explore_corpwatch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import glob\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "companies = pd.read_csv(\"corpwatch_api_tables_csv/company_info.csv\", delimiter=\"\\t\", dtype={'sector_name' :'S',\n",
    "                                                                                          'industry_name' : 'S',\n",
    "                                                                                            'sic_sector' : 'S'\n",
    "                                                                                           })\n",
    "relations = pd.read_csv(\"corpwatch_api_tables_csv/company_relations.csv\", delimiter=\"\\t\")\n",
    "locations = pd.read_csv(\"corpwatch_api_tables_csv/company_locations.csv\", delimiter=\"\\t\", low_memory=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[CorpWatch column names reference](http://api.corpwatch.org/documentation/db_dump/README.txt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge country and location datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "r = relations[['source_cw_id','target_cw_id','year','relation_origin']]\n",
    "c = companies[['cw_id', 'cik','irs_number', 'company_name', 'best_location_id','sic_sector',\n",
    "               'num_parents', 'num_children', 'top_parent_id','most_recent','year','min_year','max_year']]\n",
    "l = locations[['location_id', 'state', 'country_code']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = pd.merge(left=c, right=l, left_on=\"best_location_id\", right_on=\"location_id\", how=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = data.drop(['best_location_id', 'location_id'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cw_id</th>\n",
       "      <th>cik</th>\n",
       "      <th>irs_number</th>\n",
       "      <th>company_name</th>\n",
       "      <th>sic_sector</th>\n",
       "      <th>num_parents</th>\n",
       "      <th>num_children</th>\n",
       "      <th>top_parent_id</th>\n",
       "      <th>most_recent</th>\n",
       "      <th>year</th>\n",
       "      <th>min_year</th>\n",
       "      <th>max_year</th>\n",
       "      <th>state</th>\n",
       "      <th>country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>221759452.0</td>\n",
       "      <td>K TRON INTERNATIONAL INC</td>\n",
       "      <td>3800</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>54894</td>\n",
       "      <td>0</td>\n",
       "      <td>2010</td>\n",
       "      <td>2003</td>\n",
       "      <td>2011</td>\n",
       "      <td>NJ</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>2070.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Acme Electric Corp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>123</td>\n",
       "      <td>0</td>\n",
       "      <td>2005</td>\n",
       "      <td>2005</td>\n",
       "      <td>2008</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>2070.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Acme Electric Corp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>123</td>\n",
       "      <td>0</td>\n",
       "      <td>2006</td>\n",
       "      <td>2005</td>\n",
       "      <td>2008</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>2070.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Acme Electric Corp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>123</td>\n",
       "      <td>0</td>\n",
       "      <td>2007</td>\n",
       "      <td>2005</td>\n",
       "      <td>2008</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>2070.0</td>\n",
       "      <td>160324980.0</td>\n",
       "      <td>ACME ELECTRIC CORP</td>\n",
       "      <td>3600</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>2008</td>\n",
       "      <td>2005</td>\n",
       "      <td>2008</td>\n",
       "      <td>NY</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15</td>\n",
       "      <td>2135.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affiliated Computer Services Inc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1982</td>\n",
       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "      <td>2003</td>\n",
       "      <td>2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15</td>\n",
       "      <td>2135.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affiliated Computer Services Inc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1982</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>2003</td>\n",
       "      <td>2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>22</td>\n",
       "      <td>2491.0</td>\n",
       "      <td>880104066.0</td>\n",
       "      <td>BALLY TECHNOLOGIES INC</td>\n",
       "      <td>7300</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4095</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>2003</td>\n",
