{
 "metadata": {
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  "name": "",
  "signature": "sha256:e1f952a8600390364b6b97ee76ae75210e3615993b9f2ac1e6e271b023c664dd"
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "# Me: Chris Hausler\n",
      "# Today - Pandas and Scikit-Learn\n",
      "\n",
      "## And a lot of firsts\n",
      "   + first MPUG meeting... Hi \n",
      "   + first presentation using IPython Notebook"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## Python Data Analysis Library (pandas)\n",
      "\n",
      "pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.<br>\n",
      "<b>Creates somthing similar to R DataFrames.. but better</b>\n",
      "\n",
      "### I think it's great, but I'm still a bit clumsy with it .. also the doco is still a little hit and miss\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "#### Some imports"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import warnings\n",
      "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
      "warnings.filterwarnings(\"ignore\", category=UserWarning)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "import pylab as plt\n",
      "import matplotlib \n",
      "%matplotlib inline\n",
      "pd.__version__"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "'0.13.1'"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "### <code>pandas</code> has two main data structures: <code>Series</code> and <code>DataFrame</code>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## Series - Like a one dimensional array but better"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "values = [5,3,4,8,2,9]\n",
      "vals = pd.Series(values)\n",
      "vals"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "0    5\n",
        "1    3\n",
        "2    4\n",
        "3    8\n",
        "4    2\n",
        "5    9\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Each value is now associated with an _index_. The index itself is an object of class <code>Index</code> and can be manipulated directly."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vals.index"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "Int64Index([0, 1, 2, 3, 4, 5], dtype='int64')"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vals.values"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "array([5, 3, 4, 8, 2, 9])"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vals * 2.5"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "0    12.5\n",
        "1     7.5\n",
        "2    10.0\n",
        "3    20.0\n",
        "4     5.0\n",
        "5    22.5\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "We can give named indexes"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vals2 = pd.Series(values, index=['tom','sally','jeff','george','pablo','florence'])\n",
      "vals2"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "tom         5\n",
        "sally       3\n",
        "jeff        4\n",
        "george      8\n",
        "pablo       2\n",
        "florence    9\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "\n",
      "And use these to get the data we want"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vals2[['florence','tom']]"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "florence    9\n",
        "tom         5\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vals2[['florence','tom','kate']]"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "florence     9\n",
        "tom          5\n",
        "kate       NaN\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Dealing with missing values"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vals3 = vals2[['tom','sally','pablo','florence','ricky','katrin']]\n",
      "vals3"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "tom          5\n",
        "sally        3\n",
        "pablo        2\n",
        "florence     9\n",
        "ricky      NaN\n",
        "katrin     NaN\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Get rid of them"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vals3.dropna()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "tom         5\n",
        "sally       3\n",
        "pablo       2\n",
        "florence    9\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Fill them with a value"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "vals3.fillna(0)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "tom         5\n",
        "sally       3\n",
        "pablo       2\n",
        "florence    9\n",
        "ricky       0\n",
        "katrin      0\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Fill them with a calculated value"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vals3.fillna(vals3.mean())"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "tom         5.00\n",
        "sally       3.00\n",
        "pablo       2.00\n",
        "florence    9.00\n",
        "ricky       4.75\n",
        "katrin      4.75\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Use a function like forward fill"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vals3.fillna(method='ffill')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "tom         5\n",
        "sally       3\n",
        "pablo       2\n",
        "florence    9\n",
        "ricky       9\n",
        "katrin      9\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "A handy way to get a picture of our data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vals3.describe()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "count    4.000000\n",
        "mean     4.750000\n",
        "std      3.095696\n",
        "min      2.000000\n",
        "25%      2.750000\n",
        "50%      4.000000\n",
        "75%      6.000000\n",
        "max      9.000000\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## DataFrame - Like a 2D array... with bells and whistles"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vals.index=pd.Index(['tom','sally','pablo','florence','ricky','katrin'])\n",
      "vals3=vals3[['tom','sally','pablo','florence','billy','katrin']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# create a dataframe\n",
      "dat = pd.DataFrame({'orig':vals,'new':vals3})\n",
      "dat"
     ],
     "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>new</th>\n",
        "      <th>orig</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>billy</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>florence</th>\n",
        "      <td>  9</td>\n",
        "      <td>  8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>katrin</th>\n",
        "      <td>NaN</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pablo</th>\n",
        "      <td>  2</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ricky</th>\n",
        "      <td>NaN</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sally</th>\n",
        "      <td>  3</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>tom</th>\n",
        "      <td>  5</td>\n",
        "      <td>  5</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>7 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "          new  orig\n",
        "billy     NaN   NaN\n",
        "florence    9     8\n",
        "katrin    NaN     9\n",
        "pablo       2     4\n",
        "ricky     NaN     2\n",
        "sally       3     3\n",
        "tom         5     5\n",
        "\n",
        "[7 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Check for nulls"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dat.isnull()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "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>new</th>\n",
        "      <th>orig</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>billy</th>\n",
