diff --git a/.ipynb_checkpoints/ETL_data_project-checkpoint.ipynb b/.ipynb_checkpoints/ETL_data_project-checkpoint.ipynb
deleted file mode 100644
index d7ad3c7..0000000
--- a/.ipynb_checkpoints/ETL_data_project-checkpoint.ipynb
+++ /dev/null
@@ -1,1409 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "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": 60,
-   "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": 61,
-   "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": 99,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Financial times url to scrape\n",
-    "ft_url = 'https://www.inc.com/inc5000/2020'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# read the html file for scarping tables \n",
-    "ft_tables= pd.read_html(ft_url)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# check it's type\n",
-    "type(ft_tables)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Check how many tables are there \n",
-    "len(ft_tables)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# scrape the the first table \n",
-    "ft_tables_df = ft_tables[0]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "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>founding_year</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>2015</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>2005</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>2015</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>2015</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>2015</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  founding_year rank_year  \n",
-       "0     180306.6                       1117.4           2015      2020  \n",
-       "1      37463.5                        621.5           2005      2020  \n",
-       "2      36680.1                        616.5           2015      2020  \n",
-       "3      30547.9                        574.2           2015      2020  \n",
-       "4      24074.8                        523.0           2015      2020  "
-      ]
-     },
-     "execution_count": 67,
-     "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]','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\",\"Founding Year\":\"founding_year\",\"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": 68,
-   "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": 69,
-   "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": 70,
-   "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>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>36.5 Million</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>13.1 Million</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>97.3 Million</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>29.9 Million</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>$42.1M</td>\n",
-       "      <td>2020</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>941.0</td>\n",
-       "      <td>10.8 Million</td>\n",
-       "      <td>2ULaundry</td>\n",
-       "      <td>Charlotte</td>\n",
-       "      <td>2015.0</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>NC</td>\n",
-       "      <td>43</td>\n",
-       "      <td>Consumer</td>\n",
-       "      <td>None</td>\n",
-       "      <td>2020</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    rank     revenue_$    company_name           city  founding_year  \\\n",
-       "0    1.0  36.5 Million  LetsGetChecked       New York         2014.0   \n",
-       "1  701.0  13.1 Million     100 Thieves    Los Angeles         2017.0   \n",
-       "2  832.0  97.3 Million    10X Genomics     Pleasanton         2012.0   \n",
-       "3  124.0  29.9 Million          15Five  San Francisco         2011.0   \n",
-       "4  941.0  10.8 Million       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        $42.1M      2020  \n",
-       "4          None      2020  "
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Transforming Growjo top 1000 fastest growing companies in 2020\n",
-    "# drop null values\n",
-    "\n",
-    "growjo_df=growjo_df.dropna(axis=1,how='all')\n",
-    "# Add the year column \n",
-    "growjo_df['rank_year']= '2020'\n",
-    "\n",
-    "\n",
-    "# Normalizing revenue in to mellions \n",
-    "growjo_df['revenue_$']= (growjo_df['revenue_$']/1000000).apply(lambda x: '{:,.1f} Million'.format(x))\n",
-    "\n",
-    "\n",
-    "# remove duplicates and show clean data\n",
-    "\n",
-    "clean_growjo_df=growjo_df.drop_duplicates()\n",
-    "clean_growjo_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Transforming"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Cleaning 2019 Inc 5000 company list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Drop column we don't need\n",
-    "drop_column_2019=Inc_2019_df.drop(['Profile','_ - previous_workers','_ - founded','_ - metro','url','_ - yrs_on_list'], axis=1)\n",
-    "\n",
-    "# Add the year column \n",
-    "drop_column_2019['rank_year']= '2019'\n",
-    "\n",
-    "# Rename columns \n",
-    "rename_df_2019= drop_column_2019.rename(columns={\"_ - rank\":\"rank\",\"name\":\"company_name\",\"state\":\"state\",\"_ - revenue\":\"revenue_$\",\"_ - growth\":\"growth_rate\",\"_ - industry\":\"industry\",\"_ - workers\":\"number_of_employees\",\"city\":\"city\"})\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_$','growth_rate','city','state']]\n",
-    "#cleaned_2019_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Cleaning 2018 Inc 5000 company list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Drop column we don't need\n",
-    "\n",
