startup_funding project1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import csv\n",
    "import operator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('startup_funding.csv')\n",
    "test1=df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**PROBLEM 1**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Your Friend has developed the Product and he wants to establish the product startup and he is searching for a perfect location where getting the investment has a high chance. But due to its financial restriction, he can choose only between three locations -  Bangalore, Mumbai, and NCR. As a friend, you want to help your friend deciding the location. NCR include Gurgaon, Noida and New Delhi. Find the location where the most number of funding is done. That means, find the location where startups has received funding maximum number of times. Plot the bar graph between location and number of funding. Take city name \"Delhi\" as \"New Delhi\". Check the case-sensitiveness of cities also. That means, at some place instead of \"Bangalore\", \"bangalore\" is given. Take city name as \"Bangalore\". For few startups multiple locations are given, one Indian and one Foreign. Consider the startup if any one of the city lies in given locations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "test1.dropna(inplace = True, subset = ['CityLocation'])\n",
    "test1.reset_index(drop = True, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ind_city(city):\n",
    "    return city.split('/')[0].strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replacing the wrong written city\n",
    "test1['CityLocation'].replace(\"bangalore\", \"Bangalore\", inplace = True)\n",
    "test1['CityLocation'].replace(\"Delhi\", \"New Delhi\", inplace = True)\n",
    "test1['CityLocation'].replace(\"SFO / Bangalor\", \"Bangalore\", inplace = True)\n",
    "test1['CityLocation'].replace(\"Seattle / Bangalore\", \"Bangalore\", inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "test1['CityLocation'] = test1['CityLocation'].apply(ind_city)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "test1=test1[(test1['CityLocation']==\"Mumbai\")|(test1['CityLocation']==\"Bangalore\")|(test1['CityLocation']==\"Gurgaon\")|(test1['CityLocation']==\"New Delhi\")|(test1['CityLocation']==\"Noida\")]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Bangalore', 'Mumbai', 'New Delhi', 'Gurgaon', 'Noida'], dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#extracting the cities and their counts\n",
    "temp = test1['CityLocation'].value_counts()\n",
    "temp\n",
    "city = temp.index\n",
    "city\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([636, 449, 389, 241,  79], dtype=int64)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "No_Of_Funding = temp.values\n",
    "No_Of_Funding "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "City where most number of funding is done among Bangalore, Mumbai and NCR is ' Bangalore '.\n"
     ]
    }
   ],
   "source": [
    "print(\"City where most number of funding is done among Bangalore, Mumbai and NCR is '\",city[0],\"'.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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 Of City</th>\n",
       "      <th>Number Of Fundings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bangalore</td>\n",
       "      <td>636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mumbai</td>\n",
       "      <td>449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>New Delhi</td>\n",
       "      <td>389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Noida</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Name Of City  Number Of Fundings\n",
       "0    Bangalore                 636\n",
       "1       Mumbai                 449\n",
       "2    New Delhi                 389\n",
       "3      Gurgaon                 241\n",
       "4        Noida                  79"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.bar(city,No_Of_Funding,color='green',width=0.8)\n",
    "plt.xlabel(\"City\")\n",
    "plt.xticks(rotation = 40)\n",
    "plt.ylabel(\"Number Of Fundings\")\n",
    "plt.title(\"City vs Number Of Fundings\")\n",
    "plt.show()\n",
    "# accepts iterable items and merges them into a single tuple\n",
    "li = list(zip(city, No_Of_Funding))\n",
    "ff=pd.DataFrame(li,columns=[\"Name Of City\", \"Number Of Fundings\"])\n",
    "ff\n",
    "# x=df.columns[\"Name Of City\"]\n",
    "# y=df.columns[\"Number Of Fundings\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              Number Of Fundings\n",
      "Name Of City                    \n",
      "Bangalore                    636\n",
      "Mumbai                       449\n",
      "New Delhi                    389\n",
      "Gurgaon                      241\n",