        "      <td>  True</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>florence</th>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>katrin</th>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pablo</th>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ricky</th>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sally</th>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>tom</th>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>7 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "            new   orig\n",
        "billy      True   True\n",
        "florence  False  False\n",
        "katrin     True  False\n",
        "pablo     False  False\n",
        "ricky      True  False\n",
        "sally     False  False\n",
        "tom       False  False\n",
        "\n",
        "[7 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Drop rows with nulls"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dat.dropna()"
     ],
     "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>new</th>\n",
        "      <th>orig</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>florence</th>\n",
        "      <td> 9</td>\n",
        "      <td> 8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pablo</th>\n",
        "      <td> 2</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sally</th>\n",
        "      <td> 3</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>tom</th>\n",
        "      <td> 5</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>4 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "          new  orig\n",
        "florence    9     8\n",
        "pablo       2     4\n",
        "sally       3     3\n",
        "tom         5     5\n",
        "\n",
        "[4 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## Timeseries with pandas DataFrames - a winning combination\n",
      "\n",
      "Data from google trends.. what correlates (+ve & -ve) with the search term _Hipster_"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "####Read hipster correlations from a csv file \n",
      "Pandas supports many file formats for read and write including\n",
      "+ _csv_\n",
      "+ _json_\n",
      "+ _pickle_\n",
      "+ _the clipboard_"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hipster = pd.read_csv('hipster.csv')\n",
      "hipster[:10]"
     ],
     "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>Date</th>\n",
        "      <th>hipster</th>\n",
        "      <th>modcloth</th>\n",
        "      <th>gumtree perth</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 2004-01-04</td>\n",
        "      <td>-0.976</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2004-01-11</td>\n",
        "      <td>-0.816</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 2004-01-18</td>\n",
        "      <td>-0.837</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 2004-01-25</td>\n",
        "      <td>-0.976</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 2004-02-01</td>\n",
        "      <td>-0.722</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 2004-02-08</td>\n",
        "      <td>-0.795</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td> 2004-02-15</td>\n",
        "      <td>-0.723</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td> 2004-02-22</td>\n",
        "      <td>-0.713</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td> 2004-02-29</td>\n",
        "      <td>-0.786</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td> 2004-03-07</td>\n",
        "      <td>-0.675</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>10 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "         Date  hipster  modcloth  gumtree perth\n",
        "0  2004-01-04   -0.976    -0.817         -0.844\n",
        "1  2004-01-11   -0.816    -0.817         -0.844\n",
        "2  2004-01-18   -0.837    -0.817         -0.844\n",
        "3  2004-01-25   -0.976    -0.817         -0.844\n",
        "4  2004-02-01   -0.722    -0.817         -0.844\n",
        "5  2004-02-08   -0.795    -0.817         -0.844\n",
        "6  2004-02-15   -0.723    -0.817         -0.844\n",
        "7  2004-02-22   -0.713    -0.817         -0.844\n",
        "8  2004-02-29   -0.786    -0.817         -0.844\n",
        "9  2004-03-07   -0.675    -0.817         -0.844\n",
        "\n",
        "[10 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Set the index to a datetime"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hipster = hipster.set_index(pd.DatetimeIndex(hipster.pop('Date')))\n",
      "hipster[:10]"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "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>hipster</th>\n",
        "      <th>modcloth</th>\n",
        "      <th>gumtree perth</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2004-01-04</th>\n",
        "      <td>-0.976</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-11</th>\n",
        "      <td>-0.816</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-18</th>\n",
        "      <td>-0.837</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-25</th>\n",
        "      <td>-0.976</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-01</th>\n",
        "      <td>-0.722</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-08</th>\n",
        "      <td>-0.795</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-15</th>\n",
        "      <td>-0.723</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-22</th>\n",
        "      <td>-0.713</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-29</th>\n",
        "      <td>-0.786</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-03-07</th>\n",
        "      <td>-0.675</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>10 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "            hipster  modcloth  gumtree perth\n",
        "2004-01-04   -0.976    -0.817         -0.844\n",
        "2004-01-11   -0.816    -0.817         -0.844\n",
        "2004-01-18   -0.837    -0.817         -0.844\n",
        "2004-01-25   -0.976    -0.817         -0.844\n",
        "2004-02-01   -0.722    -0.817         -0.844\n",
        "2004-02-08   -0.795    -0.817         -0.844\n",
        "2004-02-15   -0.723    -0.817         -0.844\n",
        "2004-02-22   -0.713    -0.817         -0.844\n",
        "2004-02-29   -0.786    -0.817         -0.844\n",
        "2004-03-07   -0.675    -0.817         -0.844\n",
        "\n",
        "[10 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Now load the anti-Hipster data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "not_hipster = pd.read_csv('negative-hipster.csv')\n",
      "not_hipster = not_hipster.set_index(pd.DatetimeIndex(not_hipster.pop('Date')))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "not_hipster[:10]"
     ],
     "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>yellow pages</th>\n",
        "      <th>windows installer</th>\n",
        "      <th>techno</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2004-01-04</th>\n",
        "      <td> 1.341</td>\n",
        "      <td> 0.668</td>\n",
        "      <td> 0.871</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-11</th>\n",
        "      <td> 1.239</td>\n",
        "      <td> 1.000</td>\n",
        "      <td> 1.122</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-18</th>\n",
        "      <td> 1.022</td>\n",
        "      <td> 0.768</td>\n",
        "      <td> 1.053</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-25</th>\n",
        "      <td> 0.923</td>\n",
        "      <td> 0.943</td>\n",
        "      <td> 0.807</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-01</th>\n",
        "      <td> 0.904</td>\n",
        "      <td> 0.799</td>\n",
        "      <td> 0.612</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-08</th>\n",
        "      <td> 0.786</td>\n",
        "      <td> 0.613</td>\n",
        "      <td> 0.614</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-15</th>\n",
