Startups april 2022 analysis and visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# <b> Data cleaning, Analysing and Visualization using </b>\n",
    "    \n",
    "    \n",
    "    - Pandas  \n",
    "    - Matplotlib\n",
    "    - seaborn.\n",
    "\n",
    "<img src = \"https://www.bigscal.com/wp-content/uploads/2022/03/data-analysis-and-visualization.jpg\" width = \"570\" height = \"250\">\n",
    "\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "> Startups may be small companies but they can play a significant role in economic growth.Startup funding, or startup capital, is money that an entrepreneur uses to launch a new business. This money can be used for hiring employees, renting space, buying inventory or other operating expenses that help a business get started. \n",
    "\n",
    "> Here is the report of Startup funding for April 2022.\n",
    "\n",
    "***\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <b> Importing necessary `libraries` </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### <b> Import and reading the required Dataset </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading csv file\n",
    "df=pd.read_csv(\"Indian Startups - Funding  Investors Data April 2022.csv\",encoding='cp1252')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Founded</th>\n",
       "      <th>Location</th>\n",
       "      <th>Sector</th>\n",
       "      <th>What it does</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investor</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dyte</td>\n",
       "      <td>2020</td>\n",
       "      <td>Bengaluru</td>\n",
       "      <td>IT Services and IT Consulting</td>\n",
       "      <td>Dyte provides the easiest way to add real-time...</td>\n",
       "      <td>Felix Lee,Vivan</td>\n",
       "      <td>Multiples</td>\n",
       "      <td>$15,300,000</td>\n",
       "      <td>undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kirana Bazaar</td>\n",
       "      <td>2018</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>Oline grocery</td>\n",
       "      <td>Kirana Bazaar is a neighborhood store network ...</td>\n",
       "      <td>Abhishek Halder, Anilesh Yadav</td>\n",
       "      <td>Multiples</td>\n",
       "      <td>$32,800,000</td>\n",
       "      <td>undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Phool</td>\n",
       "      <td>2017</td>\n",
       "      <td>kanpur</td>\n",
       "      <td>Enviromental Engineering</td>\n",
       "      <td>Phool is a biomaterial startup.</td>\n",
       "      <td>Ankit Agrawal</td>\n",
       "      <td>Multiples</td>\n",
       "      <td>$9,400,000</td>\n",
       "      <td>undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Doceree</td>\n",
       "      <td>2019</td>\n",
       "      <td>Parsippany</td>\n",
       "      <td>Information Technology &amp; Services</td>\n",
       "      <td>Doceree is a fully integrated programmatic ad ...</td>\n",
       "      <td>Daleep Manhas, Harshit Jain, Stephen Hoelper</td>\n",
       "      <td>Multiples</td>\n",
       "      <td>12,000,000</td>\n",
       "      <td>undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Acko</td>\n",
       "      <td>2016</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Health insurance</td>\n",
       "      <td>Acko is a digital insurance company with a lic...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Multiples</td>\n",
       "      <td>$458,000,000</td>\n",
       "      <td>Series D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FirstCry</td>\n",
       "      <td>2010</td>\n",
       "      <td>Pune</td>\n",
       "      <td>Online shopping</td>\n",
       "      <td>FirstCry is an online shopping store offering ...</td>\n",
       "      <td>Amitava Saha, Prashant Jadhav, Sanket Hattimat...</td>\n",
       "      <td>Multiples</td>\n",
       "      <td>$74,140</td>\n",
       "      <td>undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Meesho</td>\n",
       "      <td>2015</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Online shopping</td>\n",
       "      <td>Meesho operates as an online reselling platfor...</td>\n",
       "      <td>Sanjeev Barnwal, Vidit Aatrey</td>\n",
       "      <td>Multiples</td>\n",
       "      <td>$1,100,000,000</td>\n",
       "      <td>Series F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NoBroker</td>\n",
       "      <td>2013</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Housing rent</td>\n",
       "      <td>NoBroker is a real-estate platform that makes ...</td>\n",
       "      <td>Akhil Gupta, Amit Agarwal,</td>\n",
       "      <td>Multiples</td>\n",
       "      <td>$426,000,000</td>\n",
       "      <td>Series E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Paytm</td>\n",
       "      <td>2010</td>\n",
       "      <td>Noida</td>\n",
       "      <td>Financial services</td>\n",
       "      <td>Paytm is a digital payments start-up that offe...</td>\n",
       "      <td>Akshay Khanna, Vijay Shekhar</td>\n",
       "      <td>Multiples</td>\n",
       "      <td>$1,200,000,000</td>\n",
       "      <td>undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ShareChat</td>\n",
       "      <td>2015</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Social networking</td>\n",
       "      <td>ShareChat is a social networking and regional ...</td>\n",
       "      <td>Ankush Sachdeva, Bhanu</td>\n",
       "      <td>Multiples</td>\n",
       "      <td>$1,400,000,000</td>\n",
       "      <td>Series G</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Company Name  Founded    Location                             Sector  \\\n",
       "0           Dyte     2020   Bengaluru      IT Services and IT Consulting   \n",
       "1  Kirana Bazaar     2018     Gurgaon                      Oline grocery   \n",
       "2          Phool     2017      kanpur           Enviromental Engineering   \n",
       "3        Doceree     2019  Parsippany  Information Technology & Services   \n",
       "4           Acko     2016      Mumbai                   Health insurance   \n",
       "5       FirstCry     2010        Pune                    Online shopping   \n",
       "6         Meesho     2015   Bangalore                    Online shopping   \n",
       "7       NoBroker     2013   Bangalore                       Housing rent   \n",
