Spark_fund_Investment/Final-EDA-Investment-assignment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Business objective:\n",
    "The objective is to identify the best sectors, countries, and a suitable investment type for making investments. The overall strategy is to invest where others are investing, implying that the 'best' sectors and countries are the ones 'where most investors are investing'.\n",
    "\n",
    "### Goals of data analysis:\n",
    "1. Investment type analysis: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checkpoint 1: Data Cleaning 1\n",
    "#### Load the companies and rounds data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>permalink</th>\n",
       "      <th>name</th>\n",
       "      <th>homepage_url</th>\n",
       "      <th>category_list</th>\n",
       "      <th>status</th>\n",
       "      <th>country_code</th>\n",
       "      <th>state_code</th>\n",
       "      <th>region</th>\n",
       "      <th>city</th>\n",
       "      <th>founded_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/Organization/-Fame</td>\n",
       "      <td>#fame</td>\n",
       "      <td>http://livfame.com</td>\n",
       "      <td>Media</td>\n",
       "      <td>operating</td>\n",
       "      <td>IND</td>\n",
       "      <td>16</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>/Organization/-Qounter</td>\n",
       "      <td>:Qounter</td>\n",
       "      <td>http://www.qounter.com</td>\n",
       "      <td>Application Platforms|Real Time|Social Network...</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>DE</td>\n",
       "      <td>DE - Other</td>\n",
       "      <td>Delaware City</td>\n",
       "      <td>04-09-2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/Organization/-The-One-Of-Them-Inc-</td>\n",
       "      <td>(THE) ONE of THEM,Inc.</td>\n",
       "      <td>http://oneofthem.jp</td>\n",
       "      <td>Apps|Games|Mobile</td>\n",
       "      <td>operating</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>/Organization/0-6-Com</td>\n",
       "      <td>0-6.com</td>\n",
       "      <td>http://www.0-6.com</td>\n",
       "      <td>Curated Web</td>\n",
       "      <td>operating</td>\n",
       "      <td>CHN</td>\n",
       "      <td>22</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>01-01-2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>/Organization/004-Technologies</td>\n",
       "      <td>004 Technologies</td>\n",
       "      <td>http://004gmbh.de/en/004-interact</td>\n",
       "      <td>Software</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>IL</td>\n",
       "      <td>Springfield, Illinois</td>\n",
       "      <td>Champaign</td>\n",
       "      <td>01-01-2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             permalink                    name  \\\n",
       "0                  /Organization/-Fame                   #fame   \n",
       "1               /Organization/-Qounter                :Qounter   \n",
       "2  /Organization/-The-One-Of-Them-Inc-  (THE) ONE of THEM,Inc.   \n",
       "3                /Organization/0-6-Com                 0-6.com   \n",
       "4       /Organization/004-Technologies        004 Technologies   \n",
       "\n",
       "                        homepage_url  \\\n",
       "0                 http://livfame.com   \n",
       "1             http://www.qounter.com   \n",
       "2                http://oneofthem.jp   \n",
       "3                 http://www.0-6.com   \n",
       "4  http://004gmbh.de/en/004-interact   \n",
       "\n",
       "                                       category_list     status country_code  \\\n",
       "0                                              Media  operating          IND   \n",
       "1  Application Platforms|Real Time|Social Network...  operating          USA   \n",
       "2                                  Apps|Games|Mobile  operating          NaN   \n",
       "3                                        Curated Web  operating          CHN   \n",
       "4                                           Software  operating          USA   \n",
       "\n",
       "  state_code                 region           city  founded_at  \n",
       "0         16                 Mumbai         Mumbai         NaN  \n",
       "1         DE             DE - Other  Delaware City  04-09-2014  \n",
       "2        NaN                    NaN            NaN         NaN  \n",
       "3         22                Beijing        Beijing  01-01-2007  \n",
       "4         IL  Springfield, Illinois      Champaign  01-01-2010  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "companies = pd.read_csv('companies.csv', encoding='palmos')\n",
    "companies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['permalink', 'name', 'homepage_url', 'category_list', 'status',\n",
       "       'country_code', 'state_code', 'region', 'city', 'founded_at'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "companies.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 66368 entries, 0 to 66367\n",
      "Data columns (total 10 columns):\n",
      "permalink        66368 non-null object\n",
      "name             66367 non-null object\n",
      "homepage_url     61310 non-null object\n",
      "category_list    63220 non-null object\n",
      "status           66368 non-null object\n",
      "country_code     59410 non-null object\n",
      "state_code       57821 non-null object\n",
      "region           58338 non-null object\n",
      "city             58340 non-null object\n",
      "founded_at       51147 non-null object\n",
      "dtypes: object(10)\n",
      "memory usage: 5.1+ MB\n"
     ]
    }
   ],
   "source": [
    "companies.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>company_permalink</th>\n",
       "      <th>funding_round_permalink</th>\n",
       "      <th>funding_round_type</th>\n",
       "      <th>funding_round_code</th>\n",
       "      <th>funded_at</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/-fame</td>\n",
       "      <td>/funding-round/9a01d05418af9f794eebff7ace91f638</td>\n",
       "      <td>venture</td>\n",
       "      <td>B</td>\n",
       "      <td>05-01-2015</td>\n",
       "      <td>10000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>/ORGANIZATION/-QOUNTER</td>\n",
       "      <td>/funding-round/22dacff496eb7acb2b901dec1dfe5633</td>\n",
       "      <td>venture</td>\n",
       "      <td>A</td>\n",
       "      <td>14-10-2014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/organization/-qounter</td>\n",
       "      <td>/funding-round/b44fbb94153f6cdef13083530bb48030</td>\n",
       "      <td>seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01-03-2014</td>\n",
       "      <td>700000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>/ORGANIZATION/-THE-ONE-OF-THEM-INC-</td>\n",
       "      <td>/funding-round/650b8f704416801069bb178a1418776b</td>\n",
       "      <td>venture</td>\n",
       "      <td>B</td>\n",
       "      <td>30-01-2014</td>\n",
       "      <td>3406878.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>/organization/0-6-com</td>\n",
       "      <td>/funding-round/5727accaeaa57461bd22a9bdd945382d</td>\n",
       "      <td>venture</td>\n",
       "      <td>A</td>\n",
       "      <td>19-03-2008</td>\n",
       "      <td>2000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     company_permalink  \\\n",
       "0                  /organization/-fame   \n",
       "1               /ORGANIZATION/-QOUNTER   \n",
       "2               /organization/-qounter   \n",
       "3  /ORGANIZATION/-THE-ONE-OF-THEM-INC-   \n",
       "4                /organization/0-6-com   \n",
       "\n",
       "                           funding_round_permalink funding_round_type  \\\n",
       "0  /funding-round/9a01d05418af9f794eebff7ace91f638            venture   \n",
       "1  /funding-round/22dacff496eb7acb2b901dec1dfe5633            venture   \n",
       "2  /funding-round/b44fbb94153f6cdef13083530bb48030               seed   \n",
       "3  /funding-round/650b8f704416801069bb178a1418776b            venture   \n",
       "4  /funding-round/5727accaeaa57461bd22a9bdd945382d            venture   \n",
       "\n",
       "  funding_round_code   funded_at  raised_amount_usd  \n",
       "0                  B  05-01-2015         10000000.0  \n",
       "1                  A  14-10-2014                NaN  \n",
       "2                NaN  01-03-2014           700000.0  \n",
       "3                  B  30-01-2014          3406878.0  \n",
       "4                  A  19-03-2008          2000000.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rounds2 = pd.read_csv('rounds2.csv', encoding='palmos', sep=\",\")\n",
    "rounds2 .head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['company_permalink', 'funding_round_permalink', 'funding_round_type',\n",
       "       'funding_round_code', 'funded_at', 'raised_amount_usd'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rounds2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 114949 entries, 0 to 114948\n",
      "Data columns (total 6 columns):\n",
      "company_permalink          114949 non-null object\n",
      "funding_round_permalink    114949 non-null object\n",
      "funding_round_type         114949 non-null object\n",
      "funding_round_code         31140 non-null object\n",
      "funded_at                  114949 non-null object\n",
      "raised_amount_usd          94959 non-null float64\n",
      "dtypes: float64(1), object(5)\n",
      "memory usage: 5.3+ MB\n"
     ]
    }
   ],
   "source": [
    "rounds2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table 1.1: Understand the Data Set (Question)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### How many unique companies are present in rounds2 ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique companies in rounds2 dataset: 66368\n"
     ]
    }
   ],
   "source": [
    "# How many unique companies are present in rounds2?\n",
    "rounds2['company_permalink'] = rounds2['company_permalink'].str.lower()\n",
    "unique_count  = len(rounds2['company_permalink'].unique())\n",
    "print('Unique companies in rounds2 dataset:', unique_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### How many unique companies are present in companies ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique companies in companies dataset: 66368\n"
     ]
    }
   ],
   "source": [
    "# How many unique companies are present in companies?\n",
    "companies['permalink'] = companies['permalink'].str.lower()\n",
    "unique_count  = len(companies['permalink'].unique())\n",
    "print('Unique companies in companies dataset:', unique_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Are there any companies in the rounds2 file which are not present in companies? Answer yes or no: Y/N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Are there any companies in the rounds2 file which are not present in companies? Answer yes or no: Y/N\n",
    "set_len = set(companies['permalink'].unique()).difference(set(rounds2['company_permalink'].unique()))\n",
    "print(len(set_len))\n",
    "True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merge the two data frames so that all variables (columns) in the companies frame are added to the rounds2 data frame. Name the merged frame master_frame. How many observations are present in master_frame?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(114949, 16)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_frame = pd.merge(rounds2, companies, how='left', left_on='company_permalink', right_on='permalink')\n",
    "master_frame.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 114949 entries, 0 to 114948\n",
      "Data columns (total 16 columns):\n",
      "company_permalink          114949 non-null object\n",
      "funding_round_permalink    114949 non-null object\n",
      "funding_round_type         114949 non-null object\n",
      "funding_round_code         31140 non-null object\n",
      "funded_at                  114949 non-null object\n",
      "raised_amount_usd          94959 non-null float64\n",
      "permalink                  114949 non-null object\n",
      "name                       114948 non-null object\n",
      "homepage_url               108815 non-null object\n",
      "category_list              111539 non-null object\n",
      "status                     114949 non-null object\n",
      "country_code               106271 non-null object\n",
      "state_code                 104003 non-null object\n",
      "region                     104782 non-null object\n",
      "city                       104785 non-null object\n",
      "founded_at                 94428 non-null object\n",
      "dtypes: float64(1), object(15)\n",
      "memory usage: 14.9+ MB\n"
     ]
    }
   ],
   "source": [
    "# len(master_frame['permalink'].unique())\n",
    "master_frame.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data cleaning\n",
    "* Null values percentage details\n",
    "* Dropping unnecessary columns\n",
    "* Dropping unnecessary rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company_permalink</th>\n",
       "      <th>funding_round_permalink</th>\n",
       "      <th>funding_round_type</th>\n",
       "      <th>funding_round_code</th>\n",
       "      <th>funded_at</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>permalink</th>\n",
       "      <th>name</th>\n",
       "      <th>homepage_url</th>\n",
       "      <th>category_list</th>\n",
       "      <th>status</th>\n",
       "      <th>country_code</th>\n",
       "      <th>state_code</th>\n",
       "      <th>region</th>\n",
       "      <th>city</th>\n",
       "      <th>founded_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/-fame</td>\n",
       "      <td>/funding-round/9a01d05418af9f794eebff7ace91f638</td>\n",
       "      <td>venture</td>\n",
       "      <td>B</td>\n",
       "      <td>05-01-2015</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>/organization/-fame</td>\n",
       "      <td>#fame</td>\n",
       "      <td>http://livfame.com</td>\n",