-    "drop_column_2018=Inc_2018_df.drop(['_ - id','_ - zipcode','_ - ifmid','_ - latitude','_ - longitude','_ - website','_ - state_l','_ - metrocode','_ - ifiid','_ - previous_workers','_ - metro','_ - founded','_ - url','_ - partner_lists - partner_lists','_ - yrs_on_list'], axis=1)\n",
-    "\n",
-    "# Add the year column \n",
-    "drop_column_2018['rank_year']= '2018'\n",
-    "\n",
-    "# Rename columns \n",
-    "rename_df_2018=drop_column_2018.rename(columns={\"_ - rank\":\"rank\",\"_ - company\":\"company_name\",\"_ - state_s\":\"state\",\"_ - revenue\":\"revenue_$\",\"_ - growth\":\"growth_rate\",\"_ - industry\":\"industry\",\"_ - workers\":\"number_of_employees\",\"_ - state_l\":\"state\",\"_ - city\":\"city\"})\n",
-    "rename_df_2018.head()\n",
-    "\n",
-    "# Normalizing revenue in to mellions \n",
-    "rename_df_2018['revenue_$']= (rename_df_2018['revenue_$']/1000000).apply(lambda x: '{:,.1f} Million'.format(x))\n",
-    "\n",
-    "\n",
-    "cleaned_2018_df =rename_df_2018[['rank','rank_year','company_name','industry','number_of_employees','revenue_$','growth_rate','city','state']]\n",
-    "\n",
-    "#cleaned_2018_df.head()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Cleaning 10 year (2007-2017) Inc 5000 company list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Rename columns \n",
-    "\n",
-    "drop_column_10year_df=Inc_10year_df.drop(['_ - state_l','_ - metro','_ - yrs_on_list'],axis=1)\n",
-    "\n",
-    "# Add the year column \n",
-    "rename_10year_df=drop_column_10year_df.rename(columns={\"year\":\"rank_year\",\"_ - rank\":\"rank\",\"_ - company\":\"company_name\",\"_ - website\":\"company_Website\",\"_ - state_s\":\"state\",\"_ - revenue\":\"revenue_$\",\"_ - growth\":\"growth_rate\",\"_ - industry\":\"industry\",\"_ - workers\":\"number_of_employees\",\"_ - founded\":\"founded_Year\",\"_ - city\":\"city\"})\n",
-    "\n",
-    "\n",
-    "# Normalizing revenue in to mellions \n",
-    "rename_10year_df['revenue_$']= (rename_10year_df['revenue_$']/1000000).apply(lambda x: '{:,.1f} Million'.format(x))\n",
-    "\n",
-    "cleaned_10year_df =rename_10year_df[['rank','rank_year','company_name','industry','number_of_employees','revenue_$','growth_rate','city','state']]\n",
-    "\n",
-    "# cleaned_10year_df.head()\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Combine all Inc 5000 data in to a single dataframe"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# combine Inc 5000 data in to a single dataframe\n",
-    "combine_data =[cleaned_2018_df,cleaned_2019_df,cleaned_10year_df]\n",
-    "Inc_5000_df= pd.concat(combine_data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>rank</th>\n",
-       "      <th>rank_year</th>\n",
-       "      <th>company_name</th>\n",
-       "      <th>industry</th>\n",
-       "      <th>number_of_employees</th>\n",
-       "      <th>revenue_$</th>\n",
-       "      <th>growth_rate</th>\n",
-       "      <th>city</th>\n",
-       "      <th>state</th>\n",
-       "      <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>99.0 Million</td>\n",
-       "      <td>75660.8425</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>168.8 Million</td>\n",
-       "      <td>71423.7620</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>255.0 Million</td>\n",
-       "      <td>60165.5058</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>49.2 Million</td>\n",
-       "      <td>39816.5093</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>219.6 Million</td>\n",
-       "      <td>38962.9022</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        city state  \\\n",
-       "0                 49.0   99.0 Million   75660.8425     Madison    WI   \n",
-       "1                118.0  168.8 Million   71423.7620     Boulder    CO   \n",
-       "2                865.0  255.0 Million   60165.5058     Chicago    IL   \n",
-       "3                 55.0   49.2 Million   39816.5093      Denver    CO   \n",
-       "4                104.0  219.6 Million   38962.9022  Scottsdale    AZ   \n",
-       "\n",
-       "         country  \n",
-       "0  United States  \n",
-       "1  United States  \n",
-       "2  United States  \n",
-       "3  United States  \n",
-       "4  United States  "
-      ]
-     },
-     "execution_count": 75,
-     "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": 76,
-   "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",
-       "      <th>country</th>\n",
-       "      <th>founding_year</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>99.0 Million</td>\n",
-       "      <td>75660.8425</td>\n",
-       "      <td>Madison</td>\n",
-       "      <td>WI</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>020391</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>168.8 Million</td>\n",
-       "      <td>71423.7620</td>\n",
-       "      <td>Boulder</td>\n",
-       "      <td>CO</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>016357</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>255.0 Million</td>\n",
-       "      <td>60165.5058</td>\n",
-       "      <td>Chicago</td>\n",
-       "      <td>IL</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>009922</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>49.2 Million</td>\n",
-       "      <td>39816.5093</td>\n",
-       "      <td>Denver</td>\n",
-       "      <td>CO</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>022829</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>219.6 Million</td>\n",
-       "      <td>38962.9022</td>\n",
-       "      <td>Scottsdale</td>\n",
-       "      <td>AZ</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>005896</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        city state  \\\n",