       "8          Paytm     2010       Noida                 Financial services   \n",
       "9      ShareChat     2015   Bangalore                  Social networking   \n",
       "\n",
       "                                        What it does  \\\n",
       "0  Dyte provides the easiest way to add real-time...   \n",
       "1  Kirana Bazaar is a neighborhood store network ...   \n",
       "2                    Phool is a biomaterial startup.   \n",
       "3  Doceree is a fully integrated programmatic ad ...   \n",
       "4  Acko is a digital insurance company with a lic...   \n",
       "5  FirstCry is an online shopping store offering ...   \n",
       "6  Meesho operates as an online reselling platfor...   \n",
       "7  NoBroker is a real-estate platform that makes ...   \n",
       "8  Paytm is a digital payments start-up that offe...   \n",
       "9  ShareChat is a social networking and regional ...   \n",
       "\n",
       "                                            Founders   Investor  \\\n",
       "0                                    Felix Lee,Vivan  Multiples   \n",
       "1                     Abhishek Halder, Anilesh Yadav  Multiples   \n",
       "2                                      Ankit Agrawal  Multiples   \n",
       "3       Daleep Manhas, Harshit Jain, Stephen Hoelper  Multiples   \n",
       "4                                                NaN  Multiples   \n",
       "5  Amitava Saha, Prashant Jadhav, Sanket Hattimat...  Multiples   \n",
       "6                      Sanjeev Barnwal, Vidit Aatrey  Multiples   \n",
       "7                         Akhil Gupta, Amit Agarwal,  Multiples   \n",
       "8                       Akshay Khanna, Vijay Shekhar  Multiples   \n",
       "9                             Ankush Sachdeva, Bhanu  Multiples   \n",
       "\n",
       "           Amount        Stage  \n",
       "0     $15,300,000  undisclosed  \n",
       "1     $32,800,000  undisclosed  \n",
       "2      $9,400,000  undisclosed  \n",
       "3      12,000,000  undisclosed  \n",
       "4    $458,000,000     Series D  \n",
       "5         $74,140  undisclosed  \n",
       "6  $1,100,000,000     Series F  \n",
       "7    $426,000,000     Series E  \n",
       "8  $1,200,000,000  undisclosed  \n",
       "9  $1,400,000,000     Series G  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Founded</th>\n",
       "      <th>Location</th>\n",
       "      <th>Sector</th>\n",
       "      <th>What it does</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investor</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Eximpe</td>\n",
       "      <td>2021</td>\n",
       "      <td>Eximpe is Managing sellers, buyers, processes,...</td>\n",
       "      <td>Arjun Zacharia</td>\n",
       "      <td>Axel Wehr and Rohit M.A.</td>\n",
       "      <td>$270,000,000</td>\n",
       "      <td>Seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Limechat</td>\n",
       "      <td>2020</td>\n",
       "      <td>Faridabad</td>\n",
       "      <td>Software</td>\n",
       "      <td>Limechat provides a personalised shopping expe...</td>\n",
       "      <td>Aniket Bajpai, Nikhil Gupta</td>\n",
       "      <td>Pi Ventures and Stellaris Venture Partners</td>\n",
       "      <td>$5,000,000</td>\n",
       "      <td>Seed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Medisage</td>\n",
       "      <td>2019</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Medisage Connect with Global doctors, watch vi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bessemer Venture Partners and Siddharth Sikchi</td>\n",
       "      <td>$530,000,000</td>\n",
       "      <td>undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Spyne</td>\n",
       "      <td>2018</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>Software</td>\n",
       "      <td>Spyne is a Deep Tech startup helping businesse...</td>\n",
       "      <td>Deepti Prasad, Sanjay Kumar</td>\n",
       "      <td>Pentathlon Ventures and Core91 Fund</td>\n",
       "      <td>$7,000,000</td>\n",
       "      <td>Series A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Stockal</td>\n",
       "      <td>2016</td>\n",
       "      <td>Newyork</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Stockal is the platform for cross-border savin...</td>\n",
       "      <td>Rajan K Natarajan, Sitashwa Srivastava, Vinay ...</td>\n",
       "      <td>Hashed and A'Z Angels</td>\n",
       "      <td>$14,800,000</td>\n",
       "      <td>Series A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>YouKraft</td>\n",
       "      <td>2020</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Publishing</td>\n",
       "      <td>YouKraft is an e-commerce marketplace for all ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Goel Family Office</td>\n",
       "      <td>76,000,000</td>\n",
       "      <td>Seed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>EPNS</td>\n",
       "      <td>2020</td>\n",
       "      <td>BUIDLing World's First Decentralized Notificat...</td>\n",
       "      <td>Harsh Rajat, Richa Joshi</td>\n",
       "      <td>BR Capital and LD Capital</td>\n",
       "      <td>$66,000,000</td>\n",
       "      <td>Seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Securden</td>\n",
       "      <td>2018</td>\n",
       "      <td>Delaware</td>\n",
       "      <td>Artificial Intelligence</td>\n",
       "      <td>Securden is a Windows Privilege Management Sof...</td>\n",
       "      <td>Balasubramanian Venkatramani, Kumaran Balan</td>\n",
       "      <td>Accel and Tiger Global Management</td>\n",
       "      <td>$11,700,000</td>\n",
       "      <td>Series A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Mesh</td>\n",
       "      <td>2020</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>Enterprise Software</td>\n",
       "      <td>Mesh is a social performance management platfo...</td>\n",
       "      <td>Gaurav Chaubey, Rahul Singh, Saurabh Nangia</td>\n",
       "      <td>Ankit Nagori and Sumon Sadhu</td>\n",
       "      <td>$16,100,000</td>\n",
       "      <td>Series A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Last9</td>\n",
       "      <td>2020</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>Software</td>\n",