       "      <td>Media</td>\n",
       "      <td>operating</td>\n",
       "      <td>IND</td>\n",
       "      <td>16</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>/organization/-qounter</td>\n",
       "      <td>/funding-round/22dacff496eb7acb2b901dec1dfe5633</td>\n",
       "      <td>venture</td>\n",
       "      <td>A</td>\n",
       "      <td>14-10-2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>/organization/-qounter</td>\n",
       "      <td>:Qounter</td>\n",
       "      <td>http://www.qounter.com</td>\n",
       "      <td>Application Platforms|Real Time|Social Network...</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>DE</td>\n",
       "      <td>DE - Other</td>\n",
       "      <td>Delaware City</td>\n",
       "      <td>04-09-2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/organization/-qounter</td>\n",
       "      <td>/funding-round/b44fbb94153f6cdef13083530bb48030</td>\n",
       "      <td>seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01-03-2014</td>\n",
       "      <td>700000.0</td>\n",
       "      <td>/organization/-qounter</td>\n",
       "      <td>:Qounter</td>\n",
       "      <td>http://www.qounter.com</td>\n",
       "      <td>Application Platforms|Real Time|Social Network...</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>DE</td>\n",
       "      <td>DE - Other</td>\n",
       "      <td>Delaware City</td>\n",
       "      <td>04-09-2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>/organization/-the-one-of-them-inc-</td>\n",
       "      <td>/funding-round/650b8f704416801069bb178a1418776b</td>\n",
       "      <td>venture</td>\n",
       "      <td>B</td>\n",
       "      <td>30-01-2014</td>\n",
       "      <td>3406878.0</td>\n",
       "      <td>/organization/-the-one-of-them-inc-</td>\n",
       "      <td>(THE) ONE of THEM,Inc.</td>\n",
       "      <td>http://oneofthem.jp</td>\n",
       "      <td>Apps|Games|Mobile</td>\n",
       "      <td>operating</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>/organization/0-6-com</td>\n",
       "      <td>/funding-round/5727accaeaa57461bd22a9bdd945382d</td>\n",
       "      <td>venture</td>\n",
       "      <td>A</td>\n",
       "      <td>19-03-2008</td>\n",
       "      <td>2000000.0</td>\n",
       "      <td>/organization/0-6-com</td>\n",
       "      <td>0-6.com</td>\n",
       "      <td>http://www.0-6.com</td>\n",
       "      <td>Curated Web</td>\n",
       "      <td>operating</td>\n",
       "      <td>CHN</td>\n",
       "      <td>22</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>01-01-2007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     company_permalink  \\\n",
       "0                  /organization/-fame   \n",
       "1               /organization/-qounter   \n",
       "2               /organization/-qounter   \n",
       "3  /organization/-the-one-of-them-inc-   \n",
       "4                /organization/0-6-com   \n",
       "\n",
       "                           funding_round_permalink funding_round_type  \\\n",
       "0  /funding-round/9a01d05418af9f794eebff7ace91f638            venture   \n",
       "1  /funding-round/22dacff496eb7acb2b901dec1dfe5633            venture   \n",
       "2  /funding-round/b44fbb94153f6cdef13083530bb48030               seed   \n",
       "3  /funding-round/650b8f704416801069bb178a1418776b            venture   \n",
       "4  /funding-round/5727accaeaa57461bd22a9bdd945382d            venture   \n",
       "\n",
       "  funding_round_code   funded_at  raised_amount_usd  \\\n",
       "0                  B  05-01-2015         10000000.0   \n",
       "1                  A  14-10-2014                NaN   \n",
       "2                NaN  01-03-2014           700000.0   \n",
       "3                  B  30-01-2014          3406878.0   \n",
       "4                  A  19-03-2008          2000000.0   \n",
       "\n",
       "                             permalink                    name  \\\n",
       "0                  /organization/-fame                   #fame   \n",
       "1               /organization/-qounter                :Qounter   \n",
       "2               /organization/-qounter                :Qounter   \n",
       "3  /organization/-the-one-of-them-inc-  (THE) ONE of THEM,Inc.   \n",
       "4                /organization/0-6-com                 0-6.com   \n",
       "\n",
       "             homepage_url                                      category_list  \\\n",
       "0      http://livfame.com                                              Media   \n",
       "1  http://www.qounter.com  Application Platforms|Real Time|Social Network...   \n",
       "2  http://www.qounter.com  Application Platforms|Real Time|Social Network...   \n",
       "3     http://oneofthem.jp                                  Apps|Games|Mobile   \n",
       "4      http://www.0-6.com                                        Curated Web   \n",
       "\n",
       "      status country_code state_code      region           city  founded_at  \n",
       "0  operating          IND         16      Mumbai         Mumbai         NaN  \n",
       "1  operating          USA         DE  DE - Other  Delaware City  04-09-2014  \n",
       "2  operating          USA         DE  DE - Other  Delaware City  04-09-2014  \n",
       "3  operating          NaN        NaN         NaN            NaN         NaN  \n",
       "4  operating          CHN         22     Beijing        Beijing  01-01-2007  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_frame.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Null values percentage details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Null_value_percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Column_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>company_permalink</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>funding_round_permalink</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>funding_round_type</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>funding_round_code</td>\n",
       "      <td>72.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>funded_at</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>raised_amount_usd</td>\n",
       "      <td>17.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>permalink</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>name</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>homepage_url</td>\n",
       "      <td>5.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>category_list</td>\n",
       "      <td>2.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>status</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country_code</td>\n",
       "      <td>7.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>state_code</td>\n",
       "      <td>9.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>region</td>\n",
       "      <td>8.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>city</td>\n",
       "      <td>8.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>founded_at</td>\n",
       "      <td>17.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Null_value_percentage\n",
       "Column_name                                   \n",
       "company_permalink                         0.00\n",
       "funding_round_permalink                   0.00\n",
       "funding_round_type                        0.00\n",
       "funding_round_code                       72.91\n",
       "funded_at                                 0.00\n",
       "raised_amount_usd                        17.39\n",
       "permalink                                 0.00\n",
       "name                                      0.00\n",
       "homepage_url                              5.34\n",
       "category_list                             2.97\n",
       "status                                    0.00\n",
       "country_code                              7.55\n",
       "state_code                                9.52\n",
       "region                                    8.84\n",
       "city                                      8.84\n",
       "founded_at                               17.85"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_details = pd.DataFrame(round(100*(master_frame.isnull().sum()/len(master_frame.index)), 2), columns=[\"Null_value_percentage\"])\n",
    "null_details.index.name = \"Column_name\"\n",
    "null_details"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Drop unnecessary  columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_frame = master_frame.drop(['permalink', 'funding_round_code', 'founded_at', 'funded_at', 'state_code', 'city', \n",
    "                                  'region', 'homepage_url', 'status', 'funding_round_permalink'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Column_name\n",
       "company_permalink      0.00\n",
       "funding_round_type     0.00\n",
       "raised_amount_usd     17.39\n",
       "name                   0.00\n",
       "category_list          2.97\n",
       "country_code           7.55\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(100*(master_frame.isnull().sum()/len(master_frame.index)), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Column_name</th>\n",
       "      <th>company_permalink</th>\n",
       "      <th>funding_round_type</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/-fame</td>\n",
       "      <td>venture</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>#fame</td>\n",
       "      <td>Media</td>\n",
       "      <td>IND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>/organization/-qounter</td>\n",
       "      <td>venture</td>\n",
       "      <td>NaN</td>\n",
       "      <td>:Qounter</td>\n",
       "      <td>Application Platforms|Real Time|Social Network...</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/organization/-qounter</td>\n",
       "      <td>seed</td>\n",
       "      <td>700000.0</td>\n",
       "      <td>:Qounter</td>\n",
       "      <td>Application Platforms|Real Time|Social Network...</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>/organization/-the-one-of-them-inc-</td>\n",
       "      <td>venture</td>\n",
       "      <td>3406878.0</td>\n",
       "      <td>(THE) ONE of THEM,Inc.</td>\n",
       "      <td>Apps|Games|Mobile</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>/organization/0-6-com</td>\n",
       "      <td>venture</td>\n",
       "      <td>2000000.0</td>\n",
       "      <td>0-6.com</td>\n",
       "      <td>Curated Web</td>\n",
       "      <td>CHN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Column_name                    company_permalink funding_round_type  \\\n",
       "0                            /organization/-fame            venture   \n",
       "1                         /organization/-qounter            venture   \n",
       "2                         /organization/-qounter               seed   \n",
       "3            /organization/-the-one-of-them-inc-            venture   \n",
       "4                          /organization/0-6-com            venture   \n",
       "\n",
       "Column_name  raised_amount_usd                    name  \\\n",
       "0                   10000000.0                   #fame   \n",
       "1                          NaN                :Qounter   \n",
       "2                     700000.0                :Qounter   \n",
       "3                    3406878.0  (THE) ONE of THEM,Inc.   \n",
       "4                    2000000.0                 0-6.com   \n",
       "\n",
       "Column_name                                      category_list country_code  \n",
       "0                                                        Media          IND  \n",
       "1            Application Platforms|Real Time|Social Network...          USA  \n",
       "2            Application Platforms|Real Time|Social Network...          USA  \n",
       "3                                            Apps|Games|Mobile          NaN  \n",
       "4                                                  Curated Web          CHN  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(114949, 6)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_frame.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Drop unnecessary  columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_frame = master_frame[~(master_frame['raised_amount_usd'].isnull() | master_frame['country_code'].isnull() |\n",
    "                              master_frame['category_list'].isnull())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Column_name\n",
       "company_permalink     0.0\n",
       "funding_round_type    0.0\n",
       "raised_amount_usd     0.0\n",
       "name                  0.0\n",
       "category_list         0.0\n",
       "country_code          0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(100*(master_frame.isnull().sum()/len(master_frame.index)), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(88529, 6)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_frame.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Column_name</th>\n",
       "      <th>company_permalink</th>\n",
       "      <th>funding_round_type</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/-fame</td>\n",
       "      <td>venture</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>#fame</td>\n",
       "      <td>Media</td>\n",
       "      <td>IND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/organization/-qounter</td>\n",
       "      <td>seed</td>\n",
       "      <td>700000.0</td>\n",
       "      <td>:Qounter</td>\n",
       "      <td>Application Platforms|Real Time|Social Network...</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>/organization/0-6-com</td>\n",
       "      <td>venture</td>\n",
       "      <td>2000000.0</td>\n",
       "      <td>0-6.com</td>\n",
       "      <td>Curated Web</td>\n",
       "      <td>CHN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>/organization/01games-technology</td>\n",
       "      <td>undisclosed</td>\n",
       "      <td>41250.0</td>\n",
       "      <td>01Games Technology</td>\n",
