EDA_Asset Management Company.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Asset Management "
   ]
  },
  {
   "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": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "companies=pd.read_csv(\"C:\\\\Users\\\\mohan\\\\Desktop\\\\ML\\\\Assignments\\\\Week_5- Assignment-4 (11-June-2019)\\\\Assignment-4_ Teclov-data-code-project\\\\Assignment-4\\\\companies1.txt\",sep=\"\\t\",encoding= \"ISO-8859-1\")\n",
    "rounds=pd.read_csv(\"C:\\\\Users\\\\mohan\\\\Desktop\\\\ML\\\\Assignments\\\\Week_5- Assignment-4 (11-June-2019)\\\\Assignment-4_ Teclov-data-code-project\\\\Assignment-4\\\\rounds2.csv\",encoding = 'ISO-8859-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        company_permalink                          funding_round_permalink  \\\n",
       "0     /organization/-fame  /funding-round/9a01d05418af9f794eebff7ace91f638   \n",
       "1  /ORGANIZATION/-QOUNTER  /funding-round/22dacff496eb7acb2b901dec1dfe5633   \n",
       "\n",
       "  funding_round_type funding_round_code   funded_at  raised_amount_usd  \n",
       "0            venture                  B  05-01-2015         10000000.0  \n",
       "1            venture                  A  14-10-2014                NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rounds.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "rounds.info()       # The variables funding_round_code and raised_amount_usd has missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                permalink      name            homepage_url  \\\n",
       "0     /Organization/-Fame     #fame      http://livfame.com   \n",
       "1  /Organization/-Qounter  :Qounter  http://www.qounter.com   \n",
       "\n",
       "                                       category_list     status country_code  \\\n",
       "0                                              Media  operating          IND   \n",
       "1  Application Platforms|Real Time|Social Network...  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  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "companies.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66368"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(companies['permalink'].unique())   # so the total number of unique companies are 66368 which is present in the permalink of companies Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                permalink      name            homepage_url  \\\n",
       "0     /organization/-fame     #fame      http://livfame.com   \n",
       "1  /organization/-qounter  :Qounter  http://www.qounter.com   \n",
       "\n",
       "                                       category_list     status country_code  \\\n",
       "0                                              Media  operating          IND   \n",
       "1  Application Platforms|Real Time|Social Network...  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  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "companies['permalink']=companies['permalink'].str.lower()  # Converting everything in permalink to lower case for uniformity\n",
    "companies.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90247"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding the number of unique companies in Rounds dataframe\n",
    "\n",
    "len(rounds.company_permalink.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                       114949\n",
       "unique                       90247\n",
       "top       /ORGANIZATION/SOLARFLARE\n",
       "freq                            10\n",
       "Name: company_permalink, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rounds.company_permalink.describe()  # One more way of identifying the number of unique companies by using describe function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                       114949\n",
       "unique                       66370\n",
       "top       /organization/solarflare\n",
       "freq                            19\n",
       "Name: company_permalink, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rounds['company_permalink']=rounds['company_permalink'].str.lower()  # Converting everything to lowercase for uniformity\n",
    "rounds.company_permalink.describe()                                  # unique companies reduced from 90247 to 66370 after uniformity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>29597</td>\n",
       "      <td>/organization/e-cãbica</td>\n",
       "      <td>/funding-round/8491f74869e4fe8ba9c378394f8fbdea</td>\n",
       "      <td>seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01-02-2015</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31863</td>\n",
       "      <td>/organization/energystone-games-çµç³æ¸¸æ</td>\n",
       "      <td>/funding-round/b89553f3d2279c5683ae93f45a21cfe0</td>\n",
       "      <td>seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>09-08-2014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45176</td>\n",
       "      <td>/organization/huizuche-com-æ ç§ÿè½¦</td>\n",
       "      <td>/funding-round/8f8a32dbeeb0f831a78702f83af78a36</td>\n",
       "      <td>seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18-09-2014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58473</td>\n",
       "      <td>/organization/magnet-tech-ç£ç³ç§æ</td>\n",
       "      <td>/funding-round/8fc91fbb32bc95e97f151dd0cb4166bf</td>\n",
       "      <td>seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16-08-2014</td>\n",
       "      <td>1625585.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101036</td>\n",
       "      <td>/organization/tipcat-interactive-æ²èÿä¿¡æ¯ç...</td>\n",
       "      <td>/funding-round/41005928a1439cb2d706a43cb661f60f</td>\n",
       "      <td>seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>06-09-2010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109969</td>\n",
       "      <td>/organization/weiche-tech-åè½¦ç§æ</td>\n",
       "      <td>/funding-round/f74e457f838b81fa0b29649740f186d8</td>\n",
       "      <td>venture</td>\n",
       "      <td>A</td>\n",
       "      <td>06-09-2015</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113839</td>\n",
       "      <td>/organization/zengame-ç¦",
       "æ¸¸ç§æ</td>\n",
       "      <td>/funding-round/6ba28fb4f3eadf5a9c6c81bc5dde6cdf</td>\n",
       "      <td>seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17-07-2010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        company_permalink  \\\n",
       "29597                             /organization/e-cãbica   \n",
       "31863        /organization/energystone-games-çµç³æ¸¸æ   \n",
       "45176                /organization/huizuche-com-æ ç§ÿè½¦   \n",
       "58473              /organization/magnet-tech-ç£ç³ç§æ   \n",
       "101036  /organization/tipcat-interactive-æ²èÿä¿¡æ¯ç...   \n",
       "109969             /organization/weiche-tech-åè½¦ç§æ   \n",
       "113839                 /organization/zengame-ç¦\n",
       "æ¸¸ç§æ   \n",
       "\n",
       "                                funding_round_permalink funding_round_type  \\\n",
       "29597   /funding-round/8491f74869e4fe8ba9c378394f8fbdea               seed   \n",
       "31863   /funding-round/b89553f3d2279c5683ae93f45a21cfe0               seed   \n",
       "45176   /funding-round/8f8a32dbeeb0f831a78702f83af78a36               seed   \n",
       "58473   /funding-round/8fc91fbb32bc95e97f151dd0cb4166bf               seed   \n",
       "101036  /funding-round/41005928a1439cb2d706a43cb661f60f               seed   \n",
       "109969  /funding-round/f74e457f838b81fa0b29649740f186d8            venture   \n",
       "113839  /funding-round/6ba28fb4f3eadf5a9c6c81bc5dde6cdf               seed   \n",
       "\n",
       "       funding_round_code   funded_at  raised_amount_usd  \n",
       "29597                 NaN  01-02-2015                NaN  \n",
       "31863                 NaN  09-08-2014                NaN  \n",
       "45176                 NaN  18-09-2014                NaN  \n",
       "58473                 NaN  16-08-2014          1625585.0  \n",
       "101036                NaN  06-09-2010                NaN  \n",
       "109969                  A  06-09-2015                NaN  \n",
       "113839                NaN  17-07-2010                NaN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We have 2 extra companies in the round dataframe compared to companies dataframe let's find out what is the real problem \n",
    "\n",
    "rounds.loc[~rounds['company_permalink'].isin(companies['permalink']),:] # All these permalinks have unusual characters, lets check whether the same in original dataframe too"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>29597</td>\n",
       "      <td>/ORGANIZATION/E-CÃBICA</td>\n",
       "      <td>/funding-round/8491f74869e4fe8ba9c378394f8fbdea</td>\n",
       "      <td>seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01-02-2015</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31863</td>\n",
       "      <td>/ORGANIZATION/ENERGYSTONE-GAMES-ÇµÇ³Æ¸¸Æ</td>\n",
       "      <td>/funding-round/b89553f3d2279c5683ae93f45a21cfe0</td>\n",
       "      <td>seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>09-08-2014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45176</td>\n",
       "      <td>/organization/huizuche-com-æ ç§ÿè½¦</td>\n",
       "      <td>/funding-round/8f8a32dbeeb0f831a78702f83af78a36</td>\n",
       "      <td>seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18-09-2014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58473</td>\n",
       "      <td>/ORGANIZATION/MAGNET-TECH-Ç£Ç³Ç§Æ</td>\n",
       "      <td>/funding-round/8fc91fbb32bc95e97f151dd0cb4166bf</td>\n",
       "      <td>seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16-08-2014</td>\n",
       "      <td>1625585.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101036</td>\n",
       "      <td>/organization/tipcat-interactive-æ²èÿä¿¡æ¯ç...</td>\n",
       "      <td>/funding-round/41005928a1439cb2d706a43cb661f60f</td>\n",
       "      <td>seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>06-09-2010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109969</td>\n",
       "      <td>/ORGANIZATION/WEICHE-TECH-ÅÈ½¦Ç§Æ</td>\n",
       "      <td>/funding-round/f74e457f838b81fa0b29649740f186d8</td>\n",
       "      <td>venture</td>\n",
       "      <td>A</td>\n",
       "      <td>06-09-2015</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113839</td>\n",
       "      <td>/ORGANIZATION/ZENGAME-Ç¦",
       "Æ¸¸Ç§Æ</td>\n",
       "      <td>/funding-round/6ba28fb4f3eadf5a9c6c81bc5dde6cdf</td>\n",
       "      <td>seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17-07-2010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        company_permalink  \\\n",
       "29597                             /ORGANIZATION/E-CÃBICA   \n",
       "31863        /ORGANIZATION/ENERGYSTONE-GAMES-ÇµÇ³Æ¸¸Æ   \n",
       "45176                /organization/huizuche-com-æ ç§ÿè½¦   \n",
       "58473              /ORGANIZATION/MAGNET-TECH-Ç£Ç³Ç§Æ   \n",
       "101036  /organization/tipcat-interactive-æ²èÿä¿¡æ¯ç...   \n",
       "109969             /ORGANIZATION/WEICHE-TECH-ÅÈ½¦Ç§Æ   \n",
       "113839                 /ORGANIZATION/ZENGAME-Ç¦\n",
       "Æ¸¸Ç§Æ   \n",
       "\n",
       "                                funding_round_permalink funding_round_type  \\\n",
       "29597   /funding-round/8491f74869e4fe8ba9c378394f8fbdea               seed   \n",
       "31863   /funding-round/b89553f3d2279c5683ae93f45a21cfe0               seed   \n",
       "45176   /funding-round/8f8a32dbeeb0f831a78702f83af78a36               seed   \n",
       "58473   /funding-round/8fc91fbb32bc95e97f151dd0cb4166bf               seed   \n",
       "101036  /funding-round/41005928a1439cb2d706a43cb661f60f               seed   \n",
       "109969  /funding-round/f74e457f838b81fa0b29649740f186d8            venture   \n",
       "113839  /funding-round/6ba28fb4f3eadf5a9c6c81bc5dde6cdf               seed   \n",
       "\n",
       "       funding_round_code   funded_at  raised_amount_usd  \n",
       "29597                 NaN  01-02-2015                NaN  \n",
       "31863                 NaN  09-08-2014                NaN  \n",
       "45176                 NaN  18-09-2014                NaN  \n",
       "58473                 NaN  16-08-2014          1625585.0  \n",
       "101036                NaN  06-09-2010                NaN  \n",
       "109969                  A  06-09-2015                NaN  \n",
       "113839                NaN  17-07-2010                NaN  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rounds_original=pd.read_csv(\"C:\\\\Users\\\\mohan\\\\Desktop\\\\ML\\\\Assignments\\\\Week_5- Assignment-4 (11-June-2019)\\\\Assignment-4_ Teclov-data-code-project\\\\Assignment-4\\\\rounds2.csv\",encoding = 'ISO-8859-1')\n",
    "rounds_original.loc[[29597,31863,45176,58473,101036,109969,113839],:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# It seems to appear in original dataframe too but sometimes while importing these files python might introduce these characters especially since encoding is performed.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>77</td>\n",
       "      <td>/organization/10north</td>\n",
       "      <td>/funding-round/b41ff7de932f8b6e5bbeed3966c0ed6a</td>\n",
       "      <td>equity_crowdfunding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12-08-2014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>729</td>\n",
       "      <td>/organization/51wofang-</td>\n",
       "      <td>/funding-round/346b9180d276a74e0fbb2825e66c6f5b</td>\n",
       "      <td>venture</td>\n",
       "      <td>A</td>\n",
       "      <td>06-07-2015</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2670</td>\n",
       "      <td>/organization/adslinked</td>\n",
       "      <td>/funding-round/449ae54bb63c768c232955ca6911dee4</td>\n",
       "      <td>seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29-09-2014</td>\n",
       "      <td>100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3166</td>\n",
       "      <td>/organization/aesthetic-everything-social-network</td>\n",
       "      <td>/funding-round/62593455f1a69857ed05d5734cc04132</td>\n",
       "      <td>equity_crowdfunding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12-10-2014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3291</td>\n",
       "      <td>/organization/affluent-attach-club-2</td>\n",
       "      <td>/funding-round/626678bdf1654bc4df9b1b34647a4df1</td>\n",
       "      <td>seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15-10-2014</td>\n",