       "      <td>Last9 is a Site Reliability Engineering platfo...</td>\n",
       "      <td>Nishant Modak, Piyush Verma</td>\n",
       "      <td>Better Capital and Sequoia Capital India</td>\n",
       "      <td>$13,000,000</td>\n",
       "      <td>Series A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Company Name  Founded                                           Location  \\\n",
       "85       Eximpe     2021  Eximpe is Managing sellers, buyers, processes,...   \n",
       "86     Limechat     2020                                          Faridabad   \n",
       "87     Medisage     2019                                             Mumbai   \n",
       "88        Spyne     2018                                            Gurgaon   \n",
       "89      Stockal     2016                                            Newyork   \n",
       "90     YouKraft     2020                                           Banglore   \n",
       "91         EPNS     2020  BUIDLing World's First Decentralized Notificat...   \n",
       "92     Securden     2018                                           Delaware   \n",
       "93         Mesh     2020                                           San Jose   \n",
       "94        Last9     2020                                        Santa Clara   \n",
       "\n",
       "                      Sector  \\\n",
       "85            Arjun Zacharia   \n",
       "86                  Software   \n",
       "87                Healthcare   \n",
       "88                  Software   \n",
       "89                   Finance   \n",
       "90                Publishing   \n",
       "91  Harsh Rajat, Richa Joshi   \n",
       "92   Artificial Intelligence   \n",
       "93       Enterprise Software   \n",
       "94                  Software   \n",
       "\n",
       "                                         What it does  \\\n",
       "85                           Axel Wehr and Rohit M.A.   \n",
       "86  Limechat provides a personalised shopping expe...   \n",
       "87  Medisage Connect with Global doctors, watch vi...   \n",
       "88  Spyne is a Deep Tech startup helping businesse...   \n",
       "89  Stockal is the platform for cross-border savin...   \n",
       "90  YouKraft is an e-commerce marketplace for all ...   \n",
       "91                          BR Capital and LD Capital   \n",
       "92  Securden is a Windows Privilege Management Sof...   \n",
       "93  Mesh is a social performance management platfo...   \n",
       "94  Last9 is a Site Reliability Engineering platfo...   \n",
       "\n",
       "                                             Founders  \\\n",
       "85                                       $270,000,000   \n",
       "86                        Aniket Bajpai, Nikhil Gupta   \n",
       "87                                                NaN   \n",
       "88                        Deepti Prasad, Sanjay Kumar   \n",
       "89  Rajan K Natarajan, Sitashwa Srivastava, Vinay ...   \n",
       "90                                                NaN   \n",
       "91                                        $66,000,000   \n",
       "92        Balasubramanian Venkatramani, Kumaran Balan   \n",
       "93        Gaurav Chaubey, Rahul Singh, Saurabh Nangia   \n",
       "94                        Nishant Modak, Piyush Verma   \n",
       "\n",
       "                                          Investor        Amount        Stage  \n",
       "85                                            Seed           NaN          NaN  \n",
       "86      Pi Ventures and Stellaris Venture Partners    $5,000,000         Seed  \n",
       "87  Bessemer Venture Partners and Siddharth Sikchi  $530,000,000  undisclosed  \n",
       "88             Pentathlon Ventures and Core91 Fund    $7,000,000     Series A  \n",
       "89                           Hashed and A'Z Angels   $14,800,000     Series A  \n",
       "90                              Goel Family Office    76,000,000         Seed  \n",
       "91                                            Seed           NaN          NaN  \n",
       "92               Accel and Tiger Global Management   $11,700,000     Series A  \n",
       "93                    Ankit Nagori and Sumon Sadhu   $16,100,000     Series A  \n",
       "94        Better Capital and Sequoia Capital India   $13,000,000     Series A  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows the first and last `10 rows` of the dataset. From these we can easily understand the nature of the dataset.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <b> Getting more info about the dataset. </b>\n",
    "\n",
    "- Getting shape of the dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(95, 9)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> so this dataset consist of 95 rows and 9 columns. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Checking the Datatype of each variable in Dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 95 entries, 0 to 94\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   Company Name  95 non-null     object\n",
      " 1   Founded       95 non-null     int64 \n",
      " 2   Location      94 non-null     object\n",
      " 3   Sector        95 non-null     object\n",
      " 4   What it does  95 non-null     object\n",
      " 5   Founders      85 non-null     object\n",
      " 6   Investor      85 non-null     object\n",
      " 7   Amount        93 non-null     object\n",
      " 8   Stage         92 non-null     object\n",
      "dtypes: int64(1), object(8)\n",
      "memory usage: 6.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Here most of the variables are in \"Object\" type."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Getting percentage of empty cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0409356725146197"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_cells = np.product(df.shape)\n",
    "empty_cells = df.isnull().sum().sum()\n",
    "percent_empty = (empty_cells/total_cells)*100\n",
    "percent_empty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Nearly `3%` of the data is filled with empty cells. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# <b> Data Cleaning </b>\n",
    "- Checking for duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()\n",