       "      <td>Games</td>\n",
       "      <td>HKG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>/organization/0ndine-biomedical-inc</td>\n",
       "      <td>seed</td>\n",
       "      <td>43360.0</td>\n",
       "      <td>Ondine Biomedical Inc.</td>\n",
       "      <td>Biotechnology</td>\n",
       "      <td>CAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Column_name                    company_permalink funding_round_type  \\\n",
       "0                            /organization/-fame            venture   \n",
       "2                         /organization/-qounter               seed   \n",
       "4                          /organization/0-6-com            venture   \n",
       "6               /organization/01games-technology        undisclosed   \n",
       "7            /organization/0ndine-biomedical-inc               seed   \n",
       "\n",
       "Column_name  raised_amount_usd                    name  \\\n",
       "0                   10000000.0                   #fame   \n",
       "2                     700000.0                :Qounter   \n",
       "4                    2000000.0                 0-6.com   \n",
       "6                      41250.0      01Games Technology   \n",
       "7                      43360.0  Ondine Biomedical Inc.   \n",
       "\n",
       "Column_name                                      category_list country_code  \n",
       "0                                                        Media          IND  \n",
       "2            Application Platforms|Real Time|Social Network...          USA  \n",
       "4                                                  Curated Web          CHN  \n",
       "6                                                        Games          HKG  \n",
       "7                                                Biotechnology          CAN  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_frame.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checkpoint 2: Funding Type Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1 Spark Funds wants to choose following investment types for each potential investment they will make. \n",
    "* venture\n",
    "* angel\n",
    "* seed\n",
    "* private equity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "venture                  47809\n",
       "seed                     21095\n",
       "debt_financing            6506\n",
       "angel                     4400\n",
       "grant                     1939\n",
       "private_equity            1820\n",
       "undisclosed               1345\n",
       "convertible_note          1320\n",
       "equity_crowdfunding       1128\n",
       "post_ipo_equity            598\n",
       "product_crowdfunding       330\n",
       "post_ipo_debt              151\n",
       "non_equity_assistance       60\n",
       "secondary_market            28\n",
       "Name: funding_round_type, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the unique funding_round_type \n",
    "master_frame['funding_round_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Column_name</th>\n",
       "      <th>company_permalink</th>\n",
       "      <th>funding_round_type</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/-fame</td>\n",
       "      <td>venture</td>\n",
       "      <td>10.00000</td>\n",
       "      <td>#fame</td>\n",
       "      <td>Media</td>\n",
       "      <td>IND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/organization/-qounter</td>\n",
       "      <td>seed</td>\n",
       "      <td>0.70000</td>\n",
       "      <td>:Qounter</td>\n",
       "      <td>Application Platforms|Real Time|Social Network...</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>/organization/0-6-com</td>\n",
       "      <td>venture</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0-6.com</td>\n",
       "      <td>Curated Web</td>\n",
       "      <td>CHN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>/organization/01games-technology</td>\n",
       "      <td>undisclosed</td>\n",
       "      <td>0.04125</td>\n",
       "      <td>01Games Technology</td>\n",
       "      <td>Games</td>\n",
       "      <td>HKG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>/organization/0ndine-biomedical-inc</td>\n",
       "      <td>seed</td>\n",
       "      <td>0.04336</td>\n",
       "      <td>Ondine Biomedical Inc.</td>\n",
       "      <td>Biotechnology</td>\n",
       "      <td>CAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Column_name                    company_permalink funding_round_type  \\\n",
       "0                            /organization/-fame            venture   \n",
       "2                         /organization/-qounter               seed   \n",
       "4                          /organization/0-6-com            venture   \n",
       "6               /organization/01games-technology        undisclosed   \n",
       "7            /organization/0ndine-biomedical-inc               seed   \n",
       "\n",
       "Column_name  raised_amount_usd                    name  \\\n",
       "0                     10.00000                   #fame   \n",
       "2                      0.70000                :Qounter   \n",
       "4                      2.00000                 0-6.com   \n",
       "6                      0.04125      01Games Technology   \n",
       "7                      0.04336  Ondine Biomedical Inc.   \n",
       "\n",
       "Column_name                                      category_list country_code  \n",
       "0                                                        Media          IND  \n",
       "2            Application Platforms|Real Time|Social Network...          USA  \n",
       "4                                                  Curated Web          CHN  \n",
       "6                                                        Games          HKG  \n",
       "7                                                Biotechnology          CAN  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting $ dollors to million\n",
    "master_frame['raised_amount_usd'] = master_frame['raised_amount_usd']/1000000\n",
    "master_frame.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A plot showing the representative amount of investment in each funding type. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Column_name</th>\n",
       "      <th>funding_round_type</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>venture</td>\n",
       "      <td>560523.362596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>private_equity</td>\n",
       "      <td>134568.045021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>debt_financing</td>\n",
       "      <td>111692.753486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>post_ipo_equity</td>\n",
       "      <td>39514.081027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>post_ipo_debt</td>\n",
       "      <td>25587.220256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Column_name funding_round_type  raised_amount_usd\n",
       "0                      venture      560523.362596\n",
       "1               private_equity      134568.045021\n",
       "2               debt_financing      111692.753486\n",
       "3              post_ipo_equity       39514.081027\n",
       "4                post_ipo_debt       25587.220256"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "funding_type = master_frame.pivot_table(values='raised_amount_usd', index='funding_round_type', aggfunc='sum').sort_values(by='raised_amount_usd', ascending=False).reset_index()\n",
    "funding_type.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Column_name</th>\n",
       "      <th>funding_round_type</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>venture</td>\n",
       "      <td>560523.362596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>private_equity</td>\n",
       "      <td>134568.045021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>debt_financing</td>\n",
       "      <td>111692.753486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>post_ipo_equity</td>\n",
       "      <td>39514.081027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>post_ipo_debt</td>\n",
       "      <td>25587.220256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>undisclosed</td>\n",
       "      <td>21374.284568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>seed</td>\n",
       "      <td>15774.707732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>grant</td>\n",
       "      <td>8750.121993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>angel</td>\n",
       "      <td>4274.925121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>secondary_market</td>\n",
       "      <td>2364.278903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>convertible_note</td>\n",
       "      <td>1765.086374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>equity_crowdfunding</td>\n",
       "      <td>575.164907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>product_crowdfunding</td>\n",
       "      <td>446.564880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>non_equity_assistance</td>\n",
       "      <td>28.845203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Column_name     funding_round_type  raised_amount_usd\n",
       "0                          venture      560523.362596\n",
       "1                   private_equity      134568.045021\n",
       "2                   debt_financing      111692.753486\n",
       "3                  post_ipo_equity       39514.081027\n",
       "4                    post_ipo_debt       25587.220256\n",
       "5                      undisclosed       21374.284568\n",
       "6                             seed       15774.707732\n",
       "7                            grant        8750.121993\n",
       "8                            angel        4274.925121\n",
       "9                 secondary_market        2364.278903\n",
       "10                convertible_note        1765.086374\n",
       "11             equity_crowdfunding         575.164907\n",
       "12            product_crowdfunding         446.564880\n",
       "13           non_equity_assistance          28.845203"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "funding_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "sns.barplot(x='raised_amount_usd', y=\"funding_round_type\", data=funding_type)\n",
    "plt.xlabel('raised amount in million')\n",
    "plt.savefig('FT_vs_amount.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A plot showing the representative amount of investment for 'venture', 'angel', 'seed', 'private_equity'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Column_name</th>\n",
       "      <th>funding_round_type</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>venture</td>\n",
       "      <td>560523.362596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>private_equity</td>\n",
       "      <td>134568.045021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>seed</td>\n",
       "      <td>15774.707732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>angel</td>\n",
       "      <td>4274.925121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Column_name funding_round_type  raised_amount_usd\n",
       "0                      venture      560523.362596\n",
       "1               private_equity      134568.045021\n",
       "6                         seed       15774.707732\n",
       "8                        angel        4274.925121"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "four_funding_type = funding_type[funding_type['funding_round_type'].isin(['venture', 'angel', 'seed', 'private_equity'])]\n",
    "four_funding_type.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "sns.barplot(y='raised_amount_usd', x=\"funding_round_type\", data=four_funding_type)\n",
    "plt.ylabel('raised amount in million')\n",
    "plt.title(\"representative amount of investment.\")\n",
    "plt.savefig('vasp_vs_amount.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Rows with only 4 investment type (funding_round_type)\n",
    "master_frame = master_frame[master_frame['funding_round_type'].isin(['venture', 'angel', 'seed', 'private_equity'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "venture           47809\n",
       "seed              21095\n",
       "angel              4400\n",
       "private_equity     1820\n",
       "Name: funding_round_type, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_frame['funding_round_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Column_name</th>\n",
       "      <th>funding_round_type</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>private_equity</td>\n",
       "      <td>73.938486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>venture</td>\n",
       "      <td>11.724223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>angel</td>\n",
       "      <td>0.971574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>seed</td>\n",
       "      <td>0.747794</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Column_name funding_round_type  raised_amount_usd\n",
       "0               private_equity          73.938486\n",
       "1                      venture          11.724223\n",
       "2                        angel           0.971574\n",
       "3                         seed           0.747794"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# average of FT raised_amount_usd in million\n",
    "four_funding_type_avg = master_frame.pivot_table(values='raised_amount_usd', index='funding_round_type', aggfunc='mean').sort_values(by='raised_amount_usd', ascending=False).reset_index()[:4]\n",
    "four_funding_type_avg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "sns.barplot(y='raised_amount_usd', x=\"funding_round_type\", data=four_funding_type_avg)\n",
    "plt.ylabel('raised amount in million')\n",
    "plt.title(\"Average amount of investment\")\n",
    "plt.savefig('vasp_vs_amount_avg.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Column_name</th>\n",
       "      <th>company_permalink</th>\n",