       "      <td>100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>110545</td>\n",
       "      <td>/organization/whodats-spaces</td>\n",
       "      <td>/funding-round/d5d6db3d1e6c54d71a63b3aa0c9278e6</td>\n",
       "      <td>seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28-10-2014</td>\n",
       "      <td>30000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113839</td>\n",
       "      <td>/organization/zengame-</td>\n",
       "      <td>/funding-round/6ba28fb4f3eadf5a9c6c81bc5dde6cdf</td>\n",
       "      <td>seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17-07-2010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>114946</td>\n",
       "      <td>/organization/eron</td>\n",
       "      <td>/funding-round/59f4dce44723b794f21ded3daed6e4fe</td>\n",
       "      <td>venture</td>\n",
       "      <td>A</td>\n",
       "      <td>01-08-2014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>114947</td>\n",
       "      <td>/organization/asys-2</td>\n",
       "      <td>/funding-round/35f09d0794651719b02bbfd859ba9ff5</td>\n",
       "      <td>seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01-01-2015</td>\n",
       "      <td>18192.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>114948</td>\n",
       "      <td>/organization/novatiff-reklam-ve-tantm-hizmetl...</td>\n",
       "      <td>/funding-round/af942869878d2cd788ef5189b435ebc4</td>\n",
       "      <td>grant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01-10-2013</td>\n",
       "      <td>14851.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        company_permalink  \\\n",
       "77                                  /organization/10north   \n",
       "729                               /organization/51wofang-   \n",
       "2670                              /organization/adslinked   \n",
       "3166    /organization/aesthetic-everything-social-network   \n",
       "3291                 /organization/affluent-attach-club-2   \n",
       "...                                                   ...   \n",
       "110545                       /organization/whodats-spaces   \n",
       "113839                             /organization/zengame-   \n",
       "114946                                 /organization/eron   \n",
       "114947                               /organization/asys-2   \n",
       "114948  /organization/novatiff-reklam-ve-tantm-hizmetl...   \n",
       "\n",
       "                                funding_round_permalink   funding_round_type  \\\n",
       "77      /funding-round/b41ff7de932f8b6e5bbeed3966c0ed6a  equity_crowdfunding   \n",
       "729     /funding-round/346b9180d276a74e0fbb2825e66c6f5b              venture   \n",
       "2670    /funding-round/449ae54bb63c768c232955ca6911dee4                 seed   \n",
       "3166    /funding-round/62593455f1a69857ed05d5734cc04132  equity_crowdfunding   \n",
       "3291    /funding-round/626678bdf1654bc4df9b1b34647a4df1                 seed   \n",
       "...                                                 ...                  ...   \n",
       "110545  /funding-round/d5d6db3d1e6c54d71a63b3aa0c9278e6                 seed   \n",
       "113839  /funding-round/6ba28fb4f3eadf5a9c6c81bc5dde6cdf                 seed   \n",
       "114946  /funding-round/59f4dce44723b794f21ded3daed6e4fe              venture   \n",
       "114947  /funding-round/35f09d0794651719b02bbfd859ba9ff5                 seed   \n",
       "114948  /funding-round/af942869878d2cd788ef5189b435ebc4                grant   \n",
       "\n",
       "       funding_round_code   funded_at  raised_amount_usd  \n",
       "77                    NaN  12-08-2014                NaN  \n",
       "729                     A  06-07-2015          5000000.0  \n",
       "2670                  NaN  29-09-2014           100000.0  \n",
       "3166                  NaN  12-10-2014                NaN  \n",
       "3291                  NaN  15-10-2014           100000.0  \n",
       "...                   ...         ...                ...  \n",
       "110545                NaN  28-10-2014            30000.0  \n",
       "113839                NaN  17-07-2010                NaN  \n",
       "114946                  A  01-08-2014                NaN  \n",
       "114947                NaN  01-01-2015            18192.0  \n",
       "114948                NaN  01-10-2013            14851.0  \n",
       "\n",
       "[74 rows x 6 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing the encoding in the rounds dataframe\n",
    "rounds['company_permalink']=rounds['company_permalink'].str.encode('utf-8').str.decode('ascii','ignore')\n",
    "rounds.loc[~rounds['company_permalink'].isin(companies['permalink']),:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66368"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(rounds.company_permalink.unique())   # Now the unique list is 66368 same as companies dataframe lets have a look if the same problem exists in companies dataframe too"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>43</td>\n",
       "      <td>/organization/10â°north</td>\n",
       "      <td>10Â°North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fashion</td>\n",
       "      <td>operating</td>\n",
       "      <td>CAN</td>\n",
       "      <td>ON</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>Mississauga</td>\n",
       "      <td>05-01-2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>426</td>\n",
       "      <td>/organization/51wofang-æ å¿§ææ¿</td>\n",
       "      <td>51wofang æ å¿§ææ¿</td>\n",
       "      <td>http://www.51wofang.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>closed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01-01-2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1506</td>\n",
       "      <td>/organization/adslinkedâ¢</td>\n",
       "      <td>AdsLinkedâ¢</td>\n",
       "      <td>http://www.adslinked.com</td>\n",
       "      <td>Advertising|Internet</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>1775</td>\n",
       "      <td>/organization/aesthetic-everythingâ®-social-ne...</td>\n",
       "      <td>Aesthetic EverythingÂ® Social Network</td>\n",
       "      <td>http://aestheticeverything.com/</td>\n",
       "      <td>Public Relations</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>CA</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Marina Del Rey</td>\n",
       "      <td>15-06-2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1834</td>\n",
       "      <td>/organization/affluent-attachã©-club-2</td>\n",
       "      <td>Affluent AttachÃ© Club</td>\n",
       "      <td>http://www.affluentattache.com/</td>\n",
       "      <td>Hospitality</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>CA</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Beverly Hills</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63833</td>\n",
       "      <td>/organization/whodatâs-spaces</td>\n",
       "      <td>Whodatâs Spaces</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apps</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>65778</td>\n",
       "      <td>/organization/zengame-ç¦",
       "æ¸¸ç§æ</td>\n",
       "      <td>ZenGame ç¦",
       "æ¸¸ç§æ</td>\n",
       "      <td>http://www.zen-game.com</td>\n",
       "      <td>Internet|Mobile Games|Online Gaming</td>\n",
       "      <td>closed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17-07-2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66365</td>\n",
       "      <td>/organization/ãeron</td>\n",
       "      <td>ÃERON</td>\n",
       "      <td>http://www.aeron.hu/</td>\n",
       "      <td>NaN</td>\n",
       "      <td>operating</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01-01-2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66366</td>\n",
       "      <td>/organization/ãasys-2</td>\n",
       "      <td>Ãasys</td>\n",
       "      <td>http://www.oasys.io/</td>\n",
       "      <td>Consumer Electronics|Internet of Things|Teleco...</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>CA</td>\n",
       "      <td>SF Bay Area</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>01-01-2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66367</td>\n",
       "      <td>/organization/ä°novatiff-reklam-ve-tanä±tä±m-h...</td>\n",
       "      <td>Ä°novatiff Reklam ve TanÄ±tÄ±m Hizmetleri Tic</td>\n",
       "      <td>http://inovatiff.com</td>\n",
       "      <td>Consumer Goods|E-Commerce|Internet</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               permalink  \\\n",
       "43                               /organization/10â°north   \n",
       "426                  /organization/51wofang-æ å¿§ææ¿   \n",
       "1506                          /organization/adslinkedâ¢   \n",
       "1775   /organization/aesthetic-everythingâ®-social-ne...   \n",
       "1834              /organization/affluent-attachã©-club-2   \n",
       "...                                                  ...   \n",
       "63833                    /organization/whodatâs-spaces   \n",
       "65778                 /organization/zengame-ç¦\n",
       "æ¸¸ç§æ   \n",
       "66365                               /organization/ãeron   \n",
       "66366                             /organization/ãasys-2   \n",
       "66367  /organization/ä°novatiff-reklam-ve-tanä±tä±m-h...   \n",
       "\n",
       "                                                name  \\\n",
       "43                                         10Â°North   \n",
       "426                            51wofang æ å¿§ææ¿   \n",
       "1506                                    AdsLinkedâ¢   \n",
       "1775           Aesthetic EverythingÂ® Social Network   \n",
       "1834                          Affluent AttachÃ© Club   \n",
       "...                                              ...   \n",
       "63833                              Whodatâs Spaces   \n",
       "65778                           ZenGame ç¦\n",
       "æ¸¸ç§æ   \n",
       "66365                                         ÃERON   \n",
       "66366                                         Ãasys   \n",
       "66367  Ä°novatiff Reklam ve TanÄ±tÄ±m Hizmetleri Tic   \n",
       "\n",
       "                          homepage_url  \\\n",
       "43                                 NaN   \n",
       "426            http://www.51wofang.com   \n",
       "1506          http://www.adslinked.com   \n",
       "1775   http://aestheticeverything.com/   \n",
       "1834   http://www.affluentattache.com/   \n",
       "...                                ...   \n",
       "63833                              NaN   \n",
       "65778          http://www.zen-game.com   \n",
       "66365             http://www.aeron.hu/   \n",
       "66366             http://www.oasys.io/   \n",
       "66367             http://inovatiff.com   \n",
       "\n",
       "                                           category_list     status  \\\n",
       "43                                               Fashion  operating   \n",
       "426                                                  NaN     closed   \n",
       "1506                                Advertising|Internet  operating   \n",
       "1775                                    Public Relations  operating   \n",
       "1834                                         Hospitality  operating   \n",
       "...                                                  ...        ...   \n",
       "63833                                               Apps  operating   \n",
       "65778                Internet|Mobile Games|Online Gaming     closed   \n",
       "66365                                                NaN  operating   \n",
       "66366  Consumer Electronics|Internet of Things|Teleco...  operating   \n",
       "66367                 Consumer Goods|E-Commerce|Internet  operating   \n",
       "\n",
       "      country_code state_code       region            city  founded_at  \n",
       "43             CAN         ON      Toronto     Mississauga  05-01-2013  \n",
       "426            NaN        NaN          NaN             NaN  01-01-2014  \n",
       "1506           NaN        NaN          NaN             NaN         NaN  \n",
       "1775           USA         CA  Los Angeles  Marina Del Rey  15-06-2009  \n",
       "1834           USA         CA  Los Angeles   Beverly Hills         NaN  \n",
       "...            ...        ...          ...             ...         ...  \n",
       "63833          NaN        NaN          NaN             NaN         NaN  \n",
       "65778          NaN        NaN          NaN             NaN  17-07-2010  \n",
       "66365          NaN        NaN          NaN             NaN  01-01-2011  \n",
       "66366          USA         CA  SF Bay Area   San Francisco  01-01-2014  \n",
       "66367          NaN        NaN          NaN             NaN         NaN  \n",
       "\n",
       "[68 rows x 10 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# companies present in companies df but not in rounds df\n",
    "\n",
    "companies.loc[~companies['permalink'].isin(rounds['company_permalink']),:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing encoding from companies df\n",
    "\n",
    "companies['permalink']=companies['permalink'].str.encode('utf-8').str.decode('ascii','ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [permalink, name, homepage_url, category_list, status, country_code, state_code, region, city, founded_at]\n",
       "Index: []"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "companies.loc[~companies['permalink'].isin(rounds['company_permalink']),:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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_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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [company_permalink, funding_round_permalink, funding_round_type, funding_round_code, funded_at, raised_amount_usd]\n",
       "Index: []"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rounds.loc[~rounds['company_permalink'].isin(companies['permalink']),:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The encoding issue has been resolved now lets export these new csv files\n",
    "\n",
    "#Writing rounds file\n",
    "rounds.to_csv(\"rounds_clean.csv\",sep=',',index=False)\n",
    "\n",
    "#Writing companies file\n",
    "companies.to_csv(\"companies_clean.csv\",sep='\\t',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the clean csv files\n",
    "\n",
    "rounds=pd.read_csv(\"C:\\\\Users\\\\mohan\\\\Desktop\\\\ML\\\\Assignments\\\\Week_5- Assignment-4 (11-June-2019)\\\\Assignment-4_ Teclov-data-code-project\\\\Assignment-4\\\\rounds_clean.csv\",encoding=\"ISO-8859-1\")\n",
