herrfz/Structure of a Data Analysis

## Structure of a Data Analysis
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   "cells": [
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Structure of a data analysis -- in Python"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Create/obtain the data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# unfortunately I don't find pre-packaged UCI spam data for Python\n",
      "# so we're going to have to download it from the website\n",
      "#\n",
      "# alternatively you could also use rpy interface to load the data \n",
      "# if you have the kernlab package installed\n",
      "#\n",
      "# first generate column names\n",
      "cols = ['make', 'address', 'all', 'num3d', \n",
      "        'our', 'over', 'remove', 'internet',\n",
      "        'order', 'mail', 'receive', 'will',\n",
      "        'people', 'report', 'addresses', 'free',\n",
      "        'business', 'email', 'you', 'credit',\n",
      "        'your', 'font', 'num000', 'money',\n",
      "        'hp', 'hpl', 'george', 'num650',\n",
      "        'lab', 'labs', 'telnet', 'num857',\n",
      "        'data', 'num415', 'num85', 'technology',\n",
      "        'num1999', 'parts', 'pm', 'direct',\n",
      "        'cs', 'meeting', 'original', 'project',\n",
      "        're', 'edu', 'table', 'conference',\n",
      "        'charSemicolon', 'charRoundbracket', 'charSquarebracket', 'charExclamation', \n",
      "        'charDollar', 'charHash', 'capitalAve', 'capitalLong', \n",
      "        'capitalTotal', 'type']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# download the data\n",
      "spam = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/spambase/spambase.data', names=cols)\n",
      "\n",
      "# convert the original 1/0 labels to spam/nonspam\n",
      "spam['type'] = spam['type'].apply(lambda x: 'spam' if x == 1 else 'nonspam')\n",
      "\n",
      "# get the number of rows and columns in the data\n",
      "spam.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "(4601, 58)"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here I'm going to use rpy2 interface since it uses a seed to randomly sample the data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext rmagic"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -o trainIndicator\n",
      "set.seed(3435)\n",
      "trainIndicator = rbinom(4601,size=1,prob=0.5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now the trainIndicator variable is in Python namespace, stored as numpy array"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# this is equivalent to the R table command\n",
      "pd.Series(trainIndicator).value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "0    2314\n",
        "1    2287"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## another way to do it completely in Python:\n",
      "# np.random.seed(3435)\n",
      "# trainIndicator = np.random.binomial(1, 0.5, 4601)\n",
      "# np.bincount(trainIndicator)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# split into training and test data\n",
      "trainSpam = spam[trainIndicator == 1]\n",
      "testSpam = spam[trainIndicator == 0]\n",
      "trainSpam.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "(2287, 58)"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# show column names\n",
      "#\n",
      "# we've created these columns manually above\n",
      "print trainSpam.columns "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Index([make, address, all, num3d, our, over, remove, internet, order, mail, receive, will, people, report, addresses, free, business, email, you, credit, your, font, num000, money, hp, hpl, george, num650, lab, labs, telnet, num857, data, num415, num85, technology, num1999, parts, pm, direct, cs, meeting, original, project, re, edu, table, conference, charSemicolon, charRoundbracket, charSquarebracket, charExclamation, charDollar, charHash, capitalAve, capitalLong, capitalTotal, type], dtype=object)\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# this is the equivalent of R head command\n",
      "#\n",
      "# the head() function is not so nice if there are so many columns\n",
      "trainSpam.ix[:,:20].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>make</th>\n",
        "      <th>address</th>\n",
        "      <th>all</th>\n",
        "      <th>num3d</th>\n",
        "      <th>our</th>\n",
        "      <th>over</th>\n",
        "      <th>remove</th>\n",
        "      <th>internet</th>\n",
        "      <th>order</th>\n",
        "      <th>mail</th>\n",
        "      <th>receive</th>\n",
        "      <th>will</th>\n",
        "      <th>people</th>\n",
        "      <th>report</th>\n",
        "      <th>addresses</th>\n",
        "      <th>free</th>\n",
        "      <th>business</th>\n",
        "      <th>email</th>\n",
        "      <th>you</th>\n",
        "      <th>credit</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0 </strong></td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.64</td>\n",
        "      <td> 0.64</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.32</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.64</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.32</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1.29</td>\n",
        "      <td> 1.93</td>\n",
        "      <td> 0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>6 </strong></td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1.92</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.64</td>\n",
        "      <td> 0.96</td>\n",
        "      <td> 1.28</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.96</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.32</td>\n",
        "      <td> 3.85</td>\n",
        "      <td> 0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>8 </strong></td>\n",
