ETL_data_project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Dependencies\n",
    "import pandas as pd\n",
    "import csv\n",
    "import sys, json\n",
    "import psycopg2\n",
    "from sqlalchemy import create_engine\n",
    "import pymongo\n",
    "import datetime\n",
    "import numpy as np\n",
    "np.random.seed(1)\n",
    "import requests\n",
    "from pprint import pprint\n",
    "import matplotlib.pyplot as plt\n",
    "from flask import jsonify\n",
    "import company_domain\n",
    "company_domain=company_domain.company_domain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The path to CSV files to extract all Inc 5000 company list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The path to our CSV files to extract all Inc 5000 company list\n",
    "\n",
    "# 2019 Inc 5000 company list\n",
    "Inc_2019_csv = \"Resources/inc5000-2019.csv\"\n",
    "# 2018 Inc 5000 company list\n",
    "Inc_2018_csv = \"Resources/inc5000-2018.csv\"\n",
    "# 2007-2017 Inc 5000 company list\n",
    "Inc_10year_csv =\"Resources/inc5000_all10years.csv\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read the files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read csv file\n",
    "# Read 2019 Inc 5000 company list\n",
    "Inc_2019_df = pd.read_csv(Inc_2019_csv)\n",
    "\n",
    "# Read 2018 Inc 5000 company list\n",
    "Inc_2018_df = pd.read_csv(Inc_2018_csv)\n",
    "\n",
    "\n",
    "# Read 2007-2017 Inc 5000 company list\n",
    "Inc_10year_df = pd.read_csv(Inc_10year_csv,encoding='cp1252')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scraping Financial Times Americas Fastest Growing Companies 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Financial times url to scrape\n",
    "ft_url = 'https://www.ft.com/americas-fastest-growing-companies-2020'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the html file for scarping tables \n",
    "ft_tables= pd.read_html(ft_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check it's type\n",
    "type(ft_tables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check how many tables are there \n",
    "len(ft_tables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scrape the the first table \n",
    "ft_tables_df = ft_tables[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>company_name</th>\n",
       "      <th>country</th>\n",
       "      <th>industry</th>\n",
       "      <th>growth_rate</th>\n",
       "      <th>compound_annual_growth_rate</th>\n",
       "      <th>rank_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Niantic</td>\n",
       "      <td>United States</td>\n",
       "      <td>Games industry</td>\n",
       "      <td>180306.6</td>\n",
       "      <td>1117.4</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>UiPath</td>\n",
       "      <td>United States</td>\n",
       "      <td>Technology</td>\n",
       "      <td>37463.5</td>\n",
       "      <td>621.5</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Publisher First (Freestar)</td>\n",
       "      <td>United States</td>\n",
       "      <td>Advertising</td>\n",
       "      <td>36680.1</td>\n",
       "      <td>616.5</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>FreightWise</td>\n",
       "      <td>United States</td>\n",
       "      <td>Transport</td>\n",
       "      <td>30547.9</td>\n",
       "      <td>574.2</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Veggie Noodle Co.</td>\n",
       "      <td>United States</td>\n",
       "      <td>Food &amp; Beverage</td>\n",
       "      <td>24074.8</td>\n",
       "      <td>523.0</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rank                company_name        country         industry  \\\n",
       "0     1                     Niantic  United States   Games industry   \n",
       "1     2                      UiPath  United States       Technology   \n",
       "2     3  Publisher First (Freestar)  United States      Advertising   \n",
       "3     4                 FreightWise  United States        Transport   \n",
       "4     5           Veggie Noodle Co.  United States  Food & Beverage   \n",
       "\n",
       "   growth_rate  compound_annual_growth_rate rank_year  \n",
       "0     180306.6                       1117.4      2020  \n",
       "1      37463.5                        621.5      2020  \n",
       "2      36680.1                        616.5      2020  \n",
       "3      30547.9                        574.2      2020  \n",
       "4      24074.8                        523.0      2020  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transforming scraped data\n",
    "\n",
    "# Drop column we don't need\n",
    "drop_ft_columns=ft_tables_df.drop(['Revenue 2018 [in $m]','Founding Year','Revenue 2015 [in $m]','Number of employees 2018'], axis=1)\n",
    "\n",
    "\n",
    "# Remove unnecessary sign \"*\" from the first column \n",
    "\n",
    "drop_ft_columns['Name'] = drop_ft_columns['Name'].str.replace(r'*', '')\n",
    "\n",
    "# rename columns \n",
    "rename_ft_df=drop_ft_columns.rename(columns={\"Rank\":\"rank\",\"Name\":\"company_name\",\"FT category\":\"industry\",\"Absolute growth rate [in %]\":\"growth_rate\",\"Compound annual growth rate (CAGR) [in %]\":\"compound_annual_growth_rate\",\"Country\":\"country\"})\n",
    "\n",
    "# Add the year column \n",
    "rename_ft_df['rank_year']= '2020'\n",
    "\n",
    "rename_ft_df['country']= 'United States'\n",
    "\n",
    "# drop null values Growth\n",
    "\n",
    "rename_ft_df=rename_ft_df.dropna(axis=1,how='all')\n",
    "\n",
    "# remove duplicates and show clean data\n",
