td>\n",
       "      <td>232493.0</td>\n",
       "      <td>126807.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US0608105</th>\n",
       "      <td>PUMA5 08105, California</td>\n",
       "      <td>0.116667</td>\n",
       "      <td>0.528668</td>\n",
       "      <td>0.412000</td>\n",
       "      <td>204539.0</td>\n",
       "      <td>136269.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US2503303</th>\n",
       "      <td>PUMA5 03303, Massachusetts</td>\n",
       "      <td>0.089206</td>\n",
       "      <td>0.481267</td>\n",
       "      <td>0.392061</td>\n",
       "      <td>115710.0</td>\n",
       "      <td>112634.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US2503304</th>\n",
       "      <td>PUMA5 03304, Massachusetts</td>\n",
       "      <td>0.539569</td>\n",
       "      <td>0.115997</td>\n",
       "      <td>-0.423573</td>\n",
       "      <td>117844.0</td>\n",
       "      <td>137728.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US3402301</th>\n",
       "      <td>PUMA5 02301, New Jersey</td>\n",
       "      <td>0.511410</td>\n",
       "      <td>0.258935</td>\n",
       "      <td>-0.252474</td>\n",
       "      <td>200753.0</td>\n",
       "      <td>120590.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US3500200</th>\n",
       "      <td>PUMA5 00200, New Mexico</td>\n",
       "      <td>0.213663</td>\n",
       "      <td>0.500233</td>\n",
       "      <td>0.286569</td>\n",
       "      <td>109167.0</td>\n",
       "      <td>98760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US3500300</th>\n",
       "      <td>PUMA5 00300, New Mexico</td>\n",
       "      <td>0.566291</td>\n",
       "      <td>0.284282</td>\n",
       "      <td>-0.282009</td>\n",
       "      <td>115416.0</td>\n",
       "      <td>123068.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US3603102</th>\n",
       "      <td>PUMA5 03102, New York</td>\n",
       "      <td>0.841614</td>\n",
       "      <td>0.553282</td>\n",
       "      <td>-0.288331</td>\n",
       "      <td>156857.0</td>\n",
       "      <td>149618.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US3700400</th>\n",
       "      <td>PUMA5 00400, North Carolina</td>\n",
       "      <td>0.818956</td>\n",
       "      <td>0.609494</td>\n",
       "      <td>-0.209462</td>\n",
       "      <td>223526.0</td>\n",
       "      <td>120185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US4802308</th>\n",
       "      <td>PUMA5 02308, Texas</td>\n",
       "      <td>0.157603</td>\n",
       "      <td>0.412300</td>\n",
       "      <td>0.254696</td>\n",
       "      <td>179311.0</td>\n",
       "      <td>105889.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US4802313</th>\n",
       "      <td>PUMA5 02313, Texas</td>\n",
       "      <td>0.342277</td>\n",
       "      <td>0.564901</td>\n",
       "      <td>0.222624</td>\n",
       "      <td>130403.0</td>\n",
       "      <td>116870.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US4802502</th>\n",
       "      <td>PUMA5 02502, Texas</td>\n",
       "      <td>0.226250</td>\n",
       "      <td>0.457684</td>\n",
       "      <td>0.231434</td>\n",
       "      <td>116455.0</td>\n",
       "      <td>127022.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US4802504</th>\n",
       "      <td>PUMA5 02504, Texas</td>\n",
       "      <td>0.525767</td>\n",
       "      <td>0.309175</td>\n",
       "      <td>-0.216592</td>\n",
       "      <td>226857.0</td>\n",
       "      <td>104588.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US4802507</th>\n",
       "      <td>PUMA5 02507, Texas</td>\n",
       "      <td>0.735555</td>\n",
       "      <td>0.504637</td>\n",
       "      <td>-0.230918</td>\n",
       "      <td>156369.0</td>\n",
       "      <td>125627.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US4803400</th>\n",
       "      <td>PUMA5 03400, Texas</td>\n",
       "      <td>0.386969</td>\n",
       "      <td>0.699929</td>\n",
       "      <td>0.312959</td>\n",
       "      <td>95160.0</td>\n",
       "      <td>179294.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79500US4805303</th>\n",
       "      <td>PUMA5 05303, Texas</td>\n",
       "      <td>0.515004</td>\n",
       "      <td>0.265493</td>\n",
       "      <td>-0.249511</td>\n",
       "      <td>198111.0</td>\n",
       "      <td>144000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97000US4830390</th>\n",
       "      <td>MESQUITE INDEPENDENT SCHOOL DISTRICT , Texas</td>\n",
       "      <td>0.511651</td>\n",
       "      <td>0.298632</td>\n",
       "      <td>-0.213019</td>\n",
       "      <td>155778.0</td>\n",
       "      <td>172483.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                name  \\\n",
       "geoid                                                                  \n",
       "16000US4847892                                  Mesquite city, Texas   \n",