       "      <th>funding_round_type</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/-fame</td>\n",
       "      <td>venture</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>#fame</td>\n",
       "      <td>Media</td>\n",
       "      <td>IND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/organization/-qounter</td>\n",
       "      <td>seed</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>:Qounter</td>\n",
       "      <td>Application Platforms|Real Time|Social Network...</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>/organization/0-6-com</td>\n",
       "      <td>venture</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0-6.com</td>\n",
       "      <td>Curated Web</td>\n",
       "      <td>CHN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>/organization/0ndine-biomedical-inc</td>\n",
       "      <td>seed</td>\n",
       "      <td>0.043360</td>\n",
       "      <td>Ondine Biomedical Inc.</td>\n",
       "      <td>Biotechnology</td>\n",
       "      <td>CAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>/organization/0ndine-biomedical-inc</td>\n",
       "      <td>venture</td>\n",
       "      <td>0.719491</td>\n",
       "      <td>Ondine Biomedical Inc.</td>\n",
       "      <td>Biotechnology</td>\n",
       "      <td>CAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Column_name                    company_permalink funding_round_type  \\\n",
       "0                            /organization/-fame            venture   \n",
       "2                         /organization/-qounter               seed   \n",
       "4                          /organization/0-6-com            venture   \n",
       "7            /organization/0ndine-biomedical-inc               seed   \n",
       "8            /organization/0ndine-biomedical-inc            venture   \n",
       "\n",
       "Column_name  raised_amount_usd                    name  \\\n",
       "0                    10.000000                   #fame   \n",
       "2                     0.700000                :Qounter   \n",
       "4                     2.000000                 0-6.com   \n",
       "7                     0.043360  Ondine Biomedical Inc.   \n",
       "8                     0.719491  Ondine Biomedical Inc.   \n",
       "\n",
       "Column_name                                      category_list country_code  \n",
       "0                                                        Media          IND  \n",
       "2            Application Platforms|Real Time|Social Network...          USA  \n",
       "4                                                  Curated Web          CHN  \n",
       "7                                                Biotechnology          CAN  \n",
       "8                                                Biotechnology          CAN  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_frame.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 Calculate the most representative value of the investment amount for each of the four funding types (venture, angel, seed, and private equity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "funding_round_type\n",
       "angel              0.971574\n",
       "private_equity    73.938486\n",
       "seed               0.747794\n",
       "venture           11.724223\n",
       "Name: raised_amount_usd, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average of each funding_round_type\n",
    "round(master_frame.groupby('funding_round_type')['raised_amount_usd'].mean(), 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Spark Funds wants to invest between 5 to 15 million USD per investment round, keep the row with venture type\n",
    "master_frame = master_frame[master_frame['funding_round_type'] == 'venture']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Column_name</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>47809.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>11.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>88.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>1.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>17600.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Column_name  raised_amount_usd\n",
       "count                 47809.00\n",
       "mean                     11.72\n",
       "std                      88.22\n",
       "min                       0.00\n",
       "25%                       1.60\n",
       "50%                       5.00\n",
       "75%                      12.00\n",
       "max                   17600.00"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(master_frame.describe(), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 3: Country Analysis\n",
    "* Spark Funds wants to see the top nine countries which have received the highest total funding (across ALL sectors for the chosen investment type)\n",
    "\n",
    "* For the chosen investment type, make a data frame named top9 with the top nine countries (based on the total investment amount each country has received)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>country_code</th>\n",
       "      <th>Total_amount</th>\n",
       "      <th>Total_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>USA</td>\n",
       "      <td>420068.029342</td>\n",
       "      <td>35943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>CHN</td>\n",
       "      <td>39338.918773</td>\n",
       "      <td>1548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>GBR</td>\n",
       "      <td>20072.813004</td>\n",
       "      <td>2041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>IND</td>\n",
       "      <td>14261.508718</td>\n",
       "      <td>819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>CAN</td>\n",
       "      <td>9482.217668</td>\n",
       "      <td>1246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>FRA</td>\n",
       "      <td>7226.851352</td>\n",
       "      <td>912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>ISR</td>\n",
       "      <td>6854.350477</td>\n",
       "      <td>786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>DEU</td>\n",
       "      <td>6306.921981</td>\n",
       "      <td>554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>JPN</td>\n",
       "      <td>3167.647127</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country_code   Total_amount  Total_count\n",
       "0          USA  420068.029342        35943\n",
       "1          CHN   39338.918773         1548\n",
       "2          GBR   20072.813004         2041\n",
       "3          IND   14261.508718          819\n",
       "4          CAN    9482.217668         1246\n",
       "5          FRA    7226.851352          912\n",
       "6          ISR    6854.350477          786\n",
       "7          DEU    6306.921981          554\n",
       "8          JPN    3167.647127          269"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top9 = master_frame.groupby('country_code')['raised_amount_usd'].agg(\n",
    "    ['sum', 'count']).rename(columns={'sum':'Total_amount', 'count':'Total_count'}).sort_values(by='Total_amount', ascending=False)[:9]\n",
    "top9 = top9.reset_index()\n",
    "top9\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A plot showing the top 9 countries against the total amount of investments of funding type "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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muvua7v74eH5LprslH7TCKsclOau7b+3uzydZn2kOz6OTrO/uK7r7W0nOSnJcVVWSxyZ551j/zCRPWZujAQB2BYIpAIDdUFUdmukmNReM0vOq6pKqOqOq9h+1g5JcubDaVaO2ufoBSW7q7ts2qS/b/0lVdVFVXbRhw4a74YgAgJ2RYAoAYDdTVfsmeVeSF3T315K8JcmDkxyZ5Jokf7Cx6ZLVexvq31vsPq27j+ruo9atW7eVRwAA7Cr2nLsDAABsP1V1z0yh1Nu7+91J0t3XLiz/kyTvHS+vSnLIwuoHJ7l6PF9Wvz7JflW15xg1tdgeAOB7GDEFALCbGHNAnZ7ksu5+w0L9wIVmv5jk0+P5OUmOr6p7VdVhSQ5PcmGSjyY5fNyBb69ME6Sf092d5INJnjrWPyHJe9bymACAnZsRUwAAu4/HJPmVJJ+qqotH7SWZ7qp3ZKbL7r6Q5DlJ0t2XVtXZST6T6Y5+J3f37UlSVc9Lcl6SPZKc0d2Xju29MMlZVfXqJJ/IFIQBACwlmAIA2E1094ezfB6oc1dY55Qkpyypn7tsve6+ItNd+wAAtsilfAAAAADMQjAFAAAAwCwEUwAAAADMQjAFAAAAwCwEUwAAAADMQjAFAAAAwCwEUwAAAADMQjAFAAAAwCwEUwAAAADMQjAFAAAAwCwEUwAAAADMQjAFAAAAwCwEUwAAAADMQjAFAAAAwCwEUwAAAADMQjAFAAAAwCwEUwAAAADMQjAFAAAAwCwEUwAAAADMQjAFAAAAwCwEUwAAAADMQjAFAAAAwCwEUwAAAADMQjAFAAAAwCwEUwAAAADMQjAFAAAAwCwEUwAAAADMQjAFAAAAwCwEUwAAAADMQjAFAAAAwCwEUwAAAADMQjAFAAAAwCwEUwAAAADMQjAFAAAAwCwEUwAAAADMQjAFAAAAwCwEUwAAAADMQjAFAAAAwCwEUwAAAADMQjAFAAAAwCwEUwAAAADMQjAFAAAAwCwEUwAAAADMYk2Dqao6pKo+WFWXVdWlVfX8Uf/dqvpyVV08Hk9aWOfFVbW+qj5bVU9YqB87auur6kUL9cOq6oKquryq3lFVe436vcbr9WP5oWt5rAAAAABsnbUeMWo4oa4AACAASURBVHVbkt/u7ockOSbJyVV1xFh2ancfOR7nJslYdnyShyY5Nsmbq2qPqtojyZuSPDHJEUmesbCd141tHZ7kxiQnjvqJSW7s7h9OcupoBwAAAMAOYk2Dqe6+prs/Pp7fkuSyJAetsMpxSc7q7lu7+/NJ1ic5ejzWd/cV3f2tJGclOa6qKsljk7xzrH9mkqcsbOvM8fydSR432gMAAACwA9huc0yNS+kekeSCUXpeVV1SVWdU1f6jdlCSKxdWu2rUNlc/IMlN3X3bJvU7bWssv3m037RfJ1XVRVV10YYNG+7SMQIAAACwetslmKqqfZO8K8kLuvtrSd6S5MFJjkxyTZI/2Nh0yeq9DfWVtnXnQvdp3X1Udx+1bt26FY8DAAAAgLvPmgdTVXXPTKHU27v73UnS3dd29+3d/Z0kf5LpUr1kGvF0yMLqBye5eoX69Un2q6o9N6nfaVtj+X2T3HD3Hh0AAAAA22pVwdRC8LNibUmbSnJ6ksu6+w0L9QMXmv1ikk+P5+ckOX7cUe+wJIcnuTDJR5McPu7At1emCdLP6e5O8sEkTx3rn5DkPQvbOmE8f2qSD4z2AAAAAOwAthguDRcmeeQqapt6TJJfSfKpqrp41F6S6a56R2a6tO4LSZ6TJN19aVWdneQzme7od3J3354kVfW8JOcl2SPJGd196djeC5OcVVWvTvKJTEFYxs8/r6r1mUZKHb/KYwUAAABgO1gxmKqqByQ5MMk+VfWPc8e8TfdJ8n1b2nh3fzjL53o6d4V1TklyypL6ucvW6+4rcselgIv1byZ52pb6CAAAAMA8tjRi6slJfi3T3E1vXqjfkuRla9UpAAAAAHZ9KwZT3f1nSf6sqp7e3Wdvpz4BAAAAsBtY7RxTf11VT09y6OI63f2ategUAAAAALu+Vd2VL8l/TvJLmUKp2xceAABsZ1X11tXUAAB2dKsdMfWg7n7YmvYEAIDVevjii6q6R5KfmKkvAADbbLUjpj5SVUesaU8AAFhRVb2wqm5M8vCqumE8bkxyfVa46zEAwI5qtSOmHp3kE1W1PsmtSSpJd/cj16xnAABs6vVJ/iDJ7yV50cZid5tiAQDYKa02mHrKmvYCAIAt6u5OcluS36mqH0zywCR7VtXG5f97xu4BAGy11QZT31jTXgAAsGpVdUqSX0nyd7njhjSd5EmzdQoAYBusNph6f6aTnUqyd5JDknwuyY+uUb8AANi8pyb5ke7+5twdAQC4K1YVTHX3QxZfV9XRSZ69Jj0CAGBLPp/V38QGAGCHtU0nNN19YZKj7+a+AACwOrdkujHNm6rqDRsfW1qpqg6pqg9W1WVVdWlVPX/U71dV51fV5ePn/qNeVfXGqlpfVZdU1SMXtnXCaH95VZ2wUH9UVX1qrPPG2jgBFgDAEqsaMVVV/3rh5T2SPCrJDWvSIwAAtuS/jcfWui3Jb3f3x6vq3kk+VlXnJ/nVJO/v7tdW1Ysy3fHvhUmemOTw8Xh0krckeXRV3S/Jy5MclWm6h49V1TndfeNoc1KSjyQ5N8mxSf5mm48UANilrXaOqXULz29L8r4kf3X3dwcAgC3p7tO3cb1rklwznt9SVZclOSjJcUl+ZjQ7M8mHMgVTxyV527gb4Eeqar+qOnC0Pb+7b0iSEW4dW1UfSnKf7v7bUX9bprs7C6YAgKVWO8fUy5KkqvYZr92lDwBgJlV1eaaRSnfS3T+yFds4NMkjklyQ5AdGaJXuvqaqHjCaHZTkyoXVrhq1lepXLakv2/9JmUZW5YEPfOBquw0A7GJWeynfEZm+PTtwellXJXl2d39mLTsHAMBS/2zh+d5Jnpbkvqtduar2TfKuJC/o7q+tMA3UsgW9DfXvLXafluS0JDnqqKOWtgEAdn2rnfz8tCQv6e6Du/ugJP9+1AAA2M66+9qFxxe7+z8m+b9Ws25V3TNTKPX27n73KF87LtHL+HndqF+V5JCF1Q9OcvUW6gcvqQMALLXaYOre3X3+xhfd/b4k916bLgEAsJKqevjC48iq+vWsYsTUuEPe6Uku6+7Fu/idk2TjnfVOSPKehfqzxt35jkly87jk77wkj6+q/ccd/B6f5Lyx7JaqOmbs61kL2wIA+B6rnfz8C1X14iR/Pl7/yyRfXJsuAQCwBW9aeH5bki8k+aVVrPeYJL+S5FNVdfGovSTJa5OcXVUnJvlSpksDk+muek9Ksj7JPyR5dpJ09w1V9aokHx3tXrlxIvQkv5HkrUn2yTTpuYnPAYDNWm0w9WtJXpXp5KSS/I+MExMAALav7v7JbVzvw1k+D1SSPG5J+05y8ma2dUaSM5bUL0rysG3pHwCw+1ntXfm+muQ317gvAACsQlXdO8nLkvzUKP33JK/u7lvm6xUAwNZb1RxTVfXIqjq7qi6sqo9vfKx15wAAWOqMJN/ONIfTs5J8K8mfzdojAIBtsNpL+f4i0/wDn0rynbXrDgAAq3B4dz9t4fXLFuaMAgDYaaw2mPrqwu2EAQCY1zer6p90998mybhj3jdn7hMAwFZbbTD1iqr6f5O8L8mtG4vdfc6a9AoAgJX8ZpI/r6p7jdffyHRJHwDATmW1wdQzkzw8yb6541K+TiKYAgDYzrr740keWlX3S1LjRjUAADud1QZTj+put/0FANgBVNWrkvxBd98wXu+f5AXd/fJ5ewYAsHVWdVe+JBdU1Y+uaU8AAFitn+vumza+6O4bk/z8jP0BANgmqx0xdXSSS6pqfaY5pipJd/cj16xnAABszh5VtVd3fytJqmrvJHvN3CcAgK222mDqKWvaCwAAtsZZSc6vqjMyzft5YpK3z9slAICtt6pgqrs/lyRjgs2917RHAACsqLtfU1WXJPnZTCPZX9/d/3XmbgEAbLVVBVNV9eQkpyY5OMlXk/xQksuT/NjadQ0AgM3p7vcmee+yZVX14e7+Z9u5SwAAW221k5+fkuQxST7b3YckOTbJh9aqUwAA3CXfP3cHAABWY7XB1G3dvSHJPaqquvv8JCY+BwDYMfXcHQAAWI3VTn5+c1V9f5IPJ3lbVV2X5Dtr1y0AAAAAdnWrHTH1lCTfTPKCTJfwfTnJz69RnwAAuGtq7g4AAKzGau/Kd8t4enuS0zddboJNAIAdyq/O3QEAgNVY7aV8W2KCTQCANVZVN2b5/FGVpLv7fpmefHK7dgwAYBvdXcGUCTYBANbe/efuAADA3enuCqYAAFhj3X374uuqul+SvRdKV2/fHgEA3DWrnfx8S0ywCQCwnVTVk6vq75NcleSC8fMD8/YKAGDr3V3B1K/eTdsBAGDLTknymCSf7e5Dkjwh052TAQB2KiteymeCTQCAHdJt3b2hqu5RVdXd51fVKXN3CgBga21pjikTbAIA7HhurqrvT/LhJG+rquuSfGfmPgEAbLUVgykTbAIA7JCekuSbSV6Q5FlJ7pvk52btEQDANljVHFMm2AQA2KG8uLtv7+5vd/fp3f2GJL81d6cAALbWaic/N8EmAMCO49gltSdv914AANxFqw2mbuvuDUm+O8FmkkeuYb8AANhEVT2nqj6R5Eer6uMLj8uTfGbu/gEAbK0tTX6+kQk2AQDmd3aS9yf5vSQvWqjf0t3XzdMlAIBtt9oRU4sTbH4oyZdjgk0AgO2qu2/s7vXd/bQk+yT55+Oxbt6eAQBsm9UGUybYBADYQVTVyZlGTz1wPM6uqt+ct1cAAFtvtcGUCTYBAHYcz0lydHe/pLtfkuTRSZ47c58AALbaisHUXZ1gs6oOqaoPVtVlVXVpVT1/1O9XVedX1eXj5/6jXlX1xqpaX1WXVNUjF7Z1wmh/eVWdsFB/VFV9aqzzxqqqlfYBALALqCTfXnj97VEDANipbGnE1NlJnpbk3PFz4+Mx3f2MVWz/tiS/3d0PSXJMkpOr6ohMk3W+v7sPzzSB58bJO5+Y5PDxOCnJW5IpZEry8kzfBh6d5OULQdNbRtuN620c3bW5fQAA7JSqauONa/48yUeq6qVV9dIk/zvJmfP1DABg26wYTN3VCTa7+5ru/vh4fkuSy5IclOS43HHydGamydUz6m/ryUeS7FdVByZ5QpLzu/uG7r4xyflJjh3L7tPdf9vdneRtm2xr2T4AAHZWFyZJd78+0xdz/5DkG0me293/cc6OAQBsiz233OS7E2yenOSvR+nsqnpTd795tTuqqkOTPCLJBUl+oLuvSabwqqoeMJodlOTKhdWuGrWV6lctqWeFfWzar5MyndjlgQ984GoPBwBgDt+9XK+7P5rkozP2BQDgLltVMJU7Jtj8epJU1WsyDRlfVTBVVfsmeVeSF3T318Y0UEubLqn1NtRXrbtPS3Jakhx11FFbtS4AwHa2rqo2e2fkcedkAICdxmrvyrfNE2xW1T0zhVJv7+53j/K14zK8jJ/XjfpVSQ5ZWP3gJFdvoX7wkvpK+wAA2FntkWTfJPfezAMAYKey4oipqtqzu2/LHRNsvmss+sWsYoLNcYe805Nctsk3eOckOSHJa8fP9yzUn1dVZ2Wa6PzmcRneeUleszDh+eOTvLi7b6iqW6rqmEyXCD4ryX/awj4AAHZW13T3K+fuBADA3WVLl/JdmOSR3f36qvpgkp/MNFLquWNegy15TJJfSfKpqrp41F6SKSw6u6pOTPKlTHf6S6a7/z0pyfpMk3k+O0lGAPWq3DGPwiu7+4bx/DeSvDXT5Ox/Mx5ZYR8AADurVY1YBwDYWWwpmLpLE2x294ez+ROoxy1p35kmWV+2rTOSnLGkflGShy2pf3XZPgAAdmLObQCAXcqWgikTbAIA7CAWRowDAOwSthRMbZxg07BxAAAAAO5WWwqmTLAJAAAAwJq4xxaWGykFAAAAwJrYUjBlgk0AAAAA1sSKwZQJNgEAAABYK1saMQUAAAAAa0IwBQAAAMAsBFMAAAAAzEIwBQAAAMAsBFMAAAAAzEIwBQAAAMAsBFMAAAAAzEIwBQAAAMAsBFMAAAAAzEIwBQAAAMAsBFMAAAAAzEIwBQAAAMAsBFMAAAAAzEIwBQAAAMAsBFMAAAAAzEIwBQAAAMAsBFMAALuJqjqjqq6rqk8v1H63qr5cVRePx5MWlr24qtZX1Wer6gkL9WNHbX1VvWihflhVXVBVl1fVO6pqr+13dADAzkgwBQCw+3hrkmOX1E/t7iPH49wkqaojkhyf5KFjnTdX1R5VtUeSNyV5YpIjkjxjtE2S141tHZ7kxiQnrunRAAA7PcEUAMBuorv/R5IbVtn8uCRndfet3f35JOuTHD0e67v7iu7+VpKzkhxXVZXksUneOdY/M8lT7tYDAAB2OYIpAACeV1WXjEv99h+1g5JcudDmqlHbXP2AJDd1922b1JeqqpOq6qKqumjDhg1313EAADsZwRQAwO7tLUkenOTIJNck+YNRryVtexvqS3X3ad19VHcftW7duq3rMQCwy9hz7g4AADCf7r524/Oq+pMk7x0vr0pyyELTg5NcPZ4vq1+fZL+q2nOMmlpsDwCwlBFTAAC7sao6cOHlLybZeMe+c5IcX1X3qqrDkhye5MIkH01y+LgD316ZJkg/p7s7yQeTPHWsf0KS92yPYwAAdl5GTAEA7Caq6i+T/EyS+1fVVUlenuRnqurITJfdfSHJc5Kkuy+tqrOTfCbJbUlO7u7bx3ael+S8JHskOaO7Lx27