gdetrez/gf-dev-stats.ipynb

## gf-dev-stats.ipynb
{
 "metadata": {
  "name": "",
  "signature": "sha256:5f918713e821dcc23e5dd73a180de914fccd86143a7f6fd9632be6aed8b1c37a"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Statistics of the gf-dev mailing list"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import pandas as pd\n",
      "import sqlite3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Subscribers"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The file `gf-dev.csv` has been downloaded from Google groups manage interface (accessed 2015-08-26 13:30)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "members = pd.read_csv('gf-dev.csv', skiprows=1, header=True, usecols=[0,6,7], names=['email', 'year', 'month'])\n",
      "members.info()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 180 entries, 0 to 179\n",
        "Data columns (total 3 columns):\n",
        "email    180 non-null object\n",
        "year     180 non-null int64\n",
        "month    180 non-null int64\n",
        "dtypes: int64(2), object(1)\n",
        "memory usage: 5.6+ KB\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "new_subscribers_per_month = members.groupby(['year', 'month'])['email'].count()\n",
      "new_subscribers_per_month.index = new_subscribers_per_month.index.map(lambda i: \"%d-%02d\" % i)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There is missing row in this set (months with no new subscribers), we need to complete the index"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "months = [ \"%d-%02d\" % (y,m) for y in range(2009,2016) for m in range(1 if y > 2009 else 10,13 if y < 2015 else 9)]\n",
      "new_subscribers_per_month = new_subscribers_per_month.reindex(months, fill_value=0)\n",
      "new_subscribers_per_month.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "2009-10    6\n",
        "2009-11    0\n",
        "2009-12    3\n",
        "2010-01    3\n",
        "2010-02    8\n",
        "Name: email, dtype: int64"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cumulative_subscribers_per_month = new_subscribers_per_month.cumsum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cumulative_subscribers_per_month.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7fb8ebfb3bd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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++BRy9lzL9BVDd2Ceuy9w9zXAY8BxGT5HrAr9h0v546X88Snk7LmW6cKwLbAw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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb8ebfa3e50>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Messages"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A custome script has been developed to scrape the google groups website and get all message in a mailbox. The script is `scraper.py` and uses the excellent scrapy framework. It is run like this:\n",
      "\n",
      "    scrapy runspider scraper.py -s DOWNLOAD_DELAY=2\n",
      "    \n",
      "Where we set the parameter DOWNLOAD_DELAY to to seconds to avoid being banned by google.\n",
      "\n",
      "This should create the file gf-dev.mbox with all message from the mailing list. This is then indexed using mlstats like this:\n",
      "\n",
      "    mlstats --db-driver=sqlite --db-name=gf-dev.db gf-dev.mbox"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is the database that will be used now to get some statistics on messages and senders."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "con = sqlite3.connect(\"gf-dev.db\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "Q = \"\"\"\n",
      "SELECT STRFTIME(\"%Y-%m\",messages.first_date) as month, COUNT(*) as messages\n",
      "FROM messages\n",
      "GROUP BY strftime(\"%Y-%m\", messages.first_date);\n",
      "\"\"\"\n",
      "messages = pd.read_sql_query(Q, con).set_index(['month']).messages.reindex(months, fill_value=0)\n",
      "messages.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "month\n",
        "2009-10    28\n",
        "2009-11     1\n",
        "2009-12    19\n",
        "2010-01    32\n",
        "2010-02    59\n",
        "Name: messages, dtype: int64"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "messages.plot(kind=\"bar\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7fb8ebcb6f90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GNmL4Pum+lvupquqQfpgHcDrpSpd3Aq/Oj6+RLu37Wl7+a+CW/PibXHY96dru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\n1M3MOsRJ3cysQ/4/ABJ5iJF1ogUAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb8ebcfe190>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Mean:\", messages.mean(), \"messages/months\"\n",
      "print \"Max:\", messages.max(), \"messages/months\", \"(\", messages.argmax() ,\")\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mean: 29.0422535211 messages/months\n",
        "Max: 118 messages/months ( 2010-09 )\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Posters"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Q = \"\"\"SELECT STRFTIME(\"%Y-%m\",messages.first_date) as month, COUNT(DISTINCT messages_people.email_address) as posters\n",
      "FROM messages, messages_people \n",
      "WHERE messages.message_id=messages_people.message_id \n",
      "GROUP BY strftime(\"%Y-%m\", messages.first_date);\n",
      "\"\"\"\n",
      "posters = pd.read_sql_query(Q, con).set_index(['month']).posters.reindex(months)\n",
      "posters.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "month\n",
        "2009-10     6\n",
        "2009-11     2\n",
        "2009-12    11\n",
        "2010-01     8\n",
        "2010-02    13\n",
        "Name: posters, dtype: float64"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "posters.plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7fb8ebb38b10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7fb8ebbb4d10>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Mean:\", posters.mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mean: 11.9714285714\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Saving it all"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame({'subscribers':cumulative_subscribers_per_month,'messages':messages, 'posters':posters})\n",
      "df.index = df.index.map(lambda date: date + \"-01\")\n",
      "df.index.name = 'month'\n",
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>messages</th>\n",
        "      <th>posters</th>\n",
        "      <th>subscribers</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>month</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2009-10-01</th>\n",
        "      <td>28</td>\n",
        "      <td>6</td>\n",
        "      <td>6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009-11-01</th>\n",
        "      <td>1</td>\n",
        "      <td>2</td>\n",
        "      <td>6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009-12-01</th>\n",
        "      <td>19</td>\n",
        "      <td>11</td>\n",
        "      <td>9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-01</th>\n",
        "      <td>32</td>\n",
        "      <td>8</td>\n",
        "      <td>12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-02-01</th>\n",
        "      <td>59</td>\n",
        "      <td>13</td>\n",
        "      <td>20</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "            messages  posters  subscribers\n",
        "month                                     \n",
        "2009-10-01        28        6            6\n",
        "2009-11-01         1        2            6\n",
        "2009-12-01        19       11            9\n",
        "2010-01-01        32        8           12\n",
        "2010-02-01        59       13           20"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.to_csv('gf-dev-stats.csv', na_rep='0')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.messages.max()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "118"
       ]
      }
     ],
     "prompt_number": 16
    }
   ],
   "metadata": {}
  }
 ]
}