iPython notebook: A choropleth depicting percentage of public employees that are female in each county in Iowa.

iowa_female_public_employees

{
 "metadata": {
  "name": "Iowa Choropleth Map"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "# Making an Iowa choropleth map using Python\n\n## Preface\nI'm following along with this [flowingdata tutorial](http://flowingdata.com/2009/11/12/how-to-make-a-us-county-thematic-map-using-free-tools/), with some slight tweaks. My choropleth will visualize the percentage public employees that are female in each county. I [previously calculated these percentages with pandas](http://www.seeaustinhack.com/2013/10/13/using-pandas-to-analyze-iowa-public-salaries/) using [data I scraped](http://www.seeaustinhack.com/2013/09/06/obtaining-data-with-python-scrapy/) from the Des Moines Register's Iowa public salary database. \n\n## Code\nIf you just want to see the code, I put it on GitHub: https://github.com/austinlyons/iowa_female_public_employees\n\n## SVG\nTo get an SVG with Iowa and it's counties, I took the [US county SVG from Wikimedia](http://upload.wikimedia.org/wikipedia/commons/5/5f/USA_Counties_with_FIPS_and_names.svg), opened the SVG in a text editor, and deleted everything but the Iowa counties. I edited the resulting map a bit with [Inkscape](http://inkscape.org/), making sure that each county path retained the county name and its FIPS code. To easily look up a county's FIPS code I created a .csv with Iowa counties and their respective FIPS code using [data from the EPA](http://www.epa.gov/envirofw/html/codes/ia.html)."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# imports\nimport pandas as pd\nimport numpy as np\nfrom BeautifulSoup import BeautifulSoup",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# implement county->fips lookup table with a dataframe with the county as the index\nfips = pd.read_csv('iowa_county_fips.csv', index_col=0)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# are there 99 counties in this dataframe? sanity check\nprint fips",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "<class 'pandas.core.frame.DataFrame'>\nIndex: 99 entries, ADAIR to WRIGHT\nData columns (total 1 columns):\ncode    99  non-null values\ndtypes: int64(1)\n"
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# does the data look like we expect?\nprint fips[0:10]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "             code\ncounty           \nADAIR       19001\nADAMS       19003\nALLAMAKEE   19005\nAPPANOOSE   19007\nAUDUBON     19009\nBENTON      19011\nBLACK HAWK  19013\nBOONE       19015\nBREMER      19017\nBUCHANAN    19019\n"
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# Iowa public employees dataframe: % female, % male, total # employees. County is set as the index\niowa_by_sex = pd.read_csv('iowa_public_employees_female_male_ratio.csv', index_col=0)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# Let's see a summary of this data\nprint iowa_by_sex.describe()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "                F           M  Employee Count\ncount  100.000000  100.000000      100.000000\nmean     0.514018    0.485982      576.960000\nstd      0.107958    0.107958     1882.730061\nmin      0.274510    0.000000        1.000000\n25%      0.457567    0.431299       62.750000\n50%      0.507613    0.492387      125.500000\n75%      0.568701    0.542433      345.250000\nmax      1.000000    0.725490    15229.000000\n"
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "print iowa_by_sex.loc['TAMA']",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "F                   0.664311\nM                   0.335689\nEmployee Count    283.000000\nName: TAMA, dtype: float64\n"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# add code column to our iowa dataframe\niowa_by_sex['code'] = fips['code'].astype(int)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# pandas magic! We should see the code as a new column in this row.\n# adding a column is so simple since the two dataframes \n# each use county as their index\nprint iowa_by_sex.loc['TAMA']",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "F                     0.664311\nM                     0.335689\nEmployee Count      283.000000\ncode              19171.000000\nName: TAMA, dtype: float64\n"
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# load the blank Iowa SVG\nsvg = open('iowa_counties.svg', 'r').read()",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<img src=\"http://i.imgur.com/qdh2mJh.png?1\" width=\"50%\" height=\"50%\">"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# parse SVG, defining selfClosingTags as shown here: https://josephhall.org/nqb2/index.php/flwdchrplth\nsoup = BeautifulSoup(svg, selfClosingTags=['defs','sodipodi:namedview'])",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# find counties using findAll\npaths = soup.findAll('path')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# The map colors in order from lightest to darkest\ncolors = ['#F1EEF6', '#D0D1E6', '#A6BDDB', '#74A9CF', '#2B8CBE', '#045A8D']",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# county base style. We'll add the fill color (at the end of this string) for each county path\npath_style=\"font-size:12px;fill-rule:nonzero;stroke:#000000;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:bevel;stroke-miterlimit:4;stroke-opacity:1;stroke-dasharray:none;marker-start:none;fill:\"",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# Color the counties based on percent of public employees that are female\nfor p in paths:\n \n    if p['id'] not in [\"State_Lines\", \"separator\"]: # only color the counties\n        try:\n            rate = iowa_by_sex[iowa_by_sex['code'] == int(p['id'])]['F']\n        except:\n            continue\n \n        if rate > 0.833:\n            color_class = 5\n        elif rate > 0.666:\n            color_class = 4\n        elif rate > 0.5:\n            color_class = 3\n        elif rate > 0.333:\n            color_class = 2\n        elif rate > 0.166:\n            color_class = 1\n        else:\n            color_class = 0\n \n        color = colors[color_class]\n        p['style'] = path_style + color",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# Save result\nfo = open(\"iowa_counties_colored.svg\", \"wb\")\nfo.write(soup.prettify());\nfo.close()",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "# output is an SVG of Iowa counties colored by % of public employees that are female"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<img src=\"http://i.imgur.com/s22Y080.png\" width=\"60%\" height=\"60%\">"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    }
   ],
   "metadata": {}
  }
 ]
}