{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory analyses #1 & #2: the effects of place and time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We begin with exploratory analysis of the various factors we hypothesize may influence a company's success. In this notebook, we investigate associations between company valuation at IPO or total funding amount and office location and timing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set-up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import stand-alone functions and necessary packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from modules import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "plt.style.use('seaborn-dark')\n",
    "plt.rcParams['figure.figsize'] = (10, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Connect to the MySQL database, read in cb_offices and cb_ipos and convert to dataframes, disconnect from database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conn = dbConnect()\n",
    "offices = dbTableToDataFrame(conn, 'cb_offices')\n",
    "ipos = dbTableToDataFrame(conn, 'cb_ipos')\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Look at offices and ipos dataframes & merge them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>address1</th>\n",
       "      <th>address2</th>\n",
       "      <th>city</th>\n",
       "      <th>country_code</th>\n",
       "      <th>created_at</th>\n",
       "      <th>description</th>\n",
       "      <th>id</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>object_id</th>\n",
       "      <th>office_id</th>\n",
       "      <th>region</th>\n",
       "      <th>state_code</th>\n",
       "      <th>updated_at</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>710 - 2nd Avenue</td>\n",
       "      <td>Suite 1100</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>USA</td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>47.6031220000</td>\n",
       "      <td>-122.3332530000</td>\n",
       "      <td>c:1</td>\n",
       "      <td>1</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>WA</td>\n",
       "      <td>None</td>\n",
       "      <td>98104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4900 Hopyard Rd</td>\n",
       "      <td>Suite 310</td>\n",
       "      <td>Pleasanton</td>\n",
       "      <td>USA</td>\n",
       "      <td>None</td>\n",
       "      <td>Headquarters</td>\n",
       "      <td>2</td>\n",
       "      <td>37.6929340000</td>\n",
       "      <td>-121.9049450000</td>\n",
       "      <td>c:3</td>\n",
       "      <td>3</td>\n",
       "      <td>SF Bay</td>\n",
       "      <td>CA</td>\n",
       "      <td>None</td>\n",
       "      <td>94588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>135 Mississippi St</td>\n",
       "      <td>None</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>USA</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>37.7647260000</td>\n",
       "      <td>-122.3945230000</td>\n",
       "      <td>c:4</td>\n",
       "      <td>4</td>\n",
       "      <td>SF Bay</td>\n",
       "      <td>CA</td>\n",
       "      <td>None</td>\n",
       "      <td>94107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1601 Willow Road</td>\n",
       "      <td>None</td>\n",
       "      <td>Menlo Park</td>\n",
       "      <td>USA</td>\n",
       "      <td>None</td>\n",
       "      <td>Headquarters</td>\n",
       "      <td>4</td>\n",
       "      <td>37.4160500000</td>\n",
       "      <td>-122.1518010000</td>\n",
       "      <td>c:5</td>\n",
       "      <td>5</td>\n",
       "      <td>SF Bay</td>\n",
       "      <td>CA</td>\n",
       "      <td>None</td>\n",
       "      <td>94025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Suite 200</td>\n",
       "      <td>654 High Street</td>\n",
       "      <td>Palo Alto</td>\n",
       "      <td>ISR</td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>c:7</td>\n",
       "      <td>7</td>\n",
       "      <td>SF Bay</td>\n",
       "      <td>CA</td>\n",
       "      <td>None</td>\n",
       "      <td>94301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             address1         address2           city country_code created_at  \\\n",
       "0    710 - 2nd Avenue       Suite 1100        Seattle          USA       None   \n",
       "1     4900 Hopyard Rd        Suite 310     Pleasanton          USA       None   \n",
       "2  135 Mississippi St             None  San Francisco          USA       None   \n",
       "3    1601 Willow Road             None     Menlo Park          USA       None   \n",
       "4           Suite 200  654 High Street      Palo Alto          ISR       None   \n",
       "\n",
       "    description  id       latitude        longitude object_id  office_id  \\\n",
       "0                 1  47.6031220000  -122.3332530000       c:1          1   \n",
       "1  Headquarters   2  37.6929340000  -121.9049450000       c:3          3   \n",
       "2          None   3  37.7647260000  -122.3945230000       c:4          4   \n",
       "3  Headquarters   4  37.4160500000  -122.1518010000       c:5          5   \n",
       "4                 5           None             None       c:7          7   \n",
       "\n",
       "    region state_code updated_at zip_code  \n",
       "0  Seattle         WA       None    98104  \n",
       "1   SF Bay         CA       None    94588  \n",
       "2   SF Bay         CA       None    94107  \n",
       "3   SF Bay         CA       None    94025  \n",
       "4   SF Bay         CA       None    94301  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offices.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_at</th>\n",
       "      <th>id</th>\n",
       "      <th>ipo_id</th>\n",
       "      <th>object_id</th>\n",
       "      <th>public_at</th>\n",
       "      <th>raised_amount</th>\n",
       "      <th>raised_currency_code</th>\n",
       "      <th>source_description</th>\n",
       "      <th>source_url</th>\n",
       "      <th>stock_symbol</th>\n",
       "      <th>updated_at</th>\n",
       "      <th>valuation_amount</th>\n",
       "      <th>valuation_currency_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-02-09 05:17:45</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>c:1654</td>\n",
       "      <td>1980-12-19</td>\n",
       "      <td>None</td>\n",
       "      <td>USD</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NASDAQ:AAPL</td>\n",
       "      <td>2012-04-12 04:02:59</td>\n",