    "# the dataset doesn't contain any duplicated cells"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " > The dataset doesn't contain any duplicated cells. So there is no need to drop duplicates.\n",
    " \n",
    " ---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Handling misplaced cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Founded</th>\n",
       "      <th>Location</th>\n",
       "      <th>Sector</th>\n",
       "      <th>What it does</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investor</th>\n",
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       "      <th>Stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Eximpe</td>\n",
       "      <td>2021</td>\n",
       "      <td>Eximpe is Managing sellers, buyers, processes,...</td>\n",
       "      <td>Arjun Zacharia</td>\n",
       "      <td>Axel Wehr and Rohit M.A.</td>\n",
       "      <td>$270,000,000</td>\n",
       "      <td>Seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>EPNS</td>\n",
       "      <td>2020</td>\n",
       "      <td>BUIDLing World's First Decentralized Notificat...</td>\n",
       "      <td>Harsh Rajat, Richa Joshi</td>\n",
       "      <td>BR Capital and LD Capital</td>\n",
       "      <td>$66,000,000</td>\n",
       "      <td>Seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Company Name  Founded                                           Location  \\\n",
       "85       Eximpe     2021  Eximpe is Managing sellers, buyers, processes,...   \n",
       "91         EPNS     2020  BUIDLing World's First Decentralized Notificat...   \n",
       "\n",
       "                      Sector               What it does      Founders  \\\n",
       "85            Arjun Zacharia   Axel Wehr and Rohit M.A.  $270,000,000   \n",
       "91  Harsh Rajat, Richa Joshi  BR Capital and LD Capital   $66,000,000   \n",
       "\n",
       "   Investor Amount Stage  \n",
       "85     Seed    NaN   NaN  \n",
       "91     Seed    NaN   NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "misplaced_cells = df['Amount'].isnull()\n",
    "df[misplaced_cells]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> we can notice that some values are misplaced. Here the `Amount` variable will be useful in analysis. Instead of removing the cells, Replacing them with the corresponding values from respective rows for better analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[85,'Amount']= '$270,000,000'\n",
    "df.loc[91,'Amount'] = '$66,000,000'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Founded</th>\n",
       "      <th>Location</th>\n",
       "      <th>Sector</th>\n",
       "      <th>What it does</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investor</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Eximpe</td>\n",
       "      <td>2021</td>\n",
       "      <td>Eximpe is Managing sellers, buyers, processes,...</td>\n",
       "      <td>Arjun Zacharia</td>\n",
       "      <td>Axel Wehr and Rohit M.A.</td>\n",
       "      <td>$270,000,000</td>\n",
       "      <td>Seed</td>\n",
       "      <td>$270,000,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>EPNS</td>\n",
       "      <td>2020</td>\n",
       "      <td>BUIDLing World's First Decentralized Notificat...</td>\n",
       "      <td>Harsh Rajat, Richa Joshi</td>\n",
       "      <td>BR Capital and LD Capital</td>\n",
       "      <td>$66,000,000</td>\n",
       "      <td>Seed</td>\n",
       "      <td>$66,000,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Company Name  Founded                                           Location  \\\n",
       "85       Eximpe     2021  Eximpe is Managing sellers, buyers, processes,...   \n",
       "91         EPNS     2020  BUIDLing World's First Decentralized Notificat...   \n",
       "\n",
       "                      Sector               What it does      Founders  \\\n",
       "85            Arjun Zacharia   Axel Wehr and Rohit M.A.  $270,000,000   \n",
       "91  Harsh Rajat, Richa Joshi  BR Capital and LD Capital   $66,000,000   \n",
       "\n",
       "   Investor        Amount Stage  \n",
       "85     Seed  $270,000,000   NaN  \n",
       "91     Seed   $66,000,000   NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[misplaced_cells]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> - In order to analyze `Amount` variable, we need need to convert them to `int` rather than `object`.\n",
    "> - So removing the special characters such as  `$`, `,`, `.` and variables such as `Undisclosed` is necessery to convert them as `int`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing the special characters ('$',',','.')\n",
    "df['Amount']=df['Amount'].str.replace(r'\\W','',regex=True)\n",
    "\n",
    "# Replacing 'Undisclosed' with '0'\n",
    "df['Amount']=df['Amount'].str.replace('Undisclosed','0')\n",
    "\n",
    "# Changing datatype\n",
    "df[\"Amount\"]=df[\"Amount\"].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 95 entries, 0 to 94\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   Company Name  95 non-null     object\n",
      " 1   Founded       95 non-null     int64 \n",
      " 2   Location      94 non-null     object\n",
      " 3   Sector        95 non-null     object\n",
      " 4   What it does  95 non-null     object\n",
      " 5   Founders      85 non-null     object\n",
      " 6   Investor      85 non-null     object\n",
      " 7   Amount        95 non-null     int64 \n",
      " 8   Stage         92 non-null     object\n",
      "dtypes: int64(2), object(7)\n",
      "memory usage: 6.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Dtype of `Amount` becomes `int64`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Company Name     0\n",
       "Founded          0\n",
       "Location         1\n",
       "Sector           0\n",
       "What it does     0\n",
       "Founders        10\n",
       "Investor        10\n",
       "Amount           0\n",
       "Stage            3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Now let's fill the empty cells with `Undisclosed`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Founded</th>\n",
       "      <th>Location</th>\n",
       "      <th>Sector</th>\n",
       "      <th>What it does</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investor</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dyte</td>\n",
       "      <td>2020</td>\n",
       "      <td>Bengaluru</td>\n",