-       "0                 49.0   99.0 Million   75660.8425     Madison    WI   \n",
-       "1                118.0  168.8 Million   71423.7620     Boulder    CO   \n",
-       "2                865.0  255.0 Million   60165.5058     Chicago    IL   \n",
-       "3                 55.0   49.2 Million   39816.5093      Denver    CO   \n",
-       "4                104.0  219.6 Million   38962.9022  Scottsdale    AZ   \n",
-       "\n",
-       "         country  founding_year total_funding  compound_annual_growth_rate  \\\n",
-       "0  United States            NaN           NaN                          NaN   \n",
-       "1  United States            NaN           NaN                          NaN   \n",
-       "2  United States            NaN           NaN                          NaN   \n",
-       "3  United States            NaN           NaN                          NaN   \n",
-       "4  United States            NaN           NaN                          NaN   \n",
-       "\n",
-       "       id  \n",
-       "0  020391  \n",
-       "1  016357  \n",
-       "2  009922  \n",
-       "3  022829  \n",
-       "4  005896  "
-      ]
-     },
-     "execution_count": 76,
-     "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_$','rank','rank_year','company_name','industry','number_of_employees','city','state','country'])\n",
-    "\n",
-    "\n",
-    "merged_df.head()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Ready the tables in the way to load in postgres sql database\n",
-    "\n",
-    "# company table \n",
-    "Company = merged_df[['id','company_name','number_of_employees','industry','city','state','country']]\n",
-    "# drop null values from Id \n",
-    "Company= Company.dropna(subset=['id'])\n",
-    "# drop duplicate id\n",
-    "Company = Company.drop_duplicates('id')\n",
-    "Company.set_index(\"id\",inplace=True)\n",
-    "\n",
-    "# Ranks table \n",
-    "Ranks=merged_df[['id','rank','rank_year']]\n",
-    "Ranks.set_index(\"id\",inplace=True)\n",
-    "\n",
-    "# Groth table\n",
-    "\n",
-    "Growth=merged_df[['id','growth_rate','total_funding','compound_annual_growth_rate','revenue_$']]\n",
-    "Growth.set_index(\"id\",inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create database connection\n",
-    "engine = create_engine('postgresql+psycopg2://postgres:216724401@localhost:5432/top-companies_db')\n",
-    "connection = engine.connect()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['company', 'ranks', 'growth']"
-      ]
-     },
-     "execution_count": 92,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# confirm tables\n",
-    "engine.table_names()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Company.to_sql(name='company',con=engine, if_exists='append',index=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Ranks.to_sql(name='ranks',con=engine, if_exists='append',index=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Growth.to_sql(name='growth',con=engine, if_exists='append',index=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "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",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>20391</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>16357</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>9922</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>22829</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5896</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",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      id     company_name number_of_employees                      industry  \\\n",
-       "0  20391         SwanLeap                49.0    Logistics & Transportation   \n",
-       "1  16357       PopSockets               118.0  Consumer Products & Services   \n",
-       "2   9922        Home Chef               865.0               Food & Beverage   \n",
-       "3  22829  Velocity Global                55.0  Business Products & Services   \n",
-       "4   5896     DEPCOM Power               104.0                        Energy   \n",
-       "\n",
-       "         city state        country  \n",
-       "0     Madison    WI  United States  \n",
-       "1     Boulder    CO  United States  \n",
-       "2     Chicago    IL  United States  \n",
-       "3      Denver    CO  United States  \n",
-       "4  Scottsdale    AZ  United States  "
-      ]
-     },
-     "execution_count": 96,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.read_sql_query('select * from company', con=engine).head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "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>20391</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2018</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>16357</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2018</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>9922</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2018</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>22829</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2018</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5896</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  20391     1       2018\n",
-       "1  16357     2       2018\n",
-       "2   9922     3       2018\n",
-       "3  22829     4       2018\n",
-       "4   5896     5       2018"
-      ]
-     },
-     "execution_count": 97,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.read_sql_query('select * from ranks', con=engine).head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "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_$</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>20391</td>\n",