       "      <td>None</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-02-09 05:25:18</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>c:1242</td>\n",
       "      <td>1986-03-13</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NASDAQ:MSFT</td>\n",
       "      <td>2010-12-11 12:39:46</td>\n",
       "      <td>None</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-02-09 05:40:32</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>c:342</td>\n",
       "      <td>1969-06-09</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NYSE:DIS</td>\n",
       "      <td>2010-12-23 08:58:16</td>\n",
       "      <td>None</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-02-10 22:51:24</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>c:59</td>\n",
       "      <td>2004-08-25</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NASDAQ:GOOG</td>\n",
       "      <td>2011-08-01 20:47:08</td>\n",
       "      <td>None</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-02-10 23:28:09</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>c:317</td>\n",
       "      <td>1997-05-01</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NASDAQ:AMZN</td>\n",
       "      <td>2011-08-01 21:11:22</td>\n",
       "      <td>100000000000</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           created_at  id  ipo_id object_id   public_at raised_amount  \\\n",
       "0 2008-02-09 05:17:45   1       1    c:1654  1980-12-19          None   \n",
       "1 2008-02-09 05:25:18   2       2    c:1242  1986-03-13          None   \n",
       "2 2008-02-09 05:40:32   3       3     c:342  1969-06-09          None   \n",
       "3 2008-02-10 22:51:24   4       4      c:59  2004-08-25          None   \n",
       "4 2008-02-10 23:28:09   5       5     c:317  1997-05-01          None   \n",
       "\n",
       "  raised_currency_code source_description source_url stock_symbol  \\\n",
       "0                  USD               None       None  NASDAQ:AAPL   \n",
       "1                 None               None       None  NASDAQ:MSFT   \n",
       "2                 None               None       None     NYSE:DIS   \n",
       "3                 None               None       None  NASDAQ:GOOG   \n",
       "4                 None               None       None  NASDAQ:AMZN   \n",
       "\n",
       "           updated_at valuation_amount valuation_currency_code  \n",
       "0 2012-04-12 04:02:59             None                     USD  \n",
       "1 2010-12-11 12:39:46             None                     USD  \n",
       "2 2010-12-23 08:58:16             None                     USD  \n",
       "3 2011-08-01 20:47:08             None                     USD  \n",
       "4 2011-08-01 21:11:22     100000000000                     USD  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipos.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# merge by 'object_id' field, which uniquely links the two dataframes\n",
    "offices_ipos = pd.merge(offices, ipos, on='object_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>address1</th>\n",
       "      <th>address2</th>\n",
       "      <th>city</th>\n",
       "      <th>country_code</th>\n",
       "      <th>created_at_x</th>\n",
       "      <th>description</th>\n",
       "      <th>id_x</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>object_id</th>\n",
       "      <th>...</th>\n",
       "      <th>ipo_id</th>\n",
       "      <th>public_at</th>\n",
       "      <th>raised_amount</th>\n",
       "      <th>raised_currency_code</th>\n",
       "      <th>source_description</th>\n",
       "      <th>source_url</th>\n",
       "      <th>stock_symbol</th>\n",
       "      <th>updated_at_y</th>\n",
       "      <th>valuation_amount</th>\n",
       "      <th>valuation_currency_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1601 Willow Road</td>\n",
       "      <td>None</td>\n",
       "      <td>Menlo Park</td>\n",
       "      <td>USA</td>\n",
       "      <td>None</td>\n",
       "      <td>Headquarters</td>\n",
       "      <td>4</td>\n",
       "      <td>37.4160500000</td>\n",
       "      <td>-122.1518010000</td>\n",
       "      <td>c:5</td>\n",
       "      <td>...</td>\n",
       "      <td>847</td>\n",
       "      <td>2012-05-18</td>\n",
       "      <td>18400000000</td>\n",
       "      <td>USD</td>\n",
       "      <td>Facebook Prices IPO at Record Value</td>\n",
       "      <td>http://online.wsj.com/news/articles/SB10001424...</td>\n",
       "      <td>NASDAQ:FB</td>\n",
       "      <td>2013-11-21 19:40:55</td>\n",
       "      <td>104000000000</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Dublin</td>\n",
       "      <td>IRL</td>\n",
       "      <td>None</td>\n",
       "      <td>Europe HQ</td>\n",
       "      <td>6975</td>\n",
       "      <td>53.3441040000</td>\n",
       "      <td>-6.2674940000</td>\n",
       "      <td>c:5</td>\n",
       "      <td>...</td>\n",
       "      <td>847</td>\n",
       "      <td>2012-05-18</td>\n",
       "      <td>18400000000</td>\n",
       "      <td>USD</td>\n",
       "      <td>Facebook Prices IPO at Record Value</td>\n",
       "      <td>http://online.wsj.com/news/articles/SB10001424...</td>\n",
       "      <td>NASDAQ:FB</td>\n",
       "      <td>2013-11-21 19:40:55</td>\n",
       "      <td>104000000000</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>340 Madison Ave</td>\n",
       "      <td>None</td>\n",
       "      <td>New York</td>\n",
       "      <td>USA</td>\n",
       "      <td>None</td>\n",
       "      <td>New York</td>\n",
       "      <td>9084</td>\n",
       "      <td>40.7557162000</td>\n",
       "      <td>-73.9792469000</td>\n",
       "      <td>c:5</td>\n",
       "      <td>...</td>\n",
       "      <td>847</td>\n",
       "      <td>2012-05-18</td>\n",
       "      <td>18400000000</td>\n",
       "      <td>USD</td>\n",
       "      <td>Facebook Prices IPO at Record Value</td>\n",
       "      <td>http://online.wsj.com/news/articles/SB10001424...</td>\n",
       "      <td>NASDAQ:FB</td>\n",
       "      <td>2013-11-21 19:40:55</td>\n",
       "      <td>104000000000</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1355 Market St.</td>\n",
       "      <td>None</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>USA</td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "      <td>10</td>\n",
       "      <td>37.7768052000</td>\n",
       "      <td>-122.4169244000</td>\n",
       "      <td>c:12</td>\n",
       "      <td>...</td>\n",
       "      <td>1310</td>\n",