       "      <td>IT Services and IT Consulting</td>\n",
       "      <td>Dyte provides the easiest way to add real-time...</td>\n",
       "      <td>Felix Lee,Vivan</td>\n",
       "      <td>Multiples</td>\n",
       "      <td>15300000</td>\n",
       "      <td>undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kirana Bazaar</td>\n",
       "      <td>2018</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>Oline grocery</td>\n",
       "      <td>Kirana Bazaar is a neighborhood store network ...</td>\n",
       "      <td>Abhishek Halder, Anilesh Yadav</td>\n",
       "      <td>Multiples</td>\n",
       "      <td>32800000</td>\n",
       "      <td>undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Phool</td>\n",
       "      <td>2017</td>\n",
       "      <td>kanpur</td>\n",
       "      <td>Enviromental Engineering</td>\n",
       "      <td>Phool is a biomaterial startup.</td>\n",
       "      <td>Ankit Agrawal</td>\n",
       "      <td>Multiples</td>\n",
       "      <td>9400000</td>\n",
       "      <td>undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Doceree</td>\n",
       "      <td>2019</td>\n",
       "      <td>Parsippany</td>\n",
       "      <td>Information Technology &amp; Services</td>\n",
       "      <td>Doceree is a fully integrated programmatic ad ...</td>\n",
       "      <td>Daleep Manhas, Harshit Jain, Stephen Hoelper</td>\n",
       "      <td>Multiples</td>\n",
       "      <td>12000000</td>\n",
       "      <td>undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Acko</td>\n",
       "      <td>2016</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Health insurance</td>\n",
       "      <td>Acko is a digital insurance company with a lic...</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>Multiples</td>\n",
       "      <td>458000000</td>\n",
       "      <td>Series D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>90</th>\n",
       "      <td>YouKraft</td>\n",
       "      <td>2020</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Publishing</td>\n",
       "      <td>YouKraft is an e-commerce marketplace for all ...</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>Goel Family Office</td>\n",
       "      <td>76000000</td>\n",
       "      <td>Seed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>EPNS</td>\n",
       "      <td>2020</td>\n",
       "      <td>BUIDLing World's First Decentralized Notificat...</td>\n",
       "      <td>Harsh Rajat, Richa Joshi</td>\n",
       "      <td>BR Capital and LD Capital</td>\n",
       "      <td>$66,000,000</td>\n",
       "      <td>Seed</td>\n",
       "      <td>66000000</td>\n",
       "      <td>Undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Securden</td>\n",
       "      <td>2018</td>\n",
       "      <td>Delaware</td>\n",
       "      <td>Artificial Intelligence</td>\n",
       "      <td>Securden is a Windows Privilege Management Sof...</td>\n",
       "      <td>Balasubramanian Venkatramani, Kumaran Balan</td>\n",
       "      <td>Accel and Tiger Global Management</td>\n",
       "      <td>11700000</td>\n",
       "      <td>Series A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Mesh</td>\n",
       "      <td>2020</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>Enterprise Software</td>\n",
       "      <td>Mesh is a social performance management platfo...</td>\n",
       "      <td>Gaurav Chaubey, Rahul Singh, Saurabh Nangia</td>\n",
       "      <td>Ankit Nagori and Sumon Sadhu</td>\n",
       "      <td>16100000</td>\n",
       "      <td>Series A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Last9</td>\n",
       "      <td>2020</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>Software</td>\n",
       "      <td>Last9 is a Site Reliability Engineering platfo...</td>\n",
       "      <td>Nishant Modak, Piyush Verma</td>\n",
       "      <td>Better Capital and Sequoia Capital India</td>\n",
       "      <td>13000000</td>\n",
       "      <td>Series A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company Name  Founded                                           Location  \\\n",
       "0            Dyte     2020                                          Bengaluru   \n",
       "1   Kirana Bazaar     2018                                            Gurgaon   \n",
       "2           Phool     2017                                             kanpur   \n",
       "3         Doceree     2019                                         Parsippany   \n",
       "4            Acko     2016                                             Mumbai   \n",
       "..            ...      ...                                                ...   \n",
       "90       YouKraft     2020                                           Banglore   \n",
       "91           EPNS     2020  BUIDLing World's First Decentralized Notificat...   \n",
       "92       Securden     2018                                           Delaware   \n",
       "93           Mesh     2020                                           San Jose   \n",
       "94          Last9     2020                                        Santa Clara   \n",
       "\n",
       "                               Sector  \\\n",
       "0       IT Services and IT Consulting   \n",
       "1                       Oline grocery   \n",
       "2            Enviromental Engineering   \n",
       "3   Information Technology & Services   \n",
       "4                    Health insurance   \n",
       "..                                ...   \n",
       "90                         Publishing   \n",
       "91           Harsh Rajat, Richa Joshi   \n",
       "92            Artificial Intelligence   \n",
       "93                Enterprise Software   \n",
       "94                           Software   \n",
       "\n",
       "                                         What it does  \\\n",
       "0   Dyte provides the easiest way to add real-time...   \n",
       "1   Kirana Bazaar is a neighborhood store network ...   \n",
       "2                     Phool is a biomaterial startup.   \n",
       "3   Doceree is a fully integrated programmatic ad ...   \n",
       "4   Acko is a digital insurance company with a lic...   \n",
       "..                                                ...   \n",
       "90  YouKraft is an e-commerce marketplace for all ...   \n",