    "companies=pd.read_csv(\"C:\\\\Users\\\\mohan\\\\Desktop\\\\ML\\\\Assignments\\\\Week_5- Assignment-4 (11-June-2019)\\\\Assignment-4_ Teclov-data-code-project\\\\Assignment-4\\\\companies_clean.csv\",sep=\"\\t\",encoding=\"ISO-8859-1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "66368\n",
      "66368\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "# Confirming the number of companies and list are same in both the dataframes\n",
    "\n",
    "print(len(rounds['company_permalink'].unique()))\n",
    "print(len(companies['permalink'].unique()))\n",
    "print(len(companies.loc[~companies['permalink'].isin(rounds['company_permalink']),:]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "permalink            0\n",
      "name                 1\n",
      "homepage_url      5058\n",
      "category_list     3148\n",
      "status               0\n",
      "country_code      6958\n",
      "state_code        8547\n",
      "region            8030\n",
      "city              8028\n",
      "founded_at       15221\n",
      "dtype: int64 \n",
      "\n",
      "\n",
      "\n",
      "company_permalink              0\n",
      "funding_round_permalink        0\n",
      "funding_round_type             0\n",
      "funding_round_code         83809\n",
      "funded_at                      0\n",
      "raised_amount_usd          19990\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Missing value treatment\n",
    "\n",
    "#Missing values in companies DF\n",
    "print(companies.isnull().sum(),\"\\n\\n\\n\")\n",
    "\n",
    "#Missing values in rounds DF\n",
    "print(rounds.isnull().sum())"
   ]
  },
  {
   "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></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",
       "      <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>#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",
       "      <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>: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",
       "      <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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                permalink      name            homepage_url  \\\n",
       "0     /organization/-fame     #fame      http://livfame.com   \n",
       "1  /organization/-qounter  :Qounter  http://www.qounter.com   \n",
       "\n",
       "                                       category_list     status country_code  \\\n",
       "0                                              Media  operating          IND   \n",
       "1  Application Platforms|Real Time|Social Network...  operating          USA   \n",
       "\n",
       "  state_code      region           city  founded_at       company_permalink  \\\n",
       "0         16      Mumbai         Mumbai         NaN     /organization/-fame   \n",
       "1         DE  DE - Other  Delaware City  04-09-2014  /organization/-qounter   \n",
       "\n",
       "                           funding_round_permalink funding_round_type  \\\n",
       "0  /funding-round/9a01d05418af9f794eebff7ace91f638            venture   \n",
       "1  /funding-round/22dacff496eb7acb2b901dec1dfe5633            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  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# There is no missing values in permalink and company_permalink so let us merge these dataframes\n",
    "\n",
    "master=pd.merge(companies,rounds,how=\"inner\",left_on=\"permalink\",right_on=\"company_permalink\")\n",
    "master.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The columns permalink and company_permalink are both the same removing one of them\n",
    "\n",
    "master.drop(['company_permalink'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['permalink', 'name', 'homepage_url', 'category_list', 'status',\n",
       "       'country_code', 'state_code', 'region', 'city', 'founded_at',\n",
       "       'funding_round_permalink', 'funding_round_type', 'funding_round_code',\n",
       "       'funded_at', 'raised_amount_usd'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "permalink                      0\n",
       "name                           1\n",
       "homepage_url                6134\n",
       "category_list               3410\n",
       "status                         0\n",
       "country_code                8678\n",
       "state_code                 10946\n",
       "region                     10167\n",
       "city                       10164\n",
       "founded_at                 20521\n",
       "funding_round_permalink        0\n",
       "funding_round_type             0\n",
       "funding_round_code         83809\n",
       "funded_at                      0\n",
       "raised_amount_usd          19990\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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\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",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying the number of missing values in percentage comparing to total number of values\n",
    "round(100*(master.isnull().sum())/len(master.index),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>permalink</th>\n",
       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>status</th>\n",
       "      <th>country_code</th>\n",
       "      <th>funding_round_permalink</th>\n",
       "      <th>funding_round_type</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>#fame</td>\n",
       "      <td>Media</td>\n",
       "      <td>operating</td>\n",
       "      <td>IND</td>\n",
       "      <td>/funding-round/9a01d05418af9f794eebff7ace91f638</td>\n",
       "      <td>venture</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>:Qounter</td>\n",
       "      <td>Application Platforms|Real Time|Social Network...</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>/funding-round/22dacff496eb7acb2b901dec1dfe5633</td>\n",
       "      <td>venture</td>\n",
       "      <td>14-10-2014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                permalink      name  \\\n",
       "0     /organization/-fame     #fame   \n",
       "1  /organization/-qounter  :Qounter   \n",
       "\n",
       "                                       category_list     status country_code  \\\n",
       "0                                              Media  operating          IND   \n",
       "1  Application Platforms|Real Time|Social Network...  operating          USA   \n",
       "\n",
       "                           funding_round_permalink funding_round_type  \\\n",
       "0  /funding-round/9a01d05418af9f794eebff7ace91f638            venture   \n",
       "1  /funding-round/22dacff496eb7acb2b901dec1dfe5633            venture   \n",
       "\n",
       "    funded_at  raised_amount_usd  \n",
       "0  05-01-2015         10000000.0  \n",
       "1  14-10-2014                NaN  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping all the not necesary columns from the master dataframe\n",
    "\n",
    "master=master.drop(['homepage_url','state_code','region','city','founded_at','funding_round_code'],axis=1)\n",
    "master.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "permalink                   0.00\n",
       "name                        0.00\n",
       "category_list               2.97\n",
       "status                      0.00\n",
       "country_code                7.55\n",
       "funding_round_permalink     0.00\n",
       "funding_round_type          0.00\n",
       "funded_at                   0.00\n",
       "raised_amount_usd          17.39\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying the number of missing values in percentage comparing to total number of values\n",
    "round(100*(master.isnull().sum())/len(master.index),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# raised_amount_usd is a important columns, let us analyse whether we can impute the column by any means or do we need to drop the rows having null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    9.495900e+04\n",
       "mean     1.042687e+07\n",
       "std      1.148212e+08\n",
       "min      0.000000e+00\n",
       "25%      3.225000e+05\n",
       "50%      1.680511e+06\n",
       "75%      7.000000e+06\n",
       "max      2.127194e+10\n",
       "Name: raised_amount_usd, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for imputing method\n",
    "\n",
    "master['raised_amount_usd'].describe()  # There is a outlier existing so imputing the NaNs with mean median doesnt make sense lets just drop the rows with NaNs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "permalink                  0.00\n",
       "name                       0.00\n",
       "category_list              1.10\n",
       "status                     0.00\n",
       "country_code               6.16\n",
       "funding_round_permalink    0.00\n",
       "funding_round_type         0.00\n",
       "funded_at                  0.00\n",
       "raised_amount_usd          0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retaining only those rows which doesnt have NaN value in the raised_amount_usd column\n",
    "master=master[~np.isnan(master['raised_amount_usd'])]\n",
    "\n",
    "# Displaying the number of missing values in percentage comparing to total number of values\n",
    "\n",
    "round(100*(master.isnull().sum())/len(master.index),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USA    62049\n",
       "GBR     5019\n",
       "CAN     2616\n",
       "CHN     1927\n",
       "IND     1649\n",
       "       ...  \n",
       "HND        1\n",
       "GRD        1\n",
       "GGY        1\n",
       "DZA        1\n",
       "KNA        1\n",
       "Name: country_code, Length: 134, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let us look into the country_code column since it is a category lets convert into type 'category'\n",
    "\n",
    "country_code=master['country_code'].astype('category')\n",
    "\n",
    "#displaying the frequency of each category\n",
    "\n",
    "country_code.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USA    65.34294\n",
       "GBR     5.28544\n",
       "CAN     2.75487\n",
       "CHN     2.02930\n",
       "IND     1.73654\n",
       "         ...   \n",
       "HND     0.00105\n",
       "SOM     0.00105\n",
       "MKD     0.00105\n",
       "GGY     0.00105\n",
       "PRY     0.00105\n",
       "Name: country_code, Length: 134, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(100*(master['country_code'].value_counts())/len(master.index),5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The percentage of missing values of country_code is just 6% of the whole data ,  we can either drop these values or impute with USA as this is\n",
    "# frequently repeated. Let's drop the values as we will be left with sufficient dataset even after dropping these values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "permalink                  0.00\n",
       "name                       0.00\n",
       "category_list              0.65\n",
       "status                     0.00\n",
       "country_code               0.00\n",
       "funding_round_permalink    0.00\n",
       "funding_round_type         0.00\n",
       "funded_at                  0.00\n",
       "raised_amount_usd          0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For categorical values use pd.isnull as np.isnan doesnt work with objects\n",
    "\n",
    "master=master[~pd.isnull(master['country_code'])]\n",
    "\n",
    "# Displaying the number of missing values in percentage comparing to total number of values\n",
    "\n",
    "round(100*(master.isnull().sum())/len(master.index),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "permalink                  0.0\n",
       "name                       0.0\n",
       "category_list              0.0\n",
       "status                     0.0\n",
       "country_code               0.0\n",
       "funding_round_permalink    0.0\n",
       "funding_round_type         0.0\n",
       "funded_at                  0.0\n",
       "raised_amount_usd          0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master=master[~pd.isnull(master['category_list'])]\n",
    "\n",
    "# Displaying the number of missing values in percentage comparing to total number of values\n",
    "\n",
    "round(100*(master.isnull().sum())/len(master.index),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Writing the clean dataframe \n",
    "\n",
    "master.to_csv(\"master_df.csv\",sep=',',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 88529 entries, 0 to 114947\n",
      "Data columns (total 9 columns):\n",
      "permalink                  88529 non-null object\n",
      "name                       88528 non-null object\n",
      "category_list              88529 non-null object\n",
      "status                     88529 non-null object\n",
      "country_code               88529 non-null object\n",
      "funding_round_permalink    88529 non-null object\n",
      "funding_round_type         88529 non-null object\n",
      "funded_at                  88529 non-null object\n",
      "raised_amount_usd          88529 non-null float64\n",
      "dtypes: float64(1), object(8)\n",
      "memory usage: 6.8+ MB\n"
     ]
    }
   ],
   "source": [
    "master.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77.01589400516751"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100*(len(master.index)/len(rounds.index))   # Approximately we have retained 77% of the whole data which is clean set of data for further analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sector Analysis"
   ]
  },
  {
   "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></th>\n",
       "      <th>permalink</th>\n",
       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>status</th>\n",
       "      <th>country_code</th>\n",
       "      <th>funding_round_permalink</th>\n",
       "      <th>funding_round_type</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>#fame</td>\n",
       "      <td>Media</td>\n",
       "      <td>operating</td>\n",
       "      <td>IND</td>\n",
       "      <td>/funding-round/9a01d05418af9f794eebff7ace91f638</td>\n",
       "      <td>venture</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>:Qounter</td>\n",
       "      <td>Application Platforms|Real Time|Social Network...</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>/funding-round/b44fbb94153f6cdef13083530bb48030</td>\n",
       "      <td>seed</td>\n",
       "      <td>01-03-2014</td>\n",
       "      <td>700000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/organization/0-6-com</td>\n",
       "      <td>0-6.com</td>\n",
       "      <td>Curated Web</td>\n",