       "      <td>2013-11-07</td>\n",
       "      <td>1820000000</td>\n",
       "      <td>USD</td>\n",
       "      <td>Twitter Prices IPO Above Estimates At $26 Per ...</td>\n",
       "      <td>http://techcrunch.com/2013/11/06/twitter-price...</td>\n",
       "      <td>NYSE:TWTR</td>\n",
       "      <td>2013-11-07 04:18:48</td>\n",
       "      <td>18100000000</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2145 Hamilton Avenue</td>\n",
       "      <td>None</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>USA</td>\n",
       "      <td>None</td>\n",
       "      <td>Headquarters</td>\n",
       "      <td>16</td>\n",
       "      <td>37.2950050000</td>\n",
       "      <td>-121.9300350000</td>\n",
       "      <td>c:20</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>1998-10-02</td>\n",
       "      <td>None</td>\n",
       "      <td>USD</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NASDAQ:EBAY</td>\n",
       "      <td>2012-04-12 04:24:15</td>\n",
       "      <td>None</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               address1 address2           city country_code created_at_x  \\\n",
       "0      1601 Willow Road     None     Menlo Park          USA         None   \n",
       "1                  None     None         Dublin          IRL         None   \n",
       "2       340 Madison Ave     None       New York          USA         None   \n",
       "3       1355 Market St.     None  San Francisco          USA         None   \n",
       "4  2145 Hamilton Avenue     None       San Jose          USA         None   \n",
       "\n",
       "    description  id_x       latitude        longitude object_id  \\\n",
       "0  Headquarters     4  37.4160500000  -122.1518010000       c:5   \n",
       "1     Europe HQ  6975  53.3441040000    -6.2674940000       c:5   \n",
       "2      New York  9084  40.7557162000   -73.9792469000       c:5   \n",
       "3                  10  37.7768052000  -122.4169244000      c:12   \n",
       "4  Headquarters    16  37.2950050000  -121.9300350000      c:20   \n",
       "\n",
       "            ...            ipo_id   public_at raised_amount  \\\n",
       "0           ...               847  2012-05-18   18400000000   \n",
       "1           ...               847  2012-05-18   18400000000   \n",
       "2           ...               847  2012-05-18   18400000000   \n",
       "3           ...              1310  2013-11-07    1820000000   \n",
       "4           ...                26  1998-10-02          None   \n",
       "\n",
       "  raised_currency_code                                 source_description  \\\n",
       "0                  USD                Facebook Prices IPO at Record Value   \n",
       "1                  USD                Facebook Prices IPO at Record Value   \n",
       "2                  USD                Facebook Prices IPO at Record Value   \n",
       "3                  USD  Twitter Prices IPO Above Estimates At $26 Per ...   \n",
       "4                  USD                                               None   \n",
       "\n",
       "                                          source_url  stock_symbol  \\\n",
       "0  http://online.wsj.com/news/articles/SB10001424...     NASDAQ:FB   \n",
       "1  http://online.wsj.com/news/articles/SB10001424...     NASDAQ:FB   \n",
       "2  http://online.wsj.com/news/articles/SB10001424...     NASDAQ:FB   \n",
       "3  http://techcrunch.com/2013/11/06/twitter-price...     NYSE:TWTR   \n",
       "4                                               None   NASDAQ:EBAY   \n",
       "\n",
       "         updated_at_y valuation_amount valuation_currency_code  \n",
       "0 2013-11-21 19:40:55     104000000000                     USD  \n",
       "1 2013-11-21 19:40:55     104000000000                     USD  \n",
       "2 2013-11-21 19:40:55     104000000000                     USD  \n",
       "3 2013-11-07 04:18:48      18100000000                     USD  \n",
       "4 2012-04-12 04:24:15             None                     USD  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at new merged dataframe\n",
    "offices_ipos.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1554, 27)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offices_ipos.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['address1', 'address2', 'city', 'country_code', 'created_at_x',\n",
       "       'description', 'id_x', 'latitude', 'longitude', 'object_id',\n",
       "       'office_id', 'region', 'state_code', 'updated_at_x', 'zip_code',\n",
       "       'created_at_y', 'id_y', 'ipo_id', 'public_at', 'raised_amount',\n",
       "       'raised_currency_code', 'source_description', 'source_url',\n",
       "       'stock_symbol', 'updated_at_y', 'valuation_amount',\n",
       "       'valuation_currency_code'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offices_ipos.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of valuation amount"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first want a sense for the total valuation amounts we are considering. The valuations are all < 2x10<sup>10</sup> except for 3 that are >1x10<sup>11</sup>. While there are 1,554 entries in the merged dataframe, there are only 167 entries that have valuation amount provided. We will continue with analysis of valuation amount but subsequently look at total funding amount instead later in this notebook to increase our sample size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAJhAiAIAADCh0hDl9Xr1zDPPaNiwYRo+fLj27dtXon3FihVKTExUUlKS5s6dW2uFAgAA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\nqNDrU5jDpjCHXQ1C7Qopas9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33367Zs+erfvuu08Oh0M33HCDfvjhB0nSnXfeqRdeeEFT\np04NnKdXr176/vvvlZiYKI/HozvvvFMPPPCAjh8/Xq36r732Wl133XW69957FRYWpltuuUVHjx6t\n8vG///3vNXHiRCUkJMjr9eqxxx7T9ddfL7fbrY8//lj33HOPWrdura5du1Z6rj59+mjAgAGaP39+\nid4qAJcni0E/MwAAQLXREwWgXkhOTg5MBj/XsGHDNHz48DqoCEB9R08UAACACUwsBwAAMIEQBQAA\nYAIhCgAAwARCFAAAgAmEKAAAABMIUQAAACb8f2DVQEQ0ogmWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117149748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot valuation distribution; include number of companies considering\n",
    "val = pd.to_numeric(offices_ipos[~offices_ipos.valuation_amount.isnull()].valuation_amount)\n",
    "a=sns.distplot(val, rug=True);\n",
