startup data analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "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>SNo</th>\n",
       "      <th>Date</th>\n",
       "      <th>StartupName</th>\n",
       "      <th>IndustryVertical</th>\n",
       "      <th>SubVertical</th>\n",
       "      <th>CityLocation</th>\n",
       "      <th>InvestorsName</th>\n",
       "      <th>InvestmentType</th>\n",
       "      <th>AmountInUSD</th>\n",
       "      <th>Remarks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>01/08/2017</td>\n",
       "      <td>TouchKin</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Predictive Care Platform</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Kae Capital</td>\n",
       "      <td>Private Equity</td>\n",
       "      <td>1,300,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>02/08/2017</td>\n",
       "      <td>Ethinos</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Digital Marketing Agency</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Triton Investment Advisors</td>\n",
       "      <td>Private Equity</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>02/08/2017</td>\n",
       "      <td>Leverage Edu</td>\n",
       "      <td>Consumer Internet</td>\n",
       "      <td>Online platform for Higher Education Services</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>Kashyap Deorah, Anand Sankeshwar, Deepak Jain,...</td>\n",
       "      <td>Seed Funding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>02/08/2017</td>\n",
       "      <td>Zepo</td>\n",
       "      <td>Consumer Internet</td>\n",
       "      <td>DIY Ecommerce platform</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Kunal Shah, LetsVenture, Anupam Mittal, Hetal ...</td>\n",
       "      <td>Seed Funding</td>\n",
       "      <td>500,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>02/08/2017</td>\n",
       "      <td>Click2Clinic</td>\n",
       "      <td>Consumer Internet</td>\n",
       "      <td>healthcare service aggregator</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Narottam Thudi, Shireesh Palle</td>\n",
       "      <td>Seed Funding</td>\n",
       "      <td>850,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>01/07/2017</td>\n",
       "      <td>Billion Loans</td>\n",
       "      <td>Consumer Internet</td>\n",
       "      <td>Peer to Peer Lending platform</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Reliance Corporate Advisory Services Ltd</td>\n",
       "      <td>Seed Funding</td>\n",
       "      <td>1,000,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>03/07/2017</td>\n",
       "      <td>Ecolibriumenergy</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Energy management solutions provider</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>Infuse Ventures, JLL</td>\n",
       "      <td>Private Equity</td>\n",
       "      <td>2,600,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>04/07/2017</td>\n",
       "      <td>Droom</td>\n",
       "      <td>eCommerce</td>\n",
       "      <td>Online marketplace for automobiles</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>Asset Management (Asia) Ltd, Digital Garage Inc</td>\n",
       "      <td>Private Equity</td>\n",
       "      <td>20,000,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>05/07/2017</td>\n",
       "      <td>Jumbotail</td>\n",
       "      <td>eCommerce</td>\n",
       "      <td>online marketplace for food and grocery</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Kalaari Capital, Nexus India Capital Advisors</td>\n",
       "      <td>Private Equity</td>\n",
       "      <td>8,500,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>05/07/2017</td>\n",
       "      <td>Moglix</td>\n",
       "      <td>eCommerce</td>\n",
       "      <td>B2B marketplace for Industrial products</td>\n",
       "      <td>Noida</td>\n",
       "      <td>International Finance Corporation, Rocketship,...</td>\n",
       "      <td>Private Equity</td>\n",
       "      <td>12,000,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>05/07/2017</td>\n",
       "      <td>Timesaverz</td>\n",
       "      <td>Consumer Internet</td>\n",
       "      <td>Hyperlocal home services provider</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>BCCL</td>\n",
       "      <td>Private Equity</td>\n",
       "      <td>1,000,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>06/07/2017</td>\n",
       "      <td>Minjar</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Cloud Solutions provider</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Blume Ventures, Contrarian Capital India Partn...</td>\n",