eGGSs6rq1Uk+keT07XRoAMBOSjAFALCb6O5nLClvNjzq7lOSnLKkfm6Sc5fUr8h01z4AgFVxKR8AAAAAsxBMAQAAADALwRQAAAAAsxBMAQAAADALwRQAAAAAsxBMAQAAADALwRQAAAAAsxBMAQAAADALwRQAAAAAsxBMAfz/7d1/uK5lWSf87ym8pZklKjAGKMaQk78A2SGNWSCJ4FsjvknJW0KO05480Kwpj7GxdyDMpt6Zjt7IwqgQcEaNnGGkQpHwB2igoCA/TGOLiHsgQUHTsTTxfP94rpUPi7XX3hv2Wvdaa38+x/Ecz/Oc93Xfz3nt9fy49nnf93UDAAAwCYUpAAAAACahMAUAAADAJBSmAAAAAJiEwhQAAAAAk1CYAgAAAGASClMAAAAATEJhCgAAAIBJKEwBAAAAMAmFKQAAAAAmoTAFAAAAwCQUpgAAAACYhMIUAAAAAJNY0cJUVZ1TVXdW1Y1zsdOr6n9V1XXj9ry5Zb9cVVuq6hNV9dy5+HEjtqWqXj0Xf0JVfbCqbq6qP6mqbxnxbx3Pt4zlB65kPwEAAADYeSt9xNS5SY5bIv7b3X3ouF2cJFX1pCQvSvLksc7vV9UeVbVHkt9LcnySJyU5abRNkt8c2zo4yT1JXjriL01yT3f/8yS/PdoBAAAAsIasaGGquy9PcvcONn9+krd291e7+1NJtiQ5Yty2dPct3f21JG9N8vyqqiTPTvK2sf55SU6Y29Z54/Hbkhwz2gMAAACwRkw1x9TLq+r6carfXiO2X5LPzLXZOmLbij86yRe6++uL4vfZ1lj+xdH+fqpqc1VdU1XX3HXXXQ++ZwAAAADskCkKU2clOSjJoUnuSPJbI77UEU39AOLLbev+we6zu3tTd2/ae++9l8sbAAAAgF1o1QtT3f3Z7r63u7+R5A8zO1UvmR3xdMBc0/2T3L5M/HNJHllVey6K32dbY/l3ZsdPKQQAAABgFax6YaqqHjv39AVJFq7Yd1GSF40r6j0hycFJPpTk6iQHjyvwfUtmE6Rf1N2d5D1JXjjWPyXJ2+e2dcp4/MIk7x7tAQAAAFgj9tx+kweuqt6S5Kgkj6mqrUlOS3JUVR2a2al1tyb5t0nS3TdV1QVJPpbk60lO7e57x3ZenuSSJHskOae7bxov8e+TvLWqfi3JtUn+eMT/OMmbqmpLZkdKvWgl+wkAAADAzlvRwlR3n7RE+I+XiC20f12S1y0RvzjJxUvEb8k3TwWcj/9DkhN3KlkAAAAAVtVUV+UDAAAAYDenMAUAAADAJBSmAAAAAJiEwhQAAAAAk1CYAgAAAGASClMAAAAATEJhCgAAAIBJKEwBAAAAMAmFKQAAAAAmoTAFAAAAwCQUpgAAAACYhMIUAAAAAJNQmAIAAABgEgpTAAAAAExCYQoAAACASShMAQAAADAJhSkAAAAAJqEwBQAAAMAkFKYAAAAAmITCFAAAAACTUJgCAAAAYBIKUwAAAABMQmEKAAAAgEkoTAEAAAAwCYUpAAAAACahMAUAAADAJBSmAAAAAJiEwhQAAAAAk1CYAgAAAGASClMAAAAATEJhCgAAAIBJKEwBAAAAMAmFKQCA3URVnVNVd1bVjXOxR1XVpVV187jfa8Srqs6sqi1VdX1VPX1unVNG+5ur6pS5+OFVdcNY58yqqtXtIQCw3ihMAQDsPs5Nctyi2KuTXNbdBye5bDxPkuOTHDxum5OclcwKWUlOS/KMJEckOW2hmDXabJ5bb/FrAQDch8IUAMBuorsvT3L3ovDzk5w3Hp+X5IS5+Pk9c1WSR1bVY5M8N8ml3X13d9+T5NIkx41l39HdV3Z3Jzl/blsAAEtSmAIA2L3t2913JMm432fE90vymbl2W0dsufjWJeJLqqrNVXVNVV1z1113PehOAADrk8IUAABLWWp+qH4A8SV199ndvam7N+29994PMEUAYL1TmAIA2L19dpyGl3F/54hvTXLAXLv9k9y+nfj+S8QBALZJYQoAYPd2UZKFK+udkuTtc/GTx9X5jkzyxXGq3yVJjq2qvcak58cmuWQs+1JVHTmuxnfy3LYAAJa059QJAACwOqrqLUmOSvKYqtqa2dX1fiPJBVX10iS3JTlxNL84yfOSbEnylSQvSZLuvruqXpvk6tHujO5emFD9ZZld+e9hSd4xbgAA26QwBQCwm+juk7ax6Jgl2naSU7exnXOSnLNE/JokT3kwOQIAuxen8gEAAAAwCYUpAAAAACahMAUAAADAJBSmAAAAAJiEwhQAAAAAk1CYAgAAAGASClMAAAAATEJhCgAAAIBJKEwBAAAAMAmFKQAAAAAmoTAFAAAAwCQUpgAAAACYxIoWpqrqnKq6s6punIs9qqouraqbx/1eI15VdWZVbamq66vq6XPrnDLa31xVp8zFD6+qG8Y6Z1ZVLfcaAAAAAKwdK33E1LlJjlsUe3WSy7r74CSXjedJcnySg8dtc5KzklmRKclpSZ6R5Igkp80Vms4abRfWO247rwEAAADAGrGihanuvjzJ3YvCz09y3nh8XpIT5uLn98xVSR5ZVY9N8twkl3b33d19T5JLkxw3ln1Hd1/Z3Z3k/EXbWuo1AAAAAFgjpphjat/uviNJxv0+I75fks/Mtds6YsvFty4RX+417qeqNlfVNVV1zV133fWAOwUAAADAzllLk5/XErF+APGd0t1nd/em7t6099577+zqAAAAADxAUxSmPjtOw8u4v3PEtyY5YK7d/klu3058/yXiy70GAAAAAGvEFIWpi5IsXFnvlCRvn4ufPK7Od2SSL47T8C5JcmxV7TUmPT82ySVj2Zeq6shxNb6TF21rqdcAAAAAYI3YcyU3XlVvSXJUksdU1dbMrq73G0kuqKqXJrktyYmj+cVJnpdkS5KvJHlJknT33VX12iRXj3ZndPfChOovy+zKfw9L8o5xyzKvAQAAAMAasaKFqe4+aRuLjlmibSc5dRvbOSfJOUvEr0nylCXin1/qNQAAAABYO9bS5OcAAAAA7EYUpgAAAACYhMIUAAAAAJNQmAIAAABgEgpTAAAAAExCYQoAAACASShMAQAAADAJhSkAAAAAJqEwBQAAAMAkFKYAAAAAmITCFAAAAACTUJgCAAAAYBIKUwAAAABMQmEKAAAAgEkoTAEAAAAwCYUpAAAAACahMAUAAADAJBSmAAAAAJiEwhQAAAAAk1CYAgAAAGASClMAAAAATEJhCgAAAIBJKEwBAAAAMAmFKQAAAAAmoTAFAAAAwCQUpgAAAACYhMIUAAAAAJNQmAIAAABgEgpTAAAAAExCYQoAAACASShMAQAAADAJhSkAAAAAJqEwBQAAAMAkFKYAAAAAmITCFAAAAACTUJgCAAAAYBIKUwAAAABMQmEKAAAAgEkoTAEAAAAwCYUpAAAAACahMAUAAADAJBSmAAAAAJiEwhQAAKmqW6vqhqq6rqquGbFHVdWlVXXzuN9rxKuqzqyqLVV1fVU9fW47p4z2N1fVKVP1BwBYHxSmAABYcHR3H9rdm8bzVye5rLsPTnLZeJ4kxyc5eNw2JzkrmRWykpyW5BlJjkhy2kIxCwBgKQpTAABsy/OTnDcen5fkhLn4+T1zVZJHVtVjkzw3yaXdfXd335Pk0iTHrXbSAMD6oTAFAECSdJJ3VdWHq2rziO3b3XckybjfZ8T3S/KZuXW3jti24vdTVZur6pqquuauu+7ahd0AANaTPadOAACANeGZ3X17Ve2T5NKq+vgybWuJWC8Tv3+w++wkZyfJpk2blmwDAGx8jpgCACDdffu4vzPJhZnNEfXZcYpexv2do/nWJAfMrb5/ktuXiQMALElhCgBgN1dVD6+qRyw8TnJskhuTXJRk4cp6pyR5+3h8UZKTx9X5jkzyxXGq3yVJjq2qvcak58eOGADAkpzKBwDAvkkurKpkNj58c3e/s6quTnJBVb00yW1JThztL07yvCRbknwlyUuSpLvvrqrXJrl6tDuju+9evW4AAOuNwhQAwG6uu29JcsgS8c8nOWaJeCc5dRvbOifJObs6RwBgY3IqHwAAAACTmKwwVVW3VtUNVXVdVV0zYo+qqkur6uZxv9eIV1WdWVVbqur6qnr63HZOGe1vrqpT5uKHj+1vGesudZUYAAAAACYy9RFTR3f3od29aTx/dZLLuvvgJJeN50lyfJKDx21zkrOSWSEryWlJnpHZlWNOWyhmjTab59Y7buW7AwAAAMCOmrowtdjzk5w3Hp+X5IS5+Pk9c1WSR45LFj83yaXdfXd335Pk0iTHjWXf0d1XjjkQzp/bFgAAAABrwJSFqU7yrqr6cFVtHrF9x6WGM+73GfH9knxmbt2tI7ZcfOsS8fupqs1VdU1VXXPXXXc9yC4BAAAAsKOmvCrfM7v79qraJ8mlVfXxZdouNT9UP4D4/YPdZyc5O0k2bdq0ZBsAAAAAdr3Jjpjq7tvH/Z1JLsxsjqjPjtPwMu7vHM23JjlgbvX9k9y+nfj+S8QBAAAAWCMmKUxV1cOr6hELj5Mcm+TGJBclWbiy3ilJ3j4eX5Tk5HF1viOTfHGc6ndJkmOraq8x6fmxSS4Zy75UVUeOq/GdPLctAAAAANaAqU7l2zfJhbOaUfZM8ubufmdVXZ3kgqp6aZLbkpw42l+c5HlJtiT5SpKXJEl3311Vr01y9Wh3RnffPR6/LMm5SR6W5B3jBgAAAMAaMUlhqrtvSXLIEvHPJzlmiXgnOXUb2zonyTlLxK9J8pQHnSwAAAAAK2LKq/IBAAAAsBtTmAIAAABgEgpTAAAAAExCYQoAAACASShMAQAAADAJhSkAAAAAJqEwBQAAAMAk9pw6AaZ32xlPnTqFnfa4/3jD1CkAAAAAD5IjpgAAAACYhMIUAAAAAJNQmAIAAABgEgpTAAAAAExCYQoAAACASShMAQAAADAJhSkAAAAAJqEwBQAAAMAkFKYAAAAAmITCFAAAAACTUJgCAAAAYBIKUwAAAABMQmEKAAAAgEkoTAEAAAAwCYUpAAAAACahMAUAAADAJBSmAAAAAJiEwhQAAAAAk9hz6gQAAICN55m/+8ypU9hpH3jFB6ZOAWC344gpAAAAACahMAUAAADAJBSmAAAAAJiEwhQAAAAAkzD5ORueiTcBAABgbXLEFAAAAACTUJgCAAAAYBJO5QMAgAncdsZTp05hpz3uP94wdQoAbDCOmAIAAABgEgpTAAAAAExCYQoAAACASZhjCjaA9/3gD02dwk77ocvfN3UKAAAs4/W/+GdTp7DTXv5bPzp1yxtSYgAAEi5JREFUCsBOcsQUAAAAAJNQmAIAAABgEgpTAAAAAExCYQoAAACASZj8HAAAgN3O637qhVOnsNNe81/fNnUKsMspTAFrnivCAABrjasiA+waClMAAACwwfz16949dQo77Xtf8+ypU2AC5pgCAAAAYBKOmAKY2O4wv4E9dgAAwFIUpgDgQTr99NOnTmGnrcecAQDmrcfxzHrMeaU5lQ8AAACASShMAQAAADAJhSkAAAAAJmGOKQBgWRf86RFTp7DTfvzED02dAgDAg7K7jME29BFTVXVcVX2iqrZU1aunzgcAYHdgDAYA7KgNW5iqqj2S/F6S45M8KclJVfWkabMCANjYjMEAgJ2xYQtTSY5IsqW7b+nuryV5a5LnT5wTAMBGZwwGAOyw6u6pc1gRVfXCJMd1978Zz1+c5Bnd/fJF7TYn2TyePjHJJ1Yxzcck+dwqvt5q2+j9SzZ+H/Vv/dvofdzo/Us2fh9Xu3+P7+69V/H1djvGYGuC/q1/G72PG71/ycbvo/6tf2tmDLaRJz+vJWL3q8J199lJzl75dO6vqq7p7k1TvPZq2Oj9SzZ+H/Vv/dvofdzo/Us2fh83ev92U8ZgE9O/9W+j93Gj9y/Z+H3Uv/VvLfVxI5/KtzXJAXPP909y+0S5AADsLozBAIAdtpELU1cnObiqnlBV35LkRUkumjgnAICNzhgMANhhG/ZUvu7+elW9PMklSfZIck533zRxWotNcvj6Ktro/Us2fh/1b/3b6H3c6P1LNn4fN3r/djvGYGuC/q1/G72PG71/ycbvo/6tf2umjxt28nMAAAAA1raNfCofAAAAAGuYwhQAAAAAk1CYWiFVdWBV3bgodnpV/VJVHVlVH6yq66rqr6vq9EXt3l5VV65qwjupqv5ZVb21qj5ZVR+rqour6nu21efx+Nyq+l9V9a3j+WOq6tYJ0t8hVbVvVb25qm6pqg9X1ZVV9YKqOqqqvjj+ftdX1V9W1T5jnZ+uqrvGso9X1S9M3Y/lVNWXx/2BVdVV9Yq5Za+vqp8ej8+tqk9V1Uer6m+q6vyq2m+itHfItt6jY9kvVNU/VNV3zrU/avwb/Ohc7M+r6qgJ0t8hVXXveK8t3A6ce39eO96D/2WJ9db8d8y8uffpQ6rqzKq6sapuqKqrq+oJY9mtI3Z9Vb2vqh4/bdbbN/f3u2l8tv5dVT1kLJv/nlm4/fByvy3T9GLHVNWXR+5/P/rysap6w/ibLvv9Aztjo4+/ko0/Btsdxl+JMZgx2PpgDGYMtloUpqZxXpLN3X1okqckuWBhQVU9MsnTkzxy4cO+1lRVJbkwyXu7+6DuflKS/5Bk3x1Y/d4k/3ol89sVRh//Z5LLu/u7u/vwzK4qtP9ockV3H9rdT8vs6kOnzq3+J+Nv+8wkr6mq+Utmr2V3Jnllza6gtJRXdfchSZ6Y5Nok71mm7aR24D16UmZ/txcsWnVrktesWqIP3t+P9+HC7dYRv6K7D0tyWJIfqapnLqywHr5jlvETSb4rydO6+6mZ/f2+MLf86PGZfG+SX1n99Hbawt/vyUmek+R5SU6bW37For/vX06T5i71yfH9+LQkT0pywohv7/sHdoV1Pf5KNv4YbDcdfyXGYIkx2FpnDLb+rekxmMLUNPZJckeSdPe93f2xuWU/luTPkrw1sx/itejoJP/Y3W9YCHT3dUk+swPr/n9JfqGq1voVIZ+d5GuL+vjp7v7d+Ubjx/cRSe5ZvIHu/nySLUkeu8K57ip3JbksySnLNeqZ307yt0mOX43EHoAl36PdfUVVHZTk2zP70Txp0XofTfLFqnrO6qW6crr775Ncl2R+z+p6+I7ZlscmuaO7v5Ek3b21u+/32UtyZe7b5zWvu+9MsjnJy8f3yobW3V9P8ldJ/vkI7dD3DzxI6338lWz8MdjuOP5KjMESY7C1zhhsg1irYzCFqWn8dpJPVNWFVfVvq+qhc8tOSvKWcVv8hb1WPCXJh7ex7KD5wx6T/Oyi5bcleX+SF69kgrvAk5N8ZJnlzxr9uy3JDyc5Z3GDqnpckocmuX5FMlwZv5HkF6tqjx1o+5Ek/2KF83mglnuPLnzGrkjyxBqnAcz5tayPPT1J8rC5z9uFixdW1V5JDk5y+Vx4PXzHbMsFSX509Pe3quqwbbQ7LrM97utKd9+S2e/ywnvyWYsOIz9owvR2qar6tiTHJLlhLrwz3z/wQKz38Vey8cdgu+v4KzEGS4zB1jJjsA1irY7BFKZWTm8r3t1nJNmU5F1J/u8k70xm59RnVrl8f3f/TZKvV9VTViPZXeiT84c9JnnDEm1+Pcmrso7ef1X1e+P846tHaOHwzgOSvDHJ/zvX/Ceq6qYktyT5ne7+h9XO94Hq7k8l+VBm78vtWa97FF6U5K1jj8//SHLi/MLuviJJqupZE+S2s+YPI58/JP5ZVXV9ZntU/7y7/zZZ/98x3b01s9MYfjnJN5JcVlXHzDV5T1Xdmdl/Vt48QYq7wvznavFh5J/MMr8tq5DbrnDQ+E/lB5L8RXe/Y2HBTn7/wLbsruOvZAOOwXaX8VdiDJYYg61lxmDGYCttXfworVOfT7LXotijknwuSbr7k919VmbVykOq6tGZnbu7V5JP1WxCygOzNg/zvCnJ4Q905e7ektmhrT++yzLa9W7K7BzwJEl3n5rZ32rvJdpelOQH557/yThf+VlJfquq/tlKJroCfj3Jv8/2vx8OS/LXK5/OA7Lke7SqnpbZ3qtLx2fsRVl6j9Xrsr7mOVjsinGe/1OTvKyqDh3x9fIds03d/dXufkd3vyqz9+oJc4uPTvL4zP7+Z0yR34NRVd+d2Rwwdy7TbNnflnVg4T/Oh3X36Uss39HvH9iWjTz+Sjb+GGx3Hn8lxmCJMdiaZQxmDLaSDPxWSHd/OckdC5XkqnpUZoc2vr+q/s+581cPzuxD8IXMvpyP6+4Du/vAzL7U1+IX1ruTfGtV/cxCoKq+L7Mvox31uiRr+QoG707y0Kp62Vzs27bR9geSfHJxsLuvTPKmJK/c9emtnO7+eJKPJfmRpZbXzM9ldq75O1czt52wrffo7yQ5feEz1t3flWS/WnT1kO5+V2Y/PIesZtK72tgj958y+5FJ1s93zJKq6ulV9V3j8UMym7zx0/NtxpwOP5/k5PG9uy5U1d6ZHd3w+u7e5p635X5bViXRFba97x/Yng0+/ko2/hhstx1/JcZgiTHYWmUMZgy20hSmVtbJSX5lHDL37iS/Og4DfHFmcxxcl9kP508mOSDJ45JctbDyOKTu76rqGaue+TLGB/YFSZ5Ts8vA3pTk9CS378Q2bsrycwhMavTxhCQ/VLNL9H4os6v5LPy4LJx3/NHM/p6/uI1N/WaSl1TVI1Y86V3rdfnmFXAW/OfR379J8n2ZXX3ja6ue2Q5Y5j16VGZXipl3YZYeGCz1b7AevSHJD9bs6i/r4jtmGfsk+bOaXar3+iRfT/L6xY26+47M5m84dfGyNWZhfoqbkvxlZqcX/erc8sXzG7xwxLf127Im1Wyi5a/uxCob5bPHdDbk+CvZ+GMw468kxmDJxvkdMAZbu4zBljbZZ6+WKQoCADwoVXVIkj/s7iOmzgUAYHexnsZgjpgCAFZEVf1sZntO18tVlgAA1r31NgZzxBQAAAAAk3DEFAAAAACTUJgCAAAAYBIKUwAAAABMQmEKeECq6o+q6km7YDtf3hX5rJSqOmFb/ayqn62qk1fgNf9pu1V17sIlaqvqvVW1aTy+uKoeuatfGwAAYDXtOXUCwNpUVZXZBRK+sdTy7v43q5zSVE5I8udJPrZ4QXe/YSVecEe2293PW4nXBgB4MKrq55Oc3d1fmTiPW5Ns6u7PTZkHsH2OmAL+SVUdWFV/XVW/n+QjSQ6oqrOq6pqquqmqfnWu7XuralNV7TGO6rmxqm6oql8Yyw+qqndW1Yer6oqq+hcj/oSqurKqrq6q1y6Ty/8c695UVZvn4l+uqt8cy/6yqo4YudxSVf9qtHloVb1x5HNtVR094j9dVa+f29afV9VRc9t9XVV9tKquqqp9q+pfJvlXSf5zVV1XVQctyvH0qvqluX+P36yqD1XV31TVs5bo01FV9b6qumC0+Y2q+smxzg0L25/f7jL/PrdW1WPG4383/v1vHIPB+b/lH45/w3dV1cOW2yYAwC7w80m+bakFVbXHKucCrAMKU8BiT0xyfncf1t2fTvKa7t6U5GlJfqiqnrao/aFJ9uvup3T3U5O8ccTPTvKK7j48yS8l+f0R/50kZ3X39yX522Xy+Ndj3U1Jfq6qHj3iD0/y3rHsS0l+LclzkrwgyRmjzalJMvI5Kcl5VfXQ7fT74Umu6u5Dklye5Ge6+6+SXJTkVd19aHd/cjvb2LO7j8hsQHbaNtockuSVSZ6a5MVJvmes80dJXrGd7d9PVR2e5CVJnpHkyCQ/U1WHjcUHJ/m97n5yki8k+bGd3T4AsPFU1clVdf3YIfemqnp8VV02YpdV1eNGu3+aUmA8//K4P2rslHtbVX28qv5bzfxcku9K8p6qes/COlV1RlV9MMmvVNWFc9t7TlX9j2XyPK6qPjLyvGzEHjV2YF4/diY+bcQfPXbEXVtVf5Ck5rbzU2NH4HVV9QcKZLC2KEwBi326u6+ae/7jVfWRJNcmeXKSxfMt3ZLku6vqd6vquCR/V1XfnuRfJvnTqrouyR8keexo/8wkbxmP37RMHj9XVR9NclWSAzIrsiTJ15K8czy+Icn7uvsfx+MDR/wHFrbd3R9P8ukk37Odfn8ts1P2kuTDc9vaGQsDq+XWv7q77+juryb5ZJJ3jfgNy6yznB9IcmF3/+/u/vLIYeForU9193U7kBMAsJuoqicneU2SZ48dcq9M8vrMdkw+Lcl/S3LmDmzqsMx2xj0pyXcneWZ3n5nk9iRHd/fRo93Dk9zY3c/IbCfi91bV3mPZS/LNnZqL89w7yR8m+bGR54lj0a8muXbk+h+SnD/ipyV5f3cfltmOxYXi2vcm+YmR36FJ7k3ykzvQP2CVKEwBi/3vhQdV9YTMjnY6Zvz4/0WS+xx51N33ZHYU0HszO1LpjzL7bvnCOMpo4fa986stl8A4ve6Hk3z/GIhcO/e6/9jdC+t/I8lXRx7fyDfnzass7eu57/fefF/mt3tvHtgcfF/dgfW/Ovf4G3PP5/PfGdvq6+LXeqB9AgA2lmcnedvC3EvdfXeS70/y5rH8TZnt+NqeD3X31jEGuy7b3gF2b5L/Pl6rx/Z/qmYXcfn+JO/YxnpHJrm8uz81l2dy3x2Q707y6Kr6ziQ/mOS/jvhfJLlntD8myeFJrh47TI/JrJAGrBEKU8ByviOzQtUXq2rfJMcvbjDmOXpId//3JP9Pkqd3998l+VRVnTjaVFUdMlb5QJIXjcfb2lv1nUnu6e6v1GxuqiN3Mu/LF7ZdVd+T2R6zTyS5NcmhVfWQqjogyRE7sK0vJXnETr7+aro8yQlV9W1V9fDMTmm8YuKcAIC1q7KdnYRzy/9pp15VVZJvmWuzozvA/qG77517/sYkP5XZdAt/2t1f38k8l9op14vuF7c/b25n6RO7+/RtvCYwAYUpYJu6+6OZHa10U5JzMisqLbZfkveOPVDnJvnlEf/JJC8dp+PdlOT5I/7KJKdW1dWZFaCW8s4ke1bV9Ulem9npfDvj95PsUVU3JPmTJD89Tp37QJJPZXba3H/JbIL37XlrkleN+QoO2m7rVdbdH8ns3/1DST6Y5I+6+9pJkwIA1rLLMpuq4dHJbM6mJH+V++44fP94fGtmRxsls7Hc/7ED2192p153357Z6X6/ktkYZluuzGx+0yfM5ZncdwfkUUk+N3aKzsePT7LXaH9ZkhdW1T4L26mqx+9AP4BVUt88cwUAAICNrqpOSfKqzI50ujbJ6ZnthHxMkruSvKS7bxtHzL89swMaLsvswjbfPgpCv9TdPzK29/ok13T3uVX1isymd7iju4+uqi9397cvev0XJfn57l72qPhRYPr18fp3dvdzRoHqjUmekOQrSTZ39/Wj0PaW0Yf3Jfm/khze3Z+rqp/IbOfpQ5L8Y5JTF82pCkxIYQoAAIBVMwpZ13b3H0+dCzA9hSkAAABWRVV9OLM5TJ8zploAdnMKUwAAAEymqj6Y5FsXhV/c3TdMkQ+wuhSmAAAAAJiEq/IBAAAAMAmFKQAAAAAmoTAFAAAAwCQUpgAAAACYxP8PWFnrATU5J5MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.barplot(x=\"country_code\", y=\"Total_amount\", data=top9, estimator=sum)\n",
    "plt.title(\"Total amount of investments of top 9 countries.\")\n",
    "plt.xlabel(\"raised amount in million\")\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.barplot(x=\"country_code\", y=\"Total_count\", data=top9, estimator=sum)\n",
    "plt.title(\"Count\")\n",
    "plt.savefig('top9_vs_FT.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A plot showing the representative amount of investment in top 3 english speaking countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>country_code</th>\n",
       "      <th>Total_amount</th>\n",
       "      <th>Total_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>USA</td>\n",
       "      <td>420068.029342</td>\n",
       "      <td>35943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>GBR</td>\n",
       "      <td>20072.813004</td>\n",
       "      <td>2041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>IND</td>\n",
       "      <td>14261.508718</td>\n",
       "      <td>819</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country_code   Total_amount  Total_count\n",
       "0          USA  420068.029342        35943\n",
       "2          GBR   20072.813004         2041\n",
       "3          IND   14261.508718          819"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top3_english_speaking = top9[top9['country_code'].isin(['USA', 'GBR', 'IND'])]\n",
    "top3_english_speaking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.barplot(x=\"country_code\", y=\"Total_amount\", data=top3_english_speaking, estimator=sum)\n",
    "plt.ylabel(\"raised amount in million\")\n",
    "plt.title(\"Sum\")\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.barplot(x=\"country_code\", y=\"Total_count\", data=top3_english_speaking, estimator=sum)\n",
    "plt.title(\"Count\")\n",
    "plt.savefig('top3_english_vs_amount.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_frame = master_frame[(master_frame['country_code'].isin(['USA', 'GBR', 'IND']))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Column_name</th>\n",
       "      <th>company_permalink</th>\n",
       "      <th>funding_round_type</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/-fame</td>\n",
       "      <td>venture</td>\n",
       "      <td>10.0</td>\n",
       "      <td>#fame</td>\n",
       "      <td>Media</td>\n",
       "      <td>IND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>venture</td>\n",
       "      <td>20.0</td>\n",
       "      <td>H2O.ai</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>venture</td>\n",
       "      <td>1.7</td>\n",