      "Noida                         79\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "ff_reset=ff.set_index('Name Of City')\n",
    "print(ff_reset)\n",
    "labels=['Bangalore', 'Mumbai', 'New Delhi', 'Gurgaon', 'Noida']\n",
    " \n",
    "plot = ff_reset.plot.pie(y='Number Of Fundings',labels=labels,figsize=(7,6),autopct='%1.2f%%')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now we need to find top 5 investors "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**PROBLEM 2**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even after trying for so many times, your friend’s startup could not find the investment. So you decided to take this matter in your hand and try to find the list of investors who probably can invest in your friend’s startup. Your list will increase the chance of your friend startup getting some initial investment by contacting these investors. Find the top 5 investors who have invested maximum number of times (consider repeat investments in one company also). In a startup, multiple investors might have invested. So consider each investor for that startup. Ignore undisclosed investors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>...</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2359</th>\n",
       "      <td>2367</td>\n",
       "      <td>29/01/2015</td>\n",
       "      <td>Printvenue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Asia Pacific Internet Group</td>\n",
       "      <td>Private Equity</td>\n",
       "      <td>4,500,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2360</th>\n",
       "      <td>2368</td>\n",
       "      <td>29/01/2015</td>\n",
       "      <td>Graphene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KARSEMVEN Fund</td>\n",
       "      <td>Private Equity</td>\n",
       "      <td>825,000</td>\n",
       "      <td>Govt backed VC Fund</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2361</th>\n",
       "      <td>2369</td>\n",
       "      <td>30/01/2015</td>\n",
       "      <td>Mad Street Den</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Exfinity Fund, GrowX Ventures.</td>\n",
       "      <td>Private Equity</td>\n",
       "      <td>1,500,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2362</th>\n",
       "      <td>2370</td>\n",
       "      <td>30/01/2015</td>\n",
       "      <td>Simplotel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MakeMyTrip</td>\n",
       "      <td>Private Equity</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Strategic Funding, Minority stake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2363</th>\n",
       "      <td>2371</td>\n",
       "      <td>31/01/2015</td>\n",
       "      <td>couponmachine.in</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UK based Group of Angel Investors</td>\n",
       "      <td>Seed Funding</td>\n",
       "      <td>140,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2364 rows × 10 columns</p>\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",
       "...    ...         ...               ...                ...   \n",
       "2359  2367  29/01/2015        Printvenue                NaN   \n",
       "2360  2368  29/01/2015          Graphene                NaN   \n",
       "2361  2369  30/01/2015    Mad Street Den                NaN   \n",
       "2362  2370  30/01/2015         Simplotel                NaN   \n",
       "2363  2371  31/01/2015  couponmachine.in                NaN   \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",
       "...                                             ...          ...   \n",
       "2359                                            NaN          NaN   \n",
       "2360                                            NaN          NaN   \n",
       "2361                                            NaN          NaN   \n",
       "2362                                            NaN          NaN   \n",
       "2363                                            NaN          NaN   \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",
       "...                                                 ...             ...   \n",
       "2359                        Asia Pacific Internet Group  Private Equity   \n",
       "2360                                     KARSEMVEN Fund  Private Equity   \n",
       "2361                     Exfinity Fund, GrowX Ventures.  Private Equity   \n",
       "2362                                         MakeMyTrip  Private Equity   \n",
       "2363                  UK based Group of Angel Investors    Seed Funding   \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",
       "...          ...                                ...  \n",
       "2359   4,500,000                                NaN  \n",
       "2360     825,000                Govt backed VC Fund  \n",
       "2361   1,500,000                                NaN  \n",
       "2362         NaN  Strategic Funding, Minority stake  \n",