       "      <td>Seed Funding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>06/07/2017</td>\n",
       "      <td>MyCity4kids</td>\n",
       "      <td>Consumer Internet</td>\n",
       "      <td>parenting blog and kids’ events discovery plat...</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>various</td>\n",
       "      <td>Seed Funding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>07/07/2017</td>\n",
       "      <td>Clip App</td>\n",
       "      <td>Consumer Internet</td>\n",
       "      <td>Digital Media Video platform</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India Quotient, Shunwei Capital</td>\n",
       "      <td>Seed Funding</td>\n",
       "      <td>1,000,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>07/07/2017</td>\n",
       "      <td>Upwardly.in</td>\n",
       "      <td>Consumer Internet</td>\n",
       "      <td>MF investment platform</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Sreeram Iyer, Suvo Sarkar, Anita Gupta, Likemi...</td>\n",
       "      <td>Seed Funding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>10/07/2017</td>\n",
       "      <td>Autorox.co</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Workshop Management Software Platform</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mitsui &amp; Co.</td>\n",
       "      <td>Private Equity</td>\n",
       "      <td>3,000,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>11/07/2017</td>\n",
       "      <td>Fabogo</td>\n",
       "      <td>Consumer Internet</td>\n",
       "      <td>Salon &amp; Spa Aggregation &amp; Discovery platform</td>\n",
       "      <td>Pune</td>\n",
       "      <td>Dunamis Ventures Pte Ltd</td>\n",
       "      <td>Private Equity</td>\n",
       "      <td>2,250,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>11/07/2017</td>\n",
       "      <td>Flickstree</td>\n",
       "      <td>Consumer Internet</td>\n",
       "      <td>Video Content Discovery Platform</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Venture Catalysts, Sourav Ganguly, Ankit Adity...</td>\n",
       "      <td>Seed Funding</td>\n",
       "      <td>464,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>11/07/2017</td>\n",
       "      <td>Design Cafe</td>\n",
       "      <td>Consumer Internet</td>\n",
       "      <td>Online Interior Design platform</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Fireside Ventures, Apurva Salarpuria, Sidharth...</td>\n",
       "      <td>Seed Funding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>12/07/2017</td>\n",
       "      <td>Innoviti</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Digital payments solutions</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>SBI-FMO Fund, Bessemer Venture Partners, Catam...</td>\n",
       "      <td>Private Equity</td>\n",
       "      <td>18,500,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SNo        Date       StartupName   IndustryVertical  \\\n",
       "0     0  01/08/2017          TouchKin         Technology   \n",
       "1     1  02/08/2017           Ethinos         Technology   \n",
       "2     2  02/08/2017      Leverage Edu  Consumer Internet   \n",
       "3     3  02/08/2017              Zepo  Consumer Internet   \n",
       "4     4  02/08/2017      Click2Clinic  Consumer Internet   \n",
       "5     5  01/07/2017     Billion Loans  Consumer Internet   \n",
       "6     6  03/07/2017  Ecolibriumenergy         Technology   \n",
       "7     7  04/07/2017             Droom          eCommerce   \n",
       "8     8  05/07/2017         Jumbotail          eCommerce   \n",
       "9     9  05/07/2017            Moglix          eCommerce   \n",
       "10   10  05/07/2017        Timesaverz  Consumer Internet   \n",
       "11   11  06/07/2017            Minjar         Technology   \n",
       "12   12  06/07/2017       MyCity4kids  Consumer Internet   \n",
       "13   13  07/07/2017          Clip App  Consumer Internet   \n",
       "14   14  07/07/2017       Upwardly.in  Consumer Internet   \n",
       "15   15  10/07/2017        Autorox.co         Technology   \n",
       "16   16  11/07/2017            Fabogo  Consumer Internet   \n",
       "17   17  11/07/2017        Flickstree  Consumer Internet   \n",
       "18   18  11/07/2017       Design Cafe  Consumer Internet   \n",
       "19   19  12/07/2017          Innoviti         Technology   \n",
       "\n",
       "                                          SubVertical CityLocation  \\\n",