    "a.set_title('valuation distribution (N = ' + str(len(val)) + ')');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We look at these 3 outliers and see that one is simply because the valuation amount is in JPY (currently 1 JPY = 0.0088 USD). The other two are Facebook and Amazon, which we know to be companies with very high valuations. We will look at those with valuation_currency_code 'USD' moving forward to standardize our analysis to one currency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>address1</th>\n",
       "      <th>address2</th>\n",
       "      <th>city</th>\n",
       "      <th>country_code</th>\n",
       "      <th>created_at_x</th>\n",
       "      <th>description</th>\n",
       "      <th>id_x</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>object_id</th>\n",
       "      <th>...</th>\n",
       "      <th>ipo_id</th>\n",
       "      <th>public_at</th>\n",
       "      <th>raised_amount</th>\n",
       "      <th>raised_currency_code</th>\n",
       "      <th>source_description</th>\n",
       "      <th>source_url</th>\n",
       "      <th>stock_symbol</th>\n",
       "      <th>updated_at_y</th>\n",
       "      <th>valuation_amount</th>\n",
       "      <th>valuation_currency_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1601 Willow Road</td>\n",
       "      <td>None</td>\n",
       "      <td>Menlo Park</td>\n",
       "      <td>USA</td>\n",
       "      <td>None</td>\n",
       "      <td>Headquarters</td>\n",
       "      <td>4</td>\n",
       "      <td>37.4160500000</td>\n",
       "      <td>-122.1518010000</td>\n",
       "      <td>c:5</td>\n",
       "      <td>...</td>\n",
       "      <td>847</td>\n",
       "      <td>2012-05-18</td>\n",
       "      <td>18400000000</td>\n",
       "      <td>USD</td>\n",
       "      <td>Facebook Prices IPO at Record Value</td>\n",
       "      <td>http://online.wsj.com/news/articles/SB10001424...</td>\n",
       "      <td>NASDAQ:FB</td>\n",
       "      <td>2013-11-21 19:40:55</td>\n",
       "      <td>104000000000</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Dublin</td>\n",
       "      <td>IRL</td>\n",
       "      <td>None</td>\n",
       "      <td>Europe HQ</td>\n",
       "      <td>6975</td>\n",
       "      <td>53.3441040000</td>\n",
       "      <td>-6.2674940000</td>\n",
       "      <td>c:5</td>\n",
       "      <td>...</td>\n",
       "      <td>847</td>\n",
       "      <td>2012-05-18</td>\n",
       "      <td>18400000000</td>\n",
       "      <td>USD</td>\n",
       "      <td>Facebook Prices IPO at Record Value</td>\n",
       "      <td>http://online.wsj.com/news/articles/SB10001424...</td>\n",
       "      <td>NASDAQ:FB</td>\n",
       "      <td>2013-11-21 19:40:55</td>\n",
       "      <td>104000000000</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>340 Madison Ave</td>\n",
       "      <td>None</td>\n",
       "      <td>New York</td>\n",
       "      <td>USA</td>\n",
       "      <td>None</td>\n",
       "      <td>New York</td>\n",
       "      <td>9084</td>\n",
       "      <td>40.7557162000</td>\n",
       "      <td>-73.9792469000</td>\n",
       "      <td>c:5</td>\n",
       "      <td>...</td>\n",
       "      <td>847</td>\n",
       "      <td>2012-05-18</td>\n",
       "      <td>18400000000</td>\n",
       "      <td>USD</td>\n",
       "      <td>Facebook Prices IPO at Record Value</td>\n",
       "      <td>http://online.wsj.com/news/articles/SB10001424...</td>\n",
       "      <td>NASDAQ:FB</td>\n",
       "      <td>2013-11-21 19:40:55</td>\n",
       "      <td>104000000000</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>1200 12th Ave</td>\n",
       "      <td>S # 1200</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>USA</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>288</td>\n",
       "      <td>47.5923000000</td>\n",
       "      <td>-122.3172950000</td>\n",
       "      <td>c:317</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>1997-05-01</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NASDAQ:AMZN</td>\n",
       "      <td>2011-08-01 21:11:22</td>\n",
       "      <td>100000000000</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>Roppongi-Hills Mori Tower, 6-10-1, Roppongi</td>\n",
       "      <td>Minato-ku</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>JPN</td>\n",
       "      <td>None</td>\n",
       "      <td>Headquarters</td>\n",
       "      <td>9085</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>c:15609</td>\n",
       "      <td>...</td>\n",
       "      <td>78</td>\n",
       "      <td>2008-12-17</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3632</td>\n",
       "      <td>2010-10-08 06:47:07</td>\n",
       "      <td>108960000000</td>\n",
       "      <td>JPY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        address1   address2        city  \\\n",
       "0                               1601 Willow Road       None  Menlo Park   \n",
       "1                                           None       None      Dublin   \n",
       "2                                340 Madison Ave       None    New York   \n",
       "91                                 1200 12th Ave   S # 1200     Seattle   \n",
       "360  Roppongi-Hills Mori Tower, 6-10-1, Roppongi  Minato-ku       Tokyo   \n",
       "\n",
       "    country_code created_at_x   description  id_x       latitude  \\\n",
       "0            USA         None  Headquarters     4  37.4160500000   \n",
       "1            IRL         None     Europe HQ  6975  53.3441040000   \n",
       "2            USA         None      New York  9084  40.7557162000   \n",
       "91           USA         None          None   288  47.5923000000   \n",
       "360          JPN         None  Headquarters  9085           None   \n",
       "\n",
       "           longitude object_id           ...            ipo_id   public_at  \\\n",
       "0    -122.1518010000       c:5           ...               847  2012-05-18   \n",
       "1      -6.2674940000       c:5           ...               847  2012-05-18   \n",
       "2     -73.9792469000       c:5           ...               847  2012-05-18   \n",
       "91   -122.3172950000     c:317           ...                 5  1997-05-01   \n",
       "360             None   c:15609           ...                78  2008-12-17   \n",
       "\n",
       "    raised_amount raised_currency_code                   source_description  \\\n",
       "0     18400000000                  USD  Facebook Prices IPO at Record Value   \n",
       "1     18400000000                  USD  Facebook Prices IPO at Record Value   \n",
       "2     18400000000                  USD  Facebook Prices IPO at Record Value   \n",