       "2363     140,000                                NaN  \n",
       "\n",
       "[2364 rows x 10 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('startup_funding.csv')\n",
    "\n",
    "test2=df.copy()\n",
    "\n",
    "#test2.dropna(inplace = True, subset = ['AmountInUSD'])\n",
    "test2.dropna(inplace = True, subset = ['InvestorsName'])\n",
    "#test2.dropna(inplace = True, subset = ['StartupName'])\n",
    "#correcting the spelling in investors name ignoring undisclosed investors\n",
    "test2['InvestorsName'].replace(\"Undisclosed investors\", \"Undisclosed Investors\", inplace = True)\n",
    "test2['InvestorsName'].replace(\"Undisclosed Investors\",\"\",inplace = True)\n",
    "\n",
    "test2.reset_index(drop = True, inplace = True)\n",
    "test2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to fill dictionary on the basis of investors name\n",
    "def build_dict(array):\n",
    "    d = {}\n",
    "    #d holds investors name as key and the number of investments as value\n",
    "    for i in array:\n",
    "        if ',' not in i and i != \"\":\n",
    "            d[i] = d.get(i, 0) + 1\n",
    "        else:\n",
    "            #splitting the Investors name list\n",
    "            string = i.strip().split(',')\n",
    "            for j in string:\n",
    "                if j != \"\":\n",
    "                    d[j.strip()] = d.get(j.strip(), 0) + 1\n",
    "                \n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dictionary = build_dict(test2['InvestorsName'])\n",
    "\n",
    "#build pandas dataframe of investors name and its number of investments\n",
    "invest = pd.DataFrame(list(dictionary.values()), list(dictionary.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sort the values in descending order \n",
    "invest = invest.sort_values(by = [0], ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = []\n",
    "y = []\n",
    "\n",
    "#we have to just find top 5 investors who invest maximum number of times\n",
    "for i in range(5):\n",
    "    x.append(invest.index[i])\n",
    "    y.append(invest.values[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List Of Top 5 Investors who Invested most number of times :\n",
      "1. Sequoia Capital\n",
      "2. Accel Partners\n",
      "3. Kalaari Capital\n",
      "4. SAIF Partners\n",
      "5. Indian Angel Network\n"
     ]
    }
   ],
   "source": [
    "#printing the top investors\n",
    "print(\"List Of Top 5 Investors who Invested most number of times :\")\n",
    "i = 0\n",
    "for ele in x:\n",
    "    print(i+1,end = \"\")\n",
    "    print(\".\", end = \" \")\n",
    "    print(ele)\n",
    "    i += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,color='green',linestyle='dashed', linewidth = 3, marker='o', markerfacecolor='pink', markersize=12)\n",
    "plt.xlabel(\"Investors Name\")\n",
    "plt.xticks(rotation=40)\n",
    "plt.ylabel(\"Number Of Investments\")\n",
    "plt.title(\"Investors vs Number Of Investments\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name Of Investors</th>\n",
       "      <th>Total Number Of Investments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sequoia Capital</td>\n",
       "      <td>[64]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Accel Partners</td>\n",
       "      <td>[53]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kalaari Capital</td>\n",
       "      <td>[44]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SAIF Partners</td>\n",
       "      <td>[41]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Indian Angel Network</td>\n",
       "      <td>[40]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name Of Investors Total Number Of Investments\n",
       "0       Sequoia Capital                        [64]\n",
       "1        Accel Partners                        [53]\n",
       "2       Kalaari Capital                        [44]\n",
       "3         SAIF Partners                        [41]\n",
       "4  Indian Angel Network                        [40]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " \n",
    "#pandas frame for Investors name and corresponding Number of Investments made by them\n",
    "li = list(zip(x,y))\n",
    "investor_data = pd.DataFrame(li, columns = [\"Name Of Investors\", \"Total Number Of Investments\"])\n",