-       "      <td>75660.8425</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>99.0 Million</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>16357</td>\n",
-       "      <td>71423.762</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>168.8 Million</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>9922</td>\n",
-       "      <td>60165.5058</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>255.0 Million</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>22829</td>\n",
-       "      <td>39816.5093</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>49.2 Million</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5896</td>\n",
-       "      <td>38962.9022</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>219.6 Million</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      id growth_rate compound_annual_growth_rate total_funding      revenue_$\n",
-       "0  20391  75660.8425                        None          None   99.0 Million\n",
-       "1  16357   71423.762                        None          None  168.8 Million\n",
-       "2   9922  60165.5058                        None          None  255.0 Million\n",
-       "3  22829  39816.5093                        None          None   49.2 Million\n",
-       "4   5896  38962.9022                        None          None  219.6 Million"
-      ]
-     },
-     "execution_count": 98,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.read_sql_query('select * from growth', con=engine).head()"
-   ]
-  },
-  {
-   "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
-}
diff --git a/ETL_data_project.ipynb b/ETL_data_project.ipynb
index 1613695..a4f8f1e 100644
--- a/ETL_data_project.ipynb
+++ b/ETL_data_project.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,7 +88,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,17 +98,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# read the html file for scarping tables \n",
+    "# read the html file for scraping tables \n",
     "ft_tables= pd.read_html(ft_url)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -117,19 +117,19 @@
        "list"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# check it's type\n",
+    "# check type\n",
     "type(ft_tables)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -138,7 +138,7 @@
        "1"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -150,7 +150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -264,7 +264,7 @@
        "4      24074.8                        523.0      2020  "
       ]
      },
-     "execution_count": 60,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -272,7 +272,7 @@
    "source": [
     "# Transforming scraped data\n",
     "\n",
-    "# Drop column we don't need\n",
+    "# Drop column \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",
@@ -302,18 +302,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### API Call for Scraping Growjo top 1000 fastest growing companies in 2020."
+    "### API Call for Scraping Growjo top 10,000 fastest growing companies in 2020."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "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",
+    "# We generated company information based on domains. For example,\n",
+    "# you could retrieve a company’s name, location,\n",
+    "# estimated revenue and job openings from their domain name. \n",
     "\n",
     "\n",
     "# The base url to request the Api call \n",
@@ -331,10 +332,10 @@
     "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",
+    "# 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",
+    "# A for loop to get each domain and request information \n",
     "for domain in company_domain:\n",
     "    target_url = base_url + domain\n",
     "    data = requests.get(target_url)\n",
@@ -342,7 +343,7 @@
     "        response = data.json()\n",
     "    except ValueError:\n",
     "        pass\n",
-    "# append all requested information letter to retrive\n",
+    "# append all requested information to retrieve\n",
     "    ranking.append(response['ranking'])\n",
     "    estimated_revenues.append(response['estimated_revenues'])\n",
     "    company_name.append(response['company_name'])\n",
@@ -358,11 +359,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Crerate and store the information in dictionary  \n",
+    "# Crerate and store the information in a dictionary  \n",
     "growjo_df = pd.DataFrame({\n",
     "    \"rank\":ranking,\n",
     "    \"revenue_$\":estimated_revenues,\n",
@@ -383,7 +384,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -518,13 +519,13 @@
        "4          None      2020  "
       ]
      },
-     "execution_count": 61,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Transforming Growjo top 1000 fastest growing companies in 2020\n",
+    "# Transforming Growjo top 10,000 fastest growing companies in 2020\n",
     "# drop null values\n",
     "\n",
     "growjo_df=growjo_df.dropna(axis=1,how='all')\n",
@@ -553,11 +554,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Drop column we don't need\n",
+    "# Drop column \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",
@@ -568,7 +569,7 @@
     "\n",
     "\n",
     "\n",
-    "# re-Order the data to suitable format\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()"
@@ -583,7 +584,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -712,13 +713,13 @@
        "4    AZ  "
       ]
      },
-     "execution_count": 100,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Drop column we don't need\n",
+    "# Drop column \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",