       "91           None                 None                                 None   \n",
       "360          None                 None                                 None   \n",
       "\n",
       "                                            source_url  stock_symbol  \\\n",
       "0    http://online.wsj.com/news/articles/SB10001424...     NASDAQ:FB   \n",
       "1    http://online.wsj.com/news/articles/SB10001424...     NASDAQ:FB   \n",
       "2    http://online.wsj.com/news/articles/SB10001424...     NASDAQ:FB   \n",
       "91                                                None   NASDAQ:AMZN   \n",
       "360                                               None          3632   \n",
       "\n",
       "           updated_at_y valuation_amount valuation_currency_code  \n",
       "0   2013-11-21 19:40:55     104000000000                     USD  \n",
       "1   2013-11-21 19:40:55     104000000000                     USD  \n",
       "2   2013-11-21 19:40:55     104000000000                     USD  \n",
       "91  2011-08-01 21:11:22     100000000000                     USD  \n",
       "360 2010-10-08 06:47:07     108960000000                     JPY  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at the 3 outliers with very large valuation_amount\n",
    "offices_ipos[pd.to_numeric(offices_ipos.valuation_amount) > 0.8e11]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above table reveals that we do have duplicate entries for certain companies based on multiple office locations. We need to look at the number of unique companies we are considering, which as shown below is 101 (not 167). Because the 'description field' does not sufficiently and uniformly describe the type of office for all entries, we will not filter by that column. Instead, this is just a preliminary exploratory analysis that will keep all locations in the analysis. We would ideally like to filter by 'Headquarters' or 'HQ' but the data is not sufficiently documented so this would require substantial data entry to designate the headquarters location for all companies with multiple office locations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pd.unique(offices_ipos[~offices_ipos.valuation_amount.isnull()].object_id))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Valuation amount by region"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to determine the effect of location on IPO valuation. In particular, we consider the median valuation and number of IPOs by region to determine whether certain regions are hotspots for companies that IPO. As mentioned above, we now limit our analysis to valuations in USD, which reduces our N from 167 to 161."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>valuation_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.610000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.904637e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.615982e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.860000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.340000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.150000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.040000e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       valuation_amount\n",
       "count      1.610000e+02\n",
       "mean       3.904637e+09\n",
       "std        1.615982e+10\n",
       "min        3.860000e+04\n",
       "25%        1.340000e+08\n",
       "50%        3.150000e+08\n",
       "75%        1.000000e+09\n",
       "max        1.040000e+11"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# restrict to valuations in USD currency\n",
    "offices_ipos_us = offices_ipos[offices_ipos.valuation_currency_code == 'USD']\n",
    "val_us = pd.to_numeric(offices_ipos_us[~offices_ipos_us.valuation_amount.isnull()].valuation_amount)\n",
    "region = offices_ipos_us[~offices_ipos_us.valuation_amount.isnull()].region\n",
    "# create dataframe with just region and valuation\n",
    "df_region_val = pd.concat([region, val_us], axis=1)\n",
    "df_region_val.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# perform calculations on a per region basis\n",
    "reg = df_region_val.groupby(['region'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SF Bay has the most IPOs with NY, London, and Seattle following. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>valuation_amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SF Bay</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seattle</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denver</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boston</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Los Angeles</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicago</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beijing</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             valuation_amount\n",
       "region                       \n",
       "SF Bay                     34\n",
       "New York                   11\n",
       "London                      8\n",
       "Seattle                     7\n",
       "Denver                      6\n",
       "Boston                      6\n",
       "Los Angeles                 6\n",
       "Chicago                     5\n",
       "Beijing                     4\n",
       "Singapore                   3"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort regions by number of ipos (that had valutation amount listed) \n",
    "num = reg.count()\n",
    "num.sort_values(by='valuation_amount', ascending=False)[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create an informative boxplot of valuation amount by region we include regions with >5 companies that IPO."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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79qz6MAAAAEjeQ7iOGTMmzc3NGTJkSIYMGZLtt9++FrsAAAAgyXsI1zvuuCO/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\npeEIOjcAAAAGSURBVP0Pog6qJwe0mVoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bdac8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot valuation by region\n",
    "top7 = ['SF Bay', 'New York', 'London', 'Seattle', 'Denver', 'Boston', 'Los Angeles']\n",
    "df_top7 = df_region_val[df_region_val.region.isin(top7)]\n",
    "fig, a = plt.subplots(figsize=(16,10))\n",
    "a=sns.barplot(x='region', y='valuation_amount',data=df_top7, ci=False, order=top7)\n",
    "a.set_ylabel('valuation amount')\n",
    "a.set_title('valuation by region')\n",
    "plt.xticks(rotation=90);\n",