    "investor_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**PROBLEM 3**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After re-analysing the dataset you found out that some investors have invested in the same startup at different number of funding rounds. So before finalising the previous list, you want to improvise it by finding the top 5 investors who have invested in different number of startups. This list will be more helpful than your previous list in finding the investment for your friend startup. Find the top 5 investors who have invested maximum number of times in different companies. That means, if one investor has invested multiple times in one startup, count one for that company. There are many errors in startup names. Ignore correcting all, just handle the important ones - Ola, Flipkart, Oyo and Paytm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    " test3=df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#correcting the spelling in investors name ignoring undisclosed investors\n",
    "test3['InvestorsName'].replace(\"Undisclosed investors\", \"Undisclosed Investors\", inplace = True)\n",
    "test3['InvestorsName'].replace(\"Undisclosed Investors\",\"\",inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#correcting major startup names\n",
    "test3['StartupName'].replace(\"Ola Cabs\", \"Ola\", inplace = True)\n",
    "test3['StartupName'].replace(\"OyoRooms\", \"Oyo\", inplace = True)\n",
    "test3['StartupName'].replace(\"Oyorooms\", \"Oyo\", inplace = True)\n",
    "test3['StartupName'].replace(\"OYO Rooms\", \"Oyo\", inplace = True)\n",
    "test3['StartupName'].replace(\"Olacabs\", \"Ola\", inplace = True)\n",
    "test3['StartupName'].replace(\"Flipkart.com\", \"Flipkart\", inplace = True)\n",
    "test3['StartupName'].replace(\"Oyo Rooms\", \"Oyo\", inplace = True)\n",
    "test3['StartupName'].replace(\"Paytm Marketplace\", \"Paytm\", inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#filtering the na\n",
    "test3.dropna(inplace = True, subset = [\"InvestorsName\", \"StartupName\"])\n",
    "test3.reset_index(drop = True, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    " #function to create dictionary of investor name making investment to different startups\n",
    "def Create(startup_name, Investor_name):\n",
    "    cnt = {}\n",
    "    #cnt holds investors name as key and the number of investments as value\n",
    "    name_startup = {}\n",
    "    #name_startup holds the investors name as key and list of different companies in which it invests as value\n",
    "    n = len(Investor_name)\n",
    "    for idx in range(n):\n",
    "        i = Investor_name[idx].strip()\n",
    "        name = startup_name[idx].strip()\n",
    "        if ',' not in i and i != \"\":\n",
    "            if name_startup.get(i) is None: #no startup is still funded by this investor\n",
    "                cnt[i] = 1\n",
    "                li = []\n",
    "                li.append(name)\n",
    "                name_startup[i] = li   \n",
    "            else:\n",
    "                li = name_startup[i]\n",
    "                if name not in li:             #checking the given startup has been invested before or not\n",
    "                    cnt[i] = cnt.get(i, 0) + 1\n",
    "                    li.append(name)\n",
    "                    name_startup[i]\n",
    "            \n",
    "        else:\n",
    "            #splitting the Investors name list\n",
    "            string = i.strip().split(',')\n",
    "            for j in string:\n",
    "                k = j.strip()\n",
    "                if k != \"\":\n",
    "                    if name_startup.get(k) is None:  #no startup is still funded by this investor\n",
    "                        cnt[k] = 1\n",
    "                        li = []\n",
    "                        li.append(name)\n",
    "                        name_startup[k] = li\n",
    "                    else:\n",
    "                        li = name_startup[k]\n",
    "                        if name not in li:         #checking the given startup has been invested before or not\n",
    "                            cnt[k] = cnt.get(k, 0) + 1\n",
    "                            li.append(name)\n",
    "                            name_startup[k] = li \n",
    "                \n",
    "    return cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#making dictionary\n",
    "dictionary = Create(test3['StartupName'], test3['InvestorsName'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#build pandas dataframe\n",
    "diff_invest = pd.DataFrame(list(dictionary.values()), list(dictionary.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "diff_invest = diff_invest.sort_values(by = [0], ascending = False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = []\n",
    "y = []\n",
    "\n",
    "#we have to just find top 5 investors who invest maximum number of times in different startups\n",
    "for i in range(5):\n",
    "    x.append(diff_invest.index[i])\n",
    "    y.append(diff_invest.values[i][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List Of Top 5 Investors who Invested most number of times :\n",
      "1. Sequoia Capital\n",
      "2. Accel Partners\n",
      "3. Kalaari Capital\n",
      "4. Indian Angel Network\n",
      "5. Blume Ventures\n"
     ]
    }
   ],