    "\n",
    "clean_ft_df=rename_ft_df.drop_duplicates()\n",
    "clean_ft_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### API Call for Scraping Growjo top 1000 fastest growing companies in 2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# API call from Growjo 10,000 - The Fastest Growing Companies list\n",
    "# We generated companies information based on domains. For example,\n",
    "# you could retrieve a company’s name, location, employee, estimated revenue and job openings from their domain name. \n",
    "\n",
    "\n",
    "# The base url to request the Api call \n",
    "base_url = \"https://growjo.com/api?url=\"\n",
    "\n",
    "# The information we want to extract \n",
    "ranking = []\n",
    "estimated_revenues = []\n",
    "company_name = []\n",
    "city = []\n",
    "country = []\n",
    "state = []\n",
    "employees = []\n",
    "founded = []\n",
    "industry = []\n",
    "total_funding = []\n",
    "\n",
    "# a list of domains imported as company_domain you can find the list in company_domain.py in the main folder\n",
    "\n",
    "\n",
    "# A for loop to get each domains and request information \n",
    "for domain in company_domain:\n",
    "    target_url = base_url + domain\n",
    "    data = requests.get(target_url)\n",
    "    try:\n",
    "        response = data.json()\n",
    "    except ValueError:\n",
    "        pass\n",
    "# append all requested information letter to retrive\n",
    "    ranking.append(response['ranking'])\n",
    "    estimated_revenues.append(response['estimated_revenues'])\n",
    "    company_name.append(response['company_name'])\n",
    "    founded.append(response['founded'])\n",
    "    city.append(response['city'])\n",
    "    country.append(response['country'])\n",
    "    state.append(response['state'])\n",
    "    employees.append(response['employees'])\n",
    "    industry.append(response['industry'])\n",
    "    total_funding.append(response['total_funding'])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Crerate and store the information in dictionary  \n",
    "growjo_df = pd.DataFrame({\n",
    "    \"rank\":ranking,\n",
    "    \"revenue_$\":estimated_revenues,\n",
    "    \"company_name\": company_name,\n",
    "    \"city\":city,\n",
    "    \"founding_year\":founded,\n",
    "    \"country\":country,\n",
    "    \"state\":state,\n",
    "    \"number_of_employees\":employees,\n",
    "    \"industry\":industry,\n",
    "    \"total_funding\":total_funding\n",
    "})\n",
    "\n",
    "# Remove duplicate values\n",
    "growjo_df=growjo_df.drop_duplicates()\n",
    "#growjo_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>revenue_$</th>\n",
       "      <th>company_name</th>\n",
       "      <th>city</th>\n",
       "      <th>founding_year</th>\n",
       "      <th>country</th>\n",
       "      <th>state</th>\n",
       "      <th>number_of_employees</th>\n",
       "      <th>industry</th>\n",
       "      <th>total_funding</th>\n",
       "      <th>rank_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>36450000.0</td>\n",
       "      <td>LetsGetChecked</td>\n",
       "      <td>New York</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>NY</td>\n",
       "      <td>135</td>\n",
       "      <td>Hospital/Healthcare</td>\n",
       "      <td>$113M</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>701.0</td>\n",
       "      <td>13120000.0</td>\n",
       "      <td>100 Thieves</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>CA</td>\n",
       "      <td>64</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>None</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>832.0</td>\n",
       "      <td>97340000.0</td>\n",
       "      <td>10X Genomics</td>\n",
       "      <td>Pleasanton</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>CA</td>\n",
       "      <td>628</td>\n",
       "      <td>Biotech</td>\n",
       "      <td>$242.6M</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>124.0</td>\n",
       "      <td>29870000.0</td>\n",
       "      <td>15Five</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>CA</td>\n",
       "      <td>206</td>\n",
       "      <td>Tech Services</td>\n",
       "      <td>$42.1M</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>941.0</td>\n",
       "      <td>10793000.0</td>\n",
       "      <td>2ULaundry</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>NC</td>\n",
       "      <td>43</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>None</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rank   revenue_$    company_name           city  founding_year  \\\n",
       "0    1.0  36450000.0  LetsGetChecked       New York         2014.0   \n",
       "1  701.0  13120000.0     100 Thieves    Los Angeles         2017.0   \n",
       "2  832.0  97340000.0    10X Genomics     Pleasanton         2012.0   \n",
       "3  124.0  29870000.0          15Five  San Francisco         2011.0   \n",
       "4  941.0  10793000.0       2ULaundry      Charlotte         2015.0   \n",
       "\n",
       "         country state  number_of_employees             industry  \\\n",
       "0  United States    NY                  135  Hospital/Healthcare   \n",
       "1  United States    CA                   64        Entertainment   \n",
       "2  United States    CA                  628              Biotech   \n",
       "3  United States    CA                  206        Tech Services   \n",
       "4  United States    NC                   43             Consumer   \n",
       "\n",
       "  total_funding rank_year  \n",
       "0         $113M      2020  \n",