       "      <td>2016</td>\n",
       "      <td>NV</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>22</td>\n",
       "      <td>2491.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bally Technologies Inc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4095</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>2003</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>24</td>\n",
       "      <td>2601.0</td>\n",
       "      <td>111974412.0</td>\n",
       "      <td>AEROFLEX INC</td>\n",
       "      <td>3600</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>601074</td>\n",
       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "      <td>2003</td>\n",
       "      <td>2013</td>\n",
       "      <td>NY</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>24</td>\n",
       "      <td>2601.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aeroflex Inc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>601074</td>\n",
       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>2003</td>\n",
       "      <td>2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>24</td>\n",
       "      <td>2601.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aeroflex Inc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>601074</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>2003</td>\n",
       "      <td>2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>37</td>\n",
       "      <td>3153.0</td>\n",
       "      <td>630004250.0</td>\n",
       "      <td>ALABAMA POWER CO</td>\n",
       "      <td>4900</td>\n",
       "      <td>6</td>\n",
       "      <td>36</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>2016</td>\n",
       "      <td>AL</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>37</td>\n",
       "      <td>3153.0</td>\n",
       "      <td>630004250.0</td>\n",
       "      <td>ALABAMA POWER CO</td>\n",
       "      <td>4900</td>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>2004</td>\n",
       "      <td>2003</td>\n",
       "      <td>2016</td>\n",
       "      <td>AL</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>37</td>\n",
       "      <td>3153.0</td>\n",
       "      <td>630004250.0</td>\n",
       "      <td>ALABAMA POWER CO</td>\n",
       "      <td>4900</td>\n",
       "      <td>6</td>\n",
       "      <td>36</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>2005</td>\n",
       "      <td>2003</td>\n",
       "      <td>2016</td>\n",
       "      <td>AL</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>37</td>\n",
       "      <td>3153.0</td>\n",
       "      <td>630004250.0</td>\n",
       "      <td>ALABAMA POWER CO</td>\n",
       "      <td>4900</td>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>2006</td>\n",
       "      <td>2003</td>\n",
       "      <td>2016</td>\n",
       "      <td>AL</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>37</td>\n",
       "      <td>3153.0</td>\n",
       "      <td>630004250.0</td>\n",
       "      <td>ALABAMA POWER CO</td>\n",
       "      <td>4900</td>\n",
       "      <td>5</td>\n",
       "      <td>34</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>2007</td>\n",
       "      <td>2003</td>\n",
       "      <td>2016</td>\n",
       "      <td>AL</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>37</td>\n",
       "      <td>3153.0</td>\n",
       "      <td>630004250.0</td>\n",
       "      <td>ALABAMA POWER CO</td>\n",
       "      <td>4900</td>\n",
       "      <td>5</td>\n",
       "      <td>34</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>2008</td>\n",
       "      <td>2003</td>\n",
       "      <td>2016</td>\n",
       "      <td>AL</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>37</td>\n",
       "      <td>3153.0</td>\n",
       "      <td>630004250.0</td>\n",
       "      <td>ALABAMA POWER CO</td>\n",
       "      <td>4900</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>2003</td>\n",
       "      <td>2016</td>\n",
       "      <td>AL</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>37</td>\n",
       "      <td>3153.0</td>\n",
       "      <td>630004250.0</td>\n",
       "      <td>ALABAMA POWER CO</td>\n",
       "      <td>4900</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>2010</td>\n",
       "      <td>2003</td>\n",
       "      <td>2016</td>\n",
       "      <td>AL</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    cw_id     cik   irs_number                      company_name sic_sector  \\\n",
       "0       1    20.0  221759452.0          K TRON INTERNATIONAL INC       3800   \n",
       "1      12  2070.0          NaN                Acme Electric Corp        NaN   \n",
       "2      12  2070.0          NaN                Acme Electric Corp        NaN   \n",
       "3      12  2070.0          NaN                Acme Electric Corp        NaN   \n",