   "source": [
    "#printing the top investors who invested who invested most number of times in different startups\n",
    "print(\"List Of Top 5 Investors who Invested most number of times :\")\n",
    "i = 0\n",
    "for ele in x:\n",
    "    print(i+1,end = \"\")\n",
    "    print(\".\", end = \" \")\n",
    "    print(ele)\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(y,labels=x,shadow=True,explode=(0.1,0.1,0.1,0.1,0.1),autopct = \"%.1f%%\")\n",
    "plt.grid()\n",
    "plt.xticks(rotation=40)\n",
    "plt.title(\"Top 5 Investors and corresponding Number Of Investments In Different Startups\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name Of Investors</th>\n",
       "      <th>Total Number Of Investments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sequoia Capital</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Accel Partners</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kalaari Capital</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Indian Angel Network</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Blume Ventures</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name Of Investors  Total Number Of Investments\n",
       "0       Sequoia Capital                           48\n",
       "1        Accel Partners                           47\n",
       "2       Kalaari Capital                           41\n",
       "3  Indian Angel Network                           40\n",
       "4        Blume Ventures                           36"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "li=list(zip(x,y))\n",
    "investor_data=pd.DataFrame(li,columns=[\"Name Of Investors\", \"Total Number Of Investments\"])\n",
    "investor_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**PROBLEM 4**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even after putting so much effort in finding the probable investors, it didn't turn out to be helpful for your friend. So you went to your investor friend to understand the situation better and your investor friend explained to you about the different Investment Types and their features. This new information will be helpful in finding the right investor. Since your friend startup is at an early stage startup, the best-suited investment type would be - Seed Funding and Crowdfunding. Find the top 5 investors who have invested in a different number of startups and their investment type is Crowdfunding or Seed Funding. Correct spelling of investment types are - \"Private Equity\", \"Seed Funding\", \"Debt Funding\", and \"Crowd Funding\". Keep an eye for any spelling mistake. You can find this by printing unique values from this column. There are many errors in startup names. Ignore correcting all, just handle the important ones - Ola, Flipkart, Oyo and Paytm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test4=df.copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#correcting the spelling in investors name as ignoring the undisclosed investors\n",
    "test4['InvestorsName'].replace(\"Undisclosed investors\", \"Undisclosed Investors\", inplace = True)\n",
    "test4['InvestorsName'].replace(\"Undisclosed Investors\", \"\", inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#correcting major startup names\n",
    "test4['StartupName'].replace(\"Ola Cabs\", \"Ola\", inplace = True)\n",
    "test4['StartupName'].replace(\"OyoRooms\", \"Oyo\", inplace = True)\n",
    "test4['StartupName'].replace(\"Oyorooms\", \"Oyo\", inplace = True)\n",
    "test4['StartupName'].replace(\"OYO Rooms\", \"Oyo\", inplace = True)\n",
    "test4['StartupName'].replace(\"Olacabs\", \"Ola\", inplace = True)\n",
    "test4['StartupName'].replace(\"Flipkart.com\", \"Flipkart\", inplace = True)\n",
    "test4['StartupName'].replace(\"Oyo Rooms\", \"Oyo\", inplace = True)\n",
    "test4['StartupName'].replace(\"Paytm Marketplace\", \"Paytm\", inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#correcting spelling of investment type\n",
    "test4['InvestmentType'].replace(\"PrivateEquity\",\"Private Equity\", inplace = True)\n",
    "test4['InvestmentType'].replace(\"SeedFunding\",\"Seed Funding\", inplace = True)\n",
    "test4['InvestmentType'].replace(\"Crowd funding\",\"Crowd Funding\", inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#filter na \n",
    "test4.dropna(inplace = True, subset = [\"InvestorsName\", \"StartupName\", \"InvestmentType\"])\n",
    "test4.reset_index(inplace = True, drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to create dictionary of investor name making investment to different startups and Investment type is either Seed funding or Crowd funding\n",
    "def Create(startup_name, Investor_name, Invest_type, Investment_type):\n",
    "    cnt = {}\n",
    "    #cnt holds investors name as key and the number of investments as value\n",
    "    name_startup = {}\n",