       "1          None      2020  \n",
       "2       $242.6M      2020  \n",
       "3        $42.1M      2020  \n",
       "4          None      2020  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transforming Growjo top 1000 fastest growing companies in 2020\n",
    "# drop null values\n",
    "\n",
    "growjo_df=growjo_df.dropna(axis=1,how='all')\n",
    "# Add the year column \n",
    "growjo_df['rank_year']= '2020'\n",
    "\n",
    "# remove duplicates and show clean data\n",
    "\n",
    "clean_growjo_df=growjo_df.drop_duplicates()\n",
    "clean_growjo_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transforming"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning 2019 Inc 5000 company list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop column we don't need\n",
    "drop_column_2019=Inc_2019_df.drop(['Profile','_ - previous_workers','_ - founded','_ - metro','url','_ - yrs_on_list'], axis=1)\n",
    "\n",
    "# Add the year column \n",
    "drop_column_2019['rank_year']= '2019'\n",
    "\n",
    "# Rename columns \n",
    "rename_df_2019= drop_column_2019.rename(columns={\"_ - rank\":\"rank\",\"name\":\"company_name\",\"state\":\"state\",\"_ - revenue\":\"revenue_$\",\"_ - growth\":\"growth_rate\",\"_ - industry\":\"industry\",\"_ - founded\":\"founding_year\",\"_ - workers\":\"number_of_employees\",\"city\":\"city\"})\n",
    "\n",
    "\n",
    "\n",
    "# re-Order the data to suitable format\n",
    "\n",
    "cleaned_2019_df =rename_df_2019[['rank','rank_year','company_name','industry','number_of_employees','revenue_$','growth_rate','city','state']]\n",
    "#cleaned_2019_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning 2018 Inc 5000 company list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>rank_year</th>\n",
       "      <th>company_name</th>\n",
       "      <th>industry</th>\n",
       "      <th>number_of_employees</th>\n",
       "      <th>revenue_$</th>\n",
       "      <th>growth_rate</th>\n",
       "      <th>founding_year</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>SwanLeap</td>\n",
       "      <td>Logistics &amp; Transportation</td>\n",
       "      <td>49.0</td>\n",
       "      <td>98965631.0</td>\n",
       "      <td>75660.8425</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>Madison</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>PopSockets</td>\n",
       "      <td>Consumer Products &amp; Services</td>\n",
       "      <td>118.0</td>\n",
       "      <td>168837562.0</td>\n",
       "      <td>71423.7620</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Boulder</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>Home Chef</td>\n",
       "      <td>Food &amp; Beverage</td>\n",
       "      <td>865.0</td>\n",
       "      <td>255047839.0</td>\n",
       "      <td>60165.5058</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>Velocity Global</td>\n",
       "      <td>Business Products &amp; Services</td>\n",
       "      <td>55.0</td>\n",
       "      <td>49175942.0</td>\n",
       "      <td>39816.5093</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>Denver</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>DEPCOM Power</td>\n",
       "      <td>Energy</td>\n",
       "      <td>104.0</td>\n",
       "      <td>219574136.0</td>\n",
       "      <td>38962.9022</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>Scottsdale</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rank rank_year     company_name                      industry  \\\n",
       "0   1.0      2018         SwanLeap    Logistics & Transportation   \n",
       "1   2.0      2018       PopSockets  Consumer Products & Services   \n",
       "2   3.0      2018        Home Chef               Food & Beverage   \n",
       "3   4.0      2018  Velocity Global  Business Products & Services   \n",
       "4   5.0      2018     DEPCOM Power                        Energy   \n",
       "\n",
       "   number_of_employees    revenue_$  growth_rate  founding_year        city  \\\n",
       "0                 49.0   98965631.0   75660.8425         2013.0     Madison   \n",
       "1                118.0  168837562.0   71423.7620         2010.0     Boulder   \n",
       "2                865.0  255047839.0   60165.5058         2013.0     Chicago   \n",
       "3                 55.0   49175942.0   39816.5093         2013.0      Denver   \n",
       "4                104.0  219574136.0   38962.9022         2013.0  Scottsdale   \n",
       "\n",
       "  state  \n",
       "0    WI  \n",
       "1    CO  \n",
       "2    IL  \n",
       "3    CO  \n",
       "4    AZ  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop column we don't need\n",
    "\n",
    "drop_column_2018=Inc_2018_df.drop(['_ - id','_ - zipcode','_ - ifmid','_ - latitude','_ - longitude','_ - website','_ - state_l','_ - metrocode','_ - ifiid','_ - previous_workers','_ - metro','_ - url','_ - partner_lists - partner_lists','_ - yrs_on_list'], axis=1)\n",
    "\n",
    "# Add the year column \n",
    "drop_column_2018['rank_year']= '2018'\n",
    "\n",
    "# Rename columns \n",
    "rename_df_2018=drop_column_2018.rename(columns={\"_ - rank\":\"rank\",\"_ - company\":\"company_name\",\"_ - state_s\":\"state\",\"_ - revenue\":\"revenue_$\",\"_ - founded\":\"founding_year\",\"_ - growth\":\"growth_rate\",\"_ - industry\":\"industry\",\"_ - workers\":\"number_of_employees\",\"_ - state_l\":\"state\",\"_ - city\":\"city\"})\n",
    "rename_df_2018.head()\n",
    "\n",
    "\n",
    "cleaned_2018_df =rename_df_2018[['rank','rank_year','company_name','industry','number_of_employees','revenue_$','growth_rate','founding_year','city','state']]\n",