       "91                          BR Capital and LD Capital   \n",
       "92  Securden is a Windows Privilege Management Sof...   \n",
       "93  Mesh is a social performance management platfo...   \n",
       "94  Last9 is a Site Reliability Engineering platfo...   \n",
       "\n",
       "                                        Founders  \\\n",
       "0                                Felix Lee,Vivan   \n",
       "1                 Abhishek Halder, Anilesh Yadav   \n",
       "2                                  Ankit Agrawal   \n",
       "3   Daleep Manhas, Harshit Jain, Stephen Hoelper   \n",
       "4                                    Undisclosed   \n",
       "..                                           ...   \n",
       "90                                   Undisclosed   \n",
       "91                                   $66,000,000   \n",
       "92   Balasubramanian Venkatramani, Kumaran Balan   \n",
       "93   Gaurav Chaubey, Rahul Singh, Saurabh Nangia   \n",
       "94                   Nishant Modak, Piyush Verma   \n",
       "\n",
       "                                    Investor     Amount        Stage  \n",
       "0                                  Multiples   15300000  undisclosed  \n",
       "1                                  Multiples   32800000  undisclosed  \n",
       "2                                  Multiples    9400000  undisclosed  \n",
       "3                                  Multiples   12000000  undisclosed  \n",
       "4                                  Multiples  458000000     Series D  \n",
       "..                                       ...        ...          ...  \n",
       "90                        Goel Family Office   76000000         Seed  \n",
       "91                                      Seed   66000000  Undisclosed  \n",
       "92         Accel and Tiger Global Management   11700000     Series A  \n",
       "93              Ankit Nagori and Sumon Sadhu   16100000     Series A  \n",
       "94  Better Capital and Sequoia Capital India   13000000     Series A  \n",
       "\n",
       "[95 rows x 9 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna('Undisclosed',inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Company Name    0\n",
       "Founded         0\n",
       "Location        0\n",
       "Sector          0\n",
       "What it does    0\n",
       "Founders        0\n",
       "Investor        0\n",
       "Amount          0\n",
       "Stage           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Handling inconsistent data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Looks like `Bangalore`, `Banglore`, `Bengaluru` denotes the same location `Banglore`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from thefuzz import process,fuzz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Amsterdam',\n",
       "       \"BUIDLing World's First Decentralized Notification Protocol on Ethereum\",\n",
       "       'Bangalore', 'Banglore', 'Bengaluru', 'Chennai', 'Delaware',\n",
       "       'Eximpe is Managing sellers, buyers, processes, payments, and financial services.',\n",
       "       'Faridabad', 'Gurgaon', 'Haryana', 'Hyderabad', 'Israel', 'Jaipur',\n",
       "       'Kormangala', 'Lonavala', 'London', 'Maharastra', 'Maryland',\n",
       "       'Massachusetts', 'Mumba', 'Mumbai', 'New delhi', 'Newyork',\n",
       "       'Noida', 'Oakland', 'Palo Alto', 'Parsippany', 'Petaluma', 'Pune',\n",
       "       'San Francisco', 'San Jose', 'Santa Clara', 'Surat',\n",
       "       'Tiruchirappall', 'Tokyo', 'Undisclosed', 'Vitznau, Luzern,',\n",
       "       'kanpur'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting unique values.\n",
    "Locations=df['Location'].unique()\n",
    "Locations.sort()\n",
    "Locations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Banglore', 100),\n",
       " ('Bangalore', 94),\n",
       " ('Bengaluru', 59),\n",
       " ('Kormangala', 44),\n",
       " ('kanpur', 43)]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting matches for 'Banglore'\n",
    "matches = process.extract('Banglore',Locations,limit=5,scorer=fuzz.token_sort_ratio)\n",
    "matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Banglore', 'Bangalore', 'Bengaluru']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting almost perfect matches for \"Banglore\"\n",
    "perfect_matches = [matches[0] for matches in matches if matches[1]>=58]\n",
    "perfect_matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      True\n",
       "1     False\n",
       "2     False\n",
       "3     False\n",
       "4     False\n",
       "      ...  \n",
       "90     True\n",
       "91    False\n",
       "92    False\n",
       "93    False\n",
       "94    False\n",
       "Name: Location, Length: 95, dtype: bool"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the position of rows from our dataFrame.\n",
    "rows_with_perfect_matches = df['Location'].isin(perfect_matches)\n",
    "rows_with_perfect_matches   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing \n",
    "df.loc[rows_with_perfect_matches,'Location']='Banglore' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Amsterdam',\n",
       "       \"BUIDLing World's First Decentralized Notification Protocol on Ethereum\",\n",
       "       'Banglore', 'Chennai', 'Delaware',\n",
       "       'Eximpe is Managing sellers, buyers, processes, payments, and financial services.',\n",
       "       'Faridabad', 'Gurgaon', 'Haryana', 'Hyderabad', 'Israel', 'Jaipur',\n",
       "       'Kormangala', 'Lonavala', 'London', 'Maharastra', 'Maryland',\n",
       "       'Massachusetts', 'Mumba', 'Mumbai', 'New delhi', 'Newyork',\n",
       "       'Noida', 'Oakland', 'Palo Alto', 'Parsippany', 'Petaluma', 'Pune',\n",
       "       'San Francisco', 'San Jose', 'Santa Clara', 'Surat',\n",
       "       'Tiruchirappall', 'Tokyo', 'Undisclosed', 'Vitznau, Luzern,',\n",
       "       'kanpur'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locations = df['Location'].unique()\n",
    "locations.sort()\n",
    "locations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Now `Bangalore`, `Banglore`, `Bengaluru` is replaced with `Banglore`.\n",
    "\n",
    "> Now the dataset is cleaned and ready for analysis.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <b> Data analysis and Visualization </b>\n",
