Folium Chloropleth example with loan data

folium_cholor_loans.ipynb

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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Creating a State Chloropleth Map with Folium \n",
      "_Using loan data_"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# If not yet installed: \"pip install dstk\"; \"pip install folium\"\n",
      "import folium"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get state shapes and state data\n",
      "state_geo = r'https://gist.githubusercontent.com/datadave/108b5f382c838c3963d7/raw/3036216d894d49205948dbbfd562754ef3814785/us-states.json'\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "from IPython.display import HTML # for viewing html map"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 103
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Read and select data\n",
      "df = pd.read_csv(\"https://gist.githubusercontent.com/datadave/c9b95cdd022fea1b5006/raw/ede8a9976385b61ed15e9201b6cc74a9c78505eb/loan_cartodb2_total.csv\") # Sourced from http://shortyskater456.cartodb.com/viz/40d16f7e-6d3e-11e4-a898-0e9d821ea90d/\n",
      "df = df[['addr_state','funded_amnt']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 105
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>addr_state</th>\n",
        "      <th>funded_amnt</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> AL</td>\n",
        "      <td> 13758.83721</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> AR</td>\n",
        "      <td> 12055.41775</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> AZ</td>\n",
        "      <td> 13198.65455</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> CA</td>\n",
        "      <td> 13938.36698</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> CO</td>\n",
        "      <td> 14271.60633</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 106,
       "text": [
        "  addr_state  funded_amnt\n",
        "0         AL  13758.83721\n",
        "1         AR  12055.41775\n",
        "2         AZ  13198.65455\n",
        "3         CA  13938.36698\n",
        "4         CO  14271.60633"
       ]
      }
     ],
     "prompt_number": 106
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Review the data to anticipate chloropleth range\n",
      "df.funded_amnt.hist(bins = 100)\n",
      "plt.title(\"Funded Amount\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 107,
       "text": [
        "<matplotlib.text.Text at 0x10999c250>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x108ebd6d0>"
       ]
      }
     ],
     "prompt_number": 107
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.funded_amnt = df.funded_amnt / 1000 # For easier legend formatting"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 112
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Make a map\n",
      "\n",
      "map = folium.Map(location=[40, -100], zoom_start=4) # Initialize map\n",
      "\n",
      "thresh = [10, 11.2, 12.4, 13.6, 14.8,16] # set the threshold, use histogram as guide\n",
      "\n",
      "map.geo_json(geo_path=state_geo, data=df,\n",
      "             columns=['addr_state', 'funded_amnt'], # pick columns\n",
      "             key_on='feature.id',\n",
      "             threshold_scale = thresh, # set threshold\n",
      "             fill_color='YlOrRd', fill_opacity=0.75, line_opacity=0.5, # colors\n",
      "             legend_name='Funded Amount ($1,000)') # legend\n",
      "map.create_map(path='funded_chloropleth.html') #draw map"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Locally, you can use HTML library to Display the map inline\n",
      "# HTML('<iframe src=funded_chloropleth.html width=1000 height = 500><iframe>')\n",
      "\n",
      "# For publishing on the gist, showing image via markdown"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![](http://s2.postimg.org/tkucolkxl/Screen_Shot_2014_11_19_at_12_32_07_PM.png)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}