    "\n",
    "cleaned_2018_df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning 10 year (2007-2017) Inc 5000 company list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>rank_year</th>\n",
       "      <th>company_name</th>\n",
       "      <th>industry</th>\n",
       "      <th>number_of_employees</th>\n",
       "      <th>revenue_$</th>\n",
       "      <th>growth_rate</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>Loot Crate</td>\n",
       "      <td>Consumer Products &amp; Services</td>\n",
       "      <td>218.0</td>\n",
       "      <td>116247698.0</td>\n",
       "      <td>66788.5962</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>Paint Nite</td>\n",
       "      <td>Consumer Products &amp; Services</td>\n",
       "      <td>100.0</td>\n",
       "      <td>55018793.0</td>\n",
       "      <td>36555.2472</td>\n",
       "      <td>Somerville</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>CalCom Solar</td>\n",
       "      <td>Energy</td>\n",
       "      <td>47.0</td>\n",
       "      <td>33507450.0</td>\n",
       "      <td>31633.5448</td>\n",
       "      <td>Visalia</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>eLuxurySupply.com</td>\n",
       "      <td>Retail</td>\n",
       "      <td>82.0</td>\n",
       "      <td>30695215.0</td>\n",
       "      <td>23619.7198</td>\n",
       "      <td>Evansville</td>\n",
       "      <td>IN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>Company.com</td>\n",
       "      <td>Business Products &amp; Services</td>\n",
       "      <td>48.0</td>\n",
       "      <td>33370967.0</td>\n",
       "      <td>23486.8894</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>GA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rank  rank_year       company_name                      industry  \\\n",
       "0     1       2016         Loot Crate  Consumer Products & Services   \n",
       "1     2       2016         Paint Nite  Consumer Products & Services   \n",
       "2     3       2016       CalCom Solar                        Energy   \n",
       "3     4       2016  eLuxurySupply.com                        Retail   \n",
       "4     5       2016        Company.com  Business Products & Services   \n",
       "\n",
       "   number_of_employees    revenue_$  growth_rate         city state  \n",
       "0                218.0  116247698.0   66788.5962  Los Angeles    CA  \n",
       "1                100.0   55018793.0   36555.2472   Somerville    MA  \n",
       "2                 47.0   33507450.0   31633.5448      Visalia    CA  \n",
       "3                 82.0   30695215.0   23619.7198   Evansville    IN  \n",
       "4                 48.0   33370967.0   23486.8894      Atlanta    GA  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename columns \n",
    "\n",
    "drop_column_10year_df=Inc_10year_df.drop(['_ - state_l','_ - metro','_ - yrs_on_list'],axis=1)\n",
    "\n",
    "# Add the year column \n",
    "rename_10year_df=drop_column_10year_df.rename(columns={\"year\":\"rank_year\",\"_ - rank\":\"rank\",\"_ - company\":\"company_name\",\"_ - website\":\"company_Website\",\"_ - state_s\":\"state\",\"_ - revenue\":\"revenue_$\",\"_ - growth\":\"growth_rate\",\"_ - industry\":\"industry\",\"_ - workers\":\"number_of_employees\",\"_ - city\":\"city\"})\n",
    "\n",
    "\n",
    "cleaned_10year_df =rename_10year_df[['rank','rank_year','company_name','industry','number_of_employees','revenue_$','growth_rate','city','state']]\n",
    "\n",
    "cleaned_10year_df.head()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine all Inc 5000 data in to a single dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combine Inc 5000 data in to a single dataframe\n",
    "combine_data =[cleaned_2018_df,cleaned_2019_df,cleaned_10year_df]\n",
    "Inc_5000_df= pd.concat(combine_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>rank_year</th>\n",
       "      <th>company_name</th>\n",
       "      <th>industry</th>\n",
       "      <th>number_of_employees</th>\n",
       "      <th>revenue_$</th>\n",
       "      <th>growth_rate</th>\n",
       "      <th>founding_year</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>SwanLeap</td>\n",
       "      <td>Logistics &amp; Transportation</td>\n",
       "      <td>49.0</td>\n",
       "      <td>98965631.0</td>\n",
       "      <td>75660.8425</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>Madison</td>\n",
       "      <td>WI</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>PopSockets</td>\n",
       "      <td>Consumer Products &amp; Services</td>\n",
       "      <td>118.0</td>\n",
       "      <td>168837562.0</td>\n",
       "      <td>71423.7620</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Boulder</td>\n",
       "      <td>CO</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>Home Chef</td>\n",
       "      <td>Food &amp; Beverage</td>\n",
       "      <td>865.0</td>\n",
       "      <td>255047839.0</td>\n",
       "      <td>60165.5058</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>Velocity Global</td>\n",
       "      <td>Business Products &amp; Services</td>\n",
       "      <td>55.0</td>\n",
       "      <td>49175942.0</td>\n",
       "      <td>39816.5093</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>Denver</td>\n",
       "      <td>CO</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>DEPCOM Power</td>\n",
       "      <td>Energy</td>\n",
       "      <td>104.0</td>\n",
       "      <td>219574136.0</td>\n",