       "0                            Predictive Care Platform    Bangalore   \n",
       "1                            Digital Marketing Agency       Mumbai   \n",
       "2       Online platform for Higher Education Services    New Delhi   \n",
       "3                              DIY Ecommerce platform       Mumbai   \n",
       "4                       healthcare service aggregator    Hyderabad   \n",
       "5                       Peer to Peer Lending platform    Bangalore   \n",
       "6                Energy management solutions provider    Ahmedabad   \n",
       "7                  Online marketplace for automobiles      Gurgaon   \n",
       "8             online marketplace for food and grocery    Bangalore   \n",
       "9             B2B marketplace for Industrial products        Noida   \n",
       "10                  Hyperlocal home services provider       Mumbai   \n",
       "11                           Cloud Solutions provider    Bangalore   \n",
       "12  parenting blog and kids’ events discovery plat...      Gurgaon   \n",
       "13                       Digital Media Video platform    Bangalore   \n",
       "14                             MF investment platform    Bangalore   \n",
       "15              Workshop Management Software Platform    Hyderabad   \n",
       "16       Salon & Spa Aggregation & Discovery platform         Pune   \n",
       "17                   Video Content Discovery Platform       Mumbai   \n",
       "18                    Online Interior Design platform    Bangalore   \n",
       "19                         Digital payments solutions    Bangalore   \n",
       "\n",
       "                                        InvestorsName  InvestmentType  \\\n",
       "0                                         Kae Capital  Private Equity   \n",
       "1                          Triton Investment Advisors  Private Equity   \n",
       "2   Kashyap Deorah, Anand Sankeshwar, Deepak Jain,...    Seed Funding   \n",
       "3   Kunal Shah, LetsVenture, Anupam Mittal, Hetal ...    Seed Funding   \n",
       "4                      Narottam Thudi, Shireesh Palle    Seed Funding   \n",
       "5            Reliance Corporate Advisory Services Ltd    Seed Funding   \n",
       "6                                Infuse Ventures, JLL  Private Equity   \n",
       "7     Asset Management (Asia) Ltd, Digital Garage Inc  Private Equity   \n",
       "8       Kalaari Capital, Nexus India Capital Advisors  Private Equity   \n",
       "9   International Finance Corporation, Rocketship,...  Private Equity   \n",
       "10                                               BCCL  Private Equity   \n",
       "11  Blume Ventures, Contrarian Capital India Partn...    Seed Funding   \n",
       "12                                            various    Seed Funding   \n",
       "13                    India Quotient, Shunwei Capital    Seed Funding   \n",
       "14  Sreeram Iyer, Suvo Sarkar, Anita Gupta, Likemi...    Seed Funding   \n",
       "15                                       Mitsui & Co.  Private Equity   \n",
       "16                           Dunamis Ventures Pte Ltd  Private Equity   \n",
       "17  Venture Catalysts, Sourav Ganguly, Ankit Adity...    Seed Funding   \n",
       "18  Fireside Ventures, Apurva Salarpuria, Sidharth...    Seed Funding   \n",
       "19  SBI-FMO Fund, Bessemer Venture Partners, Catam...  Private Equity   \n",
       "\n",
       "   AmountInUSD Remarks  \n",
       "0    1,300,000     NaN  \n",
       "1          NaN     NaN  \n",
       "2          NaN     NaN  \n",
       "3      500,000     NaN  \n",
       "4      850,000     NaN  \n",
       "5    1,000,000     NaN  \n",
       "6    2,600,000     NaN  \n",
       "7   20,000,000     NaN  \n",
       "8    8,500,000     NaN  \n",
       "9   12,000,000     NaN  \n",
       "10   1,000,000     NaN  \n",
       "11         NaN     NaN  \n",
       "12         NaN     NaN  \n",
       "13   1,000,000     NaN  \n",
       "14         NaN     NaN  \n",
       "15   3,000,000     NaN  \n",
       "16   2,250,000     NaN  \n",
       "17     464,000     NaN  \n",
       "18         NaN     NaN  \n",
       "19  18,500,000     NaN  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(color_codes=True)\n",
    "startup_df = pd.read_csv(r\"C:\\Users\\kartik singh\\Desktop\\kaggle data\\startup_funding.csv\")\n",
    "startup_df[\"InvestorsName\"].fillna(\"various\",inplace=True)\n",
    "startup_df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "date=[]\n",
    "name=[]\n",
    "vertical=[]\n",
    "subvertical=[]\n",