       "31400US3798037964  Philadelphia, PA Metropolitan Division, Philad...   \n",
       "79500US0400103                                  PUMA5 00103, Arizona   \n",
       "79500US0400106                                  PUMA5 00106, Arizona   \n",
       "79500US0400108                                  PUMA5 00108, Arizona   \n",
       "79500US0400109                                  PUMA5 00109, Arizona   \n",
       "79500US0400115                                  PUMA5 00115, Arizona   \n",
       "79500US0400117                                  PUMA5 00117, Arizona   \n",
       "79500US0400119                                  PUMA5 00119, Arizona   \n",
       "79500US0400121                                  PUMA5 00121, Arizona   \n",
       "79500US0400122                                  PUMA5 00122, Arizona   \n",
       "79500US0601301                               PUMA5 01301, California   \n",
       "79500US0602500                               PUMA5 02500, California   \n",
       "79500US0603701                               PUMA5 03701, California   \n",
       "79500US0603702                               PUMA5 03702, California   \n",
       "79500US0605500                               PUMA5 05500, California   \n",
       "79500US0606101                               PUMA5 06101, California   \n",
       "79500US0606102                               PUMA5 06102, California   \n",
       "79500US0606701                               PUMA5 06701, California   \n",
       "79500US0608101                               PUMA5 08101, California   \n",
       "79500US0608103                               PUMA5 08103, California   \n",
       "79500US0608104                               PUMA5 08104, California   \n",
       "79500US0608105                               PUMA5 08105, California   \n",
       "79500US2503303                            PUMA5 03303, Massachusetts   \n",
       "79500US2503304                            PUMA5 03304, Massachusetts   \n",
       "79500US3402301                               PUMA5 02301, New Jersey   \n",
       "79500US3500200                               PUMA5 00200, New Mexico   \n",
       "79500US3500300                               PUMA5 00300, New Mexico   \n",
       "79500US3603102                                 PUMA5 03102, New York   \n",
       "79500US3700400                           PUMA5 00400, North Carolina   \n",
       "79500US4802308                                    PUMA5 02308, Texas   \n",
       "79500US4802313                                    PUMA5 02313, Texas   \n",
       "79500US4802502                                    PUMA5 02502, Texas   \n",
       "79500US4802504                                    PUMA5 02504, Texas   \n",
       "79500US4802507                                    PUMA5 02507, Texas   \n",
       "79500US4803400                                    PUMA5 03400, Texas   \n",
       "79500US4805303                                    PUMA5 05303, Texas   \n",
       "97000US4830390          MESQUITE INDEPENDENT SCHOOL DISTRICT , Texas   \n",
       "\n",
       "                   white_07  white_14  white_chg   total_07   total_14  \n",
       "geoid                                                                   \n",
       "16000US4847892     0.528739  0.307210  -0.221530   126081.0   144289.0  \n",
       "31400US3798037964  0.662279  0.443912  -0.218367  3887694.0  2123257.0  \n",
       "79500US0400103     0.728518  0.406740  -0.321778   239088.0   112433.0  \n",
       "79500US0400106     0.755835  0.486296  -0.269539    95112.0   123398.0  \n",
       "79500US0400108     0.755408  0.552054  -0.203354   120850.0   105593.0  \n",
       "79500US0400109     0.354030  0.621428   0.267398   174813.0   111939.0  \n",
       "79500US0400115     0.433080  0.652907   0.219828   161229.0   104583.0  \n",
       "79500US0400117     0.178125  0.520038   0.341913   153493.0   108417.0  \n",
       "79500US0400119     0.547667  0.133314  -0.414353   148404.0   116957.0  \n",
       "79500US0400121     0.739846  0.157160  -0.582687   247273.0   143892.0  \n",
       "79500US0400122     0.764159  0.114944  -0.649215   139399.0    94724.0  \n",
       "79500US0601301     0.386017  0.121231  -0.264786   135266.0   123269.0  \n",
       "79500US0602500     0.515743  0.122027  -0.393715   198473.0   179091.0  \n",
       "79500US0603701     0.758281  0.508031  -0.250249   131781.0   134472.0  \n",
       "79500US0603702     0.708622  0.440702  -0.267920   130655.0   201617.0  \n",
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       "97000US4830390     0.511651  0.298632  -0.213019   155778.0   172483.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "race[race['white_chg'].abs() > 0.2][['name','white_07','white_14','white_chg','total_07', 'total_14']]"
   ]
  }
 ],
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