cpcloud/gist:6233458

## gistfile1.txt
{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mons"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "['Jan',\n",
        " 'Feb',\n",
        " 'Mar',\n",
        " 'Apr',\n",
        " 'May',\n",
        " 'Jun',\n",
        " 'Jul',\n",
        " 'Aug',\n",
        " 'Sep',\n",
        " 'Oct',\n",
        " 'Nov',\n",
        " 'Dec']"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# just get the first 5 for illustration purposes\n",
      "df = DataFrame(randn(201, len(mons)), columns=mons,\n",
      "               index=arange(1901, 2102))[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.iloc[:, :5] # subset of the frame"
     ],
     "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>Jan</th>\n",
        "      <th>Feb</th>\n",
        "      <th>Mar</th>\n",
        "      <th>Apr</th>\n",
        "      <th>May</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1901</th>\n",
        "      <td>-0.744</td>\n",
        "      <td>-0.163</td>\n",
        "      <td> 0.411</td>\n",
        "      <td>-1.336</td>\n",
        "      <td> 0.516</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1902</th>\n",
        "      <td>-0.357</td>\n",
        "      <td>-1.125</td>\n",
        "      <td>-2.232</td>\n",
        "      <td> 0.673</td>\n",
        "      <td> 0.736</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1903</th>\n",
        "      <td>-0.119</td>\n",
        "      <td> 0.563</td>\n",
        "      <td> 0.356</td>\n",
        "      <td> 1.526</td>\n",
        "      <td>-1.086</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1904</th>\n",
        "      <td>-0.839</td>\n",
        "      <td> 0.460</td>\n",
        "      <td> 0.312</td>\n",
        "      <td> 0.922</td>\n",
        "      <td>-1.575</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1905</th>\n",
        "      <td>-0.902</td>\n",
        "      <td>-0.671</td>\n",
        "      <td>-0.383</td>\n",
        "      <td>-1.795</td>\n",
        "      <td> 1.445</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "        Jan    Feb    Mar    Apr    May\n",
        "1901 -0.744 -0.163  0.411 -1.336  0.516\n",
        "1902 -0.357 -1.125 -2.232  0.673  0.736\n",
        "1903 -0.119  0.563  0.356  1.526 -1.086\n",
        "1904 -0.839  0.460  0.312  0.922 -1.575\n",
        "1905 -0.902 -0.671 -0.383 -1.795  1.445"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from mpl_toolkits.axes_grid1 import ImageGrid\n",
      "fig = figure(figsize=(20, 100))\n",
      "grid = ImageGrid(fig, 111, nrows_ncols=(1, 1),\n",
      "                 direction='row', axes_pad=0.05, add_all=True,\n",
      "                 label_mode='1', share_all=False,\n",
      "                 cbar_location='right', cbar_mode='single',\n",
      "                 cbar_size='10%', cbar_pad=0.05)\n",
      "\n",
      "ax = grid[0]\n",
      "ax.set_title('A', fontsize=40)\n",
      "ax.tick_params(axis='both', direction='out', labelsize=20)\n",
      "im = ax.imshow(df.values, interpolation='nearest', vmax=df.max().max(),\n",
      "               vmin=df.min().min())\n",
      "ax.cax.colorbar(im)\n",
      "ax.cax.tick_params(labelsize=20)\n",
      "ax.set_xticks(arange(df.shape[1]))\n",
      "ax.set_xticklabels(mons)\n",
      "ax.set_yticks(arange(df.shape[0]))\n",
      "ax.set_yticklabels(df.index)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAABNIAAAHyCAYAAAAum+NCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x86ad390>"
       ]
      }
     ],
     "prompt_number": 54
    }
   ],
   "metadata": {}
  }
 ]
}