       "4      12  2070.0  160324980.0                ACME ELECTRIC CORP       3600   \n",
       "5      15  2135.0          NaN  Affiliated Computer Services Inc        NaN   \n",
       "6      15  2135.0          NaN  Affiliated Computer Services Inc        NaN   \n",
       "7      22  2491.0  880104066.0            BALLY TECHNOLOGIES INC       7300   \n",
       "8      22  2491.0          NaN            Bally Technologies Inc        NaN   \n",
       "9      24  2601.0  111974412.0                      AEROFLEX INC       3600   \n",
       "10     24  2601.0          NaN                      Aeroflex Inc        NaN   \n",
       "11     24  2601.0          NaN                      Aeroflex Inc        NaN   \n",
       "12     37  3153.0  630004250.0                  ALABAMA POWER CO       4900   \n",
       "13     37  3153.0  630004250.0                  ALABAMA POWER CO       4900   \n",
       "14     37  3153.0  630004250.0                  ALABAMA POWER CO       4900   \n",
       "15     37  3153.0  630004250.0                  ALABAMA POWER CO       4900   \n",
       "16     37  3153.0  630004250.0                  ALABAMA POWER CO       4900   \n",
       "17     37  3153.0  630004250.0                  ALABAMA POWER CO       4900   \n",
       "18     37  3153.0  630004250.0                  ALABAMA POWER CO       4900   \n",
       "19     37  3153.0  630004250.0                  ALABAMA POWER CO       4900   \n",
       "\n",
       "    num_parents  num_children  top_parent_id  most_recent  year  min_year  \\\n",
       "0             1            20          54894            0  2010      2003   \n",
       "1             1             0            123            0  2005      2005   \n",
       "2             1             0            123            0  2006      2005   \n",
       "3             1             0            123            0  2007      2005   \n",
       "4             1             0            123            1  2008      2005   \n",
       "5             1             0           1982            0  2011      2003   \n",
       "6             1             0           1982            1  2012      2003   \n",
       "7             1             0           4095            0  2015      2003   \n",
       "8             1             0           4095            1  2016      2003   \n",
       "9             1             0         601074            0  2011      2003   \n",
       "10            1             0         601074            0  2012      2003   \n",
       "11            1             0         601074            1  2013      2003   \n",
       "12            6            36             37            0  2003      2003   \n",
       "13            6            37             37            0  2004      2003   \n",
       "14            6            36             37            0  2005      2003   \n",
       "15            6            37             37            0  2006      2003   \n",
       "16            5            34             37            0  2007      2003   \n",
       "17            5            34             37            0  2008      2003   \n",
       "18            5            33             37            0  2009      2003   \n",
       "19            5            25             37            0  2010      2003   \n",
       "\n",
       "    max_year state country_code  \n",
       "0       2011    NJ           US  \n",
       "1       2008   NaN           US  \n",
       "2       2008   NaN           US  \n",
       "3       2008   NaN           US  \n",
       "4       2008    NY           US  \n",
       "5       2012   NaN           US  \n",
       "6       2012   NaN           US  \n",
       "7       2016    NV           US  \n",
       "8       2016   NaN           US  \n",
       "9       2013    NY           US  \n",
       "10      2013   NaN           US  \n",
       "11      2013   NaN           US  \n",
       "12      2016    AL           US  \n",
       "13      2016    AL           US  \n",
       "14      2016    AL           US  \n",
       "15      2016    AL           US  \n",
       "16      2016    AL           US  \n",
       "17      2016    AL           US  \n",
       "18      2016    AL           US  \n",
       "19      2016    AL           US  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cw_id</th>\n",
       "      <th>cik</th>\n",
       "      <th>irs_number</th>\n",
       "      <th>num_parents</th>\n",
       "      <th>num_children</th>\n",
       "      <th>top_parent_id</th>\n",
       "      <th>most_recent</th>\n",
       "      <th>year</th>\n",
       "      <th>min_year</th>\n",
       "      <th>max_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.109283e+06</td>\n",
       "      <td>3.109064e+06</td>\n",
       "      <td>7.532160e+05</td>\n",