@@ -744,7 +745,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -860,7 +861,7 @@
        "4                 48.0   33370967.0   23486.8894      Atlanta    GA  "
       ]
      },
-     "execution_count": 102,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -890,18 +891,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# combine Inc 5000 data in to a single dataframe\n",
+    "# combine Inc 5000 data into 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,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1036,7 +1037,7 @@
        "4    AZ  United States  "
       ]
      },
-     "execution_count": 104,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1049,7 +1050,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 210,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1105,7 +1106,7 @@
        "      <td>United States</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>020280</td>\n",
+       "      <td>020212</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -1122,7 +1123,7 @@
        "      <td>United States</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>016290</td>\n",
+       "      <td>016220</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -1139,7 +1140,7 @@
        "      <td>United States</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>009834</td>\n",
+       "      <td>009822</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -1156,7 +1157,7 @@
        "      <td>United States</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>022717</td>\n",
+       "      <td>022675</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -1173,7 +1174,7 @@
        "      <td>United States</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>005862</td>\n",
+       "      <td>005807</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1195,20 +1196,20 @@
        "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  "
+       "0    WI  United States           NaN                          NaN  020212  \n",
+       "1    CO  United States           NaN                          NaN  016220  \n",
+       "2    IL  United States           NaN                          NaN  009822  \n",
+       "3    CO  United States           NaN                          NaN  022675  \n",
+       "4    AZ  United States           NaN                          NaN  005807  "
       ]
      },
-     "execution_count": 210,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Merge all data in to a single dataframe\n",
+    "# Merge all data into 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",
@@ -1228,7 +1229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 211,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1246,7 +1247,7 @@
     "Ranks=merged_df[['id','rank','rank_year']]\n",
     "Ranks.set_index(\"id\",inplace=True)\n",
     "\n",
-    "# Groth table\n",
+    "# Growth table\n",
     "Growth=merged_df[['id','growth_rate','total_funding','compound_annual_growth_rate','revenue_$']]\n",
     "Growth.set_index(\"id\",inplace=True)"
    ]
@@ -1260,18 +1261,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 226,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Create database connection\n",
-    "engine = create_engine('postgresql+psycopg2://postgres:216724401@2@localhost:5432/top-companies_db')\n",
+    "engine = create_engine('postgresql+psycopg2://{{username:password}}@localhost:5432/top-companies_db')\n",
     "connection = engine.connect()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 227,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1280,7 +1281,7 @@
        "['company', 'ranks', 'growth']"
       ]
      },
-     "execution_count": 227,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1292,37 +1293,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 228,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Import data in to company table\n",
+    "# Import data into company table\n",
     "Company.to_sql(name='company',con=engine, if_exists='append',index=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 229,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Import data in to ranks table\n",
+    "# Import data into ranks table\n",
     "Ranks.to_sql(name='ranks',con=engine, if_exists='append',index=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 230,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Import Growth in to ranks table\n",
+    "# Import Growth into ranks table\n",
     "Growth.to_sql(name='growth',con=engine, if_exists='append',index=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 231,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1359,7 +1360,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>20280</td>\n",
+       "      <td>20212</td>\n",
        "      <td>SwanLeap</td>\n",
        "      <td>49.0</td>\n",
        "      <td>Logistics &amp; Transportation</td>\n",
@@ -1370,7 +1371,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>16290</td>\n",
+       "      <td>16220</td>\n",
        "      <td>PopSockets</td>\n",
        "      <td>118.0</td>\n",
        "      <td>Consumer Products &amp; Services</td>\n",
@@ -1381,7 +1382,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>9834</td>\n",
+       "      <td>9822</td>\n",
        "      <td>Home Chef</td>\n",
        "      <td>865.0</td>\n",
        "      <td>Food &amp; Beverage</td>\n",
@@ -1392,7 +1393,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>22717</td>\n",
+       "      <td>22675</td>\n",
        "      <td>Velocity Global</td>\n",
        "      <td>55.0</td>\n",
        "      <td>Business Products &amp; Services</td>\n",
@@ -1403,7 +1404,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>5862</td>\n",