       "      <td>H2O.ai</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>venture</td>\n",
       "      <td>8.9</td>\n",
       "      <td>H2O.ai</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>/organization/1-mainstream</td>\n",
       "      <td>venture</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1 Mainstream</td>\n",
       "      <td>Apps|Cable|Distribution|Software</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Column_name           company_permalink funding_round_type  raised_amount_usd  \\\n",
       "0                   /organization/-fame            venture               10.0   \n",
       "10                 /organization/0xdata            venture               20.0   \n",
       "11                 /organization/0xdata            venture                1.7   \n",
       "12                 /organization/0xdata            venture                8.9   \n",
       "22           /organization/1-mainstream            venture                5.0   \n",
       "\n",
       "Column_name          name                     category_list country_code  \n",
       "0                   #fame                             Media          IND  \n",
       "10                 H2O.ai                         Analytics          USA  \n",
       "11                 H2O.ai                         Analytics          USA  \n",
       "12                 H2O.ai                         Analytics          USA  \n",
       "22           1 Mainstream  Apps|Cable|Distribution|Software          USA  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_frame.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checkpoint 4: Sector Analysis 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.1: Extract the primary sector of each category\n",
    "Category list is a list of multiple sub-sectors separated by a pipe (vertical bar |)\n",
    "##### Example\n",
    "* Application Platforms|Real Time|Social Network Media.\n",
    "\n",
    "According to the business rule the first string before the vertical bar will be considered the primary sector.\n",
    "\n",
    "'Application Platforms' will be considered the primary sector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract the primary vector value\n",
    "master_frame['category_list'] = master_frame['category_list'].apply(lambda x:x.split(\"|\")[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Column_name</th>\n",
       "      <th>company_permalink</th>\n",
       "      <th>funding_round_type</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/-fame</td>\n",
       "      <td>venture</td>\n",
       "      <td>10.0</td>\n",
       "      <td>#fame</td>\n",
       "      <td>Media</td>\n",
       "      <td>IND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>venture</td>\n",
       "      <td>20.0</td>\n",
       "      <td>H2O.ai</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>venture</td>\n",
       "      <td>1.7</td>\n",
       "      <td>H2O.ai</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>venture</td>\n",
       "      <td>8.9</td>\n",
       "      <td>H2O.ai</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>/organization/1-mainstream</td>\n",
       "      <td>venture</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1 Mainstream</td>\n",
       "      <td>Apps</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Column_name           company_permalink funding_round_type  raised_amount_usd  \\\n",
       "0                   /organization/-fame            venture               10.0   \n",
       "10                 /organization/0xdata            venture               20.0   \n",
       "11                 /organization/0xdata            venture                1.7   \n",
       "12                 /organization/0xdata            venture                8.9   \n",
       "22           /organization/1-mainstream            venture                5.0   \n",
       "\n",
       "Column_name          name category_list country_code  \n",
       "0                   #fame         Media          IND  \n",
       "10                 H2O.ai     Analytics          USA  \n",
       "11                 H2O.ai     Analytics          USA  \n",
       "12                 H2O.ai     Analytics          USA  \n",
       "22           1 Mainstream          Apps          USA  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_frame.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  4.2: Map each primary sector to one of the eight main sectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>category_list</th>\n",
       "      <th>Automotive &amp; Sports</th>\n",
       "      <th>Blanks</th>\n",
       "      <th>Cleantech / Semiconductors</th>\n",
       "      <th>Entertainment</th>\n",
       "      <th>Health</th>\n",
       "      <th>Manufacturing</th>\n",
       "      <th>News, Search and Messaging</th>\n",
       "      <th>Others</th>\n",
       "      <th>Social, Finance, Analytics, Advertising</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3D</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3D Printing</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3D Technology</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   category_list  Automotive & Sports  Blanks  Cleantech / Semiconductors  \\\n",
       "0            NaN                    0       1                           0   \n",
       "1             3D                    0       0                           0   \n",
       "2    3D Printing                    0       0                           0   \n",
       "3  3D Technology                    0       0                           0   \n",
       "4     Accounting                    0       0                           0   \n",
       "\n",
       "   Entertainment  Health  Manufacturing  News, Search and Messaging  Others  \\\n",
       "0              0       0              0                           0       0   \n",
       "1              0       0              1                           0       0   \n",
       "2              0       0              1                           0       0   \n",
       "3              0       0              1                           0       0   \n",
       "4              0       0              0                           0       0   \n",
       "\n",
       "   Social, Finance, Analytics, Advertising  \n",
       "0                                        0  \n",
       "1                                        0  \n",
       "2                                        0  \n",
       "3                                        0  \n",
       "4                                        1  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapping = pd.read_csv('mapping.csv')\n",
    "mapping.category_list = mapping.category_list.replace({'0':'na', '2.na' :'2.0'}, regex=True)\n",
    "mapping.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>category_list</th>\n",
       "      <th>main_sector</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3D</td>\n",
       "      <td>Manufacturing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3D Printing</td>\n",
       "      <td>Manufacturing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3D Technology</td>\n",
       "      <td>Manufacturing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Advanced Materials</td>\n",
       "      <td>Manufacturing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>Manufacturing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         category_list    main_sector\n",
       "1                   3D  Manufacturing\n",
       "2          3D Printing  Manufacturing\n",
       "3        3D Technology  Manufacturing\n",
       "7   Advanced Materials  Manufacturing\n",
       "15         Agriculture  Manufacturing"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapping = pd.melt(mapping, id_vars =['category_list'], value_vars =['Manufacturing','Automotive & Sports',\n",
    "                                                              'Cleantech / Semiconductors','Entertainment',\n",
    "                                                             'Health','News, Search and Messaging','Others',\n",
    "                                                             'Social, Finance, Analytics, Advertising']) \n",
    "mapping = mapping[~(mapping.value == 0)]\n",
    "mapping = mapping.drop('value', axis = 1)\n",
    "mapping = mapping.rename(columns = {\"variable\":\"main_sector\"})\n",
    "mapping.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_frame = master_frame.merge(mapping, how = 'left', on='category_list')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>company_permalink</th>\n",
       "      <th>funding_round_type</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>country_code</th>\n",
       "      <th>main_sector</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/-fame</td>\n",
       "      <td>venture</td>\n",
       "      <td>10.0</td>\n",
       "      <td>#fame</td>\n",
       "      <td>Media</td>\n",
       "      <td>IND</td>\n",
       "      <td>Entertainment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>venture</td>\n",
       "      <td>20.0</td>\n",
       "      <td>H2O.ai</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>USA</td>\n",
       "      <td>Social, Finance, Analytics, Advertising</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>venture</td>\n",
       "      <td>1.7</td>\n",
       "      <td>H2O.ai</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>USA</td>\n",
       "      <td>Social, Finance, Analytics, Advertising</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>venture</td>\n",
       "      <td>8.9</td>\n",
       "      <td>H2O.ai</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>USA</td>\n",
       "      <td>Social, Finance, Analytics, Advertising</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>/organization/1-mainstream</td>\n",
       "      <td>venture</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1 Mainstream</td>\n",
       "      <td>Apps</td>\n",
       "      <td>USA</td>\n",
       "      <td>News, Search and Messaging</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            company_permalink funding_round_type  raised_amount_usd  \\\n",
       "0         /organization/-fame            venture               10.0   \n",
       "1        /organization/0xdata            venture               20.0   \n",
       "2        /organization/0xdata            venture                1.7   \n",
       "3        /organization/0xdata            venture                8.9   \n",
       "4  /organization/1-mainstream            venture                5.0   \n",
       "\n",
       "           name category_list country_code  \\\n",
       "0         #fame         Media          IND   \n",
       "1        H2O.ai     Analytics          USA   \n",
       "2        H2O.ai     Analytics          USA   \n",
       "3        H2O.ai     Analytics          USA   \n",
       "4  1 Mainstream          Apps          USA   \n",
       "\n",
       "                               main_sector  \n",
       "0                            Entertainment  \n",
       "1  Social, Finance, Analytics, Advertising  \n",
       "2  Social, Finance, Analytics, Advertising  \n",
       "3  Social, Finance, Analytics, Advertising  \n",
       "4               News, Search and Messaging  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_frame.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remove main_sector row which has null value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "company_permalink       0\n",
       "funding_round_type      0\n",
       "raised_amount_usd       0\n",
       "name                    0\n",
       "category_list           0\n",
       "country_code            0\n",
       "main_sector           165\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_frame.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Nanotechnology', 'Natural Gas Uses',\n",
       "       'Natural Language Processing', 'Enterprise 2.0',\n",
       "       'Adaptive Equipment', 'Racing', 'Specialty Retail',\n",
       "       'Biotechnology and Semiconductor', 'Rapidly Expanding',\n",
       "       'Navigation', 'Product Search', 'GreenTech', 'Retirement'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_frame[master_frame.main_sector.isnull()].category_list.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "company_permalink     0\n",
       "funding_round_type    0\n",
       "raised_amount_usd     0\n",
       "name                  0\n",
       "category_list         0\n",
       "country_code          0\n",
       "main_sector           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_frame = master_frame[~(master_frame.main_sector.isnull())]\n",
    "master_frame.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 38638 entries, 0 to 38802\n",
      "Data columns (total 7 columns):\n",
      "company_permalink     38638 non-null object\n",
      "funding_round_type    38638 non-null object\n",
      "raised_amount_usd     38638 non-null float64\n",
      "name                  38638 non-null object\n",
      "category_list         38638 non-null object\n",
      "country_code          38638 non-null object\n",
      "main_sector           38638 non-null object\n",
      "dtypes: float64(1), object(6)\n",
      "memory usage: 2.4+ MB\n"
     ]
    }
   ],
   "source": [
    "master_frame.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company_permalink</th>\n",
       "      <th>funding_round_type</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>country_code</th>\n",
       "      <th>main_sector</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/-fame</td>\n",
       "      <td>venture</td>\n",
       "      <td>10.0</td>\n",
       "      <td>#fame</td>\n",
       "      <td>Media</td>\n",
       "      <td>IND</td>\n",
       "      <td>Entertainment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>venture</td>\n",
       "      <td>20.0</td>\n",
       "      <td>H2O.ai</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>USA</td>\n",
