nkrumm/100 years of US Presidential Elections, Clustered.ipynb

## 100 years of US Presidential Elections, Clustered.ipynb
{
 "metadata": {
  "name": "100 years of US Presidential Elections, Clustered"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "%matplotlib inline",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import pandas as pd\nimport numpy as np\nimport sklearn.metrics.pairwise\nimport scipy\nimport pylab\nimport scipy.cluster.hierarchy as sch\nfrom matplotlib import colors",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# was copied from Google Doc at https://docs.google.com/spreadsheets/d/1J9n6ty6ZMBdRTRxA2frTTypDP5Mpd0t3-5Yus24HYZ4/edit#gid=0\ndf = pd.read_clipboard().set_index(\"State\")",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df.head()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>1916</th>\n      <th>1920</th>\n      <th>1924</th>\n      <th>1928</th>\n      <th>1932</th>\n      <th>1936</th>\n      <th>1940</th>\n      <th>1944</th>\n      <th>1948</th>\n      <th>1952</th>\n      <th>...</th>\n      <th>1976</th>\n      <th>1980</th>\n      <th>1984</th>\n      <th>1988</th>\n      <th>1992</th>\n      <th>1996</th>\n      <th>2000</th>\n      <th>2004</th>\n      <th>2008</th>\n      <th>2012</th>\n    </tr>\n    <tr>\n      <th>State</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Alabama</th>\n      <td>D</td>\n      <td>D</td>\n      <td>D</td>\n      <td>D</td>\n      <td>D</td>\n      <td>D</td>\n      <td>D</td>\n      <td>D</td>\n      <td>O</td>\n      <td>D</td>\n      <td>...</td>\n      <td>D</td>\n      <td>R</td>\n      <td>R</td>\n      <td>R</td>\n      <td>R</td>\n      <td>R</td>\n      <td>R</td>\n      <td>R</td>\n      <td>R</td>\n      <td>R</td>\n    </tr>\n    <tr>\n      <th>Alaska</th>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>...</td>\n      <td>R</td>\n      <td>R</td>\n      <td>R</td>\n      <td>R</td>\n      <td>R</td>\n      <td>R</td>\n      <td>R</td>\n      <td>R</td>\n      <td>R</td>\n      <td>R</td>\n    </tr>\n    <tr>\n      <th>Arizona</th>\n      <td>D</td>\n      <td>R</td>\n      <td>R</td>\n      <td>R</td>\n      <td>D</td>\n      <td>D</td>\n      <td>D</td>\n      <td>D</td>\n      <td>D</td>\n      <td>R</td>\n      <td>...</td>\n      <td>R</td>\n      <td>R</td>\n      <td>R</td>\n      <td>R</td>\n      <td>R</td>\n      <td>D</td>\n      <td>R</td>\n      <td>R</td>\n      <td>R</td>\n      <td>R</td>\n    </tr>\n    <tr>\n      <th>Arkansas</th>\n      <td>D</td>\n      <td>D</td>\n      <td>D</td>\n      <td>D</td>\n      <td>D</td>\n      <td>D</td>\n      <td>D</td>\n      <td>D</td>\n      <td>D</td>\n      <td>D</td>\n      <td>...</td>\n      <td>D</td>\n      <td>R</td>\n      <td>R</td>\n      <td>R</td>\n      <td>D</td>\n      <td>D</td>\n      <td>R</td>\n      <td>R</td>\n      <td>R</td>\n      <td>R</td>\n    </tr>\n    <tr>\n      <th>California</th>\n      <td>D</td>\n      <td>R</td>\n      <td>R</td>\n      <td>R</td>\n      <td>D</td>\n      <td>D</td>\n      <td>D</td>\n      <td>D</td>\n      <td>D</td>\n      <td>R</td>\n      <td>...</td>\n      <td>R</td>\n      <td>R</td>\n      <td>R</td>\n      <td>R</td>\n      <td>D</td>\n      <td>D</td>\n      <td>D</td>\n      <td>D</td>\n      <td>D</td>\n      <td>D</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 25 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": "           1916 1920 1924 1928 1932 1936 1940 1944 1948 1952 ...  1976 1980  \\\nState                                                        ...              \nAlabama       D    D    D    D    D    D    D    D    O    D ...     D    R   \nAlaska        -    -    -    -    -    -    -    -    -    - ...     R    R   \nArizona       D    R    R    R    D    D    D    D    D    R ...     R    R   \nArkansas      D    D    D    D    D    D    D    D    D    D ...     D    R   \nCalifornia    D    R    R    R    D    D    D    D    D    R ...     R    R   \n\n           1984 1988 1992 1996 2000 2004 2008 2012  \nState                                               \nAlabama       R    R    R    R    R    R    R    R  \nAlaska        R    R    R    R    R    R    R    R  \nArizona       R    R    R    D    R    R    R    R  \nArkansas      R    R    D    D    R    R    R    R  \nCalifornia    R    R    D    D    D    D    D    D  \n\n[5 rows x 25 columns]"
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df_factors = df.apply(lambda x: x.replace(\"D\", 1))\\\n               .apply(lambda x: x.replace(\"R\", -1))\\\n               .apply(lambda x: x.replace(\"-\", 0))\\\n               .apply(lambda x: x.replace(\"O\", 0))",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# optional, weight more recent years more heavily\n# set to 1 if no weighting desired; 3 is a good balance\nweight = 3\ndf_factors_w = df_factors * np.linspace(1, weight, df_factors.shape[1])",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "D = sklearn.metrics.pairwise.pairwise_distances(df_factors_w, metric=\"euclidean\")",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig = pylab.figure(figsize=(20,20))\nax1 = fig.add_axes([0.09,0.1,0.2,0.7])\n# create dendrogram on left\nY = sch.linkage(D, method='weighted')\nZ1 = sch.dendrogram(Y, orientation='right', labels=df_factors.index)\n# fix aesthetics\nax1.set_xticks([])\nax1.spines[\"top\"].set_visible(False)\nax1.spines[\"left\"].set_visible(False)\nax1.spines[\"right\"].set_visible(False)\nax1.spines[\"bottom\"].set_visible(False)\n\n# Plot distance matrix.\naxmatrix = fig.add_axes([0.35,0.1,0.6,0.7])\nidx1 = Z1['leaves']\nmat = df_factors.values[idx1,:]\n\n# set up colors\ncmap = colors.ListedColormap([\"red\",\"lightgray\",\"blue\"])\n# show main grid\nim = axmatrix.matshow(mat, aspect='auto', origin='lower', cmap=cmap)\n# fix labels + add grids\naxmatrix.set_xticks(np.arange(0,25))\naxmatrix.set_xticklabels(df.columns.values, rotation=90, ha=\"center\")\naxmatrix.set_yticks(np.arange(0,51)-0.5)\naxmatrix.set_yticklabels([])\naxmatrix.grid(c='white', axis=\"y\", which=\"both\", ls='solid', lw=1)\n#fig.savefig(\"/Users/nkrumm/Desktop/states.pdf\")",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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       "text": "<matplotlib.figure.Figure at 0x115001510>"
      }
     ],
     "prompt_number": 8
    }
   ],
   "metadata": {}
  }
 ]
}