    "    #name_startup holds the investors name as key and list of different companies in which it invests as value\n",
    "    n = len(Investor_name)\n",
    "    for idx in range(n):\n",
    "        type_of_invest = Invest_type[idx].strip()\n",
    "        if type_of_invest not in Investment_type: #if investement type is either seed or crowd then we will look into the startup name or investors name\n",
    "            continue\n",
    "        i = Investor_name[idx].strip()\n",
    "        name = startup_name[idx].strip()\n",
    "        if ',' not in i and i != \"\":\n",
    "            if name_startup.get(i) is None:    #no startup is still funded by this investor till now\n",
    "                cnt[i] = 1\n",
    "                li = []\n",
    "                li.append(name)\n",
    "                name_startup[i] = li   \n",
    "            else:\n",
    "                li = name_startup[i]\n",
    "                if name not in li:             #checking the given startup has been invested before or not\n",
    "                    cnt[i] = cnt.get(i, 0) + 1\n",
    "                    li.append(name)\n",
    "                    name_startup[i]   \n",
    "        else:\n",
    "            string = i.strip().split(',')\n",
    "            for j in string:\n",
    "                k = j.strip()\n",
    "                if k != \"\":\n",
    "                    if name_startup.get(k) is None:    #no startup is still funded by this investor till now\n",
    "                        cnt[k] = 1\n",
    "                        li = []\n",
    "                        li.append(name)\n",
    "                        name_startup[k] = li\n",
    "                    else:\n",
    "                        li = name_startup[k]\n",
    "                        if name not in li:            #checking the given startup has been invested before or not\n",
    "                            cnt[k] = cnt.get(k, 0) + 1\n",
    "                            li.append(name)\n",
    "                            name_startup[k] = li \n",
    "                \n",
    "    return cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#making dictionary to keep record of every investor\n",
    "dictionary = Create(test4['StartupName'], test4['InvestorsName'], test4['InvestmentType'], [\"Crowd Funding\", \"Seed Funding\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#build pandas dataframe\n",
    "diff_invest_type = pd.DataFrame(list(dictionary.values()), list(dictionary.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sort in descending order on the basis of values\n",
    "diff_invest_type = diff_invest_type.sort_values(by = [0], ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1=[]\n",
    "y1=[]\n",
    "for i in range(5):\n",
    "    x1.append(diff_invest_type.index[i])\n",
    "    y1.append(diff_invest_type.index[i][0])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List Of Top 5 Investors who Invested most number of times of type Seed funding or Crowd funding :\n",
      "1. Indian Angel Network\n",
      "2. Rajan Anandan\n",
      "3. LetsVenture\n",
      "4. Anupam Mittal\n",
      "5. Group of Angel Investors\n"
     ]
    }
   ],
   "source": [
    "#printing the top investors who invested who invested most number of times in different startups and Investment type is either Seed funding or Crowd funding\n",
    "print(\"List Of Top 5 Investors who Invested most number of times of type Seed funding or Crowd funding :\")\n",
    "i = 0\n",
    "for ele in x1:\n",
    "    print(i+1,end = \"\")\n",
    "    print(\".\", end = \" \")\n",
    "    print(ele)\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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 Of Investors</th>\n",
       "      <th>Total Number Of Investments of type Seed funding or Crowd funding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sequoia Capital</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Accel Partners</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kalaari Capital</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Indian Angel Network</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Blume Ventures</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name Of Investors  \\\n",
       "0       Sequoia Capital   \n",
       "1        Accel Partners   \n",
       "2       Kalaari Capital   \n",
       "3  Indian Angel Network   \n",
       "4        Blume Ventures   \n",
       "\n",
       "   Total Number Of Investments of type Seed funding or Crowd funding  \n",
       "0                                                 48                  \n",
       "1                                                 47                  \n",
       "2                                                 41                  \n",
       "3                                                 40                  \n",