       "      <td>operating</td>\n",
       "      <td>CHN</td>\n",
       "      <td>/funding-round/5727accaeaa57461bd22a9bdd945382d</td>\n",
       "      <td>venture</td>\n",
       "      <td>19-03-2008</td>\n",
       "      <td>2000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>/organization/01games-technology</td>\n",
       "      <td>01Games Technology</td>\n",
       "      <td>Games</td>\n",
       "      <td>operating</td>\n",
       "      <td>HKG</td>\n",
       "      <td>/funding-round/7d53696f2b4f607a2f2a8cbb83d01839</td>\n",
       "      <td>undisclosed</td>\n",
       "      <td>01-07-2014</td>\n",
       "      <td>41250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>/organization/0ndine-biomedical-inc</td>\n",
       "      <td>Ondine Biomedical Inc.</td>\n",
       "      <td>Biotechnology</td>\n",
       "      <td>operating</td>\n",
       "      <td>CAN</td>\n",
       "      <td>/funding-round/2b9d3ac293d5cdccbecff5c8cb0f327d</td>\n",
       "      <td>seed</td>\n",
       "      <td>11-09-2009</td>\n",
       "      <td>43360.0</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/0-6-com                 0-6.com   \n",
       "3     /organization/01games-technology      01Games Technology   \n",
       "4  /organization/0ndine-biomedical-inc  Ondine Biomedical Inc.   \n",
       "\n",
       "                                       category_list     status country_code  \\\n",
       "0                                              Media  operating          IND   \n",
       "1  Application Platforms|Real Time|Social Network...  operating          USA   \n",
       "2                                        Curated Web  operating          CHN   \n",
       "3                                              Games  operating          HKG   \n",
       "4                                      Biotechnology  operating          CAN   \n",
       "\n",
       "                           funding_round_permalink funding_round_type  \\\n",
       "0  /funding-round/9a01d05418af9f794eebff7ace91f638            venture   \n",
       "1  /funding-round/b44fbb94153f6cdef13083530bb48030               seed   \n",
       "2  /funding-round/5727accaeaa57461bd22a9bdd945382d            venture   \n",
       "3  /funding-round/7d53696f2b4f607a2f2a8cbb83d01839        undisclosed   \n",
       "4  /funding-round/2b9d3ac293d5cdccbecff5c8cb0f327d               seed   \n",
       "\n",
       "    funded_at  raised_amount_usd  \n",
       "0  05-01-2015         10000000.0  \n",
       "1  01-03-2014           700000.0  \n",
       "2  19-03-2008          2000000.0  \n",
       "3  01-07-2014            41250.0  \n",
       "4  11-09-2009            43360.0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"C:\\\\Users\\\\mohan\\\\Desktop\\\\ML\\\\Assignments\\\\Week_5- Assignment-4 (11-June-2019)\\\\Assignment-4_ Teclov-data-code-project\\\\Assignment-4\\\\master_df.csv\",sep=\",\",encoding=\"ISO-8859-1\")\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['funding_round_type'].value_counts()  # Since we have so many funding types narrowing it down to four categories: venture,seed,angel,private_equity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df[(df.funding_round_type==\"venture\") |\n",
    "      (df.funding_round_type==\"seed\") |\n",
    "      (df.funding_round_type==\"angel\") |\n",
    "      (df.funding_round_type==\"private_equity\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['funding_round_type'].value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(df['raised_amount_usd'])\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7.512400e+04\n",
       "mean     9.519475e+06\n",
       "std      7.792778e+07\n",
       "min      0.000000e+00\n",
       "25%      4.705852e+05\n",
       "50%      2.000000e+06\n",
       "75%      8.000000e+06\n",
       "max      1.760000e+10\n",
       "Name: raised_amount_usd, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['raised_amount_usd'].describe()  # The mean is around 9.5 million USD and the median is around 2 million USD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['funding_round_type'],y=df['raised_amount_usd'])\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>funding_round_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>angel</td>\n",
       "      <td>414906.0</td>\n",
       "      <td>9.715739e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>private_equity</td>\n",
       "      <td>20000000.0</td>\n",
       "      <td>7.393849e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>seed</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>7.477937e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>venture</td>\n",
       "      <td>5000000.0</td>\n",
       "      <td>1.172422e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              median              mean\n",
       "                   raised_amount_usd raised_amount_usd\n",
       "funding_round_type                                    \n",
       "angel                       414906.0      9.715739e+05\n",
       "private_equity            20000000.0      7.393849e+07\n",
       "seed                        300000.0      7.477937e+05\n",
       "venture                    5000000.0      1.172422e+07"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(values='raised_amount_usd',index='funding_round_type',aggfunc=[np.median,np.mean])\n",
    "\n",
    "#Since the presence of outliers mean will not be suitable value to continue further , it is advisable to consider the median values for all the fundings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "funding_round_type\n",
       "private_equity    20000000.0\n",
       "venture            5000000.0\n",
       "angel               414906.0\n",
       "seed                300000.0\n",
       "Name: raised_amount_usd, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# comparing the median across different funding types\n",
    "\n",
    "df.groupby('funding_round_type')['raised_amount_usd'].median().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since in the problem statement the Teclov company investment range is 5 to 15million USD , only \"venture\" funding type can be choose as investment type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Country Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 75124 entries, 0 to 88528\n",
      "Data columns (total 9 columns):\n",
      "permalink                  75124 non-null object\n",
      "name                       75123 non-null object\n",
      "category_list              75124 non-null object\n",
      "status                     75124 non-null object\n",
      "country_code               75124 non-null object\n",
      "funding_round_permalink    75124 non-null object\n",
      "funding_round_type         75124 non-null object\n",
      "funded_at                  75124 non-null object\n",
      "raised_amount_usd          75124 non-null float64\n",
      "dtypes: float64(1), object(8)\n",
      "memory usage: 8.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['funding_round_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USA    51585\n",
       "GBR     4049\n",
       "CAN     2193\n",
       "CHN     1810\n",
       "IND     1556\n",
       "       ...  \n",
       "MKD        1\n",
       "SEN        1\n",
       "PSE        1\n",
       "BRB        1\n",
       "PRY        1\n",
       "Name: country_code, Length: 131, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['country_code'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 75124 entries, 0 to 88528\n",
      "Data columns (total 9 columns):\n",
      "permalink                  75124 non-null object\n",
      "name                       75123 non-null object\n",
      "category_list              75124 non-null object\n",
      "status                     75124 non-null object\n",
      "country_code               75124 non-null object\n",
      "funding_round_permalink    75124 non-null object\n",
      "funding_round_type         75124 non-null object\n",
      "funded_at                  75124 non-null object\n",
      "raised_amount_usd          75124 non-null float64\n",
      "dtypes: float64(1), object(8)\n",
      "memory usage: 8.2+ MB\n",
      "None \n",
      "\n",
      "\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 47809 entries, 0 to 88523\n",
      "Data columns (total 9 columns):\n",
      "permalink                  47809 non-null object\n",
      "name                       47809 non-null object\n",
      "category_list              47809 non-null object\n",
      "status                     47809 non-null object\n",
      "country_code               47809 non-null object\n",
      "funding_round_permalink    47809 non-null object\n",
      "funding_round_type         47809 non-null object\n",
      "funded_at                  47809 non-null object\n",
      "raised_amount_usd          47809 non-null float64\n",
      "dtypes: float64(1), object(8)\n",
      "memory usage: 3.6+ MB\n",
      "None \n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Since we have figured it out , our investment can be made on venture sector removing all the other funding sectors\n",
    "\n",
    "print(df.info(),\"\\n\\n\\n\")\n",
    "df=df[df.funding_round_type=='venture']\n",
    "print(df.info(),\"\\n\\n\\n\")"
   ]
  },
  {
   "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>permalink</th>\n",
       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>status</th>\n",
       "      <th>country_code</th>\n",
       "      <th>funding_round_permalink</th>\n",
       "      <th>funding_round_type</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>#fame</td>\n",
       "      <td>Media</td>\n",
       "      <td>operating</td>\n",
       "      <td>IND</td>\n",
       "      <td>/funding-round/9a01d05418af9f794eebff7ace91f638</td>\n",
       "      <td>venture</td>\n",
       "      <td>05-01-2015</td>\n",
       "      <td>10000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/organization/0-6-com</td>\n",
       "      <td>0-6.com</td>\n",
       "      <td>Curated Web</td>\n",
       "      <td>operating</td>\n",
       "      <td>CHN</td>\n",
       "      <td>/funding-round/5727accaeaa57461bd22a9bdd945382d</td>\n",
       "      <td>venture</td>\n",
       "      <td>19-03-2008</td>\n",
       "      <td>2000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>/organization/0ndine-biomedical-inc</td>\n",
       "      <td>Ondine Biomedical Inc.</td>\n",
       "      <td>Biotechnology</td>\n",
       "      <td>operating</td>\n",
       "      <td>CAN</td>\n",
       "      <td>/funding-round/954b9499724b946ad8c396a57a5f3b72</td>\n",
       "      <td>venture</td>\n",
       "      <td>21-12-2009</td>\n",
       "      <td>719491.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>H2O.ai</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>/funding-round/3bb2ee4a2d89251a10aaa735b1180e44</td>\n",
       "      <td>venture</td>\n",
       "      <td>09-11-2015</td>\n",
       "      <td>20000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>H2O.ai</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>/funding-round/ae2a174c06517c2394aed45006322a7e</td>\n",
       "      <td>venture</td>\n",
       "      <td>03-01-2013</td>\n",
       "      <td>1700000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>H2O.ai</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>/funding-round/e1cfcbe1bdf4c70277c5f29a3482f24e</td>\n",
       "      <td>venture</td>\n",
       "      <td>19-07-2014</td>\n",
       "      <td>8900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>/organization/1-mainstream</td>\n",
       "      <td>1 Mainstream</td>\n",
       "      <td>Apps|Cable|Distribution|Software</td>\n",
       "      <td>acquired</td>\n",
       "      <td>USA</td>\n",
       "      <td>/funding-round/b952cbaf401f310927430c97b68162ea</td>\n",
       "      <td>venture</td>\n",
       "      <td>17-03-2015</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              permalink                    name  \\\n",
       "0                   /organization/-fame                   #fame   \n",
       "2                 /organization/0-6-com                 0-6.com   \n",
       "5   /organization/0ndine-biomedical-inc  Ondine Biomedical Inc.   \n",
       "7                  /organization/0xdata                  H2O.ai   \n",
       "8                  /organization/0xdata                  H2O.ai   \n",
       "9                  /organization/0xdata                  H2O.ai   \n",
       "15           /organization/1-mainstream            1 Mainstream   \n",
       "\n",
       "                       category_list     status country_code  \\\n",
       "0                              Media  operating          IND   \n",
       "2                        Curated Web  operating          CHN   \n",
       "5                      Biotechnology  operating          CAN   \n",
       "7                          Analytics  operating          USA   \n",
       "8                          Analytics  operating          USA   \n",
       "9                          Analytics  operating          USA   \n",
       "15  Apps|Cable|Distribution|Software   acquired          USA   \n",
       "\n",
       "                            funding_round_permalink funding_round_type  \\\n",
       "0   /funding-round/9a01d05418af9f794eebff7ace91f638            venture   \n",
       "2   /funding-round/5727accaeaa57461bd22a9bdd945382d            venture   \n",
       "5   /funding-round/954b9499724b946ad8c396a57a5f3b72            venture   \n",
       "7   /funding-round/3bb2ee4a2d89251a10aaa735b1180e44            venture   \n",
       "8   /funding-round/ae2a174c06517c2394aed45006322a7e            venture   \n",
       "9   /funding-round/e1cfcbe1bdf4c70277c5f29a3482f24e            venture   \n",
       "15  /funding-round/b952cbaf401f310927430c97b68162ea            venture   \n",
       "\n",
       "     funded_at  raised_amount_usd  \n",
       "0   05-01-2015         10000000.0  \n",
       "2   19-03-2008          2000000.0  \n",
       "5   21-12-2009           719491.0  \n",
       "7   09-11-2015         20000000.0  \n",
       "8   03-01-2013          1700000.0  \n",
       "9   19-07-2014          8900000.0  \n",
       "15  17-03-2015          5000000.0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We are interested only in the top 9 countries which has the highest amount of funding\n",
    "\n",
    "dfnew=df.groupby('country_code')['raised_amount_usd'].sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country_code\n",
       "USA    4.200680e+11\n",
       "CHN    3.933892e+10\n",
       "GBR    2.007281e+10\n",