    "city=[]\n",
    "investors=[]\n",
    "Type=[]\n",
    "amount=[]\n",
    "for j,i in startup_df.iterrows():\n",
    "    date.append(i[\"Date\"])\n",
    "    name.append(i[\"StartupName\"])\n",
    "    vertical.append(i[\"IndustryVertical\"])\n",
    "    subvertical.append(i[\"SubVertical\"])\n",
    "    city.append(i[\"CityLocation\"])\n",
    "    investors.append(i[\"InvestorsName\"])\n",
    "    Type.append(i[\"InvestmentType\"])\n",
    "    amount.append(i[\"AmountInUSD\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "city_unique = np.unique(city)\n",
    "city_count = []\n",
    "for i in city_unique:\n",
    "    city_count.append(city.count(i)) #count of every city in the contribution\n",
    "city_count_df = pd.DataFrame(city_count, index=city_unique)   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1428: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  stat_data = remove_na(group_data)\n"
     ]
    },
    {
     "data": {
      "image/png": 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F+datWxEQEIDhw4fjxx9/RO/evcUwP3DgADZs2IDs7Gyx9nPixAl4eXnB29sb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bdb5d71860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "sns.countplot(x=\"CityLocation\", data=startup_df)\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.rcParams[\"figure.figsize\"] = [50,60]\n",
    "plt.show()\n",
    "#this graph shows which cities are best for finding investments for your company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2372"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(investors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "investors_count=[]\n",
    "for i in investors:\n",
    "    investors_count.append(len(i.split(\",\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>name</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TouchKin</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ethinos</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Leverage Edu</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Zepo</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Click2Clinic</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name  count\n",
       "0      TouchKin    1.0\n",
       "1       Ethinos    1.0\n",
       "2  Leverage Edu    7.0\n",
       "3          Zepo    4.0\n",
       "4  Click2Clinic    2.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_df=pd.DataFrame(name,columns=[\"name\"])\n",
    "investor_count_df=pd.DataFrame(investors_count,columns=[\"count\"]).astype(np.float32)\n",
    "data_df = pd.DataFrame()\n",
    "name_df=name_df.join(investor_count_df)\n",
    "name_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.set()\n",
    "#sns.barplot(x=\"name\",y=\"count\", data=name_df)\n",
    "#plt.xticks(rotation=\"vertical\")\n",
    "#plt.rcParams[\"figure.figsize\"] = [50,60]\n",
    "#plt.show()\n",
    "#trying to figure out number of investors attracted by a company.\n",
    "#mission failed: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Innov8\n",
      "MyChild App\n"
     ]
    }
   ],
   "source": [
    "for j,i in name_df.iterrows():\n",
    "    if i[\"count\"] == max(name_df[\"count\"]):\n",
    "        print(i[\"name\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>name</th>\n",
       "      <th>count</th>\n",
       "      <th>vertical</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TouchKin</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Technology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ethinos</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Technology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Leverage Edu</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Consumer Internet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Zepo</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Consumer Internet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Click2Clinic</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Consumer Internet</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name  count           vertical\n",
       "0      TouchKin    1.0         Technology\n",
       "1       Ethinos    1.0         Technology\n",
       "2  Leverage Edu    7.0  Consumer Internet\n",
       "3          Zepo    4.0  Consumer Internet\n",
       "4  Click2Clinic    2.0  Consumer Internet"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vertical_df=pd.DataFrame(vertical,columns=[\"vertical\"])\n",
    "name_df = name_df.join(vertical_df)\n",
    "name_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vertical_unique = np.unique(vertical)\n",