        "      <td> 0.729</td>\n",
        "      <td> 0.956</td>\n",
        "      <td> 0.391</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-22</th>\n",
        "      <td> 0.537</td>\n",
        "      <td> 0.667</td>\n",
        "      <td> 1.124</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-29</th>\n",
        "      <td> 0.534</td>\n",
        "      <td> 1.415</td>\n",
        "      <td> 1.078</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-03-07</th>\n",
        "      <td> 0.229</td>\n",
        "      <td> 0.220</td>\n",
        "      <td> 1.918</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>10 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "            yellow pages  windows installer  techno\n",
        "2004-01-04         1.341              0.668   0.871\n",
        "2004-01-11         1.239              1.000   1.122\n",
        "2004-01-18         1.022              0.768   1.053\n",
        "2004-01-25         0.923              0.943   0.807\n",
        "2004-02-01         0.904              0.799   0.612\n",
        "2004-02-08         0.786              0.613   0.614\n",
        "2004-02-15         0.729              0.956   0.391\n",
        "2004-02-22         0.537              0.667   1.124\n",
        "2004-02-29         0.534              1.415   1.078\n",
        "2004-03-07         0.229              0.220   1.918\n",
        "\n",
        "[10 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Check the values of one column"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hipster.hipster.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "2004-01-04   -0.976\n",
        "2004-01-11   -0.816\n",
        "2004-01-18   -0.837\n",
        "2004-01-25   -0.976\n",
        "2004-02-01   -0.722\n",
        "Name: hipster, dtype: float64"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Check another, but get them as an <code>numpy.ndarray</code>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hipster['gumtree perth'].values[:20]"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "array([-0.844, -0.844, -0.844, -0.844, -0.844, -0.844, -0.844, -0.844,\n",
        "       -0.844, -0.844, -0.844, -0.844, -0.844, -0.844, -0.844, -0.844,\n",
        "       -0.844, -0.844, -0.844, -0.844])"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "View the data types, they don't need to be homogenous"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hipster.dtypes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "hipster          float64\n",
        "modcloth         float64\n",
        "gumtree perth    float64\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Joins on indexes are easy!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trend = hipster.join(not_hipster, how='inner')\n",
      "trend.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>hipster</th>\n",
        "      <th>modcloth</th>\n",
        "      <th>gumtree perth</th>\n",
        "      <th>yellow pages</th>\n",
        "      <th>windows installer</th>\n",
        "      <th>techno</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2004-01-04</th>\n",
        "      <td>-0.976</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "      <td> 1.341</td>\n",
        "      <td> 0.668</td>\n",
        "      <td> 0.871</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-11</th>\n",
        "      <td>-0.816</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "      <td> 1.239</td>\n",
        "      <td> 1.000</td>\n",
        "      <td> 1.122</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-18</th>\n",
        "      <td>-0.837</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "      <td> 1.022</td>\n",
        "      <td> 0.768</td>\n",
        "      <td> 1.053</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-25</th>\n",
        "      <td>-0.976</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "      <td> 0.923</td>\n",
        "      <td> 0.943</td>\n",
        "      <td> 0.807</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-01</th>\n",
        "      <td>-0.722</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "      <td> 0.904</td>\n",
        "      <td> 0.799</td>\n",
        "      <td> 0.612</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "            hipster  modcloth  gumtree perth  yellow pages  windows installer  \\\n",
        "2004-01-04   -0.976    -0.817         -0.844         1.341              0.668   \n",
        "2004-01-11   -0.816    -0.817         -0.844         1.239              1.000   \n",
        "2004-01-18   -0.837    -0.817         -0.844         1.022              0.768   \n",
        "2004-01-25   -0.976    -0.817         -0.844         0.923              0.943   \n",
        "2004-02-01   -0.722    -0.817         -0.844         0.904              0.799   \n",
        "\n",
        "            techno  \n",
        "2004-01-04   0.871  \n",
        "2004-01-11   1.122  \n",
        "2004-01-18   1.053  \n",
        "2004-01-25   0.807  \n",
        "2004-02-01   0.612  \n",
        "\n",
        "[5 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "We can check the column names and values"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trend.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "Index([u'hipster', u'modcloth', u'gumtree perth', u'yellow pages', u'windows installer', u'techno'], dtype='object')"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trend.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "array([[-0.976, -0.817, -0.844,  1.341,  0.668,  0.871],\n",
        "       [-0.816, -0.817, -0.844,  1.239,  1.   ,  1.122],\n",
        "       [-0.837, -0.817, -0.844,  1.022,  0.768,  1.053],\n",
        "       ..., \n",
        "       [ 1.142,  1.175,  1.394, -1.69 , -1.77 , -1.836],\n",
        "       [ 1.187,  1.221,  1.403, -1.706, -1.752, -1.796],\n",
        "       [ 1.514,  1.216,  1.365, -1.72 , -1.701, -1.883]])"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Filtering on date ranges is simple"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trend['2012-01-01':].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>hipster</th>\n",
        "      <th>modcloth</th>\n",
        "      <th>gumtree perth</th>\n",
        "      <th>yellow pages</th>\n",
        "      <th>windows installer</th>\n",
        "      <th>techno</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2012-01-01</th>\n",
        "      <td> 1.411</td>\n",
        "      <td> 1.192</td>\n",
        "      <td> 1.774</td>\n",
        "      <td>-1.077</td>\n",
        "      <td>-1.134</td>\n",
        "      <td>-1.285</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-08</th>\n",
        "      <td> 1.513</td>\n",
        "      <td> 1.111</td>\n",
        "      <td> 1.579</td>\n",
        "      <td>-0.995</td>\n",
        "      <td>-1.183</td>\n",
        "      <td>-1.189</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-15</th>\n",
        "      <td> 1.523</td>\n",
        "      <td> 1.427</td>\n",
        "      <td> 1.613</td>\n",
        "      <td>-1.027</td>\n",
        "      <td>-1.161</td>\n",
        "      <td>-1.337</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-22</th>\n",
        "      <td> 1.600</td>\n",
        "      <td> 1.490</td>\n",
        "      <td> 1.514</td>\n",
        "      <td>-1.140</td>\n",
        "      <td>-1.177</td>\n",
        "      <td>-1.345</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-29</th>\n",
        "      <td> 1.459</td>\n",
        "      <td> 1.561</td>\n",
        "      <td> 1.511</td>\n",
        "      <td>-1.046</td>\n",
        "      <td>-1.224</td>\n",
        "      <td>-1.233</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "            hipster  modcloth  gumtree perth  yellow pages  windows installer  \\\n",