       "      <td>3.109283e+06</td>\n",
       "      <td>3.109283e+06</td>\n",
       "      <td>3.109283e+06</td>\n",
       "      <td>3.109283e+06</td>\n",
       "      <td>3.109283e+06</td>\n",
       "      <td>3.109283e+06</td>\n",
       "      <td>3.109283e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.345818e+05</td>\n",
       "      <td>5.110050e+05</td>\n",
       "      <td>2.965487e+08</td>\n",
       "      <td>7.102178e-01</td>\n",
       "      <td>7.102178e-01</td>\n",
       "      <td>1.444733e+05</td>\n",
       "      <td>2.908429e-01</td>\n",
       "      <td>2.009659e+03</td>\n",
       "      <td>2.006609e+03</td>\n",
       "      <td>2.012741e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.617719e+05</td>\n",
       "      <td>6.459057e+05</td>\n",
       "      <td>3.068956e+08</td>\n",
       "      <td>8.207103e-01</td>\n",
       "      <td>1.397378e+01</td>\n",
       "      <td>2.412987e+05</td>\n",
       "      <td>4.541513e-01</td>\n",
       "      <td>3.956335e+00</td>\n",
       "      <td>3.770880e+00</td>\n",
       "      <td>3.793737e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.003000e+03</td>\n",
       "      <td>2.003000e+03</td>\n",
       "      <td>2.003000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.140550e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.300000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.006000e+03</td>\n",
       "      <td>2.003000e+03</td>\n",
       "      <td>2.010000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.362770e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.232687e+08</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.645100e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.010000e+03</td>\n",
       "      <td>2.005000e+03</td>\n",
       "      <td>2.015000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.608560e+05</td>\n",
       "      <td>1.233001e+06</td>\n",
       "      <td>4.733366e+08</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.430890e+05</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.013000e+03</td>\n",
       "      <td>2.009000e+03</td>\n",
       "      <td>2.016000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.243790e+05</td>\n",
       "      <td>1.674680e+06</td>\n",
       "      <td>1.000000e+09</td>\n",
       "      <td>6.700000e+01</td>\n",
       "      <td>2.808000e+03</td>\n",
       "      <td>9.241600e+05</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.016000e+03</td>\n",
       "      <td>2.016000e+03</td>\n",
       "      <td>2.016000e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              cw_id           cik    irs_number   num_parents  num_children  \\\n",
       "count  3.109283e+06  3.109064e+06  7.532160e+05  3.109283e+06  3.109283e+06   \n",
       "mean   3.345818e+05  5.110050e+05  2.965487e+08  7.102178e-01  7.102178e-01   \n",
       "std    2.617719e+05  6.459057e+05  3.068956e+08  8.207103e-01  1.397378e+01   \n",
       "min    1.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "25%    1.140550e+05  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "50%    2.362770e+05  0.000000e+00  2.232687e+08  1.000000e+00  0.000000e+00   \n",
       "75%    5.608560e+05  1.233001e+06  4.733366e+08  1.000000e+00  0.000000e+00   \n",
       "max    9.243790e+05  1.674680e+06  1.000000e+09  6.700000e+01  2.808000e+03   \n",
       "\n",
       "       top_parent_id   most_recent          year      min_year      max_year  \n",
       "count   3.109283e+06  3.109283e+06  3.109283e+06  3.109283e+06  3.109283e+06  \n",
       "mean    1.444733e+05  2.908429e-01  2.009659e+03  2.006609e+03  2.012741e+03  \n",
       "std     2.412987e+05  4.541513e-01  3.956335e+00  3.770880e+00  3.793737e+00  \n",
       "min     1.000000e+00  0.000000e+00  2.003000e+03  2.003000e+03  2.003000e+03  \n",
       "25%     5.300000e+03  0.000000e+00  2.006000e+03  2.003000e+03  2.010000e+03  \n",
       "50%     1.645100e+04  0.000000e+00  2.010000e+03  2.005000e+03  2.015000e+03  \n",
       "75%     2.430890e+05  1.000000e+00  2.013000e+03  2.009000e+03  2.016000e+03  \n",
       "max     9.241600e+05  1.000000e+00  2.016000e+03  2.016000e+03  2.016000e+03  "