       "IND    1.426151e+10\n",
       "CAN    9.482218e+09\n",
       "FRA    7.226851e+09\n",
       "ISR    6.854350e+09\n",
       "DEU    6.306922e+09\n",
       "JPN    3.167647e+09\n",
       "Name: raised_amount_usd, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfnew.head(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a top9 new dataframe which has the values of the top 9 countries\n",
    "top9=df[(df.country_code==\"USA\")|\n",
    "      (df.country_code==\"CHN\")|\n",
    "      (df.country_code==\"GBR\")|\n",
    "      (df.country_code==\"IND\")|\n",
    "      (df.country_code==\"CAN\")|\n",
    "      (df.country_code==\"FRA\")|\n",
    "      (df.country_code==\"ISR\")|\n",
    "      (df.country_code==\"DEU\")|\n",
    "      (df.country_code==\"JPN\")]\n",
    "# or a easy code would be\n",
    "\n",
    "#top9=dfnew[:9]"
   ]
  },
  {
   "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>permalink</th>\n",
       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>status</th>\n",
       "      <th>country_code</th>\n",
       "      <th>funding_round_permalink</th>\n",
       "      <th>funding_round_type</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>#fame</td>\n",
       "      <td>Media</td>\n",
       "      <td>operating</td>\n",
       "      <td>IND</td>\n",
       "      <td>/funding-round/9a01d05418af9f794eebff7ace91f638</td>\n",
       "      <td>venture</td>\n",
       "      <td>05-01-2015</td>\n",
       "      <td>10000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/organization/0-6-com</td>\n",
       "      <td>0-6.com</td>\n",
       "      <td>Curated Web</td>\n",
       "      <td>operating</td>\n",
       "      <td>CHN</td>\n",
       "      <td>/funding-round/5727accaeaa57461bd22a9bdd945382d</td>\n",
       "      <td>venture</td>\n",
       "      <td>19-03-2008</td>\n",
       "      <td>2000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>/organization/0ndine-biomedical-inc</td>\n",
       "      <td>Ondine Biomedical Inc.</td>\n",
       "      <td>Biotechnology</td>\n",
       "      <td>operating</td>\n",
       "      <td>CAN</td>\n",
       "      <td>/funding-round/954b9499724b946ad8c396a57a5f3b72</td>\n",
       "      <td>venture</td>\n",
       "      <td>21-12-2009</td>\n",
       "      <td>719491.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>H2O.ai</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>/funding-round/3bb2ee4a2d89251a10aaa735b1180e44</td>\n",
       "      <td>venture</td>\n",
       "      <td>09-11-2015</td>\n",
       "      <td>20000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>H2O.ai</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>/funding-round/ae2a174c06517c2394aed45006322a7e</td>\n",
       "      <td>venture</td>\n",
       "      <td>03-01-2013</td>\n",
       "      <td>1700000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88505</td>\n",
       "      <td>/organization/zyomyx-inc</td>\n",
       "      <td>ZYOMYX</td>\n",
       "      <td>Biotechnology</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>/funding-round/b4fce7f8a13267353d57db09099800e8</td>\n",
       "      <td>venture</td>\n",
       "      <td>11-07-2013</td>\n",
       "      <td>14219999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88506</td>\n",
       "      <td>/organization/zyomyx-inc</td>\n",
       "      <td>ZYOMYX</td>\n",
       "      <td>Biotechnology</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>/funding-round/ea5b21da6cf999ff4d5667431fe318d7</td>\n",
       "      <td>venture</td>\n",
       "      <td>29-01-2010</td>\n",
       "      <td>555016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88516</td>\n",
       "      <td>/organization/zyrra</td>\n",
       "      <td>Zyrra</td>\n",
       "      <td>E-Commerce</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>/funding-round/7a0fa791c96c9c447ee54662395beab9</td>\n",
       "      <td>venture</td>\n",
       "      <td>18-10-2012</td>\n",
       "      <td>140500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88518</td>\n",
       "      <td>/organization/zystor</td>\n",
       "      <td>Zystor</td>\n",
       "      <td>Biotechnology</td>\n",
       "      <td>acquired</td>\n",
       "      <td>USA</td>\n",
       "      <td>/funding-round/7ea37c0ff4132b084832039a9380d34e</td>\n",
       "      <td>venture</td>\n",
       "      <td>16-12-2004</td>\n",
       "      <td>8500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88523</td>\n",
       "      <td>/organization/zznode-science-and-technology-co...</td>\n",
       "      <td>ZZNode Science and Technology</td>\n",
       "      <td>Enterprise Software</td>\n",
       "      <td>operating</td>\n",
       "      <td>CHN</td>\n",
       "      <td>/funding-round/171693e1e07e0230defd8fddb6de4515</td>\n",
       "      <td>venture</td>\n",
       "      <td>01-04-2012</td>\n",
       "      <td>1587301.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>44118 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               permalink  \\\n",
       "0                                    /organization/-fame   \n",
       "2                                  /organization/0-6-com   \n",
       "5                    /organization/0ndine-biomedical-inc   \n",
       "7                                   /organization/0xdata   \n",
       "8                                   /organization/0xdata   \n",
       "...                                                  ...   \n",
       "88505                           /organization/zyomyx-inc   \n",
       "88506                           /organization/zyomyx-inc   \n",
       "88516                                /organization/zyrra   \n",
       "88518                               /organization/zystor   \n",
       "88523  /organization/zznode-science-and-technology-co...   \n",
       "\n",
       "                                name        category_list     status  \\\n",
       "0                              #fame                Media  operating   \n",
       "2                            0-6.com          Curated Web  operating   \n",
       "5             Ondine Biomedical Inc.        Biotechnology  operating   \n",
       "7                             H2O.ai            Analytics  operating   \n",
       "8                             H2O.ai            Analytics  operating   \n",
       "...                              ...                  ...        ...   \n",
       "88505                         ZYOMYX        Biotechnology  operating   \n",
       "88506                         ZYOMYX        Biotechnology  operating   \n",
       "88516                          Zyrra           E-Commerce  operating   \n",
       "88518                         Zystor        Biotechnology   acquired   \n",
       "88523  ZZNode Science and Technology  Enterprise Software  operating   \n",
       "\n",
       "      country_code                          funding_round_permalink  \\\n",
       "0              IND  /funding-round/9a01d05418af9f794eebff7ace91f638   \n",
       "2              CHN  /funding-round/5727accaeaa57461bd22a9bdd945382d   \n",
       "5              CAN  /funding-round/954b9499724b946ad8c396a57a5f3b72   \n",
       "7              USA  /funding-round/3bb2ee4a2d89251a10aaa735b1180e44   \n",
       "8              USA  /funding-round/ae2a174c06517c2394aed45006322a7e   \n",
       "...            ...                                              ...   \n",
       "88505          USA  /funding-round/b4fce7f8a13267353d57db09099800e8   \n",
       "88506          USA  /funding-round/ea5b21da6cf999ff4d5667431fe318d7   \n",
       "88516          USA  /funding-round/7a0fa791c96c9c447ee54662395beab9   \n",
       "88518          USA  /funding-round/7ea37c0ff4132b084832039a9380d34e   \n",
       "88523          CHN  /funding-round/171693e1e07e0230defd8fddb6de4515   \n",
       "\n",
       "      funding_round_type   funded_at  raised_amount_usd  \n",
       "0                venture  05-01-2015         10000000.0  \n",
       "2                venture  19-03-2008          2000000.0  \n",
       "5                venture  21-12-2009           719491.0  \n",
       "7                venture  09-11-2015         20000000.0  \n",
       "8                venture  03-01-2013          1700000.0  \n",
       "...                  ...         ...                ...  \n",
       "88505            venture  11-07-2013         14219999.0  \n",
       "88506            venture  29-01-2010           555016.0  \n",
       "88516            venture  18-10-2012           140500.0  \n",
       "88518            venture  16-12-2004          8500000.0  \n",
       "88523            venture  01-04-2012          1587301.0  \n",
       "\n",
       "[44118 rows x 9 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,10))\n",
    "sns.boxplot(x='country_code',y='raised_amount_usd',data=top9)\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>permalink</th>\n",
       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>status</th>\n",
       "      <th>country_code</th>\n",
       "      <th>funding_round_permalink</th>\n",
       "      <th>funding_round_type</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>#fame</td>\n",
       "      <td>Media</td>\n",
       "      <td>operating</td>\n",
       "      <td>IND</td>\n",
       "      <td>/funding-round/9a01d05418af9f794eebff7ace91f638</td>\n",
       "      <td>venture</td>\n",
       "      <td>05-01-2015</td>\n",
       "      <td>10000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>H2O.ai</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>/funding-round/3bb2ee4a2d89251a10aaa735b1180e44</td>\n",
       "      <td>venture</td>\n",
       "      <td>09-11-2015</td>\n",
       "      <td>20000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>H2O.ai</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>/funding-round/ae2a174c06517c2394aed45006322a7e</td>\n",
       "      <td>venture</td>\n",
       "      <td>03-01-2013</td>\n",
       "      <td>1700000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>H2O.ai</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>/funding-round/e1cfcbe1bdf4c70277c5f29a3482f24e</td>\n",
       "      <td>venture</td>\n",
       "      <td>19-07-2014</td>\n",
       "      <td>8900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>/organization/1-mainstream</td>\n",
       "      <td>1 Mainstream</td>\n",
       "      <td>Apps|Cable|Distribution|Software</td>\n",
       "      <td>acquired</td>\n",
       "      <td>USA</td>\n",
       "      <td>/funding-round/b952cbaf401f310927430c97b68162ea</td>\n",
       "      <td>venture</td>\n",
       "      <td>17-03-2015</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     permalink          name  \\\n",
       "0          /organization/-fame         #fame   \n",
       "7         /organization/0xdata        H2O.ai   \n",
       "8         /organization/0xdata        H2O.ai   \n",
       "9         /organization/0xdata        H2O.ai   \n",
       "15  /organization/1-mainstream  1 Mainstream   \n",
       "\n",
       "                       category_list     status country_code  \\\n",
       "0                              Media  operating          IND   \n",
       "7                          Analytics  operating          USA   \n",
       "8                          Analytics  operating          USA   \n",
       "9                          Analytics  operating          USA   \n",
       "15  Apps|Cable|Distribution|Software   acquired          USA   \n",
       "\n",
       "                            funding_round_permalink funding_round_type  \\\n",
       "0   /funding-round/9a01d05418af9f794eebff7ace91f638            venture   \n",
       "7   /funding-round/3bb2ee4a2d89251a10aaa735b1180e44            venture   \n",
       "8   /funding-round/ae2a174c06517c2394aed45006322a7e            venture   \n",
       "9   /funding-round/e1cfcbe1bdf4c70277c5f29a3482f24e            venture   \n",
       "15  /funding-round/b952cbaf401f310927430c97b68162ea            venture   \n",
       "\n",
       "     funded_at  raised_amount_usd  \n",
       "0   05-01-2015         10000000.0  \n",
       "7   09-11-2015         20000000.0  \n",
       "8   03-01-2013          1700000.0  \n",
       "9   19-07-2014          8900000.0  \n",
       "15  17-03-2015          5000000.0  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecting the top 3 English speaking countries among the list\n",
    "\n",
    "df=df[(df.country_code==\"USA\")|\n",
    "      (df.country_code==\"GBR\")|\n",
    "      (df.country_code==\"IND\")]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 38803 entries, 0 to 88518\n",
      "Data columns (total 9 columns):\n",
      "permalink                  38803 non-null object\n",
      "name                       38803 non-null object\n",
      "category_list              38803 non-null object\n",
      "status                     38803 non-null object\n",
      "country_code               38803 non-null object\n",
      "funding_round_permalink    38803 non-null object\n",
      "funding_round_type         38803 non-null object\n",
      "funded_at                  38803 non-null object\n",
      "raised_amount_usd          38803 non-null float64\n",
      "dtypes: float64(1), object(8)\n",
      "memory usage: 3.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.boxplot(x=df['country_code'],y=df['raised_amount_usd'])\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country_code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>GBR</td>\n",
       "      <td>4260000.0</td>\n",
       "      <td>9.834793e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>IND</td>\n",
       "      <td>7500000.0</td>\n",
       "      <td>1.741332e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>USA</td>\n",
       "      <td>5000000.0</td>\n",
       "      <td>1.168706e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        median              mean\n",
       "             raised_amount_usd raised_amount_usd\n",
       "country_code                                    \n",
       "GBR                  4260000.0      9.834793e+06\n",
       "IND                  7500000.0      1.741332e+07\n",
       "USA                  5000000.0      1.168706e+07"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(values='raised_amount_usd',index='country_code',aggfunc=[np.median,np.mean])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sector Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping=pd.read_csv(\"C:\\\\Users\\\\mohan\\\\Desktop\\\\ML\\\\Assignments\\\\Week_5- Assignment-4 (11-June-2019)\\\\Assignment-4_ Teclov-data-code-project\\\\Assignment-4\\\\mapping.csv\",sep=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapping.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