    "plt.savefig('results/valuation_region.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While SF Bay has the most companies that IPO (above barplot is sorted from most companies that IPO to least), the region with the highest mean valuation amount is Seattle. However, the N for Seattle is only 7 so the effects of outliers like Microsoft are more significant (refer to wider boxplot for Seattle below). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BAwdWaxwAAAA6qaqF69y5czNixIgkyd57752HH354jft33333LF26ND179kylUklDQ0O1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\nws/9MnWl3/uc22L9GipVeul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isHBVGHaQIwCIiYlBUFAQAgMD2ZxBhcaOHYtvvvnGulyM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\nFI2FKxERERERESnavwD9DJZr5pUadgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118a71e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot valuation by region\n",
    "top7 = ['SF Bay', 'New York', 'London', 'Seattle', 'Denver', 'Boston', 'Los Angeles']\n",
    "df_top7 = df_region_val[df_region_val.region.isin(top7)]\n",
    "fig, a = plt.subplots(figsize=(16,10))\n",
    "a=sns.boxplot(x='region', y='valuation_amount',data=df_top7, order=top7)\n",
    "a.set_ylabel('valuation amount')\n",
    "a.set_title('valuation by region')\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis of number of IPOs over time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to looking at the value and region of companies that IPO we also look at historical trends in terms of the number of IPOs over time to determine how timing may affect a company's success. We no longer restrict analysis to companies where valuation amount is provided. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# now don't filter to ones with valuation amount only\n",
    "dt = pd.to_datetime(offices_ipos.public_at)\n",
    "valAll = pd.to_numeric(offices_ipos.valuation_amount)\n",
    "regionAll = offices_ipos.region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_region_dt = pd.concat([regionAll, dt], axis=1)\n",
    "time = df_region_dt.groupby([dt.dt.year])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>public_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>public_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013.0</th>\n",
       "      <td>154</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004.0</th>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011.0</th>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010.0</th>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012.0</th>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009.0</th>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999.0</th>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007.0</th>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008.0</th>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000.0</th>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           region  public_at\n",
       "public_at                   \n",
       "2013.0        154        154\n",
       "2004.0         88         88\n",
       "2011.0         83         83\n",
       "2010.0         77         77\n",
       "2012.0         51         51\n",
       "2009.0         47         47\n",
       "1999.0         47         47\n",
       "2007.0         46         46\n",
       "2008.0         44         44\n",
       "2000.0         43         43"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top counts by year\n",
    "num = time.count()\n",
    "num.sort_values(by='public_at', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAECe4rVdk9n59NNPNWvWLM2ZM0dlypRRUFCQUlNT3a+npqZeFswAAAAKMp+dNbls2TIt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119128e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# barplot showing number of ipos by year\n",
    "a=sns.barplot(num.public_at.index, num.public_at)\n",
    "a.set_ylabel('number of ipos')\n",
    "a.set_xlabel('year')\n",
    "a.set_title('number of ipos by year');\n",
    "a.set_xticklabels(labels=num.public_at.index.astype(int), rotation=90);\n",
    "plt.savefig('results/ipos_year.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More recent years have more IPOs. However, we cannot rule out the possibility that the Crunchbase dataset is also simply becoming more complete."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Incorporate cb_objects into analysis to consider funding_total_usd metric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While we had been considering valuation amount from the IPO data, we also consider funding_total_usd for the below analysis because it is a much richer dataset. We have N=27,874 instead of N=167."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Connect to database and look at dataframe from cb_objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conn = dbConnect()\n",
    "objs = dbTableToDataFrame(conn, 'cb_objects')\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category_code</th>\n",
       "      <th>city</th>\n",
       "      <th>closed_at</th>\n",
       "      <th>country_code</th>\n",
       "      <th>created_at</th>\n",
       "      <th>created_by</th>\n",
       "      <th>description</th>\n",
       "      <th>domain</th>\n",
       "      <th>entity_id</th>\n",
       "      <th>entity_type</th>\n",
       "      <th>...</th>\n",
       "      <th>parent_id</th>\n",
       "      <th>permalink</th>\n",
       "      <th>region</th>\n",
       "      <th>relationships</th>\n",
       "      <th>short_description</th>\n",
       "      <th>state_code</th>\n",
       "      <th>status</th>\n",
       "      <th>tag_list</th>\n",
       "      <th>twitter_username</th>\n",
       "      <th>updated_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>web</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>None</td>\n",
       "      <td>USA</td>\n",
       "      <td>2007-05-25 06:51:27</td>\n",
       "      <td>initial-importer</td>\n",
       "      <td>Technology Platform Company</td>\n",
       "      <td>wetpaint-inc.com</td>\n",
       "      <td>1</td>\n",
       "      <td>Company</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>/company/wetpaint</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>17.0</td>\n",
       "      <td>None</td>\n",
       "      <td>WA</td>\n",
       "      <td>operating</td>\n",
       "      <td>wiki, seattle, elowitz, media-industry, media-...</td>\n",
       "      <td>BachelrWetpaint</td>\n",
       "      <td>2013-04-13 03:29:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>games_video</td>\n",