       "      <td>38962.9022</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>Scottsdale</td>\n",
       "      <td>AZ</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rank rank_year     company_name                      industry  \\\n",
       "0   1.0      2018         SwanLeap    Logistics & Transportation   \n",
       "1   2.0      2018       PopSockets  Consumer Products & Services   \n",
       "2   3.0      2018        Home Chef               Food & Beverage   \n",
       "3   4.0      2018  Velocity Global  Business Products & Services   \n",
       "4   5.0      2018     DEPCOM Power                        Energy   \n",
       "\n",
       "   number_of_employees    revenue_$  growth_rate  founding_year        city  \\\n",
       "0                 49.0   98965631.0   75660.8425         2013.0     Madison   \n",
       "1                118.0  168837562.0   71423.7620         2010.0     Boulder   \n",
       "2                865.0  255047839.0   60165.5058         2013.0     Chicago   \n",
       "3                 55.0   49175942.0   39816.5093         2013.0      Denver   \n",
       "4                104.0  219574136.0   38962.9022         2013.0  Scottsdale   \n",
       "\n",
       "  state        country  \n",
       "0    WI  United States  \n",
       "1    CO  United States  \n",
       "2    IL  United States  \n",
       "3    CO  United States  \n",
       "4    AZ  United States  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add the year column \n",
    "Inc_5000_df['country']= 'United States'\n",
    "Inc_5000_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>rank_year</th>\n",
       "      <th>company_name</th>\n",
       "      <th>industry</th>\n",
       "      <th>number_of_employees</th>\n",
       "      <th>revenue_$</th>\n",
       "      <th>growth_rate</th>\n",
       "      <th>founding_year</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>total_funding</th>\n",
       "      <th>compound_annual_growth_rate</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>SwanLeap</td>\n",
       "      <td>Logistics &amp; Transportation</td>\n",
       "      <td>49.0</td>\n",
       "      <td>98965631.0</td>\n",
       "      <td>75660.8425</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>Madison</td>\n",
       "      <td>WI</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>020280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>PopSockets</td>\n",
       "      <td>Consumer Products &amp; Services</td>\n",
       "      <td>118.0</td>\n",
       "      <td>168837562.0</td>\n",
       "      <td>71423.7620</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Boulder</td>\n",
       "      <td>CO</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>016290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>Home Chef</td>\n",
       "      <td>Food &amp; Beverage</td>\n",
       "      <td>865.0</td>\n",
       "      <td>255047839.0</td>\n",
       "      <td>60165.5058</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>009834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>Velocity Global</td>\n",
       "      <td>Business Products &amp; Services</td>\n",
       "      <td>55.0</td>\n",
       "      <td>49175942.0</td>\n",
       "      <td>39816.5093</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>Denver</td>\n",
       "      <td>CO</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>022717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>DEPCOM Power</td>\n",
       "      <td>Energy</td>\n",
       "      <td>104.0</td>\n",
       "      <td>219574136.0</td>\n",
       "      <td>38962.9022</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>Scottsdale</td>\n",
       "      <td>AZ</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>005862</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rank rank_year     company_name                      industry  \\\n",
       "0   1.0      2018         SwanLeap    Logistics & Transportation   \n",
       "1   2.0      2018       PopSockets  Consumer Products & Services   \n",
       "2   3.0      2018        Home Chef               Food & Beverage   \n",
       "3   4.0      2018  Velocity Global  Business Products & Services   \n",
       "4   5.0      2018     DEPCOM Power                        Energy   \n",
       "\n",
       "   number_of_employees    revenue_$  growth_rate  founding_year        city  \\\n",
       "0                 49.0   98965631.0   75660.8425         2013.0     Madison   \n",
       "1                118.0  168837562.0   71423.7620         2010.0     Boulder   \n",
       "2                865.0  255047839.0   60165.5058         2013.0     Chicago   \n",
       "3                 55.0   49175942.0   39816.5093         2013.0      Denver   \n",
       "4                104.0  219574136.0   38962.9022         2013.0  Scottsdale   \n",
       "\n",
       "  state        country total_funding  compound_annual_growth_rate      id  \n",
       "0    WI  United States           NaN                          NaN  020280  \n",
       "1    CO  United States           NaN                          NaN  016290  \n",
       "2    IL  United States           NaN                          NaN  009834  \n",
       "3    CO  United States           NaN                          NaN  022717  \n",
       "4    AZ  United States           NaN                          NaN  005862  "
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge all data in to a single dataframe\n",
    "merge_all =[Inc_5000_df,clean_growjo_df,clean_ft_df]\n",
    "merged_df= pd.concat(merge_all)\n",
    "merged_df.head()\n",
    "\n",