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of children in over the years"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2009469 children companies & 1099814 parent companies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "children = data[data['num_parents'] != 0][['cik','year']].drop_duplicates().groupby('year').count() #have at least one parent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "parents = data[data['num_children'] != 0][['cik','year']].drop_duplicates().groupby('year')['cik'].count() #have at least one child"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "parentless = data[data['num_parents'] == 0][['cik','year']].drop_duplicates().groupby('year')['cik'].count() #have no parent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "childless = data[data['num_children'] == 0][['cik','year']].drop_duplicates().groupby('year')['cik'].count() #have at no child"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### amount of companies per year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cik</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>78643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>91731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>90227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>90304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>90455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>81855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>89134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>94356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>96047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>96745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>99689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>98442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>105045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>66738</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         cik\n",
       "year        \n",
       "2003   78643\n",
       "2004   91731\n",
       "2005   90227\n",
       "2006   90304\n",
       "2007   90455\n",
       "2008   81855\n",
       "2009   89134\n",
       "2010   94356\n",
       "2011   96047\n",
       "2012   96745\n",
       "2013   99689\n",
       "2014   98442\n",
       "2015  105045\n",
       "2016   66738"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['cik', 'year']].drop_duplicates().groupby('year').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f84f79f4150>]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ILQkzy0s0vjBFCfnkE7j5Znj66aQjca4yFWPYbXqhwlJJGPnmy4iUkNtug6OP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\nKmjyg8ztAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f84ae148e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(children)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The amount of companies that say to have at least one child company is on decline (while the amount of companies stays roughly the same)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f84f7941050>]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FcL+IpqXOA4YBlwG4+2JgMrAYmApclPab/mLgN8AyoLahxCDwxz/CoEFwxBFhCwwlBikF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\nQUREMig5iIhIBiUHERHJoOQgIiIZ/j9JAGTEoxR43wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f84f60379d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(parents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f84f7872610>]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WJ0xIS43U93NMnVruyMxar3//xld4rq2tpba2tiTvq4jCHhAk3QbMjYjp2b+v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\nagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f84f7957f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(parentless)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f84f77b4910>]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PtFxsZnlxH0qRytWHsn59mpvwu9/BgAEt/vLWDBdcACtWpNnujVm1Cj7+cfjl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\nLBdOKGZmlgsnFDMzy4UTipmZ5eL/A5Q1LtUCx3W5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f84f788b4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(childless)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extractive industry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the SICs of extractive industries. Only 2-digits depth present in the dataset. Chosen SICs that are all extractive. [sic codes reference](https://www.sec.gov/info/edgar/siccodes.htm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#esic = pd.read_csv(\"extraction_sics.csv\", header=None, names=[\"SIC\"])[\"SIC\"].tolist()\n",
    "#esic2 = [str(e)[:2] for e in esic]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "esic2 = ['10','11','12','13','14','33']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "get all rows where the SIC starts with `esic2` elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "edata = data.ix[data['sic_sector'].str.contains(r'^'+'..|'.join(esic2), na=False)].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sic_sector\n",
       "1000    6641\n",
       "1200     709\n",
       "1300    8068\n",
       "1400    1034\n",
       "3300    1445\n",
       "Name: cik, dtype: int64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edata.groupby('sic_sector')['cik'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>cw_id</th>\n",
       "      <th>cik</th>\n",
       "      <th>irs_number</th>\n",
       "      <th>num_parents</th>\n",
       "      <th>num_children</th>\n",