+       "      <td>5807</td>\n",
        "      <td>DEPCOM Power</td>\n",
        "      <td>104.0</td>\n",
        "      <td>Energy</td>\n",
@@ -1418,11 +1419,11 @@
       ],
       "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",
+       "0  20212         SwanLeap                49.0    Logistics & Transportation   \n",
+       "1  16220       PopSockets               118.0  Consumer Products & Services   \n",
+       "2   9822        Home Chef               865.0               Food & Beverage   \n",
+       "3  22675  Velocity Global                55.0  Business Products & Services   \n",
+       "4   5807     DEPCOM Power               104.0                        Energy   \n",
        "\n",
        "         city state        country  founding_year  \n",
        "0     Madison    WI  United States         2013.0  \n",
@@ -1432,19 +1433,19 @@
        "4  Scottsdale    AZ  United States         2013.0  "
       ]
      },
-     "execution_count": 231,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# pd read sql the selected company file\n",
+    "# pd read sql for the selected company file\n",
     "pd.read_sql_query('select * from company', con=engine).head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 232,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -1476,31 +1477,31 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>20280</td>\n",
+       "      <td>20212</td>\n",
        "      <td>1</td>\n",
        "      <td>2018</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>16290</td>\n",
+       "      <td>16220</td>\n",
        "      <td>2</td>\n",
        "      <td>2018</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>9834</td>\n",
+       "      <td>9822</td>\n",
        "      <td>3</td>\n",
        "      <td>2018</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>22717</td>\n",
+       "      <td>22675</td>\n",
        "      <td>4</td>\n",
        "      <td>2018</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>5862</td>\n",
+       "      <td>5807</td>\n",
        "      <td>5</td>\n",
        "      <td>2018</td>\n",
        "    </tr>\n",
@@ -1510,26 +1511,26 @@
       ],
       "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"
+       "0  20212     1       2018\n",
+       "1  16220     2       2018\n",
+       "2   9822     3       2018\n",
+       "3  22675     4       2018\n",
+       "4   5807     5       2018"
       ]
      },
-     "execution_count": 232,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# pd read sql the selected ranks file\n",
+    "# pd read sql for the selected ranks file\n",
     "pd.read_sql_query('select * from ranks', con=engine).head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 233,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -1563,7 +1564,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>20280</td>\n",
+       "      <td>20212</td>\n",
        "      <td>75660.8425</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
@@ -1571,7 +1572,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>16290</td>\n",
+       "      <td>16220</td>\n",
        "      <td>71423.762</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
@@ -1579,7 +1580,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>9834</td>\n",
+       "      <td>9822</td>\n",
        "      <td>60165.5058</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
@@ -1587,7 +1588,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>22717</td>\n",
+       "      <td>22675</td>\n",
        "      <td>39816.5093</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
@@ -1595,7 +1596,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>5862</td>\n",
+       "      <td>5807</td>\n",
        "      <td>38962.9022</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
@@ -1607,20 +1608,20 @@
       ],
       "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"
+       "0  20212  75660.8425                        None          None   98965631.0\n",
+       "1  16220   71423.762                        None          None  168837562.0\n",
+       "2   9822  60165.5058                        None          None  255047839.0\n",
+       "3  22675  39816.5093                        None          None   49175942.0\n",
+       "4   5807  38962.9022                        None          None  219574136.0"
       ]
      },
-     "execution_count": 233,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# pd read sql the selected growth file\n",
+    "# pd read sql for the selected growth file\n",
     "pd.read_sql_query('select * from growth', con=engine).head()"
    ]
   },
@@ -1633,7 +1634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 234,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -1663,7 +1664,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>24115</td>\n",
+       "      <td>24072</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1671,10 +1672,10 @@
       ],
       "text/plain": [
        "   Total Companies\n",
-       "0            24115"
+       "0            24072"
       ]
      },
-     "execution_count": 234,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1686,7 +1687,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 235,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -1732,7 +1733,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Health</th>\n",
-       "      <td>1714</td>\n",
+       "      <td>1715</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Software</th>\n",
@@ -1740,7 +1741,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Construction</th>\n",
-       "      <td>1477</td>\n",
+       "      <td>1476</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Consumer Products &amp; Services</th>\n",