    "# Add unique id for company name \n",
    "id_df=merged_df.groupby(['company_name'], sort=True).ngroup().apply('{:006}'.format)\n",
    "\n",
    "merged_df['id']=id_df\n",
    "\n",
    "# Drop a duplicate value if there is any \n",
    "merged_df=merged_df.drop_duplicates('id')\n",
    "merged_df=merged_df.dropna(subset=['company_name','id','revenue_$','rank','rank_year','company_name','industry','number_of_employees','city','state','country'])\n",
    "merged_df['revenue_$'] = pd.to_numeric(merged_df['revenue_$'], errors='coerce')\n",
    "merged_df['growth_rate'] = pd.to_numeric(merged_df['growth_rate'], errors='coerce')\n",
    "merged_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ready the tables in the way to load in postgres sql database\n",
    "\n",
    "# company table \n",
    "Company = merged_df[['id','company_name','number_of_employees','industry','city','state','country','founding_year']]\n",
    "# drop null values from Id \n",
    "Company= Company.dropna(subset=['id'])\n",
    "# drop duplicate id and set index\n",
    "Company = Company.drop_duplicates('id')\n",
    "Company.set_index(\"id\",inplace=True)\n",
    "\n",
    "# Ranks table \n",
    "Ranks=merged_df[['id','rank','rank_year']]\n",
    "Ranks.set_index(\"id\",inplace=True)\n",
    "\n",
    "# Groth table\n",
    "Growth=merged_df[['id','growth_rate','total_funding','compound_annual_growth_rate','revenue_$']]\n",
    "Growth.set_index(\"id\",inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create database connection\n",
    "engine = create_engine('postgresql+psycopg2://postgres:216724401@2@localhost:5432/top-companies_db')\n",
    "connection = engine.connect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['company', 'ranks', 'growth']"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confirm tables\n",
    "engine.table_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import data in to company table\n",
    "Company.to_sql(name='company',con=engine, if_exists='append',index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import data in to ranks table\n",
    "Ranks.to_sql(name='ranks',con=engine, if_exists='append',index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Growth in to ranks table\n",
    "Growth.to_sql(name='growth',con=engine, if_exists='append',index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>company_name</th>\n",
       "      <th>number_of_employees</th>\n",
       "      <th>industry</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>founding_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20280</td>\n",
       "      <td>SwanLeap</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Logistics &amp; Transportation</td>\n",
       "      <td>Madison</td>\n",
       "      <td>WI</td>\n",
       "      <td>United States</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16290</td>\n",
       "      <td>PopSockets</td>\n",
       "      <td>118.0</td>\n",
       "      <td>Consumer Products &amp; Services</td>\n",
       "      <td>Boulder</td>\n",
       "      <td>CO</td>\n",
       "      <td>United States</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9834</td>\n",
       "      <td>Home Chef</td>\n",
       "      <td>865.0</td>\n",
       "      <td>Food &amp; Beverage</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "      <td>United States</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22717</td>\n",
       "      <td>Velocity Global</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Business Products &amp; Services</td>\n",
       "      <td>Denver</td>\n",
       "      <td>CO</td>\n",
       "      <td>United States</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5862</td>\n",
       "      <td>DEPCOM Power</td>\n",
       "      <td>104.0</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Scottsdale</td>\n",
       "      <td>AZ</td>\n",
       "      <td>United States</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id     company_name number_of_employees                      industry  \\\n",
       "0  20280         SwanLeap                49.0    Logistics & Transportation   \n",
       "1  16290       PopSockets               118.0  Consumer Products & Services   \n",
       "2   9834        Home Chef               865.0               Food & Beverage   \n",
       "3  22717  Velocity Global                55.0  Business Products & Services   \n",
       "4   5862     DEPCOM Power               104.0                        Energy   \n",
       "\n",
       "         city state        country  founding_year  \n",
       "0     Madison    WI  United States         2013.0  \n",
       "1     Boulder    CO  United States         2010.0  \n",
       "2     Chicago    IL  United States         2013.0  \n",
       "3      Denver    CO  United States         2013.0  \n",
       "4  Scottsdale    AZ  United States         2013.0  "
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pd read sql the selected company file\n",
    "pd.read_sql_query('select * from company', con=engine).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>rank</th>\n",
       "      <th>rank_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20280</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16290</td>\n",
       "      <td>2</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9834</td>\n",
       "      <td>3</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22717</td>\n",
       "      <td>4</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5862</td>\n",
       "      <td>5</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  rank  rank_year\n",