        "2012-01-01    1.411     1.192          1.774        -1.077             -1.134   \n",
        "2012-01-08    1.513     1.111          1.579        -0.995             -1.183   \n",
        "2012-01-15    1.523     1.427          1.613        -1.027             -1.161   \n",
        "2012-01-22    1.600     1.490          1.514        -1.140             -1.177   \n",
        "2012-01-29    1.459     1.561          1.511        -1.046             -1.224   \n",
        "\n",
        "            techno  \n",
        "2012-01-01  -1.285  \n",
        "2012-01-08  -1.189  \n",
        "2012-01-15  -1.337  \n",
        "2012-01-22  -1.345  \n",
        "2012-01-29  -1.233  \n",
        "\n",
        "[5 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trend['2012-01-01': '2013-01-01'].tail(3)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "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>hipster</th>\n",
        "      <th>modcloth</th>\n",
        "      <th>gumtree perth</th>\n",
        "      <th>yellow pages</th>\n",
        "      <th>windows installer</th>\n",
        "      <th>techno</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2012-12-16</th>\n",
        "      <td> 1.645</td>\n",
        "      <td> 1.175</td>\n",
        "      <td> 1.407</td>\n",
        "      <td>-1.433</td>\n",
        "      <td>-1.515</td>\n",
        "      <td>-1.687</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-23</th>\n",
        "      <td> 1.591</td>\n",
        "      <td> 1.695</td>\n",
        "      <td> 1.625</td>\n",
        "      <td>-1.698</td>\n",
        "      <td>-1.655</td>\n",
        "      <td>-1.504</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-30</th>\n",
        "      <td> 1.596</td>\n",
        "      <td> 1.515</td>\n",
        "      <td> 1.868</td>\n",
        "      <td>-1.515</td>\n",
        "      <td>-1.598</td>\n",
        "      <td>-1.674</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "            hipster  modcloth  gumtree perth  yellow pages  windows installer  \\\n",
        "2012-12-16    1.645     1.175          1.407        -1.433             -1.515   \n",
        "2012-12-23    1.591     1.695          1.625        -1.698             -1.655   \n",
        "2012-12-30    1.596     1.515          1.868        -1.515             -1.598   \n",
        "\n",
        "            techno  \n",
        "2012-12-16  -1.687  \n",
        "2012-12-23  -1.504  \n",
        "2012-12-30  -1.674  \n",
        "\n",
        "[3 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "We can also grab a single date, or a subset of columns"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trend.ix['2012-01-01', ['hipster', 'modcloth']]"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "hipster     1.411\n",
        "modcloth    1.192\n",
        "Name: 2012-01-01 00:00:00, dtype: float64"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "Or do some boolean filtering"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trend[trend.techno < 0].head()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "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>hipster</th>\n",
        "      <th>modcloth</th>\n",
        "      <th>gumtree perth</th>\n",
        "      <th>yellow pages</th>\n",
        "      <th>windows installer</th>\n",
        "      <th>techno</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2004-04-11</th>\n",
        "      <td>-0.510</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "      <td> 0.521</td>\n",
        "      <td> 0.301</td>\n",
        "      <td>-0.270</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2006-01-29</th>\n",
        "      <td>-0.838</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "      <td> 1.421</td>\n",
        "      <td> 1.309</td>\n",
        "      <td>-0.081</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2006-06-25</th>\n",
        "      <td>-0.799</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.833</td>\n",
        "      <td> 1.142</td>\n",
        "      <td> 1.458</td>\n",
        "      <td>-0.070</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-24</th>\n",
        "      <td>-0.454</td>\n",
        "      <td>-0.183</td>\n",
        "      <td>-0.107</td>\n",
        "      <td>-0.017</td>\n",
        "      <td> 0.053</td>\n",
        "      <td>-0.010</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-31</th>\n",
        "      <td>-0.381</td>\n",
        "      <td>-0.276</td>\n",
        "      <td>-0.142</td>\n",
        "      <td> 0.187</td>\n",
        "      <td> 0.116</td>\n",
        "      <td>-0.044</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "            hipster  modcloth  gumtree perth  yellow pages  windows installer  \\\n",
        "2004-04-11   -0.510    -0.817         -0.844         0.521              0.301   \n",
        "2006-01-29   -0.838    -0.817         -0.844         1.421              1.309   \n",
        "2006-06-25   -0.799    -0.817         -0.833         1.142              1.458   \n",
        "2010-01-24   -0.454    -0.183         -0.107        -0.017              0.053   \n",
        "2010-01-31   -0.381    -0.276         -0.142         0.187              0.116   \n",
        "\n",
        "            techno  \n",
        "2004-04-11  -0.270  \n",
        "2006-01-29  -0.081  \n",
        "2006-06-25  -0.070  \n",
        "2010-01-24  -0.010  \n",
        "2010-01-31  -0.044  \n",
        "\n",
        "[5 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Plotting is built in and easier for dates than matplotlib"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "_ = trend.plot(figsize=(10, 6))\n",
      "_ = plt.legend(loc='best', ncol=2)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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x5QLe/bEiMeEzP5+6vC1Yzp3h1CkxNPyFF4BoLkvX9FJdq5EXNuiwa2Ay0bx6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\nnOuuu861T5ZlHA6HazsgIACNpm2CTJtLZ1ujrCkI3c4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+f0Sh0KLTdalTpsaIevTZQCSl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\n7yOBoK0oLNyGyTQQpdJw2bLZ2R9RWPg/PD2vo6DgO0pLfyU8/GHs9jK6dXM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       "text": [
        "<matplotlib.figure.Figure at 0x407cb50>"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "We can also do it for a single column"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "_ = trend.hipster.cumsum().plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9/Os3ABcAG4H+wMv+9kOBcuAnWENwZZLn9v9NIu6Vl8N119kMmFoUJRieZzWY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\nfgpGMFIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x44662d0>"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Or split the columns out to subplots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "axs = trend.plot(subplots=True, figsize=(10, 10))"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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0tLRgZGSE0aNHY82aNWjbti3MzMxw5syZBlbEYFSf+oqxKgQvR5U/gBcAjoPn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\nRSU3/D/fmjflJIiD7gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3011a90>"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Resampling data is also straight forward."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# resample by month\n",
      "trend.resample('M', how='mean').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>hipster</th>\n",
        "      <th>modcloth</th>\n",
        "      <th>gumtree perth</th>\n",
        "      <th>yellow pages</th>\n",
        "      <th>windows installer</th>\n",
        "      <th>techno</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2004-01-31</th>\n",
        "      <td>-0.90125</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "      <td> 1.13125</td>\n",