    "\n",
    "> For convenience I'm adding a extra variable to the dataset - `Amount_million`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     15.3\n",
       "1     32.8\n",
       "2      9.4\n",
       "3     12.0\n",
       "4    458.0\n",
       "Name: Amount_million, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting amount in million.\n",
    "df['Amount_million']=(df['Amount']/1000000)\n",
    "df['Amount_million'].head()"
   ]
  },
  {
   "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>Company Name</th>\n",
       "      <th>Founded</th>\n",
       "      <th>Location</th>\n",
       "      <th>Sector</th>\n",
       "      <th>What it does</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investor</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Stage</th>\n",
       "      <th>Amount_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dyte</td>\n",
       "      <td>2020</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>IT Services and IT Consulting</td>\n",
       "      <td>Dyte provides the easiest way to add real-time...</td>\n",
       "      <td>Felix Lee,Vivan</td>\n",
       "      <td>Multiples</td>\n",
       "      <td>15300000</td>\n",
       "      <td>undisclosed</td>\n",
       "      <td>15.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kirana Bazaar</td>\n",
       "      <td>2018</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>Oline grocery</td>\n",
       "      <td>Kirana Bazaar is a neighborhood store network ...</td>\n",
       "      <td>Abhishek Halder, Anilesh Yadav</td>\n",
       "      <td>Multiples</td>\n",
       "      <td>32800000</td>\n",
       "      <td>undisclosed</td>\n",
       "      <td>32.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Phool</td>\n",
       "      <td>2017</td>\n",
       "      <td>kanpur</td>\n",
       "      <td>Enviromental Engineering</td>\n",
       "      <td>Phool is a biomaterial startup.</td>\n",
       "      <td>Ankit Agrawal</td>\n",
       "      <td>Multiples</td>\n",
       "      <td>9400000</td>\n",
       "      <td>undisclosed</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Doceree</td>\n",
       "      <td>2019</td>\n",
       "      <td>Parsippany</td>\n",
       "      <td>Information Technology &amp; Services</td>\n",
       "      <td>Doceree is a fully integrated programmatic ad ...</td>\n",
       "      <td>Daleep Manhas, Harshit Jain, Stephen Hoelper</td>\n",
       "      <td>Multiples</td>\n",
       "      <td>12000000</td>\n",
       "      <td>undisclosed</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Acko</td>\n",
       "      <td>2016</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Health insurance</td>\n",
       "      <td>Acko is a digital insurance company with a lic...</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>Multiples</td>\n",
       "      <td>458000000</td>\n",
       "      <td>Series D</td>\n",
       "      <td>458.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Company Name  Founded    Location                             Sector  \\\n",
       "0           Dyte     2020    Banglore      IT Services and IT Consulting   \n",
       "1  Kirana Bazaar     2018     Gurgaon                      Oline grocery   \n",
       "2          Phool     2017      kanpur           Enviromental Engineering   \n",
       "3        Doceree     2019  Parsippany  Information Technology & Services   \n",
       "4           Acko     2016      Mumbai                   Health insurance   \n",
       "\n",
       "                                        What it does  \\\n",
       "0  Dyte provides the easiest way to add real-time...   \n",
       "1  Kirana Bazaar is a neighborhood store network ...   \n",
       "2                    Phool is a biomaterial startup.   \n",
       "3  Doceree is a fully integrated programmatic ad ...   \n",
       "4  Acko is a digital insurance company with a lic...   \n",
       "\n",
       "                                       Founders   Investor     Amount  \\\n",
       "0                               Felix Lee,Vivan  Multiples   15300000   \n",
       "1                Abhishek Halder, Anilesh Yadav  Multiples   32800000   \n",
       "2                                 Ankit Agrawal  Multiples    9400000   \n",
       "3  Daleep Manhas, Harshit Jain, Stephen Hoelper  Multiples   12000000   \n",
       "4                                   Undisclosed  Multiples  458000000   \n",
       "\n",
       "         Stage  Amount_million  \n",
       "0  undisclosed            15.3  \n",
       "1  undisclosed            32.8  \n",
       "2  undisclosed             9.4  \n",
       "3  undisclosed            12.0  \n",
       "4     Series D           458.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FUNDING PER REGION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Amount_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Palo Alto</td>\n",
       "      <td>18300.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Banglore</td>\n",
       "      <td>9681.304316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Kormangala</td>\n",
       "      <td>5000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Mumbai</td>\n",
       "      <td>3980.500023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Noida</td>\n",
       "      <td>2529.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>2496.401655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>1249.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Faridabad</td>\n",
       "      <td>904.300842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Oakland</td>\n",
       "      <td>728.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>New delhi</td>\n",
       "      <td>700.500485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Location  Amount_million\n",
       "24      Palo Alto    18300.000000\n",
       "2        Banglore     9681.304316\n",
       "12     Kormangala     5000.000000\n",
       "19         Mumbai     3980.500023\n",
       "22          Noida     2529.500000\n",
       "7         Gurgaon     2496.401655\n",
       "28  San Francisco     1249.700000\n",
       "6       Faridabad      904.300842\n",
       "23        Oakland      728.100000\n",