    "leading_verticals_investors_count = []\n",
    "for j,i in name_df.iterrows():\n",
    "    if i[\"count\"] >6:\n",
    "        leading_verticals_investors_count.append(i[\"vertical\"])\n",
    "leading_verticals_investors_count = np.unique(leading_verticals_investors_count) \n",
    "len(leading_verticals_investors_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.0 Alternate Mobile Monetization platform\n",
      "1502.0 Consumer Internet\n",
      "8.0 Crowd funding platform\n",
      "7.0 Data Science & UX design Learning platform\n",
      "7.0 Doctor Appointment booking app\n",
      "111.0 ECommerce\n",
      "8.0 Easy Business Loans platform\n",
      "9.0 Ecommerce\n",
      "43.0 Education\n",
      "20.0 Finance\n",
      "7.0 Fund Raising Platform\n",
      "7.0 Last Minute Hotel Booking App\n",
      "50.0 Logistics\n",
      "8.0 Mobile Food Ordering app\n",
      "7.0 Mobile Growth HackingPlatform\n",
      "7.0 Online Counselling and psychological support platform\n",
      "7.0 P2P Payments platform\n",
      "7.0 Parents & Kids Online platform\n",
      "582.0 Technology\n",
      "334.0 eCommerce\n",
      "0 nan\n"
     ]
    }
   ],
   "source": [
    "for i in leading_verticals_investors_count:\n",
    "    cnt=0\n",
    "    for j,k in name_df.iterrows():\n",
    "        if k[\"vertical\"] == i:\n",
    "            cnt=cnt+k[\"count\"]\n",
    "    print(cnt,i)        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#from the about data we can see that \"Consumer Internet\" is the vertical with the most number of investors which means more\n",
    "#people are interested in this vertical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "CI_fundtype=[]\n",
    "for j,i in startup_df.iterrows():\n",
    "    if i[\"IndustryVertical\"] == \"Consumer Internet\":\n",
    "        CI_fundtype.append(i[\"InvestmentType\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "1 Debt Funding\n",
      "298 Private Equity\n",
      "473 Seed Funding\n"
     ]
    }
   ],
   "source": [
    "CI_fundtype_unique=np.unique(CI_fundtype)\n",
    "print(len(CI_fundtype_unique))\n",
    "for i in CI_fundtype_unique:\n",
    "    cnt=0\n",
    "    for j in CI_fundtype:\n",
    "        if j==i:\n",
    "            cnt=cnt+1\n",
    "    print(cnt,i)   \n",
    "#from the output below we get the info that the most demanding startup industry vertical get most of its funding from SEED FUNDING.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "150 Private Equity\n",
      "163 Seed Funding\n"
     ]
    }
   ],
   "source": [
    "tech_fundtype=[]\n",
    "for j,i in startup_df.iterrows():\n",
    "    if i[\"IndustryVertical\"] == \"Technology\":\n",
    "        tech_fundtype.append(i[\"InvestmentType\"])\n",
    "tech_fundtype_unique=np.unique(tech_fundtype)\n",
    "print(len(tech_fundtype_unique))\n",
    "for i in tech_fundtype_unique:\n",
    "    cnt=0\n",
    "    for j in tech_fundtype:\n",
    "        if j==i:\n",
    "            cnt=cnt+1\n",
    "    print(cnt,i)\n",
    "#below output shows that technology which is second most demanding vertical has most of its funding mainly from Private Equity and Seed Funding.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "123 Private Equity\n",
      "104 Seed Funding\n"
     ]
    }
   ],
   "source": [
    "EC_fundtype=[]\n",
    "for j,i in startup_df.iterrows():\n",
    "    if (i[\"IndustryVertical\"] == \"ECommerce\") or (i[\"IndustryVertical\"] == \"Ecommerce\") or (i[\"IndustryVertical\"] == \"eCommerce\"):\n",
    "        EC_fundtype.append(i[\"InvestmentType\"])\n",
    "EC_fundtype_unique=np.unique(EC_fundtype)\n",
    "print(len(EC_fundtype_unique))\n",
    "for i in EC_fundtype_unique:\n",
    "    cnt=0\n",
    "    for j in EC_fundtype:\n",
    "        if j==i:\n",
    "            cnt=cnt+1\n",
    "    print(cnt,i)   \n",
    "#below output shows that E-Commerce have their funding mainly of type Private Equity and Seed Funding.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def abc(verti):\n",
    "    a_list = []\n",
    "    for j,i in startup_df.iterrows():\n",
    "        if (i[\"IndustryVertical\"] == verti):\n",
    "            a_list.append((i[\"InvestmentType\"]))\n",
    "    a_list_unique = np.unique(a_list)\n",
    "    b_list=[]\n",
    "    for i in a_list_unique:\n",
    "        cnt=0\n",
    "        for j in a_list:\n",
    "            if j==i:\n",
    "                cnt=cnt+1\n",
    "        b_list.append([cnt,i]) \n",