        "      <td> 0.84475</td>\n",
        "      <td> 0.96325</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-29</th>\n",
        "      <td>-0.74780</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "      <td> 0.69800</td>\n",
        "      <td> 0.89000</td>\n",
        "      <td> 0.76380</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-03-31</th>\n",
        "      <td>-0.78950</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "      <td> 0.35650</td>\n",
        "      <td> 0.73125</td>\n",
        "      <td> 1.09175</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-04-30</th>\n",
        "      <td>-0.70400</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "      <td> 0.48125</td>\n",
        "      <td> 0.89125</td>\n",
        "      <td> 0.41950</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-05-31</th>\n",
        "      <td>-0.81820</td>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.844</td>\n",
        "      <td> 0.34780</td>\n",
        "      <td> 0.62040</td>\n",
        "      <td> 0.72860</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "            hipster  modcloth  gumtree perth  yellow pages  windows installer  \\\n",
        "2004-01-31 -0.90125    -0.817         -0.844       1.13125            0.84475   \n",
        "2004-02-29 -0.74780    -0.817         -0.844       0.69800            0.89000   \n",
        "2004-03-31 -0.78950    -0.817         -0.844       0.35650            0.73125   \n",
        "2004-04-30 -0.70400    -0.817         -0.844       0.48125            0.89125   \n",
        "2004-05-31 -0.81820    -0.817         -0.844       0.34780            0.62040   \n",
        "\n",
        "             techno  \n",
        "2004-01-31  0.96325  \n",
        "2004-02-29  0.76380  \n",
        "2004-03-31  1.09175  \n",
        "2004-04-30  0.41950  \n",
        "2004-05-31  0.72860  \n",
        "\n",
        "[5 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "and Here by year,\n",
      "but one can do business day, week, month, quarter annual and a bunch of others"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# resample by year\n",
      "_ = trend.resample('A', how='mean').plot(figsize=(10, 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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EbCow4BbXkBExIYQQJkFR1Jro9ddh6FD1uZQLKQQtDCLs+TDcGrjd+iKlpbB379V+r7w8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ZSUsPAff/BQuSc9V3ap9fZlZSfZtStCasVEkyY1YkII01EKli2rqgULCyNnzT6e/D6azn0z+Mn9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\nyvk6Rx198qjac/MetdFho9oVtUsdfvSwy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ipP8rvteotVE7qOJgVYiJmB3wk0u5c3039w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\nERERERERERFr/D8C32o7VSQTiQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4090810>"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Other fancy plots include a scatter matrix including a kernel density estimation (KDE)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# look at the relations\n",
      "_ = pd.scatter_matrix(trend, figsize=(12,8), diagonal='kde')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8pR7YDtiBXCAUd87qU2bMmMHGje5Uedu2DWf48OHNOujAgfDYY+ByuUe5JZLrQWlpKX/7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\njlotw8fHk6Ymf+66q32zAmq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aMFyuw8jlu2hqmoA7F0Em7dvfSqdOGXh6RlJYmI/d/hB6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PJEnMnbuEdev2M2hQPNddN4o2bbYTGdmFhIQENm9O5/PPcxEEkX/9q4U+feSLYa9ePenVqyeL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wkICOC992axdetROnQYT3JyEaWlCtTqAk6eLMdiicfDQ3XOE+UJE4ZQVbURT09Nk5f6f0lK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t6w0Z7lYoFDz4oOjgIQgCfn5tmDjxf2zfPpN33z2Bt/dhnn12KO+88x07dvjg7ByNSpVN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\nEPxf5oLFPjpNsLh/XIb435S4nT8fEashYoAbgTycr5tZBbyOCNsY/TyE2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       "text": [
        "<matplotlib.figure.Figure at 0x55ea410>"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "#Titanic: Machine Learning from Disaster (kaggle.com)\n",
      "Load the data, explore it and learn from it"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.read_csv('train.csv', header=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "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>PassengerId</th>\n",
        "      <th>Survived</th>\n",
        "      <th>Pclass</th>\n",
        "      <th>Name</th>\n",
        "      <th>Sex</th>\n",
        "      <th>Age</th>\n",
        "      <th>SibSp</th>\n",
        "      <th>Parch</th>\n",
        "      <th>Ticket</th>\n",
        "      <th>Fare</th>\n",
        "      <th>Cabin</th>\n",
        "      <th>Embarked</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>                           Braund, Mr. Owen Harris</td>\n",
        "      <td>   male</td>\n",
        "      <td> 22</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>        A/5 21171</td>\n",
        "      <td>  7.2500</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
        "      <td> female</td>\n",
        "      <td> 38</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>         PC 17599</td>\n",
        "      <td> 71.2833</td>\n",
        "      <td>  C85</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td>                            Heikkinen, Miss. Laina</td>\n",
        "      <td> female</td>\n",
        "      <td> 26</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> STON/O2. 3101282</td>\n",
        "      <td>  7.9250</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>      Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
        "      <td> female</td>\n",
        "      <td> 35</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>           113803</td>\n",
        "      <td> 53.1000</td>\n",
        "      <td> C123</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>                          Allen, Mr. William Henry</td>\n",
        "      <td>   male</td>\n",
        "      <td> 35</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>           373450</td>\n",
        "      <td>  8.0500</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 12 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "   PassengerId  Survived  Pclass  \\\n",
        "0            1         0       3   \n",
        "1            2         1       1   \n",
        "2            3         1       3   \n",
        "3            4         1       1   \n",
        "4            5         0       3   \n",
        "\n",
        "                                                Name     Sex  Age  SibSp  \\\n",
        "0                            Braund, Mr. Owen Harris    male   22      1   \n",
        "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
        "2                             Heikkinen, Miss. Laina  female   26      0   \n",
        "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
        "4                           Allen, Mr. William Henry    male   35      0   \n",
        "\n",
        "   Parch            Ticket     Fare Cabin Embarked  \n",
        "0      0         A/5 21171   7.2500   NaN        S  \n",
        "1      0          PC 17599  71.2833   C85        C  \n",
        "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
        "3      0            113803  53.1000  C123        S  \n",
        "4      0            373450   8.0500   NaN        S  \n",
        "\n",
        "[5 rows x 12 columns]"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Lets look at the data types here (this time they're heterogeneous)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.dtypes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "PassengerId      int64\n",
        "Survived         int64\n",
        "Pclass           int64\n",
        "Name            object\n",