       "      <td>Social, Finance, Analytics, Advertising</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>venture</td>\n",
       "      <td>1.7</td>\n",
       "      <td>H2O.ai</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>USA</td>\n",
       "      <td>Social, Finance, Analytics, Advertising</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>venture</td>\n",
       "      <td>8.9</td>\n",
       "      <td>H2O.ai</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>USA</td>\n",
       "      <td>Social, Finance, Analytics, Advertising</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>/organization/1-mainstream</td>\n",
       "      <td>venture</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1 Mainstream</td>\n",
       "      <td>Apps</td>\n",
       "      <td>USA</td>\n",
       "      <td>News, Search and Messaging</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            company_permalink funding_round_type  raised_amount_usd  \\\n",
       "0         /organization/-fame            venture               10.0   \n",
       "1        /organization/0xdata            venture               20.0   \n",
       "2        /organization/0xdata            venture                1.7   \n",
       "3        /organization/0xdata            venture                8.9   \n",
       "4  /organization/1-mainstream            venture                5.0   \n",
       "\n",
       "           name category_list country_code  \\\n",
       "0         #fame         Media          IND   \n",
       "1        H2O.ai     Analytics          USA   \n",
       "2        H2O.ai     Analytics          USA   \n",
       "3        H2O.ai     Analytics          USA   \n",
       "4  1 Mainstream          Apps          USA   \n",
       "\n",
       "                               main_sector  \n",
       "0                            Entertainment  \n",
       "1  Social, Finance, Analytics, Advertising  \n",
       "2  Social, Finance, Analytics, Advertising  \n",
       "3  Social, Finance, Analytics, Advertising  \n",
       "4               News, Search and Messaging  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_frame.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checkpoint 5: Sector Analysis 2\n",
    "\n",
    "#### 5. 1 Create three separate data frames D1, D2 and D3 \n",
    "\n",
    "* Create three separate data frames D1, D2 and D3 for each of the three countries containing the observations of funding type FT falling within the 5-15 million USD range. The three data frames should contain -\n",
    "\n",
    "    * All the columns of the master_frame along with the main sector and the primary sector.\n",
    "\n",
    "    * Calculate the total number of investments in each main sector.\n",
    "\n",
    "    * Total amount of investments in each main sector.\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_master_frame = master_frame[(master_frame['raised_amount_usd'] >= 5) & \n",
    "             (master_frame['raised_amount_usd'] <= 15) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "D1 = new_master_frame[new_master_frame['country_code'] == 'IND']\n",
    "D2 = new_master_frame[new_master_frame['country_code'] == 'GBR']\n",
    "D3 = new_master_frame[new_master_frame['country_code'] == 'USA']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IND\n",
    "  * Total number of investments in each main sector\n",
    "  * Total amount of investments in each main sector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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_count</th>\n",
       "      <th>Total_amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>main_sector</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Others</td>\n",
       "      <td>110</td>\n",
       "      <td>1013.409507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Social, Finance, Analytics, Advertising</td>\n",
       "      <td>60</td>\n",
       "      <td>550.549550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>News, Search and Messaging</td>\n",
       "      <td>52</td>\n",
       "      <td>433.834545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Total_count  Total_amount\n",
       "main_sector                                                       \n",
       "Others                                           110   1013.409507\n",
       "Social, Finance, Analytics, Advertising           60    550.549550\n",
       "News, Search and Messaging                        52    433.834545"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D1_total = D1.pivot_table(values = 'raised_amount_usd',index = ['main_sector'], aggfunc = {'sum', 'count'}).sort_values(by='sum', ascending=False)\n",
    "D1_total.columns = ['Total_count', 'Total_amount']\n",
    "# top 3\n",
    "D1_total.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"Total Amount (IND)\")\n",
    "sns.barplot(y=D1_total.index, x='Total_amount', data=D1_total)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Total count (IND)\")\n",
    "sns.barplot(y=D1_total.index, x='Total_count', data=D1_total)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 3 company with the highest investment for main sector\n",
    " 1. Others\n",
    " 2. Social, Finance, Analytics, Advertising\n",
    " 3. News, Search and Messaging\t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>raised_amount_usd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company_permalink</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>/organization/firstcry-com</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>/organization/myntra</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>/organization/commonfloor</td>\n",
       "      <td>32.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            raised_amount_usd\n",
       "company_permalink                            \n",
       "/organization/firstcry-com               39.0\n",
       "/organization/myntra                     38.0\n",
       "/organization/commonfloor                32.9"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top = D1[D1['main_sector'] == \"Others\" ]\n",
    "top_company = top.pivot_table(values='raised_amount_usd', index='company_permalink', aggfunc='sum').sort_values(by='raised_amount_usd', ascending=False)\n",
    "top_company.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "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>raised_amount_usd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company_permalink</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>/organization/manthan-systems</td>\n",
       "      <td>50.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>/organization/komli-media</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>/organization/shopclues-com</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               raised_amount_usd\n",
       "company_permalink                               \n",
       "/organization/manthan-systems               50.7\n",
       "/organization/komli-media                   28.0\n",
       "/organization/shopclues-com                 25.0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top = D1[D1['main_sector'] == \"Social, Finance, Analytics, Advertising\" ]\n",
    "top_company = top.pivot_table(values='raised_amount_usd', index='company_permalink', aggfunc='sum').sort_values(by='raised_amount_usd', ascending=False)\n",
    "top_company.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>raised_amount_usd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company_permalink</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>/organization/gupshup-technology-india-pvt-ltd</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>/organization/imimobile</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>/organization/sms-gupshup</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                raised_amount_usd\n",
       "company_permalink                                                \n",
       "/organization/gupshup-technology-india-pvt-ltd               33.0\n",
       "/organization/imimobile                                      23.0\n",
       "/organization/sms-gupshup                                    22.0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top = D1[D1['main_sector'] == \"News, Search and Messaging\" ]\n",
    "top_company = top.pivot_table(values='raised_amount_usd', index='company_permalink', aggfunc='sum').sort_values(by='raised_amount_usd', ascending=False)\n",
    "top_company.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GBR\n",
    "  * Total number of investments in each main sector\n",
    "  * Total amount of investments in each main sector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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_count</th>\n",
       "      <th>Total_amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>main_sector</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Others</td>\n",
       "      <td>147</td>\n",
       "      <td>1283.624289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Cleantech / Semiconductors</td>\n",
       "      <td>128</td>\n",
       "      <td>1150.139665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Social, Finance, Analytics, Advertising</td>\n",
       "      <td>133</td>\n",
       "      <td>1089.404014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Total_count  Total_amount\n",
       "main_sector                                                       \n",
       "Others                                           147   1283.624289\n",
       "Cleantech / Semiconductors                       128   1150.139665\n",
       "Social, Finance, Analytics, Advertising          133   1089.404014"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D2_total = D2.pivot_table(values = 'raised_amount_usd',index = ['main_sector'], aggfunc = {'sum','count'}).sort_values(by='sum', ascending=False)\n",
    "D2_total.columns = ['Total_count', 'Total_amount']\n",
    "# top 3\n",
    "D2_total.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"Total Amount (GBR)\")\n",
    "sns.barplot(y=D2_total.index, x='Total_count', data=D2_total)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Total count (GBR)\")\n",
    "sns.barplot(y=D2_total.index, x='Total_amount', data=D2_total)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 3 company with the highest investment for main sector\n",
    " 1. Others\n",
    " 2. Cleantech / Semiconductors\n",
    " 3. Social, Finance, Analytics, Advertising"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>raised_amount_usd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company_permalink</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>/organization/electric-cloud</td>\n",
       "      <td>37.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>/organization/sensage</td>\n",
       "      <td>36.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>/organization/enigmatic</td>\n",
       "      <td>32.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              raised_amount_usd\n",
       "company_permalink                              \n",
       "/organization/electric-cloud              37.00\n",
       "/organization/sensage                     36.25\n",
       "/organization/enigmatic                   32.50"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top = D2[D2['main_sector'] == \"Others\" ]\n",
    "top_company = top.pivot_table(values='raised_amount_usd', index='company_permalink', aggfunc='sum').sort_values(by='raised_amount_usd', ascending=False)\n",
    "top_company.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>raised_amount_usd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company_permalink</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>/organization/eusa-pharma</td>\n",
       "      <td>35.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>/organization/horizon-discovery</td>\n",
       "      <td>31.353719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>/organization/f-star-biotechnologische-forschungs-und-entwicklungsges-m-b-h</td>\n",
       "      <td>29.132906</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    raised_amount_usd\n",
       "company_permalink                                                    \n",
       "/organization/eusa-pharma                                   35.600000\n",
       "/organization/horizon-discovery                             31.353719\n",
       "/organization/f-star-biotechnologische-forschun...          29.132906"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top = D2[D2['main_sector'] == \"Cleantech / Semiconductors\" ]\n",
    "top_company = top.pivot_table(values='raised_amount_usd', index='company_permalink', aggfunc='sum').sort_values(by='raised_amount_usd', ascending=False)\n",
    "top_company.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>raised_amount_usd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company_permalink</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>/organization/celltick-technologies</td>\n",
       "      <td>37.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>/organization/mythings</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>/organization/zopa</td>\n",
       "      <td>32.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     raised_amount_usd\n",
       "company_permalink                                     \n",
       "/organization/celltick-technologies               37.5\n",
       "/organization/mythings                            34.0\n",
       "/organization/zopa                                32.9"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top = D2[D2['main_sector'] == \"Social, Finance, Analytics, Advertising\" ]\n",
    "top_company = top.pivot_table(values='raised_amount_usd', index='company_permalink', aggfunc='sum').sort_values(by='raised_amount_usd', ascending=False)\n",
    "top_company.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### USA\n",
    "  * Total number of investments in each main sector\n",
    "  * Total amount of investments in each main sector."