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       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>status</th>\n",
       "      <th>country_code</th>\n",
       "      <th>funding_round_permalink</th>\n",
       "      <th>funding_round_type</th>\n",
       "      <th>funded_at</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>main_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/-fame</td>\n",
       "      <td>#fame</td>\n",
       "      <td>Media</td>\n",
       "      <td>operating</td>\n",
       "      <td>IND</td>\n",
       "      <td>/funding-round/9a01d05418af9f794eebff7ace91f638</td>\n",
       "      <td>venture</td>\n",
       "      <td>05-01-2015</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>Media</td>\n",
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       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>H2O.ai</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
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       "      <td>venture</td>\n",
       "      <td>09-11-2015</td>\n",
       "      <td>20000000.0</td>\n",
       "      <td>Analytics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>H2O.ai</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>/funding-round/ae2a174c06517c2394aed45006322a7e</td>\n",
       "      <td>venture</td>\n",
       "      <td>03-01-2013</td>\n",
       "      <td>1700000.0</td>\n",
       "      <td>Analytics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>H2O.ai</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>/funding-round/e1cfcbe1bdf4c70277c5f29a3482f24e</td>\n",
       "      <td>venture</td>\n",
       "      <td>19-07-2014</td>\n",
       "      <td>8900000.0</td>\n",
       "      <td>Analytics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>/organization/1-mainstream</td>\n",
       "      <td>1 Mainstream</td>\n",
       "      <td>Apps|Cable|Distribution|Software</td>\n",
       "      <td>acquired</td>\n",
       "      <td>USA</td>\n",
       "      <td>/funding-round/b952cbaf401f310927430c97b68162ea</td>\n",
       "      <td>venture</td>\n",
       "      <td>17-03-2015</td>\n",
       "      <td>5000000.0</td>\n",
       "      <td>Apps</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>/organization/10-minutes-with</td>\n",
       "      <td>10 Minutes With</td>\n",
       "      <td>Education</td>\n",
       "      <td>operating</td>\n",
       "      <td>GBR</td>\n",
       "      <td>/funding-round/0faccbbcc5818dc5326469f13f5a8ac8</td>\n",
       "      <td>venture</td>\n",
       "      <td>09-10-2014</td>\n",
       "      <td>4000000.0</td>\n",
       "      <td>Education</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        permalink             name  \\\n",
       "0             /organization/-fame            #fame   \n",
       "7            /organization/0xdata           H2O.ai   \n",
       "8            /organization/0xdata           H2O.ai   \n",
       "9            /organization/0xdata           H2O.ai   \n",
       "15     /organization/1-mainstream     1 Mainstream   \n",
       "21  /organization/10-minutes-with  10 Minutes With   \n",
       "\n",
       "                       category_list     status country_code  \\\n",
       "0                              Media  operating          IND   \n",
       "7                          Analytics  operating          USA   \n",
       "8                          Analytics  operating          USA   \n",
       "9                          Analytics  operating          USA   \n",
       "15  Apps|Cable|Distribution|Software   acquired          USA   \n",
       "21                         Education  operating          GBR   \n",
       "\n",
       "                            funding_round_permalink funding_round_type  \\\n",
       "0   /funding-round/9a01d05418af9f794eebff7ace91f638            venture   \n",
       "7   /funding-round/3bb2ee4a2d89251a10aaa735b1180e44            venture   \n",
       "8   /funding-round/ae2a174c06517c2394aed45006322a7e            venture   \n",
       "9   /funding-round/e1cfcbe1bdf4c70277c5f29a3482f24e            venture   \n",
       "15  /funding-round/b952cbaf401f310927430c97b68162ea            venture   \n",
       "21  /funding-round/0faccbbcc5818dc5326469f13f5a8ac8            venture   \n",
       "\n",
       "     funded_at  raised_amount_usd main_category  \n",
       "0   05-01-2015         10000000.0         Media  \n",
       "7   09-11-2015         20000000.0     Analytics  \n",
       "8   03-01-2013          1700000.0     Analytics  \n",
       "9   19-07-2014          8900000.0     Analytics  \n",
       "15  17-03-2015          5000000.0          Apps  \n",
       "21  09-10-2014          4000000.0     Education  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting the main category from the df dataframe\n",
    "\n",
    "df['main_category']=df['category_list'].apply(lambda x:x.split(\"|\")[0])\n",
    "df.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping the category_list column\n",
    "\n",
    "df.drop(['category_list'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>/organization/-fame</td>\n",
       "      <td>#fame</td>\n",
       "      <td>operating</td>\n",
       "      <td>IND</td>\n",
       "      <td>/funding-round/9a01d05418af9f794eebff7ace91f638</td>\n",
       "      <td>venture</td>\n",
       "      <td>05-01-2015</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>Media</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>H2O.ai</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>/funding-round/3bb2ee4a2d89251a10aaa735b1180e44</td>\n",
       "      <td>venture</td>\n",
       "      <td>09-11-2015</td>\n",
       "      <td>20000000.0</td>\n",
       "      <td>Analytics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>H2O.ai</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>/funding-round/ae2a174c06517c2394aed45006322a7e</td>\n",
       "      <td>venture</td>\n",
       "      <td>03-01-2013</td>\n",
       "      <td>1700000.0</td>\n",
       "      <td>Analytics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>H2O.ai</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>/funding-round/e1cfcbe1bdf4c70277c5f29a3482f24e</td>\n",
       "      <td>venture</td>\n",
       "      <td>19-07-2014</td>\n",
       "      <td>8900000.0</td>\n",
       "      <td>Analytics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>/organization/1-mainstream</td>\n",
       "      <td>1 Mainstream</td>\n",
       "      <td>acquired</td>\n",
       "      <td>USA</td>\n",
       "      <td>/funding-round/b952cbaf401f310927430c97b68162ea</td>\n",
       "      <td>venture</td>\n",
       "      <td>17-03-2015</td>\n",
       "      <td>5000000.0</td>\n",
       "      <td>Apps</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     permalink          name     status country_code  \\\n",
       "0          /organization/-fame         #fame  operating          IND   \n",
       "7         /organization/0xdata        H2O.ai  operating          USA   \n",
       "8         /organization/0xdata        H2O.ai  operating          USA   \n",
       "9         /organization/0xdata        H2O.ai  operating          USA   \n",
       "15  /organization/1-mainstream  1 Mainstream   acquired          USA   \n",
       "\n",
       "                            funding_round_permalink funding_round_type  \\\n",
       "0   /funding-round/9a01d05418af9f794eebff7ace91f638            venture   \n",
       "7   /funding-round/3bb2ee4a2d89251a10aaa735b1180e44            venture   \n",
       "8   /funding-round/ae2a174c06517c2394aed45006322a7e            venture   \n",
       "9   /funding-round/e1cfcbe1bdf4c70277c5f29a3482f24e            venture   \n",
       "15  /funding-round/b952cbaf401f310927430c97b68162ea            venture   \n",
       "\n",
       "     funded_at  raised_amount_usd main_category  \n",
       "0   05-01-2015         10000000.0         Media  \n",
       "7   09-11-2015         20000000.0     Analytics  \n",
       "8   03-01-2013          1700000.0     Analytics  \n",
       "9   19-07-2014          8900000.0     Analytics  \n",
       "15  17-03-2015          5000000.0          Apps  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "category_list                              1\n",
       "Automotive & Sports                        0\n",
       "Blanks                                     0\n",
       "Cleantech / Semiconductors                 0\n",
       "Entertainment                              0\n",
       "Health                                     0\n",
       "Manufacturing                              0\n",
       "News, Search and Messaging                 0\n",
       "Others                                     0\n",
       "Social, Finance, Analytics, Advertising    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapping.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "category_list                              0\n",
       "Automotive & Sports                        0\n",
       "Blanks                                     0\n",
       "Cleantech / Semiconductors                 0\n",
       "Entertainment                              0\n",
       "Health                                     0\n",
       "Manufacturing                              0\n",
       "News, Search and Messaging                 0\n",
       "Others                                     0\n",
       "Social, Finance, Analytics, Advertising    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removing the row with the missing values\n",
    "\n",
    "mapping=mapping[~pd.isnull(mapping['category_list'])]\n",
    "mapping.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category_list  Automotive & Sports  Blanks  Cleantech / Semiconductors  \\\n",
       "1            3D                    0       0                           0   \n",
       "2   3D Printing                    0       0                           0   \n",
       "\n",
       "   Entertainment  Health  Manufacturing  News, Search and Messaging  Others  \\\n",
       "1              0       0              1                           0       0   \n",
       "2              0       0              1                           0       0   \n",
       "\n",
       "   Social, Finance, Analytics, Advertising  \n",
       "1                                        0  \n",
       "2                                        0  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapping.head(2)"
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  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
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       "              permalink    name     status country_code  \\\n",
       "0   /organization/-fame   #fame  operating          IND   \n",
       "7  /organization/0xdata  H2O.ai  operating          USA   \n",
       "\n",
       "                           funding_round_permalink funding_round_type  \\\n",
       "0  /funding-round/9a01d05418af9f794eebff7ace91f638            venture   \n",
       "7  /funding-round/3bb2ee4a2d89251a10aaa735b1180e44            venture   \n",
       "\n",
       "    funded_at  raised_amount_usd main_category  \n",
       "0  05-01-2015         10000000.0         Media  \n",
       "7  09-11-2015         20000000.0     Analytics  "
      ]
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     "execution_count": 75,
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   "source": [
    "df.head(2)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping['category_list']=mapping['category_list'].str.lower()\n",
    "df['main_category']=df['main_category'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
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       "      <td>88270</td>\n",
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       "      <td>88291</td>\n",
       "      <td>/organization/zopa</td>\n",
       "      <td>Zopa</td>\n",
       "      <td>operating</td>\n",
       "      <td>GBR</td>\n",
       "      <td>/funding-round/2a55d435c3433d8f903526c050c19361</td>\n",
       "      <td>venture</td>\n",
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       "      <td>12900000.0</td>\n",
       "      <td>finance</td>\n",
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       "      <td>88292</td>\n",
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       "      <td>Zopa</td>\n",
       "      <td>operating</td>\n",
       "      <td>GBR</td>\n",
       "      <td>/funding-round/4b0740cb83da8d2af9d221e5455f8923</td>\n",
       "      <td>venture</td>\n",
       "      <td>01-03-2006</td>\n",
       "      <td>15000000.0</td>\n",
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       "      <td>/organization/zopa</td>\n",
       "      <td>Zopa</td>\n",
       "      <td>operating</td>\n",
       "      <td>GBR</td>\n",
       "      <td>/funding-round/54dbfbd899caf7d1d4b2b7676065f303</td>\n",
       "      <td>venture</td>\n",
       "      <td>01-07-2006</td>\n",
       "      <td>5000000.0</td>\n",
       "      <td>finance</td>\n",
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       "      <td>88294</td>\n",
       "      <td>/organization/zopa</td>\n",
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      ],
      "text/plain": [
       "                         permalink           name     status country_code  \\\n",
       "7             /organization/0xdata         H2O.ai  operating          USA   \n",
       "8             /organization/0xdata         H2O.ai  operating          USA   \n",
       "9             /organization/0xdata         H2O.ai  operating          USA   \n",
       "47           /organization/100plus        100Plus   acquired          USA   \n",
       "136    /organization/1world-online  1World Online  operating          USA   \n",
       "...                            ...            ...        ...          ...   \n",
       "88270         /organization/zoopla         Zoopla        ipo          GBR   \n",
       "88291           /organization/zopa           Zopa  operating          GBR   \n",
       "88292           /organization/zopa           Zopa  operating          GBR   \n",
       "88293           /organization/zopa           Zopa  operating          GBR   \n",
       "88294           /organization/zopa           Zopa  operating          GBR   \n",
       "\n",
       "                               funding_round_permalink funding_round_type  \\\n",
       "7      /funding-round/3bb2ee4a2d89251a10aaa735b1180e44            venture   \n",