       "0  20280     1       2018\n",
       "1  16290     2       2018\n",
       "2   9834     3       2018\n",
       "3  22717     4       2018\n",
       "4   5862     5       2018"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pd read sql the selected ranks file\n",
    "pd.read_sql_query('select * from ranks', con=engine).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>growth_rate</th>\n",
       "      <th>compound_annual_growth_rate</th>\n",
       "      <th>total_funding</th>\n",
       "      <th>revenue_$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20280</td>\n",
       "      <td>75660.8425</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>98965631.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16290</td>\n",
       "      <td>71423.762</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>168837562.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9834</td>\n",
       "      <td>60165.5058</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>255047839.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22717</td>\n",
       "      <td>39816.5093</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>49175942.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5862</td>\n",
       "      <td>38962.9022</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>219574136.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id growth_rate compound_annual_growth_rate total_funding    revenue_$\n",
       "0  20280  75660.8425                        None          None   98965631.0\n",
       "1  16290   71423.762                        None          None  168837562.0\n",
       "2   9834  60165.5058                        None          None  255047839.0\n",
       "3  22717  39816.5093                        None          None   49175942.0\n",
       "4   5862  38962.9022                        None          None  219574136.0"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pd read sql the selected growth file\n",
    "pd.read_sql_query('select * from growth', con=engine).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aggregate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Companies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Companies\n",
       "0            24115"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total number of companies\n",
    "pd.read_sql_query('SELECT COUNT(company_name) As \"Total Companies\" FROM Company', con=engine)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Companies Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>industry</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Business Products &amp; Services</th>\n",
       "      <td>2430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Advertising &amp; Marketing</th>\n",
       "      <td>2199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT Services</th>\n",
       "      <td>1986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health</th>\n",
       "      <td>1714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Software</th>\n",
       "      <td>1630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Construction</th>\n",
       "      <td>1477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Consumer Products &amp; Services</th>\n",
       "      <td>1272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manufacturing</th>\n",
       "      <td>1146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Financial Services</th>\n",
       "      <td>1137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Government Services</th>\n",
       "      <td>1066</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Companies Count\n",
       "industry                                     \n",
       "Business Products & Services             2430\n",
       "Advertising & Marketing                  2199\n",
       "IT Services                              1986\n",
       "Health                                   1714\n",
       "Software                                 1630\n",
       "Construction                             1477\n",
       "Consumer Products & Services             1272\n",
       "Manufacturing                            1146\n",
       "Financial Services                       1137\n",
       "Government Services                      1066"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Query Top 10 industries the companies sourced\n",
    "top_ten_ind=pd.read_sql_query('SELECT industry, COUNT(company_name) As \"Companies Count\" FROM Company GROUP BY industry ORDER BY \"Companies Count\" DESC LIMIT 10', con=engine)\n",
    "top_ind_df = top_ten_ind.set_index('industry')\n",
    "top_ind_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Companies Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>3528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>1699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <td>1639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VA</th>\n",
       "      <td>1258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IL</th>\n",
       "      <td>1134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GA</th>\n",
       "      <td>971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA</th>\n",
       "      <td>835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OH</th>\n",
       "      <td>778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ</th>\n",
       "      <td>774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Companies Count\n",
       "state                 \n",
       "CA                3528\n",
       "TX                2001\n",
       "NY                1699\n",