   ]
  },
  {
   "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>Total_count</th>\n",
       "      <th>Total_amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>main_sector</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Others</td>\n",
       "      <td>2950</td>\n",
       "      <td>26321.007002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Social, Finance, Analytics, Advertising</td>\n",
       "      <td>2714</td>\n",
       "      <td>23807.376964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Cleantech / Semiconductors</td>\n",
       "      <td>2300</td>\n",
       "      <td>21206.628192</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Total_count  Total_amount\n",
       "main_sector                                                       \n",
       "Others                                          2950  26321.007002\n",
       "Social, Finance, Analytics, Advertising         2714  23807.376964\n",
       "Cleantech / Semiconductors                      2300  21206.628192"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D3_total = D3.pivot_table(values = 'raised_amount_usd',index = ['main_sector'], aggfunc = {'sum','count'}).sort_values(by='sum', ascending=False)\n",
    "D3_total.columns = ['Total_count', 'Total_amount']\n",
    "# top 3\n",
    "D3_total.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"Total Amount (USA)\")\n",
    "sns.barplot(y=D3_total.index, x='Total_amount', data=D3_total)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Total count (USA)\")\n",
    "sns.barplot(y=D3_total.index, x='Total_count', data=D3_total)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 3 company with the highest investment for main sector\n",
    " 1. Others\n",
    " 2. Social, Finance, Analytics, Advertising\n",
    " 3. Cleantech / Semiconductors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>raised_amount_usd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company_permalink</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>/organization/virtustream</td>\n",
       "      <td>64.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>/organization/capella</td>\n",
       "      <td>54.968051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>/organization/airtight-networks</td>\n",
       "      <td>54.201907</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 raised_amount_usd\n",
       "company_permalink                                 \n",
       "/organization/virtustream                64.300000\n",
       "/organization/capella                    54.968051\n",
       "/organization/airtight-networks          54.201907"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top = D3[D3['main_sector'] == \"Others\" ]\n",
    "top_company = top.pivot_table(values='raised_amount_usd', index='company_permalink', aggfunc='sum').sort_values(by='raised_amount_usd', ascending=False)\n",
    "top_company.head(3)"
   ]
  },
  {
   "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>raised_amount_usd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company_permalink</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>/organization/shotspotter</td>\n",
       "      <td>67.933006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>/organization/demandbase</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>/organization/intacct</td>\n",
       "      <td>61.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           raised_amount_usd\n",
       "company_permalink                           \n",
       "/organization/shotspotter          67.933006\n",
       "/organization/demandbase           63.000000\n",
       "/organization/intacct              61.800000"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top = D3[D3['main_sector'] == \"Social, Finance, Analytics, Advertising\" ]\n",
    "top_company = top.pivot_table(values='raised_amount_usd', index='company_permalink', aggfunc='sum').sort_values(by='raised_amount_usd', ascending=False)\n",
    "top_company.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>raised_amount_usd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company_permalink</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>/organization/biodesix</td>\n",
       "      <td>75.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>/organization/tigo-energy</td>\n",
       "      <td>68.739496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>/organization/forsight-labs</td>\n",
       "      <td>61.300001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             raised_amount_usd\n",
       "company_permalink                             \n",
       "/organization/biodesix               75.300000\n",
       "/organization/tigo-energy            68.739496\n",
       "/organization/forsight-labs          61.300001"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top = D3[D3['main_sector'] == \"Cleantech / Semiconductors\" ]\n",
    "top_company = top.pivot_table(values='raised_amount_usd', index='company_permalink', aggfunc='sum').sort_values(by='raised_amount_usd', ascending=False)\n",
    "top_company.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  5.2: Sector-wise Investment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IND investments count 2949.5436019999997\n",
      "GBR investments count 5365.228300000001\n",
      "USA investments count 107318.29466399997\n"
     ]
    }
   ],
   "source": [
    "#Total number of investments (count)\n",
    "print(\"IND investments count\", D1_total.Total_amount.sum())\n",
    "print(\"GBR investments count\", D2_total.Total_amount.sum())\n",
    "print(\"USA investments count\", D3_total.Total_amount.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IND investments amount 328\n",
      "GBR investments amount 619\n",
      "USA investments amount 12012\n"
     ]
    }
   ],
   "source": [
    "#Total number of investments (count)\n",
    "print(\"IND investments amount\", round(D1_total.Total_count.sum(), 12))\n",
    "print(\"GBR investments amount\", round(D2_total.Total_count.sum(), 12))\n",
    "print(\"USA investments amount\", round(D3_total.Total_count.sum(), 12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A plot showing the number of investments in the top 3 sectors of the top 3 countries\n",
    "   1. This plot display the top 3 sectors each in Country 1, Country 2, and Country 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>main_sector</th>\n",
       "      <th>country_code</th>\n",
       "      <th>Total_amount</th>\n",
       "      <th>Total_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Automotive &amp; Sports</td>\n",
       "      <td>GBR</td>\n",
       "      <td>167.051565</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Automotive &amp; Sports</td>\n",
       "      <td>IND</td>\n",
       "      <td>136.900000</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Automotive &amp; Sports</td>\n",
       "      <td>USA</td>\n",
       "      <td>1454.104361</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Cleantech / Semiconductors</td>\n",
       "      <td>GBR</td>\n",
       "      <td>1150.139665</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Cleantech / Semiconductors</td>\n",
       "      <td>IND</td>\n",
       "      <td>165.380000</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  main_sector country_code  Total_amount  Total_count\n",
       "0         Automotive & Sports          GBR    167.051565           16\n",
       "1         Automotive & Sports          IND    136.900000           13\n",
       "2         Automotive & Sports          USA   1454.104361          167\n",
       "3  Cleantech / Semiconductors          GBR   1150.139665          128\n",
       "4  Cleantech / Semiconductors          IND    165.380000           20"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "company_all = new_master_frame.pivot_table(values='raised_amount_usd', index=['main_sector', 'country_code'], aggfunc=['sum', 'count'])\n",
    "company_all.reset_index(inplace=True)\n",
    "company_all.columns = ['main_sector', 'country_code', 'Total_amount', 'Total_count']\n",
    "company_all.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 3 sector country wise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind = company_all[company_all['country_code']=='IND'].sort_values(by=['Total_amount'], ascending=False)\n",
    "gbr = company_all[company_all['country_code']=='GBR'].sort_values(by=['Total_amount'], ascending=False)\n",
    "usa = company_all[company_all['country_code']=='USA'].sort_values(by=['Total_amount'], ascending=False)"
   ]
  },
  {
   "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>main_sector</th>\n",
       "      <th>country_code</th>\n",
       "      <th>Total_amount</th>\n",
       "      <th>Total_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>Others</td>\n",
       "      <td>USA</td>\n",
       "      <td>26321.007002</td>\n",
       "      <td>2950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>Social, Finance, Analytics, Advertising</td>\n",
       "      <td>USA</td>\n",
       "      <td>23807.376964</td>\n",
       "      <td>2714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Cleantech / Semiconductors</td>\n",
       "      <td>USA</td>\n",
       "      <td>21206.628192</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>Others</td>\n",
       "      <td>GBR</td>\n",
       "      <td>1283.624289</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Cleantech / Semiconductors</td>\n",
       "      <td>GBR</td>\n",
       "      <td>1150.139665</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>Social, Finance, Analytics, Advertising</td>\n",
       "      <td>GBR</td>\n",
       "      <td>1089.404014</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>Others</td>\n",
       "      <td>IND</td>\n",
       "      <td>1013.409507</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>Social, Finance, Analytics, Advertising</td>\n",
       "      <td>IND</td>\n",
       "      <td>550.549550</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>News, Search and Messaging</td>\n",
       "      <td>IND</td>\n",
       "      <td>433.834545</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                main_sector country_code  Total_amount  \\\n",
       "20                                   Others          USA  26321.007002   \n",
       "23  Social, Finance, Analytics, Advertising          USA  23807.376964   \n",
       "5                Cleantech / Semiconductors          USA  21206.628192   \n",
       "18                                   Others          GBR   1283.624289   \n",
       "3                Cleantech / Semiconductors          GBR   1150.139665   \n",
       "21  Social, Finance, Analytics, Advertising          GBR   1089.404014   \n",
       "19                                   Others          IND   1013.409507   \n",
       "22  Social, Finance, Analytics, Advertising          IND    550.549550   \n",
       "16               News, Search and Messaging          IND    433.834545   \n",
       "\n",
       "    Total_count  \n",
       "20         2950  \n",
       "23         2714  \n",
       "5          2300  \n",
       "18          147  \n",
       "3           128  \n",
       "21          133  \n",
       "19          110  \n",
       "22           60  \n",
       "16           52  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_3_country_sector = [usa[:3], gbr[:3], ind[:3]]\n",
    "result = pd.concat(top_3_country_sector)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 3 sectors of the top 3 countries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.barplot(y='country_code', x='Total_amount', hue=\"main_sector\", data=result, estimator=np.sum)\n",
    "plt.xlabel(\"raised amount in million\")\n",
    "plt.savefig('top3sector_vs_top3count.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>main_sector</th>\n",
       "      <th>country_code</th>\n",
       "      <th>Total_amount</th>\n",
       "      <th>Total_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>Others</td>\n",
       "      <td>USA</td>\n",
       "      <td>26321.007002</td>\n",
       "      <td>2950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>Social, Finance, Analytics, Advertising</td>\n",
       "      <td>USA</td>\n",
       "      <td>23807.376964</td>\n",
       "      <td>2714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Cleantech / Semiconductors</td>\n",
       "      <td>USA</td>\n",
       "      <td>21206.628192</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>News, Search and Messaging</td>\n",
       "      <td>USA</td>\n",
       "      <td>13959.567428</td>\n",
       "      <td>1582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Health</td>\n",
       "      <td>USA</td>\n",
       "      <td>8211.859357</td>\n",
       "      <td>909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                main_sector country_code  Total_amount  \\\n",
       "20                                   Others          USA  26321.007002   \n",
       "23  Social, Finance, Analytics, Advertising          USA  23807.376964   \n",
       "5                Cleantech / Semiconductors          USA  21206.628192   \n",
       "17               News, Search and Messaging          USA  13959.567428   \n",
       "11                                   Health          USA   8211.859357   \n",
       "\n",
       "    Total_count  \n",
       "20         2950  \n",
       "23         2714  \n",
       "5          2300  \n",
       "17         1582  \n",
       "11          909  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_country_sector = [usa, gbr, ind]\n",
    "all_result = pd.concat(all_country_sector)\n",
    "all_result.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### All sectors of the top 3 countries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.barplot(y='country_code', x='Total_amount', hue=\"main_sector\", data=all_result)\n",
    "plt.xlabel(\"raised amount in million\")\n",
    "plt.savefig('allsector_vs_top3count.png')\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}