       "8      /funding-round/ae2a174c06517c2394aed45006322a7e            venture   \n",
       "9      /funding-round/e1cfcbe1bdf4c70277c5f29a3482f24e            venture   \n",
       "47     /funding-round/b5facb0d9dea2f0352b5834892c88c53            venture   \n",
       "136    /funding-round/32936e588a134502712877150198a0b3            venture   \n",
       "...                                                ...                ...   \n",
       "88270  /funding-round/98da1f441a55c9a9629a256828923e38            venture   \n",
       "88291  /funding-round/2a55d435c3433d8f903526c050c19361            venture   \n",
       "88292  /funding-round/4b0740cb83da8d2af9d221e5455f8923            venture   \n",
       "88293  /funding-round/54dbfbd899caf7d1d4b2b7676065f303            venture   \n",
       "88294  /funding-round/720b9f244c1f4d4fed63361d3bb0aa22            venture   \n",
       "\n",
       "        funded_at  raised_amount_usd        main_category  \n",
       "7      09-11-2015         20000000.0            analytics  \n",
       "8      03-01-2013          1700000.0            analytics  \n",
       "9      19-07-2014          8900000.0            analytics  \n",
       "47     02-11-2011           500000.0            analytics  \n",
       "136    13-08-2015          2500000.0            analytics  \n",
       "...           ...                ...                  ...  \n",
       "88270  19-01-2009          5440000.0  property management  \n",
       "88291  20-03-2007         12900000.0              finance  \n",
       "88292  01-03-2006         15000000.0              finance  \n",
       "88293  01-07-2006          5000000.0              finance  \n",
       "88294  01-01-2005          1000000.0              finance  \n",
       "\n",
       "[2616 rows x 9 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging all the main_category values with the mapping file's category column , all the values in the main_category column should be present\n",
    "# in the category_list column of the mapping files\n",
    "\n",
    "df[~df['main_category'].isin(mapping['category_list'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
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       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>a0lytics</td>\n",
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       "      <td>33</td>\n",
       "      <td>aquaculture</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>b2b express delivery</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>670</td>\n",
       "      <td>virtual workforces</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>672</td>\n",
       "      <td>waste ma0gement</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>682</td>\n",
       "      <td>weddings</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>683</td>\n",
       "      <td>wholesale</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>686</td>\n",
       "      <td>women</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>175 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             category_list  Automotive & Sports  Blanks  \\\n",
       "16   air pollution control                    0       0   \n",
       "20     alter0tive medicine                    0       0   \n",
       "22                a0lytics                    0       0   \n",
       "33             aquaculture                    0       0   \n",
       "49    b2b express delivery                    0       0   \n",
       "..                     ...                  ...     ...   \n",
       "670     virtual workforces                    0       0   \n",
       "672        waste ma0gement                    0       0   \n",
       "682               weddings                    0       0   \n",
       "683              wholesale                    0       0   \n",
       "686                  women                    0       0   \n",
       "\n",
       "     Cleantech / Semiconductors  Entertainment  Health  Manufacturing  \\\n",
       "16                            1              0       0              0   \n",
       "20                            0              0       1              0   \n",
       "22                            0              0       0              0   \n",
       "33                            1              0       0              0   \n",
       "49                            0              0       0              0   \n",
       "..                          ...            ...     ...            ...   \n",
       "670                           0              1       0              0   \n",
       "672                           1              0       0              0   \n",
       "682                           0              1       0              0   \n",
       "683                           0              0       0              0   \n",
       "686                           0              0       0              0   \n",
       "\n",
       "     News, Search and Messaging  Others  \\\n",
       "16                            0       0   \n",
       "20                            0       0   \n",
       "22                            0       0   \n",
       "33                            0       0   \n",
       "49                            0       0   \n",
       "..                          ...     ...   \n",
       "670                           0       0   \n",
       "672                           0       0   \n",
       "682                           0       0   \n",
       "683                           0       1   \n",
       "686                           0       1   \n",
       "\n",
       "     Social, Finance, Analytics, Advertising  \n",
       "16                                         0  \n",
       "20                                         0  \n",
       "22                                         1  \n",
       "33                                         0  \n",
       "49                                         1  \n",
       "..                                       ...  \n",
       "670                                        0  \n",
       "672                                        0  \n",
       "682                                        0  \n",
       "683                                        0  \n",
       "686                                        0  \n",
       "\n",
       "[175 rows x 10 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# values which are in the category_list but not in the main_category\n",
    "\n",
    "mapping[~mapping['category_list'].isin(df['main_category'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1                    3d\n",
       "2           3d printing\n",
       "3         3d technology\n",
       "4            accounting\n",
       "5      active lifestyle\n",
       "             ...       \n",
       "683           wholesale\n",
       "684    wine and spirits\n",
       "685            wireless\n",
       "686               women\n",
       "687        young adults\n",
       "Name: category_list, Length: 687, dtype: object"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As we look into the dataset we observe that in many of the places 'na' is replaced by '0' , such as a0lytics , waste ma0gement\n",
    "# replacing '0' with'na'\n",
    "\n",
    "mapping['category_list']=mapping['category_list'].apply(lambda x:x.replace('0','na'))\n",
    "mapping.category_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>permalink</th>\n",
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       "      <th>funding_round_type</th>\n",
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       "      <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",
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       "      <th>Others</th>\n",
       "      <th>Social, Finance, Analytics, Advertising</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
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       "      <td>10000000.0</td>\n",
       "      <td>media</td>\n",
       "      <td>media</td>\n",
       "      <td>0</td>\n",
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       "      <td>/organization/90min</td>\n",
       "      <td>90min</td>\n",
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       "      <td>GBR</td>\n",
       "      <td>/funding-round/21a2cbf6f2fb2a1c2a61e04bf930dfe6</td>\n",
       "      <td>venture</td>\n",
       "      <td>06-10-2015</td>\n",
       "      <td>15000000.0</td>\n",
       "      <td>media</td>\n",
       "      <td>media</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/organization/90min</td>\n",
       "      <td>90min</td>\n",
       "      <td>operating</td>\n",
       "      <td>GBR</td>\n",
       "      <td>/funding-round/bd626ed022f5c66574b1afe234f3c90d</td>\n",
       "      <td>venture</td>\n",
       "      <td>07-05-2013</td>\n",
       "      <td>5800000.0</td>\n",
       "      <td>media</td>\n",
       "      <td>media</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>/organization/90min</td>\n",
       "      <td>90min</td>\n",
       "      <td>operating</td>\n",
       "      <td>GBR</td>\n",
       "      <td>/funding-round/fd4b15e8c97ee2ffc0acccdbe1a98810</td>\n",
       "      <td>venture</td>\n",
       "      <td>26-03-2014</td>\n",
       "      <td>18000000.0</td>\n",
       "      <td>media</td>\n",
       "      <td>media</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>/organization/all-def-digital</td>\n",
       "      <td>All Def Digital</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>/funding-round/452a2342fe720285c3b92e9bd927d9ba</td>\n",
       "      <td>venture</td>\n",
       "      <td>06-08-2014</td>\n",
       "      <td>5000000.0</td>\n",
       "      <td>media</td>\n",
       "      <td>media</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       permalink             name     status country_code  \\\n",
       "0            /organization/-fame            #fame  operating          IND   \n",
       "1            /organization/90min            90min  operating          GBR   \n",
       "2            /organization/90min            90min  operating          GBR   \n",
       "3            /organization/90min            90min  operating          GBR   \n",
       "4  /organization/all-def-digital  All Def Digital  operating          USA   \n",
       "\n",
       "                           funding_round_permalink funding_round_type  \\\n",
       "0  /funding-round/9a01d05418af9f794eebff7ace91f638            venture   \n",
       "1  /funding-round/21a2cbf6f2fb2a1c2a61e04bf930dfe6            venture   \n",
       "2  /funding-round/bd626ed022f5c66574b1afe234f3c90d            venture   \n",
       "3  /funding-round/fd4b15e8c97ee2ffc0acccdbe1a98810            venture   \n",
       "4  /funding-round/452a2342fe720285c3b92e9bd927d9ba            venture   \n",
       "\n",
       "    funded_at  raised_amount_usd main_category category_list  \\\n",
       "0  05-01-2015         10000000.0         media         media   \n",
       "1  06-10-2015         15000000.0         media         media   \n",
       "2  07-05-2013          5800000.0         media         media   \n",
       "3  26-03-2014         18000000.0         media         media   \n",
       "4  06-08-2014          5000000.0         media         media   \n",
       "\n",
       "   Automotive & Sports  Blanks  Cleantech / Semiconductors  Entertainment  \\\n",
       "0                    0       0                           0              1   \n",
       "1                    0       0                           0              1   \n",
       "2                    0       0                           0              1   \n",
       "3                    0       0                           0              1   \n",
       "4                    0       0                           0              1   \n",
       "\n",
       "   Health  Manufacturing  News, Search and Messaging  Others  \\\n",
       "0       0              0                           0       0   \n",
       "1       0              0                           0       0   \n",
       "2       0              0                           0       0   \n",
       "3       0              0                           0       0   \n",
       "4       0              0                           0       0   \n",
       "\n",
       "   Social, Finance, Analytics, Advertising  \n",
       "0                                        0  \n",
       "1                                        0  \n",
       "2                                        0  \n",
       "3                                        0  \n",
       "4                                        0  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merging the two dataframes\n",
    "\n",
    "df=pd.merge(df,mapping,how=\"inner\",left_on=\"main_category\",right_on=\"category_list\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>permalink</th>\n",
       "      <th>name</th>\n",
       "      <th>status</th>\n",
       "      <th>country_code</th>\n",
       "      <th>funding_round_permalink</th>\n",
       "      <th>funding_round_type</th>\n",
       "      <th>funded_at</th>\n",
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       "      <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",
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       "      <td>venture</td>\n",
       "      <td>06-10-2015</td>\n",
       "      <td>15000000.0</td>\n",
       "      <td>media</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             permalink   name     status country_code  \\\n",
       "0  /organization/-fame  #fame  operating          IND   \n",
       "1  /organization/90min  90min  operating          GBR   \n",
       "\n",
       "                           funding_round_permalink funding_round_type  \\\n",
       "0  /funding-round/9a01d05418af9f794eebff7ace91f638            venture   \n",
       "1  /funding-round/21a2cbf6f2fb2a1c2a61e04bf930dfe6            venture   \n",
       "\n",
       "    funded_at  raised_amount_usd main_category  Automotive & Sports  Blanks  \\\n",
       "0  05-01-2015         10000000.0         media                    0       0   \n",
       "1  06-10-2015         15000000.0         media                    0       0   \n",
       "\n",
       "   Cleantech / Semiconductors  Entertainment  Health  Manufacturing  \\\n",
       "0                           0              1       0              0   \n",
       "1                           0              1       0              0   \n",
       "\n",
       "   News, Search and Messaging  Others  Social, Finance, Analytics, Advertising  \n",