       "4                                                 36                  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#plot\n",
    "plt.bar( x, y, color = ['red', 'yellow'])\n",
    "plt.grid()\n",
    "plt.xlabel(\"Investors Name\")\n",
    "plt.ylabel(\"Number Of Investments\")\n",
    "plt.xticks(rotation=40)\n",
    "plt.title(\"Top 5 Investors and corresponding Number Of Investments In Different Startups of Investment type Crown Funding or Seed Funding\")\n",
    "plt.show()\n",
    "\n",
    "#pandas frame for Investors name and corresponding Number of Investments made by them in different startups and Investment type is either Seed funding or Crowd funding\n",
    "li = list(zip(x,y))\n",
    "investor_data = pd.DataFrame(li, columns = [\"Name Of Investors\", \"Total Number Of Investments of type Seed funding or Crowd funding\"])\n",
    "investor_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**PROBLEM 5**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Due to your immense help, your friend startup successfully got seed funding and it is on the operational mode. Now your friend wants to expand his startup and he is looking for new investors for his startup. Now you again come as a saviour to help your friend and want to create a list of probable new new investors. Before moving forward you remember your investor friend advice that finding the investors by analysing the investment type. Since your friend startup is not in early phase it is in growth stage so the best-suited investment type is Private Equity. Find the top 5 investors who have invested in a different number of startups and their investment type is Private Equity. Correct spelling of investment types are - \"Private Equity\", \"Seed Funding\", \"Debt Funding\", and \"Crowd Funding\". Keep an eye for any spelling mistake. You can find this by printing unique values from this column.There are many errors in startup names. Ignore correcting all, just handle the important ones - Ola, Flipkart, Oyo and Paytm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "test5=df.copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "#correcting the spelling in investors name ignoring undisclosed investors\n",
    "test5['InvestorsName'].replace(\"Undisclosed investors\", \"Undisclosed Investors\", inplace = True)\n",
    "test5['InvestorsName'].replace(\"Undisclosed Investors\",\"\",inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#correcting major startup names\n",
    "test5['StartupName'].replace(\"Ola Cabs\", \"Ola\", inplace = True)\n",
    "test5['StartupName'].replace(\"OyoRooms\", \"Oyo\", inplace = True)\n",
    "test5['StartupName'].replace(\"Oyorooms\", \"Oyo\", inplace = True)\n",
    "test5['StartupName'].replace(\"OYO Rooms\", \"Oyo\", inplace = True)\n",
    "test5['StartupName'].replace(\"Olacabs\", \"Ola\", inplace = True)\n",
    "test5['StartupName'].replace(\"Flipkart.com\", \"Flipkart\", inplace = True)\n",
    "test5['StartupName'].replace(\"Oyo Rooms\", \"Oyo\", inplace = True)\n",
    "test5['StartupName'].replace(\"Paytm Marketplace\", \"Paytm\", inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#correcting spelling of investment type\n",
    "test5['InvestmentType'].replace(\"PrivateEquity\",\"Private Equity\", inplace = True)\n",
    "test5['InvestmentType'].replace(\"SeedFunding\",\"Seed Funding\", inplace = True)\n",
    "test5['InvestmentType'].replace(\"Crowd funding\",\"Crowd Funding\", inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#filter na \n",
    "test5.dropna(inplace = True, subset = [\"InvestorsName\", \"StartupName\", \"InvestmentType\"])\n",
    "test5.reset_index(inplace = True, drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to create dictionary of investor name making investment to different startups and Investment type is either Seed funding or Crowd funding\n",
    "def Create(startup_name, Investor_name, Invest_type, Investment_type):\n",
    "    cnt = {}\n",
    "    #cnt holds investors name as key and the number of investments as value\n",
    "    name_startup = {}\n",
    "    #name_startup holds the investors name as key and list of different companies in which it invests as value\n",
    "    n = len(Investor_name)\n",
    "    for idx in range(n):\n",
    "        type_of_invest = Invest_type[idx].strip()\n",
    "        if type_of_invest not in Investment_type: #if investement type is either seed or crowd then we will look into the startup name or investors name\n",
    "            continue\n",
    "        i = Investor_name[idx].strip()\n",
    "        name = startup_name[idx].strip()\n",
    "        if ',' not in i and i != \"\":\n",
    "            if name_startup.get(i) is None:    #no startup is still funded by this investor till now\n",
    "                cnt[i] = 1\n",