        "      <td> 0.15</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.46</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.61</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.30</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.92</td>\n",
        "      <td> 0.76</td>\n",
        "      <td> 0.76</td>\n",
        "      <td> 0.92</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.15</td>\n",
        "      <td> 1.23</td>\n",
        "      <td> 3.53</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>11</strong></td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.25</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.38</td>\n",
        "      <td> 0.25</td>\n",
        "      <td> 0.25</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.12</td>\n",
        "      <td> 0.12</td>\n",
        "      <td> 0.12</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 1.16</td>\n",
        "      <td> 0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>13</strong></td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.90</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.90</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.90</td>\n",
        "      <td> 0.90</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.90</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 2.72</td>\n",
        "      <td> 0.00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
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       "text": [
        "    make  address   all  num3d   our  over  remove  internet  order  mail  receive  will  \\\n",
        "0   0.00     0.64  0.64      0  0.32  0.00    0.00         0   0.00  0.00     0.00  0.64   \n",
        "6   0.00     0.00  0.00      0  1.92  0.00    0.00         0   0.00  0.64     0.96  1.28   \n",
        "8   0.15     0.00  0.46      0  0.61  0.00    0.30         0   0.92  0.76     0.76  0.92   \n",
        "11  0.00     0.00  0.25      0  0.38  0.25    0.25         0   0.00  0.00     0.12  0.12   \n",
        "13  0.00     0.00  0.00      0  0.90  0.00    0.90         0   0.00  0.90     0.90  0.00   \n",
        "\n",
        "    people  report  addresses  free  business  email   you  credit  \n",
        "0     0.00       0          0  0.32         0   1.29  1.93    0.00  \n",
        "6     0.00       0          0  0.96         0   0.32  3.85    0.00  \n",
        "8     0.00       0          0  0.00         0   0.15  1.23    3.53  \n",
        "11    0.12       0          0  0.00         0   0.00  1.16    0.00  \n",
        "13    0.90       0          0  0.00         0   0.00  2.72    0.00  "
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# again, compute frequency counts, similar to R table command\n",
      "trainSpam['type'].value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "nonspam    1381\n",
        "spam        906"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# we have different plotting commands for different plots\n",
      "# in contrast to R where only one plot command is needed\n",
      "\n",
      "from pandas.tools.plotting import boxplot\n",
      "\n",
      "# create a temporary DataFrame to produce a boxplot\n",
      "df = pd.DataFrame(trainSpam[['capitalAve', 'type']])\n",
      "\n",
      "boxplot(trainSpam, column='capitalAve', by='type');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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8CFJLV+OKO1F7tUaj594pLCwkOzubgQMHUlpaSmBgIACBgYGUlpYCsHfvXmJj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\n"
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# take the log\n",
      "#\n",
      "# here we use the apply() function to compute the log\n",
      "df['capitalAve'] = df['capitalAve'].apply(lambda x: np.log10(x + 1))\n",
      "\n",
      "boxplot(df, column='capitalAve', by='type');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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x8UFRUZFp/dKlS/Dx8WljmEREZC0WE31ZWRkMBgMA4+XeX331FSIjI83aTJw40TRmOCMj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\nSUVORK5CYII=\n"
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# relationships between predictors\n",
      "from pandas.tools.plotting import scatter_matrix\n",
      "\n",
      "scatter_matrix(trainSpam[['make', 'address', 'all', 'num3d']], figsize=(7, 7), marker='o');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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b7R0MhmQSExM9HZpoILkVFG4pLQ0gKuouSkt9MBiiCQiwcvp0jqfDEsCJE8V0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2vYufMQaWlOLrzwPBkNe5K6Uw6aprFp02bWr9+JzWbmggsmM3To0D6K8PTQk/qwbdt2Vq36CJPJ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\n"
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# hierarchical clustring\n",
      "# Warning: \n",
      "# - you need the hcluster package\n",
      "# - I'm not 100% sure the result is equal to the one in video\n",
      "# I'm not familiar with the underlying analysis; \n",
      "# this is just to show how you can create a similar diagram as in video\n",
      "from hcluster import pdist, linkage, dendrogram\n",
      "\n",
      "dendrogram(linkage(pdist(trainSpam.ix[:,:56].T)), labels=trainSpam.ix[:,:56].columns);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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Bc+fOxebNmyWvKyIiAg4ODnjwwQfx+uuvm9yfr776SvgyMz7ODz30EL777jtR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\nSUVORK5CYII=\n"
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# take the log\n",
      "# same remark as above\n",
      "dendrogram(linkage(pdist(trainSpam.ix[:,:55].T.apply(lambda x: np.log10(x + 1)))), labels=trainSpam.ix[:,:55].columns);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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KHgkfJqCurfQ1oidBpQJ2Xl4eSktLUbduXeTm5uLQoUNYsIA3WJA8DTSS5VsLSwuh1WmR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\nnSRo6NNYD059k/tOAfmGU0U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      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}