       "0                           0       0                                        0  \n",
       "1                           0       0                                        0  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dropping the category list\n",
    "\n",
    "df.drop(['category_list'],axis=1,inplace=True)\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Automotive & Sports', 'Blanks', 'Cleantech / Semiconductors',\n",
      "       'Entertainment', 'Health', 'Manufacturing',\n",
      "       'News, Search and Messaging', 'Others',\n",
      "       'Social, Finance, Analytics, Advertising'],\n",
      "      dtype='object') \n",
      "\n",
      "\n",
      "['country_code' 'funded_at' 'funding_round_permalink' 'funding_round_type'\n",
      " 'main_category' 'name' 'permalink' 'raised_amount_usd' 'status']\n"
     ]
    }
   ],
   "source": [
    "# converting the wide dataframe to long dataframe\n",
    "\n",
    "# storing the value variables in one series\n",
    "\n",
    "value_vars=df.columns[9:18]\n",
    "\n",
    "# take the setdiff() to get the rest of the variables\n",
    "\n",
    "id_vars=np.setdiff1d(df.columns,value_vars)  # one more way of getting the values\n",
    "\n",
    "#id_vars=df.columns[0:9]\n",
    "\n",
    "print(value_vars,\"\\n\\n\")\n",
    "print(id_vars)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Automotive &amp; Sports</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>GBR</td>\n",
       "      <td>07-05-2013</td>\n",
       "      <td>/funding-round/bd626ed022f5c66574b1afe234f3c90d</td>\n",
       "      <td>venture</td>\n",
       "      <td>media</td>\n",
       "      <td>90min</td>\n",
       "      <td>/organization/90min</td>\n",
       "      <td>5800000.0</td>\n",
       "      <td>operating</td>\n",
       "      <td>Automotive &amp; Sports</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country_code   funded_at                          funding_round_permalink  \\\n",
       "0          IND  05-01-2015  /funding-round/9a01d05418af9f794eebff7ace91f638   \n",
       "1          GBR  06-10-2015  /funding-round/21a2cbf6f2fb2a1c2a61e04bf930dfe6   \n",
       "2          GBR  07-05-2013  /funding-round/bd626ed022f5c66574b1afe234f3c90d   \n",
       "\n",
       "  funding_round_type main_category   name            permalink  \\\n",
       "0            venture         media  #fame  /organization/-fame   \n",
       "1            venture         media  90min  /organization/90min   \n",
       "2            venture         media  90min  /organization/90min   \n",
       "\n",
       "   raised_amount_usd     status             variable  value  \n",
       "0         10000000.0  operating  Automotive & Sports      0  \n",
       "1         15000000.0  operating  Automotive & Sports      0  \n",
       "2          5800000.0  operating  Automotive & Sports      0  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# converting wide to long dataframe\n",
    "\n",
    "long_df=pd.melt(df,\n",
    "               id_vars=list(id_vars),\n",
    "               value_vars=list(value_vars))\n",
    "long_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# removing all the rows which have value=0\n",
    "\n",
    "long_df=long_df[long_df.value==1]\n",
    "long_df=long_df.drop('value',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>funded_at</th>\n",
       "      <th>funding_round_permalink</th>\n",
       "      <th>funding_round_type</th>\n",
       "      <th>main_category</th>\n",
       "      <th>name</th>\n",
       "      <th>permalink</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>status</th>\n",
       "      <th>variable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>25828</td>\n",
       "      <td>USA</td>\n",
       "      <td>05-11-2012</td>\n",
       "      <td>/funding-round/2785595770e91ab8fd4854ef125ec563</td>\n",
       "      <td>venture</td>\n",
       "      <td>drones</td>\n",
       "      <td>3D Robotics</td>\n",
       "      <td>/organization/3d-robotics</td>\n",
       "      <td>5000000.0</td>\n",
       "      <td>operating</td>\n",
       "      <td>Automotive &amp; Sports</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25829</td>\n",
       "      <td>USA</td>\n",
       "      <td>23-04-2015</td>\n",
       "      <td>/funding-round/7ca0d4dc119b6d65eebfb352c3544542</td>\n",
       "      <td>venture</td>\n",
       "      <td>drones</td>\n",
       "      <td>3D Robotics</td>\n",
       "      <td>/organization/3d-robotics</td>\n",
       "      <td>14000000.0</td>\n",
       "      <td>operating</td>\n",
       "      <td>Automotive &amp; Sports</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25830</td>\n",
       "      <td>USA</td>\n",
       "      <td>12-09-2013</td>\n",
       "      <td>/funding-round/d6221c11246b0a536ee2cadd9fcf54d3</td>\n",
       "      <td>venture</td>\n",
       "      <td>drones</td>\n",
       "      <td>3D Robotics</td>\n",
       "      <td>/organization/3d-robotics</td>\n",
       "      <td>30000000.0</td>\n",
       "      <td>operating</td>\n",
       "      <td>Automotive &amp; Sports</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      country_code   funded_at  \\\n",
       "25828          USA  05-11-2012   \n",
       "25829          USA  23-04-2015   \n",
       "25830          USA  12-09-2013   \n",
       "\n",
       "                               funding_round_permalink funding_round_type  \\\n",
       "25828  /funding-round/2785595770e91ab8fd4854ef125ec563            venture   \n",
       "25829  /funding-round/7ca0d4dc119b6d65eebfb352c3544542            venture   \n",
       "25830  /funding-round/d6221c11246b0a536ee2cadd9fcf54d3            venture   \n",
       "\n",
       "      main_category         name                  permalink  \\\n",
       "25828        drones  3D Robotics  /organization/3d-robotics   \n",
       "25829        drones  3D Robotics  /organization/3d-robotics   \n",
       "25830        drones  3D Robotics  /organization/3d-robotics   \n",
       "\n",
       "       raised_amount_usd     status             variable  \n",
       "25828          5000000.0  operating  Automotive & Sports  \n",
       "25829         14000000.0  operating  Automotive & Sports  \n",
       "25830         30000000.0  operating  Automotive & Sports  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38788"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(long_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# renaming the variable column to sector\n",
    "\n",
    "long_df=long_df.rename(columns={'variable':'sector'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 38788 entries, 25828 to 349075\n",
      "Data columns (total 10 columns):\n",
      "country_code               38788 non-null object\n",
      "funded_at                  38788 non-null object\n",
      "funding_round_permalink    38788 non-null object\n",
      "funding_round_type         38788 non-null object\n",
      "main_category              38788 non-null object\n",
      "name                       38788 non-null object\n",
      "permalink                  38788 non-null object\n",
      "raised_amount_usd          38788 non-null float64\n",
      "status                     38788 non-null object\n",
      "sector                     38788 non-null object\n",
      "dtypes: float64(1), object(9)\n",
      "memory usage: 3.3+ MB\n"
     ]
    }
   ],
   "source": [
    "long_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# summarising the sector wise number and sum of venture investments across three countries\n",
    "\n",
    "# filter the investment range between 5 and 15m\n",
    "\n",
    "df=long_df[(long_df['raised_amount_usd']>= 5000000) & (long_df['raised_amount_usd']<=15000000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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></th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country_code</th>\n",
       "      <th>sector</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"8\" valign=\"top\">GBR</td>\n",
       "      <td>Automotive &amp; Sports</td>\n",
       "      <td>16</td>\n",
       "      <td>1.670516e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Cleantech / Semiconductors</td>\n",
       "      <td>130</td>\n",
       "      <td>1.163990e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Entertainment</td>\n",
       "      <td>56</td>\n",
       "      <td>4.827847e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Health</td>\n",
       "      <td>24</td>\n",
       "      <td>2.145375e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>42</td>\n",
       "      <td>3.619403e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>News, Search and Messaging</td>\n",
       "      <td>73</td>\n",
       "      <td>6.157462e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Others</td>\n",
       "      <td>147</td>\n",
       "      <td>1.283624e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Social, Finance, Analytics, Advertising</td>\n",
       "      <td>133</td>\n",
       "      <td>1.089404e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"8\" valign=\"top\">IND</td>\n",
       "      <td>Automotive &amp; Sports</td>\n",
       "      <td>13</td>\n",
       "      <td>1.369000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Cleantech / Semiconductors</td>\n",
       "      <td>20</td>\n",
       "      <td>1.653800e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Entertainment</td>\n",
       "      <td>33</td>\n",
       "      <td>2.808300e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Health</td>\n",
       "      <td>19</td>\n",
       "      <td>1.677400e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>21</td>\n",
       "      <td>2.009000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>News, Search and Messaging</td>\n",
       "      <td>52</td>\n",
       "      <td>4.338345e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Others</td>\n",
       "      <td>110</td>\n",
       "      <td>1.013410e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Social, Finance, Analytics, Advertising</td>\n",
       "      <td>60</td>\n",
       "      <td>5.505496e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"8\" valign=\"top\">USA</td>\n",
       "      <td>Automotive &amp; Sports</td>\n",
       "      <td>167</td>\n",
       "      <td>1.454104e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Cleantech / Semiconductors</td>\n",
       "      <td>2350</td>\n",
       "      <td>2.163343e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Entertainment</td>\n",
       "      <td>591</td>\n",
       "      <td>5.099198e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Health</td>\n",
       "      <td>909</td>\n",
       "      <td>8.211859e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>799</td>\n",
       "      <td>7.258553e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>News, Search and Messaging</td>\n",
       "      <td>1583</td>\n",
       "      <td>1.397157e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Others</td>\n",
       "      <td>2950</td>\n",
       "      <td>2.632101e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Social, Finance, Analytics, Advertising</td>\n",
       "      <td>2714</td>\n",
       "      <td>2.380738e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      count           sum\n",
       "country_code sector                                                      \n",
       "GBR          Automotive & Sports                         16  1.670516e+08\n",
       "             Cleantech / Semiconductors                 130  1.163990e+09\n",
       "             Entertainment                               56  4.827847e+08\n",
       "             Health                                      24  2.145375e+08\n",
       "             Manufacturing                               42  3.619403e+08\n",
       "             News, Search and Messaging                  73  6.157462e+08\n",
       "             Others                                     147  1.283624e+09\n",
       "             Social, Finance, Analytics, Advertising    133  1.089404e+09\n",
       "IND          Automotive & Sports                         13  1.369000e+08\n",
       "             Cleantech / Semiconductors                  20  1.653800e+08\n",
       "             Entertainment                               33  2.808300e+08\n",
       "             Health                                      19  1.677400e+08\n",
       "             Manufacturing                               21  2.009000e+08\n",
       "             News, Search and Messaging                  52  4.338345e+08\n",
       "             Others                                     110  1.013410e+09\n",
       "             Social, Finance, Analytics, Advertising     60  5.505496e+08\n",
       "USA          Automotive & Sports                        167  1.454104e+09\n",
       "             Cleantech / Semiconductors                2350  2.163343e+10\n",
       "             Entertainment                              591  5.099198e+09\n",
       "             Health                                     909  8.211859e+09\n",
       "             Manufacturing                              799  7.258553e+09\n",
       "             News, Search and Messaging                1583  1.397157e+10\n",
       "             Others                                    2950  2.632101e+10\n",
       "             Social, Finance, Analytics, Advertising   2714  2.380738e+10"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['country_code','sector']).raised_amount_usd.agg(['count','sum'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of Investments')"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the graphs\n",
    "\n",
    "plt.figure(figsize=(20,14))\n",
    "\n",
    "plt.subplot(2,1,1)\n",
    "\n",
    "p=sns.barplot(x=df['sector'],y=df['raised_amount_usd'],hue=df['country_code'],estimator=np.sum)\n",
    "plt.title(\"Total Invested amount (USD)\")\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "\n",
    "q=sns.countplot(x=df['sector'],hue=df['country_code'])\n",
    "plt.title(\"Number of Investments\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Thus, the top country in terms of the number of investments (and the total amount invested) is the USA. The sectors 'Others', 'Social, Finance, Analytics and Advertising' and 'Cleantech/Semiconductors' are the most heavily invested ones.\n",
    "\n",
    "In case you don't want to consider 'Others' as a sector, 'News, Search and Messaging' is the next best sector."
   ]
  }
 ],
 "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": 4
}