    "                li = []\n",
    "                li.append(name)\n",
    "                name_startup[i] = li   \n",
    "            else:\n",
    "                li = name_startup[i]\n",
    "                if name not in li:             #checking the given startup has been invested before or not\n",
    "                    cnt[i] = cnt.get(i, 0) + 1\n",
    "                    li.append(name)\n",
    "                    name_startup[i]   \n",
    "        else:\n",
    "            string = i.strip().split(',')\n",
    "            for j in string:\n",
    "                k = j.strip()\n",
    "                if k != \"\":\n",
    "                    if name_startup.get(k) is None:    #no startup is still funded by this investor till now\n",
    "                        cnt[k] = 1\n",
    "                        li = []\n",
    "                        li.append(name)\n",
    "                        name_startup[k] = li\n",
    "                    else:\n",
    "                        li = name_startup[k]\n",
    "                        if name not in li:            #checking the given startup has been invested before or not\n",
    "                            cnt[k] = cnt.get(k, 0) + 1\n",
    "                            li.append(name)\n",
    "                            name_startup[k] = li \n",
    "                \n",
    "    return cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "#making dictionary to keep record of every investor\n",
    "dictionary = Create(test5['StartupName'], test5['InvestorsName'], test5['InvestmentType'], [\"Private Equity\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#build pandas dataframe\n",
    "diff_invest_type = pd.DataFrame(list(dictionary.values()), list(dictionary.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "diff_invest_type = diff_invest_type.sort_values(by = [0], ascending = False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = []\n",
    "y = []\n",
    "#we have to just find top 5 Investors\n",
    "for i in range(5):\n",
    "    x.append(diff_invest_type.index[i])\n",
    "    y.append(diff_invest_type.values[i][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List Of Top 5 Investors who Invested most number of times of type Private Equity:\n",
      "1. Sequoia Capital\n",
      "2. Accel Partners\n",
      "3. Kalaari Capital\n",
      "4. Blume Ventures\n",
      "5. SAIF Partners\n"
     ]
    }
   ],
   "source": [
    "#printing the top investors who invested who invested most number of times in different startups and Investment type is Private Equity\n",
    "print(\"List Of Top 5 Investors who Invested most number of times of type Private Equity:\")\n",
    "i = 0\n",
    "for ele in x:\n",
    "    print(i+1,end = \"\")\n",
    "    print(\".\", end = \" \")\n",
    "    print(ele)\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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",
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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>Name Of Investors</th>\n",
       "      <th>Total Number Of Investments of type Private Equity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sequoia Capital</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Accel Partners</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kalaari Capital</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Blume Ventures</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SAIF Partners</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Name Of Investors  Total Number Of Investments of type Private Equity\n",
       "0   Sequoia Capital                                                 45 \n",
       "1    Accel Partners                                                 43 \n",
       "2   Kalaari Capital                                                 35 \n",
       "3    Blume Ventures                                                 27 \n",
       "4     SAIF Partners                                                 24 "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#plot\n",
    "plt.bar(x, y)\n",
    "plt.grid()\n",
    "plt.xlabel(\"Investors Name\")\n",
    "plt.ylabel(\"Number Of Investments\")\n",
    "plt.xticks(rotation=40)\n",
    "plt.title(\"Top 5 Investors and corresponding Number Of Investments In Different Startups of Investment type Private Equity\")\n",
    "plt.show()\n",
    "\n",
    "#pandas frame for Investors name and corresponding Number of Investments made by them in different startups and Investment type is either Seed funding or Crowd funding\n",
    "li = list(zip(x,y))\n",
    "investor_data = pd.DataFrame(li, columns = [\"Name Of Investors\", \"Total Number Of Investments of type Private Equity\"])\n",
    "investor_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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