       "      <th>top_parent_id</th>\n",
       "      <th>most_recent</th>\n",
       "      <th>year</th>\n",
       "      <th>min_year</th>\n",
       "      <th>max_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.789700e+04</td>\n",
       "      <td>17897.000000</td>\n",
       "      <td>1.789700e+04</td>\n",
       "      <td>1.746300e+04</td>\n",
       "      <td>17897.000000</td>\n",
       "      <td>17897.000000</td>\n",
       "      <td>17897.000000</td>\n",
       "      <td>17897.000000</td>\n",
       "      <td>17897.000000</td>\n",
       "      <td>17897.000000</td>\n",
       "      <td>17897.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.226199e+06</td>\n",
       "      <td>99825.714142</td>\n",
       "      <td>1.073994e+06</td>\n",
       "      <td>4.029286e+08</td>\n",
       "      <td>0.043136</td>\n",
       "      <td>3.640945</td>\n",
       "      <td>97418.020674</td>\n",
       "      <td>0.143097</td>\n",
       "      <td>2009.325250</td>\n",
       "      <td>2004.671230</td>\n",
       "      <td>2013.908867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.636393e+05</td>\n",
       "      <td>177501.581103</td>\n",
       "      <td>3.756819e+05</td>\n",
       "      <td>3.796470e+08</td>\n",
       "      <td>0.222090</td>\n",
       "      <td>18.575200</td>\n",
       "      <td>177168.280946</td>\n",
       "      <td>0.350181</td>\n",
       "      <td>3.803891</td>\n",
       "      <td>2.496461</td>\n",
       "      <td>3.007537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.408000e+03</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.853000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>2003.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.114776e+06</td>\n",
       "      <td>11891.000000</td>\n",
       "      <td>9.125000e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11782.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.242939e+06</td>\n",
       "      <td>29935.000000</td>\n",
       "      <td>1.133932e+06</td>\n",
       "      <td>2.660539e+08</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27334.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.393069e+06</td>\n",
       "      <td>53993.000000</td>\n",
       "      <td>1.351765e+06</td>\n",
       "      <td>7.703236e+08</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>52120.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.107218e+06</td>\n",
       "      <td>922096.000000</td>\n",
       "      <td>1.664576e+06</td>\n",
       "      <td>1.000000e+09</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>501.000000</td>\n",
       "      <td>922096.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              index          cw_id           cik    irs_number   num_parents  \\\n",
       "count  1.789700e+04   17897.000000  1.789700e+04  1.746300e+04  17897.000000   \n",
       "mean   2.226199e+06   99825.714142  1.073994e+06  4.029286e+08      0.043136   \n",
       "std    4.636393e+05  177501.581103  3.756819e+05  3.796470e+08      0.222090   \n",
       "min    1.408000e+03       6.000000  1.853000e+03  0.000000e+00      0.000000   \n",
       "25%    2.114776e+06   11891.000000  9.125000e+05  0.000000e+00      0.000000   \n",
       "50%    2.242939e+06   29935.000000  1.133932e+06  2.660539e+08      0.000000   \n",
       "75%    2.393069e+06   53993.000000  1.351765e+06  7.703236e+08      0.000000   \n",
       "max    3.107218e+06  922096.000000  1.664576e+06  1.000000e+09      5.000000   \n",
       "\n",
       "       num_children  top_parent_id   most_recent          year      min_year  \\\n",
       "count  17897.000000   17897.000000  17897.000000  17897.000000  17897.000000   \n",
       "mean       3.640945   97418.020674      0.143097   2009.325250   2004.671230   \n",
       "std       18.575200  177168.280946      0.350181      3.803891      2.496461   \n",
       "min        0.000000       6.000000      0.000000   2003.000000   2003.000000   \n",
       "25%        0.000000   11782.000000      0.000000   2006.000000   2003.000000   \n",
       "50%        0.000000   27334.000000      0.000000   2009.000000   2003.000000   \n",
       "75%        0.000000   52120.000000      0.000000   2012.000000   2006.000000   \n",
       "max      501.000000  922096.000000      1.000000   2016.000000   2016.000000   \n",
       "\n",
       "           max_year  \n",
       "count  17897.000000  \n",
       "mean    2013.908867  \n",
       "std        3.007537  \n",
       "min     2003.000000  \n",
       "25%     2013.000000  \n",
       "50%     2016.000000  \n",
       "75%     2016.000000  \n",
       "max     2016.000000  "
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edata.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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