        "Sex             object\n",
        "Age            float64\n",
        "SibSp            int64\n",
        "Parch            int64\n",
        "Ticket          object\n",
        "Fare           float64\n",
        "Cabin           object\n",
        "Embarked        object\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "We can also get a more verbose summary"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.info()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 891 entries, 0 to 890\n",
        "Data columns (total 12 columns):\n",
        "PassengerId    891 non-null int64\n",
        "Survived       891 non-null int64\n",
        "Pclass         891 non-null int64\n",
        "Name           891 non-null object\n",
        "Sex            891 non-null object\n",
        "Age            714 non-null float64\n",
        "SibSp          891 non-null int64\n",
        "Parch          891 non-null int64\n",
        "Ticket         891 non-null object\n",
        "Fare           891 non-null float64\n",
        "Cabin          204 non-null object\n",
        "Embarked       889 non-null object\n",
        "dtypes: float64(2), int64(5), object(5)"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "DataFrames can be grouped, like in SQL\n",
      "(_it sucked to be a young male on the titanic_)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_grouped = df.groupby(['Pclass', 'Sex'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_grouped[['Age', 'Survived']].mean()"
     ],
     "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></th>\n",
        "      <th>Age</th>\n",
        "      <th>Survived</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Pclass</th>\n",
        "      <th>Sex</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
        "      <th>female</th>\n",
        "      <td> 34.611765</td>\n",
        "      <td> 0.968085</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>male</th>\n",
        "      <td> 41.281386</td>\n",
        "      <td> 0.368852</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
        "      <th>female</th>\n",
        "      <td> 28.722973</td>\n",
        "      <td> 0.921053</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>male</th>\n",
        "      <td> 30.740707</td>\n",
        "      <td> 0.157407</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
        "      <th>female</th>\n",
        "      <td> 21.750000</td>\n",
        "      <td> 0.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>male</th>\n",
        "      <td> 26.507589</td>\n",
        "      <td> 0.135447</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>6 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "                     Age  Survived\n",
        "Pclass Sex                        \n",
        "1      female  34.611765  0.968085\n",
        "       male    41.281386  0.368852\n",
        "2      female  28.722973  0.921053\n",
        "       male    30.740707  0.157407\n",
        "3      female  21.750000  0.500000\n",
        "       male    26.507589  0.135447\n",
        "\n",
        "[6 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Histograms are straightforward"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = df['Age'].dropna().hist(bins=20, range=(0,100), alpha = .5)\n",
      "ax.set_xlabel('Age')\n",
      "ax.set_ylabel('Passenger Count')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "<matplotlib.text.Text at 0x7576ed0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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xRaIvx0xVxwI9uL8V5Pt3eg/uU/lLwDrgOlwL3MLf6XKf73DcFsJTjPBv1SzF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\nERFpBf8PUCvZncntagMAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x76e3d50>"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "So are boxplots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bp = df.boxplot(column='Age', by='Pclass', grid=False)\n",
      "for i in set(df.Pclass):\n",
      "    y = df.Age[df.Pclass==i].dropna()\n",
      "    # Add some random \"jitter\" to the x-axis\n",
      "    x = np.random.normal(i, 0.04, size=len(y))\n",
      "    plt.plot(x, y, 'r.', alpha=0.2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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vvGPVN0TXOygzT7QMzHz3xkYpJMzPF+VcVibFZxkZctMOhayA6ty5\nI+XnQdmq5R4P0Xd104JbupSPPbxi+DGlpdo1MJmxy7KoyFo2cf16K6ialycKY+lS2LjRGuy6nGLi\niZaBme+eni7yGhyUmMnixRIYT02VCta8PNl2551i0VdXD4+beFC2GlCNB3swrqFBLL09eySlatYs\nme7t3GlVyl199fBe7rHy5ZUpY1w9wHftEsu8q0uah61ZI9P6vXtFWSxe7OymWb48ZoOpCV2L4khc\nNQs7d8qzWWtyxx2yrbVVYiUlJfI4f15aStTUwF13yVgtKRE3TRw9oJJdlprnPlnsd3Vzqbb0dLHy\nCgvFd9vWJlH5o0flF3HFFVbvb/syfdoPPLEEg5LR1Noqbpc5c+Sm29Iicurvlxt4WprItrVVHpdc\nIkrCLIBRpo1BAuM3O7dvH/uYX/96Atdi/es2VLmPF3Nab+ZIm3nTR4+KYli4UCx67e+evDgtz3b8\nuNU9cP16SZfr7RUlb7ppfJQjnUjiagL37/8uMgNxnV1+OfzudxJArauTSvG2Nise1tUlC9aHw3JD\nH0+F6tT8t2YcVe7jpaxMrO+5c8WSW7JEtqWkyOo9N9wg+e6BAOzYMeSbJytL894TRV2dZMX09Ii7\nxZQXiJyOHhUZtbXJDGv1agnEnTghrpimJlEO6emJ/X/4FVN+3d0ij7IyuPlmmWEtWiSrMAWDcO21\n8MMfSiD1+HH48IflBr13L9x3nwRcx7HGgttRn/tEcerxHr3/0CH5cWVkiGWh0/lpY/Q2ElWs4hBp\n9NJJOvsoH2oJsZYqQkRYyUHyaOEcuXSRQQ9pvEURIfrIo9loJ9FHG9ljtpPIzhZXrzIxRvi5xxpL\n9rH4yisy0+rulhnZsmWTUujqc/caTnnt0QHR3buhqYlfv5zOpue+OPI881cxOKjT+Rlg9Gm80R7i\nVLMEvNd0w2ojEGe2mGhIg758PvWpfn76h+LhXT6bQ3D+oLho7lgP3m0kmJw0Nkqh4MAArFwp2TE7\ndkjaY0uLpKv294vMIhH4+c9Fdvn5Uqdw/LjETQoKJBa2aJF8Zl6efF4cQXI3osrdiegAqLnKkj0g\nOmsWhMP8dGcWmyorpYmR/bysLMmZBXXHJBrTDZOeLgO7q8uSo+lmW78eamt5HWCjYRWaGVJVVeLS\nKSgQJaMzsJklP1/cMf391jg6e1bkePGiyDVkqLJrroGXXhK/+vnzIsP+frkBXLworrc1ayT5ob1d\nrAKPBsnjUe6LgH8B8pDYwqPAPyDrqP4EWMzo66i6j1h57faAaEYG5OTQQ9haZNd+nir05CEYlPYQ\nAwNi9dnlaM+EWrWKo2A1ejO3h8NWIYzOwGaecFhuymbPpuimb4WFVg/+ri6R29tvi1JftUpm1L29\n0ub5iitkJn3ypNw0PNyrPx6fe4HxqAJmA3uA24BPA2eB7cCDQDbwUNS57vS5m3ntZuMop0Wwly6F\nykpSP3gDfYPGPL2mRnyDJSWeneq5mliN4GzutOCaMiKDUfvsdQ1nz4pSCIe1ZmGaGOHnHq3pW329\njLm335YxWVsrqy499pi40U6csCz0ggIpYpozR5T6m2+K1Z6RIQFah4U73Oxzj6f9QDOi2AE6gMNA\nIXAL8ISx/QlE4XsD05ozG0eZjcDsjaROnoTNm+m3O2A7O4dP+5XkIlYjOFtLgqU4NJQCq67h7FmJ\nwXiowVTSM1rTtwsXxHfe2Aj798vx+/fLvu5uy03a3y9+9xdflPcZGeJaHRiQpmI7dyb2/zgNjLe3\nTDFQDrwG5ANnjO1njPfeIhSSCLw5HY9+P9bxijuwye3jDzs0lOrrs9wA9voGlXHiCYctZV1aKjfh\n8nJ5NldgysmR1wUF4sJJTZVjzXPDNteqhxhPKuRs4GXgL4FfAG2IK8bkPOKHt+NOt4xJ9DTeYVq/\ndas8HI9X3MFocjP3Rfd6VxlPG+Pq5x6JiNV97bXwk59I/cmuXWLZX7ggiryvT2pSCgtl/333SYA1\nOxueekrWYJ0zx9E142a3TLzKPRX4d+BXwLeNbUeACsRtswB4CVgZdd7gli1bht5UVFRQUVER559U\nFMWPjKpQR6svMfPhf/MbyYrJyRH3WW6upENGIuI2LSuTpfcyM6W3UHq6HFtYKFlv8V5LAqisrKSy\nsnLo/bZt22ASyj2A+NTPAV+xbd9ubHsECaTOwysBVUVREsaoCrW62sp4im7PW10N+/bBq69aqauz\nZkkMzKwmnz1bWhM0Noqif+UVseYzMuCjH/Wd5X4N8BtgP1abha8DrwPPAEXEToX0nnI3p4VNTfID\nMhd6cFq2y17yrtkzyYcpo927JQfazKe+9FLNhkkgwxTqWMvqtbeLos7Pl2PeeEOUe2urHL92rVjy\nkYg83v9+2bZiBbz8sij755+XlgVz5oyQuZuVezx57r8jduD1/RO8JvdiZla0tkp1XG6u5MFHF0KY\nS3719jrvVxKPKaMzZ8Q/e/Gi9Cl597tliq4dPBOPU0dVe4fWSMQqaCosFF96erp0/jx1SjTzwoVy\nE58/X9wzOTlSyDR/vpxfUiI3CI/JXCtUnYjVfqCx0WoLGwpJClZTk/wozLQr865v7zzo4UIJV9PY\nKPnrDQ1Dlt3uI2E2zj0oqa7vec/IRR2UmWW0jqr2Dq3PPivG1okT4ks3fe379lnZTuXl8oguOvRo\nBpQqdyditR84e1Z8c8EgXHYZBIP8eMth7npvVEk7DO88qNWqyUl+vjyysuSGPn8+//azWWx8f5sM\ndLP60UPWnFswG8GlUMZSaqmjlAGGjyFzXz3r+QlPEGE2ZUQYpJ02suk5FuASBkhngLNkcKr6It94\n8goOfyZtxPkl1Dv+jexsXIsqdyditR8Ih2WKFw5LWXMwyDd/NsBdX+0Yedc3S96V5MVsK9DbK3GR\nSIQ+GqyWvx605tzAcB93EGJ24bTtu7dAZtGn0uHGG6GxkX98Zg5ffNd+CZIuXw733svHr15ltZcY\n9tneu3nrAtlORC+Wa76//XarHcHhwxCJ0E2a+GmbmyUVq7parHwl+UlLEwWfkyOy3bCBX3KbLJNY\nXi5xldTURF+lMhZ1dXDPPTJG//M/xaj64z+mkLdkLAYCsi0tbWjc+gFV7k5El6nbS57nz5dtRvl5\nJkbLgfPntSzdbZhrbebni4991SoOs0YUQU+PtpJwC+3t4mf/0pdk/N1/P/T0cJEsyYAJhyWf/dQp\nX41PVe7jJarFQATjfVqaTuPdxmjtIrSVhHtwklU4DBgJDUVF8N73+m58qs99vJj9v43y8yG3TEaG\nPJeWyjTR7B6pmRbJiynLQAAOHIBQiK0PlwFBx30qyyRi1y7Jjjl/Xnrxt7bCBz4g7QROn4a2Ns4z\nG1LPS3OwQEAaim3YIK4ZH8hSLffxEuWy+epnDbfM22/L/q4umQKa3SN9MgV0JdHdPzs62PLx2pj7\nVJZJREuLpCQ3N8Prr4u7tL5eFHtfH7S28t5lbdIGuKND1lF95x1fuWZUucdLXZ1jwPSBP3FwyyxZ\nolN6N6HuGfcRDktO+/nz0j4gJcXqCvnWW9DRwZqbCqXHTHu7KPSBAbkJ+ESWukB2vMRqWBTdNdC+\noIB2DnQH8XSFVFkmF7298OijsoDKW29JxemqVTJz/uY3JR3y5EmpTJ0921rLOCdHWg14RJZT0RVy\nonhHuY/WsEhRlOkjumLcHHuxxqR9e2qqvO/tlcLDDRs8NXZVuU8U+49q+XLt4+0lYikMJfkYa9Zs\njklTpqdPizJPTZU018ZG8b2Hw55LdJjsMnv+xbb82tCq9x74QSgMl60tuDa08IqSPMSKe0TXo5gy\nPX9e3DBtbRJALSiQmJjPguOq3EcjjmCaKgOXEkO2svaBklREV4zHwpSpkdzwzC+jGoL5LDiubpnR\niCOYluz9npUYxJDtkDzVbeM+TJkODMCRI6y7YzH7DqZb1r0Hg+Pqlpko0dM+xTuMJdsYbhsliTFl\n2tMDRUWk0yvbotuI+GQ8q3JXFCd8NoX3FIbs+gnJVMynDf3ULTNJ1C3jLYbk6cEpvG8wZBdcWUpk\n74HYC2p7gMm6Zf4ZOAMcsG3LAV4AaoDnkcWxFcX1bNlivPDZFN5TGLIbIOjrGVg8yv0HwKaobQ8h\nyn05sNN4730cWhAMKQPFE2j2kwuxj8uamqHXWx+OSDD8nXeGrcHgF+J1yxQDzwFrjPdHgOsRi74A\nqARWOpznLbdMrGIKRVESh31cNjdLIz/7GPXwuJ2ObJl8RLFjPOdP8HPchY+neIqStNjHZUnJyDHq\n03E7Fdkyg8bD+8RbTKEoysxhH5erV490w/jUNTPRxTpMd0wzsABoiXXgVpsTs6KigoqKign+ySTA\nDLIpipI8RI/LvLzhNQorVjhvcyGVlZVUVlbGdexEfe7bgXPAI0gwdR7OQVVv+dwVz7N1qwZVXY9T\nt0iPdnWdbFfIp5DgaS5isf8P4JfAM0AR0AB8BLjgcK7nlbsqA2+hdQtJiNkKoqlJLPBweGRLCIcO\nrn//nQB/urYSdu+WRTxKSuDzn5clMT2CtvydRlQZeAuVZxJiZrscPAiZmVBYODLrxSEjZl2gin3f\n2AFvvilCLSyEzZvl4RFGU+66QLbiT8ZqDKaNw5KHUAi6u4cvZem0HGJ397B9C2iS1Mi334aFC+Vx\nySXiovGBTNVynyRq6bmUGLnPQ/L0cG6063BayjKO5RDLA3vY+3StrLU6ezZs3Cj7PCRTtdwVJRoH\nS29c+5WZw54NE0shO2Sy9RGGxYtlFabyckmDNIOqPpCpdoVU/EmM3OehdhJa0+BObK0IjrJcZJiV\nBQeMBmJZWb6RqSr3SaK9ZVxKMCiZFzCsZ/tQ5pM2DnMntj78f/sFY2lMc3m9rq7h/d09jvrcFf/i\n0dxnX+OjHHfQVEhFccYMwgUCYt1pZoz7sQdWGxrEkj99Gnp7xfe+erWn5KvL7CmKE6brxZy265J6\n7sfuTjNdNOfPSwpUV5ev5KvKXVHMroHNzaLgfbgkmyfZvRuefBKef15y3VNSfJElY6LKXVHMzJhF\ni/je91AL3ivMmiXPixbJIh6zZ3vKJTMWqtwnifaV8QDmVD4c5p+/77++317lx7/IgHnzIDUVbr3V\nE0VL40EDqpNkSaCO43u1TN11OLUXiERYHqqlpn+URbG1LUFysWsXtLSIX33BAujvl8Zhq1eTGorQ\n99zzEjDv65OHsY9g0BOy1IDqNDKHdg3GuYRAIDD0WFdayobycq5cs4bloZBsD4U4xgsEzPcOj3Wl\npSrvZKKlRRR6czO8+ipcvAhHjkBtLf2kwYc+JA3DOjuH7QOG5cR7UZbafmCSRAhBn5apu4Fhs8gY\nuc/FxdDQ8MXYH2I/T+WdeMJhkUd6uqQ6DgyIEO2yCYUkW2ZwcPg+j7eYULfMJAkGIkSO1Do3M1KS\nl+gc98ZGyM/nzuta+NlvYvQMt5+n8p55nNwovb2wcydUVEhTMRDfekMD60rb2XcgJMcdPizPmzdL\nd0nwhCy1iGka0a6QLqeqike+GaFx50G6AplcPHeR2bmZnAkWUv6+bLb82F9BuKRmPJ06q6rYUB5h\nz6tGiuuiRZ7qBmmiXSGnkRG9ZTwQpPEVoRAPfqQWNjTBggV8+e5zfHt7BDL64I71ib46xU60G8Vp\nrNXVwaFD8OqrfPuKTvip0UOovV2Uut394vGxqpb7VKN9wN1FJMIvP/tv/PrNBeT3NLDvcJDVq1I4\nl1rAFR/M5Z5vqvyShmg3itNYq6oS5V5TA8eOSSrkwoWytN6mTbBqlfV5Hhir02m5bwK+DQSBx5AF\ns/2Nx4M0niMY5NavLOPWjg4IlbL2Xak8+3+7jECryi+piO7Z7jTWzOBpSoocOzgoqzetWTNSeXt8\nrE4mFTII/COi4FcBHwPKpuKi3ETlj3401D+aSET7gLsRm8zOsV8WYk5NHdYbXNsRJCFpaeJPb262\n5LR8uYy922+nMi1N0iQPHZJga7QMPT5WJ2O5XwHUAg3G+6eBW4HDk7ympMKY9sQkD7gU+SLbgGMx\njvOdeyoZifaxGl0Dn3wqxA/+UMZAIMgsdvA//+LDMFjNuqvTufWLxWLd1da6ctruaTo7JVB68KDI\nMisLfv97WU4vHKby+HEqiovFiq+pkawa++LYDqs3eYnJWO6FwCnb+9PGNk8xODg46uOBBx5gz6uv\n8tobb1DT3x/zOCUJiC5aMd4313UweKyWhgYI0c+Fll7ebumlsTlF/LEenba7juiZlNnwLS0NMjMl\ndS0cFkXf0SHyTUmRY4uK4IYbEv0/mFEmY7mrxgLIzZWpnYtzZX1DtI/18GHo7ubPHwrx5+WlEISF\nKZfwt9/NkGKX8vLYCzIrM495c+7uZnkoRB2wFKgHbgLOI7Po00AvsBd45umnKQJ2Av333DPiI71s\neE0mW+YqYCvicwf4OjDA8KBqFbBuEn9DURRFic0+4PKp/tAQUAcUA2mIIvddQFVRFMWLbAaOIoHV\nryf4WhRFURRFURRFUUbyz8AZ4ECiL0SZNIuAl4CDQDXwpcRejjIJ0oHXEDfxIeBbib0cxY1cC5Sj\nyt0LFGAFpWYjrkaNH7kXY309QsAfgGsSeC0JQxfrmDi/ReqWFPfTjFh6AB1IId7CxF2OMkk6jec0\npJL+fAKvJWGocleU4RQjM7LXEnwdysRJQW7WZxB326HEXo7iRopRt4yXmA3sBm5L9IUoU8JcxC1T\nkeDrSAhquSuKkAr8K/BD4BcJvhZlangb+A9gY6IvJBGoclcUqdT+v8j0/dsJvhZlcuQC84zXGcCN\nSCcCRYmbp4BGoAdpoPbpxF6OMgmuQVpnVCGKYC9WWw3FXawB3kRkuR/488RejqIoiqIoiqIoiqIo\niqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiuIVioEupHgFIIIUIh0AnkEqFWOxFfjqFF1HOlI80wPk\nTNFnKsqE0PYDileoBdYbrzuRzo5rgF7g86OcNziF19CN9IVvnMLPVJQJocpd8Tq/A0qN159CVouv\nAp5wOPZ+4HVj/8+wLP4/QmYBVcDLxrbLkLbAe43PLEVRFEWZUooZ3nq53XgOAb8EPoco46NY7hKz\nudQWLLeM3ZXyl8AXjdf7gQXG6yzj+R+Au2x/J912bj3qllESjFruihfJQCzqN4AGZL3b9yH+d3NV\nngsO561BVtjaD3wcWGVs/z1i6X8GUeQArwJ/Afw35ObSPbX/BUWZHKGxD1EU19GF+NztDCKtfZ0w\n/e6PA7cgs4C7sRZ5eAC4ArgZ2ANsQLqC/gH4ELADmR28NBUXryhTgVruil/YhfjOTXdJtm2fqfRn\nI89SQ/wAAACYSURBVOuppgKfsO1fivjitwCtwKVACTIr+A7i+lkzTdetKBNCLXfFizhlwBwCvoEE\nRCNI2uS9Ucc/jARJW43n2cb27cAy5CbwIuK2eRD4JNAHNBmfrSiKokwhxSTXWrYaUFUSjrplFC/Q\njyyG/OZYB04zZhFTCFnZSVEURVEURVEURVEURVEURVEURVEURVEURVEURZl2/h+0xKlKpz+TbgAA\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x711c3d0>"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "If we want to do some learning on this data.. lets convert gender to a binary numeric"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['isFemale'] = df['Sex'].map( {'female': 1, 'male': 0} ).astype(int)\n",
      "df[['Sex','isFemale']].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>Sex</th>\n",
        "      <th>isFemale</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>   male</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> female</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> female</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> female</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>   male</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "      Sex  isFemale\n",
        "0    male         0\n",
        "1  female         1\n",
        "2  female         1\n",
        "3  female         1\n",
        "4    male         0\n",
        "\n",
        "[5 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Find non-numeric columns so we can drop them later"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "drop_cols = df.columns[df.dtypes.map(lambda x: x=='object')]\n",
      "drop_cols"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "Index([u'Name', u'Sex', u'Ticket', u'Cabin', u'Embarked'], dtype='object')"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.info()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 891 entries, 0 to 890\n",
        "Data columns (total 13 columns):\n",
        "PassengerId    891 non-null int64\n",
        "Survived       891 non-null int64\n",
        "Pclass         891 non-null int64\n",
        "Name           891 non-null object\n",
        "Sex            891 non-null object\n",
        "Age            714 non-null float64\n",
        "SibSp          891 non-null int64\n",
        "Parch          891 non-null int64\n",
        "Ticket         891 non-null object\n",
        "Fare           891 non-null float64\n",
        "Cabin          204 non-null object\n",
        "Embarked       889 non-null object\n",
        "isFemale       891 non-null int64\n",
        "dtypes: float64(2), int64(6), object(5)"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Setup our data to learn from"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = pd.DataFrame(df[[c for c in df.columns if c != 'Survived']])\n",
      "X = X.drop(drop_cols, axis=1) \n",
      "X = X.drop('PassengerId', axis=1)\n",
      "y = df.Survived\n",
      "print X.head()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   Pclass  Age  SibSp  Parch     Fare  isFemale\n",
        "0       3   22      1      0   7.2500         0\n",
        "1       1   38      1      0  71.2833         1\n",
        "2       3   26      0      0   7.9250         1\n",
        "3       1   35      1      0  53.1000         1\n",
        "4       3   35      0      0   8.0500         0\n",
        "\n",
        "[5 rows x 6 columns]\n"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Have a quick look at the class distribution"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y.groupby(y.values).count()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "0    549\n",
        "1    342\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "and fill in some NaNs for age"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X['Age'] = X.Age.fillna(X.Age.median())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 53
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "<h1>scikit-learn</h1>\n",
      "<h2>Machine Learning in Python</h2>\n",
      "<ul>\n",