       "      <td>Culver City</td>\n",
       "      <td>None</td>\n",
       "      <td>USA</td>\n",
       "      <td>2007-05-31 21:11:51</td>\n",
       "      <td>initial-importer</td>\n",
       "      <td>None</td>\n",
       "      <td>flektor.com</td>\n",
       "      <td>10</td>\n",
       "      <td>Company</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>/company/flektor</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>6.0</td>\n",
       "      <td>None</td>\n",
       "      <td>CA</td>\n",
       "      <td>acquired</td>\n",
       "      <td>flektor, photo, video</td>\n",
       "      <td>None</td>\n",
       "      <td>2008-05-23 23:23:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>games_video</td>\n",
       "      <td>San Mateo</td>\n",
       "      <td>None</td>\n",
       "      <td>USA</td>\n",
       "      <td>2007-08-06 23:52:45</td>\n",
       "      <td>initial-importer</td>\n",
       "      <td></td>\n",
       "      <td>there.com</td>\n",
       "      <td>100</td>\n",
       "      <td>Company</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>/company/there</td>\n",
       "      <td>SF Bay</td>\n",
       "      <td>12.0</td>\n",
       "      <td>None</td>\n",
       "      <td>CA</td>\n",
       "      <td>acquired</td>\n",
       "      <td>virtualworld, there, teens</td>\n",
       "      <td>None</td>\n",
       "      <td>2013-11-04 02:09:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>network_hosting</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2008-08-24 16:51:57</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>mywebbo.com</td>\n",
       "      <td>10000</td>\n",
       "      <td>Company</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>/company/mywebbo</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>operating</td>\n",
       "      <td>social-network, new, website, web, friends, ch...</td>\n",
       "      <td>None</td>\n",
       "      <td>2008-09-06 14:19:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>games_video</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2008-08-24 17:10:34</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>themoviestreamer.com</td>\n",
       "      <td>10001</td>\n",
       "      <td>Company</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>/company/the-movie-streamer</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>operating</td>\n",
       "      <td>watch, full-length, moives, online, for, free,...</td>\n",
       "      <td>None</td>\n",
       "      <td>2008-09-06 14:19:18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     category_code         city closed_at country_code          created_at  \\\n",
       "0              web      Seattle      None          USA 2007-05-25 06:51:27   \n",
       "1      games_video  Culver City      None          USA 2007-05-31 21:11:51   \n",
       "2      games_video    San Mateo      None          USA 2007-08-06 23:52:45   \n",
       "3  network_hosting         None      None         None 2008-08-24 16:51:57   \n",
       "4      games_video         None      None         None 2008-08-24 17:10:34   \n",
       "\n",
       "         created_by                  description                domain  \\\n",
       "0  initial-importer  Technology Platform Company      wetpaint-inc.com   \n",
       "1  initial-importer                         None           flektor.com   \n",
       "2  initial-importer                                          there.com   \n",
       "3              None                         None           mywebbo.com   \n",
       "4              None                         None  themoviestreamer.com   \n",
       "\n",
       "   entity_id entity_type         ...         parent_id  \\\n",
       "0          1     Company         ...              None   \n",
       "1         10     Company         ...              None   \n",
       "2        100     Company         ...              None   \n",
       "3      10000     Company         ...              None   \n",
       "4      10001     Company         ...              None   \n",
       "\n",
       "                     permalink       region relationships  short_description  \\\n",
       "0            /company/wetpaint      Seattle          17.0               None   \n",
       "1             /company/flektor  Los Angeles           6.0               None   \n",
       "2               /company/there       SF Bay          12.0               None   \n",
       "3             /company/mywebbo      unknown           NaN               None   \n",
       "4  /company/the-movie-streamer      unknown           NaN               None   \n",
       "\n",
       "  state_code     status                                           tag_list  \\\n",
       "0         WA  operating  wiki, seattle, elowitz, media-industry, media-...   \n",
       "1         CA   acquired                              flektor, photo, video   \n",
       "2         CA   acquired                         virtualworld, there, teens   \n",
       "3       None  operating  social-network, new, website, web, friends, ch...   \n",
       "4       None  operating  watch, full-length, moives, online, for, free,...   \n",
       "\n",
       "   twitter_username          updated_at  \n",
       "0   BachelrWetpaint 2013-04-13 03:29:00  \n",
       "1              None 2008-05-23 23:23:14  \n",
       "2              None 2013-11-04 02:09:48  \n",
       "3              None 2008-09-06 14:19:18  \n",
       "4              None 2008-09-06 14:19:18  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "objs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['category_code', 'city', 'closed_at', 'country_code', 'created_at',\n",
       "       'created_by', 'description', 'domain', 'entity_id', 'entity_type',\n",
       "       'first_funding_at', 'first_investment_at', 'first_milestone_at',\n",
       "       'founded_at', 'funding_rounds', 'funding_total_usd', 'homepage_url',\n",
       "       'id', 'invested_companies', 'investment_rounds', 'last_funding_at',\n",
       "       'last_investment_at', 'last_milestone_at', 'logo_height', 'logo_url',\n",
       "       'logo_width', 'milestones', 'name', 'normalized_name', 'overview',\n",
       "       'parent_id', 'permalink', 'region', 'relationships',\n",
       "       'short_description', 'state_code', 'status', 'tag_list',\n",