    "    return b_list    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Agra', 'Ahmedabad', 'Bangalore', 'Bangalore / Palo Alto',\n",
       "       'Bangalore / SFO', 'Bangalore / San Mateo', 'Bangalore / USA',\n",
       "       'Bangalore/ Bangkok', 'Belgaum', 'Bhopal', 'Boston', 'Chandigarh',\n",
       "       'Chennai', 'Chennai/ Singapore', 'Coimbatore',\n",
       "       'Dallas / Hyderabad', 'Delhi', 'Goa', 'Goa/Hyderabad', 'Gurgaon',\n",
       "       'Gurgaon / SFO', 'Gwalior', 'Hubli', 'Hyderabad', 'Hyderabad/USA',\n",
       "       'India / US', 'Indore', 'Jaipur', 'Jodhpur', 'Kanpur', 'Karur',\n",
       "       'Kerala', 'Kochi', 'Kolkata', 'Kozhikode', 'London', 'Lucknow',\n",
       "       'Missourie', 'Mumbai', 'Mumbai / Global', 'Mumbai / NY',\n",
       "       'Mumbai / UK', 'Nagpur', 'New Delhi', 'New Delhi / California',\n",
       "       'New Delhi / US', 'New Delhi/ Houston', 'New York/ India', 'Noida',\n",
       "       'Noida / Singapore', 'Panaji', 'Pune', 'Pune / Dubai',\n",
       "       'Pune / Singapore', 'Pune / US', 'Pune/Seattle', 'SFO / Bangalore',\n",
       "       'Seattle / Bangalore', 'Siliguri', 'Singapore', 'Surat',\n",
       "       'Trivandrum', 'US', 'US/India', 'USA', 'USA/India', 'Udaipur',\n",
       "       'Udupi', 'Vadodara', 'Varanasi', 'bangalore', 'nan'], dtype='<U22')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Debt Funding': 1, 'Private Equity': 11, 'Seed Funding': 23}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def city_wise_type_list(city):\n",
    "    a_list=[]\n",
    "    b_list = {}\n",
    "    for j,i in startup_df.iterrows():\n",
    "        if i[\"CityLocation\"] == city:\n",
    "            a_list.append(i[\"InvestmentType\"])\n",
    "    a_list_unique = np.unique(a_list)\n",
    "    for i in a_list_unique:\n",
    "        cnt=0\n",
    "        for j in a_list:\n",
    "            if i==j:\n",
    "                cnt=cnt+1\n",
    "        b_list[i]=cnt \n",
    "    return b_list    \n",
    "city_wise_type_list(\"Ahmedabad\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "type_unique = np.unique(Type)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph_df = pd.DataFrame(index=city_unique)\n",
    "graph_df = graph_df.join(pd.DataFrame(columns=type_unique))\n",
    "len(graph_df)\n",
    "for key,value in city_wise_type_list(\"Agra\").items():\n",
    "    graph_df.loc[\"Agra\":\"Agra\",key:key] = value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in city_unique:\n",
    "    for key,value in city_wise_type_list(i).items():\n",
    "        graph_df.loc[i:i,key:key] = value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph_df = graph_df.fillna(0)\n",
    "#this gives us the info that which city is giving which type of funding "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "a float is required",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-27-cdcdfecf2b7f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mNaN_amount\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mmath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"kjh\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: a float is required"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "import math\n",
    "NaN_amount=[]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "startup_df.iloc[0,8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
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   },
   "outputs": [],
   "source": []
  },
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   "cell_type": "code",
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   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
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   "cell_type": "code",
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   },
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   "source": []
  },
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   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": []
  },
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   "cell_type": "code",
   "execution_count": null,
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   },
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  },
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   "cell_type": "code",
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   "cell_type": "code",
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  {
   "cell_type": "code",
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  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "code",
   "execution_count": null,
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