      "<li>Simple and efficient tools for data mining and data analysis</li>\n",
      "<li>Accessible to everybody, and reusable in various contexts</li>\n",
      "<li>Built on NumPy, SciPy, and matplotlib</li>\n",
      "<li>Open source, commercially usable - BSD license</li>\n",
      "</ul>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "###What is Machine Learning?\n",
      "_Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data_\n",
      "<b>thanks wikipedia</b>\n",
      "\n",
      "<img src='plot_separating_hyperplane_11.png'/>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Prediction with scikit-learn is easy - who will survive?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.linear_model import LogisticRegression\n",
      "from sklearn.metrics import accuracy_score as acc"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# create our classifier\n",
      "clf = LogisticRegression()\n",
      "# fit it to the data\n",
      "clf.fit(X, y)\n",
      "# and predict\n",
      "preds = clf.predict(X)\n",
      "res_acc = acc(y, preds)\n",
      "print 'Accuracy Score: {:.2f}'.format(res_acc)\n",
      "print 'Not too bad'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Accuracy Score: 0.80\n",
        "Not too bad\n"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "##Cross-validation is a fairer performance estimate\n",
      "<img src='crossval.jpg' />"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.cross_validation import KFold"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cv = KFold(n=len(y), n_folds=5, shuffle=True)\n",
      "preds = np.zeros_like(y)\n",
      "for train, test in cv:\n",
      "    clf = LogisticRegression()\n",
      "    clf.fit(X.ix[train], y.ix[train])\n",
      "    preds[test] = clf.predict(X.ix[test])\n",
      "res_acc = acc(y, preds)\n",
      "print 'Accuracy Score: {:.2f}'.format(res_acc)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Accuracy Score: 0.79\n"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "And cross-validation can be done more easily"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# scikits can actually take care of this for us\n",
      "from sklearn.cross_validation import cross_val_score\n",
      "\n",
      "# here\n",
      "clf = LogisticRegression()\n",
      "scores = cross_val_score(clf, X, y, cv=5, scoring='accuracy')\n",
      "# to here\n",
      "\n",
      "print scores\n",
      "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.81564246  0.76966292  0.75842697  0.83707865  0.78651685]\n",
        "Accuracy: 0.79 (+/- 0.06)\n"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "### dealing with categorical data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.Embarked.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "0    S\n",
        "1    C\n",
        "2    S\n",
        "3    S\n",
        "4    S\n",
        "Name: Embarked, dtype: object"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "set(df.Embarked.fillna('O'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "{'C', 'O', 'Q', 'S'}"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Use the LabelEncoder"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import preprocessing\n",
      "df.Embarked = df.Embarked.fillna('O')\n",
      "le = preprocessing.LabelEncoder()\n",
      "le.fit(df.Embarked.values)\n",
      "le.classes_\n"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 61,
       "text": [
        "array(['C', 'O', 'Q', 'S'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X['Embarked'] = le.transform(df.Embarked.values)\n",
      "X.Embarked.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "0    3\n",
        "1    0\n",
        "2    3\n",
        "3    3\n",
        "4    3\n",
        "Name: Embarked, dtype: int64"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "### tuning classifier parameters"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for C in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
      "    clf = LogisticRegression(C=C, penalty='l1')\n",
      "    scores = cross_val_score(clf, X, y, cv=5, scoring='accuracy')    \n",
      "    print(\"n_estimators: {:3.3f}\\tAccuracy: {:.2f} (+/- {:.2f})\"\n",
      "          .format(C, scores.mean(), scores.std() * 2))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "n_estimators: 0.001\tAccuracy: 0.67 (+/- 0.03)\n",
        "n_estimators: 0.010\tAccuracy: 0.67 (+/- 0.03)\n",
        "n_estimators: 0.100\tAccuracy: 0.79 (+/- 0.05)\n",
        "n_estimators: 1.000\tAccuracy: 0.80 (+/- 0.06)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "n_estimators: 10.000\tAccuracy: 0.79 (+/- 0.04)\n",
        "n_estimators: 100.000\tAccuracy: 0.79 (+/- 0.04)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "### Comparing classifiers is easy"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# normalise the data\n",
      "from sklearn.preprocessing import StandardScaler\n",
      "X = StandardScaler().fit_transform(X)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.linear_model import LogisticRegression\n",
      "from sklearn.neighbors import KNeighborsClassifier\n",
      "from sklearn.svm import SVC\n",
      "from sklearn.tree import DecisionTreeClassifier\n",
      "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
      "from sklearn.naive_bayes import GaussianNB\n",
      "from sklearn.lda import LDA\n",
      "from sklearn.qda import QDA\n",
      "\n",
      "names = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Decision Tree\",\n",
      "         \"Random Forest\", \"AdaBoost\", \"Naive Bayes\", \"LDA\",\n",
      "         \"QDA\", \"Logistic Regression\"]\n",
      "classifiers = [\n",
      "    KNeighborsClassifier(3),\n",
      "    SVC(kernel=\"linear\", C=0.025),\n",
      "    SVC(gamma=2, C=1),\n",
      "    DecisionTreeClassifier(),\n",
      "    RandomForestClassifier(),\n",
      "    AdaBoostClassifier(),\n",
      "    GaussianNB(),\n",
      "    LDA(),\n",
      "    QDA(),\n",
      "    LogisticRegression(class_weight='auto')]\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# fit each classifier and find the mean performance\n",
      "res = []\n",
      "for name, clf in zip(names, classifiers):\n",
      "    scores = cross_val_score(clf, X, y, cv=5, scoring='accuracy')\n",
      "    res.append(scores.mean())"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettyplotlib as ppl\n",
      "res = np.array(res)\n",
      "names = np.array(names)\n",
      "idx = np.argsort(res)[::-1]\n",
      "fig, ax = plt.subplots(1, figsize=(14, 6))\n",
      "ppl.bar(ax, np.arange(len(res)), res[idx], annotate=True,\n",
      "        xticklabels=names[idx], grid='y')\n",
      "plt.xticks(rotation=30)\n",
      "_ = ax.set_ylim(res.min() * 0.95, res.max() * 1.05)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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5JUmSJKlWQwFNKfB9IqjpBZwDHJG3zy3AxzL/rgWeBDYABwMXA0cDfTK/a1TT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\nSZIkSZLUTX8CTR9knafcSGIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x41f1dd0>"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Models can be pickled"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# models can be saved\n",
      "import pickle\n",
      "s = pickle.dumps(clf)\n",
      "\n",
      "    "
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [],
     "prompt_number": 69
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## And there is a whole lot scikit-learn can do..\n",
      "+ ###supervised learning\n",
      "+ ###model evaluation\n",
      "+ ###unsupervised learning\n",
      "+ ###feature selection\n",
      "+ ###feature extraction\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "_by Andreas Mueller_\n",
      "<img src='cheat_sheet.png'/>"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}