       "       'twitter_username', 'updated_at'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "objs.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(462651, 40)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "objs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27874"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(objs.funding_total_usd[~objs.funding_total_usd.isnull()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Look at the influence of state on total funding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The greatest mean funding is in WA, CA, MA, MD, and AL. However when looking at boxplots with the median indicated, which is a better metric so we are not sensitive to extreme outliers, we see that the median is highest in CA, MA, NJ, NH, and ME."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ly5dPAwYM8OgJDcNQu3btFBAQID8/P9WoUUMHDhy4o+IBAAAyGrfC1549ezRgwACVLFlS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\nwhcAAICJCF8AAAAmInwBAACY6P8BK5B8CmyFE9MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15dd87b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot mean funding_total_usd by state\n",
    "a=sns.barplot(objs['state_code'], pd.to_numeric(objs['funding_total_usd']), ci=False);\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pDnA3Ae4mWC3ZsFqypddwN8FVV9cqQItHIBBI+VEVOpX5osTLyxuFdetekZaJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118b4b4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boxplot of funding_total_usd by state\n",
    "a=sns.boxplot(objs['state_code'], pd.to_numeric(objs['funding_total_usd']));\n",
    "a.set_ylim(0, 2.5e7)\n",
    "plt.xticks(rotation=90);\n",
    "plt.savefig('results/funding_state.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just as we looked at the number of IPOs over time, we now look at the emergence of new companies over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# group by date founded to do analysis on the emergence of new companies over time\n",
    "dt = pd.to_datetime(objs.founded_at)\n",
    "df_fund_dt = pd.concat([objs.funding_total_usd, dt], axis=1)\n",
    "founded = df_fund_dt.groupby([dt.dt.year])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "founded_at\n",
       "2011.0    11884\n",
       "2012.0    11158\n",
       "2010.0    10858\n",
       "2009.0     9805\n",
       "2008.0     8502\n",
       "2007.0     6765\n",
       "2013.0     6280\n",
       "2006.0     4689\n",
       "2005.0     3580\n",
       "2004.0     2828\n",
       "Name: founded_at, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top counts by year\n",
    "num_found = founded.count()\n",
    "num_found.sort_values(by='founded_at', ascending=False)['founded_at'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jRznuDQCAy03pop0urecVf5NL651L9epB5/waF9cFAAAwAUIbAACACRDaAAAATIDQBgAA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\nACZAaAMAADABQhsAAIAJENoAAABM4P8Bhbi9pva4/gUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13db412b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot number of new companies by year; zooming in on 1990 and later where there is more data\n",
    "a=sns.barplot(num_found.founded_at.index.astype(int)[(num_found.founded_at.index.astype(int) >= 1990) \n",
    "                                                     & (num_found.founded_at.index.astype(int) < 2013)], \n",
    "              num_found.founded_at[(num_found.founded_at.index.astype(int) >= 1990) \n",
    "                                   & (num_found.founded_at.index.astype(int) < 2013)])\n",
    "a.set_ylabel('number of new companies')\n",
    "a.set_xlabel('year')\n",
    "a.set_title('number of new companies by year');\n",
    "plt.xticks(rotation=90);\n",
    "plt.savefig('results/new_year.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# company lifespan: closed_at - founded_at\n",
    "start = pd.to_datetime(objs.founded_at)\n",
    "end = pd.to_datetime(objs.closed_at)\n",
    "life = end.dt.year - start.dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BAABQeAQxG9kZxByGIZ/Xqd44PWIAANiFIGajgTliha8jJvVX149RXR8AALsQxGyUXnS7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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x141327cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a=sns.distplot(life[~life.isnull()]);\n",
    "a.set_xlim(-5, 50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    27.000000\n",
       "mean     -7.851852\n",
       "std      10.614590\n",
       "min     -40.000000\n",
       "25%      -9.500000\n",
       "50%      -3.000000\n",
       "75%      -1.000000\n",
       "max      -1.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# there are 27 with a life < 0 showing data entry for this data set was flawed. \n",
    "life[life < 0].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2013.000000\n",
       "mean        4.384004\n",
       "std         4.359592\n",
       "min         1.000000\n",
       "25%         2.000000\n",
       "50%         3.000000\n",
       "75%         5.000000\n",
       "max        53.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# consider those with positive life\n",
    "life_pos = life[life>0]\n",
    "life_pos[~life_pos.isnull()].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AfsWtHactW7aopKREq1ev1vjx4zVr1ixPzwUAAOB33ApOu3btUs+ePSVJd9xxhz777DOP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x141327d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# see whether companies tend to have longer of shorter lifespans based on when they were founded\n",
    "a=sns.barplot(start[~life.isnull()].dt.year.astype(int), life[~life.isnull()], ci=None);\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9vTV27Fi9/fbbio2N1VdffZXuZ6ZMmTJq3769+vXrpx9//NHxOTcdOcu24LR+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x147a4a860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# see whether companies tend to have longer or shorter lifespans based on region\n",
    "a=sns.barplot(objs.state_code[~objs.state_code.isnull()], life[~objs.state_code.isnull()], ci=None);\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Zu+mC6N82tX24qcVLVJdPidO//vUv3HvvvZgwYQJ+//13pKamYuvWrVCr1Q3/cQB17/4g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\nQRhcn9SDoHYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a701898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# see whether companies tend to have longer or shorter lifespans based on region\n",
    "a=sns.boxplot(objs.state_code[~objs.state_code.isnull()], life[~objs.state_code.isnull()]);\n",
    "a.set_ylim(0,15);\n",
    "plt.xticks(rotation=90);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}