       "20      New delhi      700.500485"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "funding_per_region = df.groupby(['Location'])['Amount_million'].sum().reset_index()\n",
    "funding_per_region = funding_per_region.sort_values('Amount_million',ascending=False).head(10)\n",
    "funding_per_region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(10,5))\n",
    "sns.set_palette('Set2')\n",
    "sns.set_style('dark')\n",
    "plt.title(\"FUNDING PER REGION\",family='serif',color='k',size='large')\n",
    "sns.barplot(x = 'Amount_million', y = 'Location', data = funding_per_region)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This barplot shows the top 10 fundings per region.\n",
    "- Startups from `Palo Alto` a city of California received fundings over $18,000 million\n",
    "- Followed by stratups founded at `Banglore` received fundings over $9,000 million.\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FUNDING OVER SECTORS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sector</th>\n",
       "      <th>Amount_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Financial services</td>\n",
       "      <td>21816.502497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Travel Arrangements</td>\n",
       "      <td>5550.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Logistics</td>\n",
       "      <td>2500.000485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>E-commerce</td>\n",
       "      <td>1970.004316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Internet Publishing</td>\n",
       "      <td>1700.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Sector  Amount_million\n",
       "20   Financial services    21816.502497\n",
       "56  Travel Arrangements     5550.000000\n",
       "42            Logistics     2500.000485\n",
       "10           E-commerce     1970.004316\n",
       "41  Internet Publishing     1700.000000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fund_sector = df.groupby('Sector')['Amount_million'].sum().reset_index()\n",
    "fund_sector = fund_sector.sort_values('Amount_million',ascending=False).head()\n",
    "fund_sector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(10,5))\n",
    "sns.set_palette('Set2')\n",
    "sns.set_style('dark')\n",
    "plt.title(\"TOP 5 FUNDING OVER SECTOR\",family='serif',color='k',size='large')\n",
    "sns.barplot(y = 'Amount_million', x = 'Sector', data = fund_sector.head())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This barplot shows the fundings over different sectors.\n",
    "- Startups based on `Financial services` got the most funding followed by `Travel arrangements` and `Logistics Sector`\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FUNDING OVER COMPANY."
   ]
  },
  {
   "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>Company Name</th>\n",
       "      <th>Amount_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Accel</td>\n",
       "      <td>18300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Ola</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Mahindra Logistics</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Verse Innovation</td>\n",
       "      <td>1700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>ShareChat</td>\n",
       "      <td>1400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Company Name  Amount_million\n",
       "0                Accel         18300.0\n",
       "60                 Ola          5000.0\n",
       "46  Mahindra Logistics          2500.0\n",
       "84    Verse Innovation          1700.0\n",
       "71           ShareChat          1400.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "company = df.groupby('Company Name')['Amount_million'].sum().reset_index()\n",
    "company = company.sort_values('Amount_million',ascending=False)\n",
    "company.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(10,5))\n",
    "sns.set_palette('Set2')\n",
    "sns.set_style('dark')\n",
    "plt.title(\"FUNDING OVER COMPANIES\",family='serif',color='k',size='large')\n",
    "sns.barplot(x='Company Name',y='Amount_million',data=company.head())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This barplot shows top 5 fundings over Companies.\n",
    "- `Accel` got funding over $18,000 million.\n",
    "- Followed by `Ola` - $5,000 million and `Mahindra logistics` - $2,500 million.\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NUMBER OF STARTUPS PER REGION "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loc_order=df['Location'].value_counts().head(10).index\n",
    "fig,ax = plt.subplots(figsize=(10,5))\n",
    "sns.set_palette('Set2')\n",
    "sns.set_style('dark')\n",
    "plt.title(\"STARTUPS PER REGION\",family='serif',color='k',size='large')\n",
    "sns.countplot(x='Location',data=df,order=loc_order)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Nearly 25 companies from `Banglore` raised funds.\n",
    "- Followed by `Mumbai` - nearly 10 and `Gurgaon` - nearly 8 companies.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FUNDINGS PER SECTOR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stage_order = df['Stage'].value_counts().head().index\n",
    "fig,ax = plt.subplots(figsize=(10,5))\n",
    "sns.set_style('dark')\n",
    "sns.countplot(x='Stage',data=df,order=stage_order,palette='Set3')\n",
    "plt.title(\"FUNDINGS PER SECTOR\",family='serif',color='k',size='large')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the top 5 Fundings over Sectors.\n",
    "- Here most of the sectors are left disclosed.\n",
    "- Leaving that, there are nearly 10 `Series F` and nearly 10 `Series E` sector.\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SATRTUPS OVER TIME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(10,5))\n",
    "sns.set_style('dark')\n",
    "sns.histplot(x='Founded',data=df,color='teal')\n",
    "plt.title(\"STARTUPS OVER TIME\",family='serif',color='k',size='large')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Large number of startups that founded after year `2000` raised funds while comparing to the startups that founded before year 2000.\n",
    "---\n",
    "---"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('base')",
   "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": "3.9.12"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "c95be5be5ce4f665ea8150410cab273f96bf53dea87635cb260e9a4d2a7d5564"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}