@@ -1768,16 +1769,16 @@
        "Business Products & Services             2430\n",
        "Advertising & Marketing                  2199\n",
        "IT Services                              1986\n",
-       "Health                                   1714\n",
+       "Health                                   1715\n",
        "Software                                 1630\n",
-       "Construction                             1477\n",
+       "Construction                             1476\n",
        "Consumer Products & Services             1272\n",
        "Manufacturing                            1146\n",
        "Financial Services                       1137\n",
        "Government Services                      1066"
       ]
      },
-     "execution_count": 235,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1791,7 +1792,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 236,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -1825,27 +1826,27 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CA</th>\n",
-       "      <td>3528</td>\n",
+       "      <td>3502</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TX</th>\n",
-       "      <td>2001</td>\n",
+       "      <td>1995</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NY</th>\n",
-       "      <td>1699</td>\n",
+       "      <td>1696</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FL</th>\n",
-       "      <td>1639</td>\n",
+       "      <td>1637</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>VA</th>\n",
-       "      <td>1258</td>\n",
+       "      <td>1259</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>IL</th>\n",
-       "      <td>1134</td>\n",
+       "      <td>1136</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>GA</th>\n",
@@ -1861,7 +1862,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NJ</th>\n",
-       "      <td>774</td>\n",
+       "      <td>775</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1870,19 +1871,19 @@
       "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",
+       "CA                3502\n",
+       "TX                1995\n",
+       "NY                1696\n",
+       "FL                1637\n",
+       "VA                1259\n",
+       "IL                1136\n",
        "GA                 971\n",
        "PA                 835\n",
        "OH                 778\n",
-       "NJ                 774"
+       "NJ                 775"
       ]
      },
-     "execution_count": 236,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1896,7 +1897,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 237,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -1930,7 +1931,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>New York</th>\n",
-       "      <td>717</td>\n",
+       "      <td>714</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Chicago</th>\n",
@@ -1942,7 +1943,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Houston</th>\n",
-       "      <td>388</td>\n",
+       "      <td>389</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Austin</th>\n",
@@ -1950,11 +1951,11 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>San Francisco</th>\n",
-       "      <td>365</td>\n",
+       "      <td>360</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>San Diego</th>\n",
-       "      <td>315</td>\n",
+       "      <td>318</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Dallas</th>\n",
@@ -1962,11 +1963,11 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Los Angeles</th>\n",
-       "      <td>243</td>\n",
+       "      <td>242</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Denver</th>\n",
-       "      <td>218</td>\n",
+       "      <td>220</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1975,19 +1976,19 @@
       "text/plain": [
        "               Companies Count\n",
        "city                          \n",
-       "New York                   717\n",
+       "New York                   714\n",
        "Chicago                    458\n",
        "Atlanta                    404\n",
-       "Houston                    388\n",
+       "Houston                    389\n",
        "Austin                     372\n",
-       "San Francisco              365\n",
-       "San Diego                  315\n",
+       "San Francisco              360\n",
+       "San Diego                  318\n",
        "Dallas                     286\n",
-       "Los Angeles                243\n",
-       "Denver                     218"
+       "Los Angeles                242\n",
+       "Denver                     220"
       ]
      },
-     "execution_count": 237,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2003,7 +2004,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 238,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -2094,7 +2095,7 @@
        "Kevin.Murphy                              99.0"
       ]
      },
-     "execution_count": 238,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2109,12 +2110,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 239,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1080x720 with 4 Axes>"
       ]
@@ -2143,27 +2144,6 @@
     "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": {
diff --git a/Image/Aggrigate_plot.png b/Image/Aggrigate_plot.png
index 5f235ed..6d9cc10 100644
Binary files a/Image/Aggrigate_plot.png and b/Image/Aggrigate_plot.png differ
diff --git a/__pycache__/company_domain.cpython-37.pyc b/__pycache__/company_domain.cpython-37.pyc
deleted file mode 100644
index 083ea80..0000000
Binary files a/__pycache__/company_domain.cpython-37.pyc and /dev/null differ