       "FL                1639\n",
       "VA                1258\n",
       "IL                1134\n",
       "GA                 971\n",
       "PA                 835\n",
       "OH                 778\n",
       "NJ                 774"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Query Top 10 state the companies sourced\n",
    "top_ten_state=pd.read_sql_query('SELECT state, COUNT(company_name) As \"Companies Count\" FROM Company GROUP BY state ORDER BY \"Companies Count\" DESC LIMIT 10', con=engine)\n",
    "top_ten_state_df = top_ten_state.set_index('state')\n",
    "top_ten_state_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Companies Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicago</th>\n",
       "      <td>458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atlanta</th>\n",
       "      <td>404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Houston</th>\n",
       "      <td>388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austin</th>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Francisco</th>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Diego</th>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dallas</th>\n",
       "      <td>286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Los Angeles</th>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denver</th>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Companies Count\n",
       "city                          \n",
       "New York                   717\n",
       "Chicago                    458\n",
       "Atlanta                    404\n",
       "Houston                    388\n",
       "Austin                     372\n",
       "San Francisco              365\n",
       "San Diego                  315\n",
       "Dallas                     286\n",
       "Los Angeles                243\n",
       "Denver                     218"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Query Top 10 city the companies sourced\n",
    "top_ten_city=pd.read_sql_query('SELECT city, COUNT(company_name) As \"Companies Count\" FROM Company GROUP BY city ORDER BY \"Companies Count\" DESC LIMIT 10', con=engine)\n",
    "top_ten_city_df = top_ten_city.set_index('city')\n",
    "top_ten_city_df \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number_of_employees</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Connexion Point</th>\n",
       "      <td>998.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Truly Nolen of America</th>\n",
       "      <td>994.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cottonwood Financial</th>\n",
       "      <td>993.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V-Soft Consulting Group</th>\n",
       "      <td>993.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apex Systems</th>\n",
       "      <td>991.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinkedIn</th>\n",
       "      <td>990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Otter Products</th>\n",
       "      <td>990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MY Shower Door / D3 Glass</th>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lewis-Price &amp; Associates</th>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kevin.Murphy</th>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           number_of_employees\n",
       "company_name                                  \n",
       "Connexion Point                          998.0\n",
       "Truly Nolen of America                   994.0\n",
       "Cottonwood Financial                     993.0\n",
       "V-Soft Consulting Group                  993.0\n",
       "Apex Systems                             991.0\n",
       "LinkedIn                                 990.0\n",
       "Otter Products                           990.0\n",
       "MY Shower Door / D3 Glass                 99.0\n",
       "Lewis-Price & Associates                  99.0\n",
       "Kevin.Murphy                              99.0"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Query Top 10 city the companies sourced\n",
    "top_ten_employee=pd.read_sql_query('SELECT company_name, number_of_employees FROM Company ORDER BY number_of_employees DESC LIMIT 10', con=engine)\n",
    "top_ten_employee_df=top_ten_employee.set_index('company_name')\n",
    "top_ten_employee_df['number_of_employees'] = pd.to_numeric(top_ten_employee_df['number_of_employees'], errors='coerce')\n",
    "top_ten_employee_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Aggrigate plot on high growth company enteries\n",
    "# sort industry plot to make more visible\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2)\n",
    "\n",
    "fig.suptitle(\"Aggrigate plot on high growth company enteries by metrics\",fontsize = 20)\n",
    "# Top ten industries sourced \n",
    "sortedind=top_ind_df.sort_values(by=\"Companies Count\", ascending=True)\n",
    "sortedind.plot.barh(figsize=(15,10), color=['#002878'],fontsize = 14,ax=axes[0,0], title=\"Top ten industry\")\n",
    "top_ten_state_df.plot.barh(figsize=(15,10), color=['#002878'],fontsize = 14,ax=axes[0,1],title=\"Top ten state\")\n",
    "top_ten_city_df.plot.bar(figsize=(15,10), color=['#002878'],fontsize = 14,ax=axes[1,1],title=\"Top ten city\")\n",
    "sortedemployee=top_ten_employee_df.sort_values(by=\"number_of_employees\", ascending=True)\n",
    "sortedemployee.plot.barh(figsize=(15,10), color=['#002878'],fontsize = 14,ax=axes[1,0],title=\"Top ten employees\")\n",
    "\n",
    "fig.savefig('image/Aggrigate_plot.png',bbox_inches = 'tight')   # save the figure to file\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}