Project1_Case Study - II code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Case Study : Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 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": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lQGFmZqUcKMzMrJQDhZmZlXKgMDOzUg4UZmZWyoHCzMxKOVCYmVkpBwozMyvlQGFmZqUcKMzMrNTQ/i5Au6277roxZsyY/i6Gmdky5eabb34qIobXmrfcBYoxY8bQ1eXbV5uZtULSw/XmuenJzMxKOVCYmVkpBwozMyvlQGFmZqUcKMzMrJQDhZmZlXKgMDOzUg4UZmZWqqOBQtI7JN1WeLwg6duS1pZ0laT78/NaOb8knSRptqRZkrbqZHnNzKzDgSIi7o2ILSJiC2Br4BXgIuAwYGZEjAVm5mmAicDY/JgKnNKnBZSWn4eZWZv0Z9PTBOAfEfEwMAmYntOnA7vm15OAMyO5ARgmaYPOF9XMbPDqz0CxB3BOfj0iIh4HyM/r5fSRwKOFZbpz2hIkTZXUJalr3rx5fVhkM7PBp18ChaSVgE8B5zfKWiMtlkqImBYR4yJi3PDhNS9+aGZmPdRfNYqJwC0R8WSefrLSpJSf5+b0bmB0YblRwJyOldLMzPotUHyBxc1OADOAyfn1ZODiQvpeefTTeOD5ShOVmZl1RsfvRyFpNeDjwNcKyT8EzpM0BXgE2D2nXwbsBMwmjZDap4NFNTMz+iFQRMQrwDpVaU+TRkFV5w1gvw4VzczMavCZ2WZmVsqBwszMSjlQmJlZKQcKMzMr5UBhZmalHCjMzKyUA4WZmZVyoDAzs1IOFGZmVsqBwszMSjlQmJlZKQcKMzMr5UBhZmalOn71WLOBSEfWupnisim+t9RNIM16xTUKMzMr5UBhZmalHCjMzKyUA4WZmZVyoDAzs1IOFGZmVqrjgULSMEkXSLpH0t2StpW0tqSrJN2fn9fKeSXpJEmzJc2StFWny2tmNtj1R43iROCKiHgnsDlwN3AYMDMixgIz8zTARGBsfkwFTul8cc3MBreOBgpJawAfAk4HiIjXIuI5YBIwPWebDuyaX08CzozkBmCYpA06WWYzs8Gu0zWKjYF5wBmSbpV0mqQ3ASMi4nGA/Lxezj8SeLSwfHdOMzOzDul0oBgKbAWcEhFbAi+zuJmpllrXVVjq+gSSpkrqktQ1b9689pTUzMyAzgeKbqA7Im7M0xeQAseTlSal/Dy3kH90YflRwJzqlUbEtIgYFxHjhg8f3meFNzMbjDoaKCLiCeBRSe/ISROAu4AZwOScNhm4OL+eAeyVRz+NB56vNFGZmVln9MfVY78JnCVpJeABYB9SwDpP0hTgEWD3nPcyYCdgNvBKzmtmZh3U8UAREbcB42rMmlAjbwD79XmhzMysLp+ZbWZmpRwozMyslAOFmZmVcqAwM7NSDhRmZlbKgcLMzEo5UJiZWSkHCjMzK+VAYWZmpRwozMyslAOFmZmVcqAwM7NSDhRmZlbKgcLMzEo5UJiZWSkHCjMzK+VAYWZmpRwozMyslAOFmZmVcqAwM7NSDhRmZlaq44FC0kOSbpd0m6SunLa2pKsk3Z+f18rpknSSpNmSZknaqtPlNTMb7PqrRrF9RGwREePy9GHAzIgYC8zM0wATgbH5MRU4peMlNTMb5AZK09MkYHp+PR3YtZB+ZiQ3AMMkbdAfBTQzG6z6I1AEcKWkmyVNzWkjIuJxgPy8Xk4fCTxaWLY7py1B0lRJXZK65s2b14dFNzMbfIb2wza3i4g5ktYDrpJ0T0le1UiLpRIipgHTAMaNG7fUfDMz67mO1ygiYk5+ngtcBGwDPFlpUsrPc3P2bmB0YfFRwJzOldbMzDoaKCS9SdKbK6+BHYA7gBnA5JxtMnBxfj0D2CuPfhoPPF9pojIzs87odNPTCOAiSZVtnx0RV0i6CThP0hTgEWD3nP8yYCdgNvAKsE+Hy2tmNuh1NFBExAPA5jXSnwYm1EgPYL8OFM3MzOoYKMNjzcxsgHKgMDOzUg4UZmZWyoHCzMxKOVCYmVkpBwozMyvlQGFmZqUcKMzMrFTTgULSByVNKkyvK+nsfAOi4yWt2DdFNDOz/tRKjeIYYLPC9Imks6lvAPYGjmxfsczMOkXL0aNvtBIo3gHcDCBpNeDTwAERsS/wXeDz7S+emZn1t1YCxUrAgvx6O9J1on6fp+8DfOc5M7PlUCuB4h5gx/x6T+D6iHgxT78FeKadBTMzs4GhlavH/jdwfr4U+Jqk+1lX7Ajc2s6CmZnZwNB0oIiIGZI2AbYEbo+I+wqzrwf+3u7CmZlZ/2tleOxepDvMXVgVJAAuIHV2m5nZcqaVPoozgLfWmbdRnm9mZsuZVgJF2SDddYAXelkWMzMbgEr7KPKZ2MVO6/+UNK8q2yrAB4Gb2lw2MzMbABp1Zq8HvLsw/VZg/ao8rwFXAt9vY7nMzGyAKA0UEXEqcCqApGuAr0fEPb3dqKQhQBfwWETsImkj4FxgbeAW4EsR8ZqklYEzga2Bp4HPR8RDvd2+mZk1r+k+iojYvh1BIjsAuLsw/SPghIgYCzwLTMnpU4BnI+JtwAk5n5mZdVArJ9wh6S3ALsAoUt9EUUTEoU2sYxSwM/A/wHckCfgo8MWcZTpwBHAKqX/kiJx+AfBTSYqIaKXcZmbWc00HCkmfBs4BhgBzSX0TRQE0DBTAj0kXEXxznl4HeC4iFubpbmBkfj0SeBQgIhZKej7nf6qqbFOBqQAbbrhhs2/JzMya0Mrw2B+QOq1HRMTIiNio6rFxoxVI2gWYGxE3F5NrZI0m5i1OiJgWEeMiYtzw4cObeCtmZtasVpqeRgPfjIjeXPxvO+BTknYiNV2tQaphDJM0NNcqRgFzcv7uvN1uSUNJ15jyxQfNzDqolRrFX+nlZToi4vCIGBURY4A9gKsjYk/gGmC3nG0ycHF+PSNPk+df7f4JM7POaqVG8R3gLEkvAVcBz1VniIhXeliOQ4FzJX2fdBXa03P66cCvJc0m1ST26OH6zcysh1oJFLPy8xnU6CfIhjS7soi4Frg2v34A2KZGngXA7i2U0czM2qyVQPFl6gcIMzNbTrVyP4pf9WE5zMxsgGqlM9vMzAahVk64m0eDpqeIWK/XJTIzswGllT6Kk1k6UKxNuvzGGiweqWRmZsuRVvoojqiVnq/VdB6wsNZ8MzNbtvW6jyKfAHcasH/vi2NmZgNNuzqzNwZWatO6zMxsAGmlM/sbNZJXAjYB9gTOb1ehzMxs4GilM/unNdJeJV2472fAkW0pkZmZDSitdGb7nAszs0HIf/5mZlaqpUAhaWNJp0i6XdJj+flnkhretMjMzJZNrXRmb026b8QC4FLgSWAE8FlgT0nbR8QtfVJKMzPrN610Zh9HulfExOJ9JyStBlyW53+0vcUzM7P+1krT0zbAMdU3J8rTxwHva2fBzMxsYGglUMwH1qkzb21Sk5SZmS1nWgkUvwd+KOkDxcQ8fTRwSTsLZmZmA0Or98y+GPhTvuT4k8B6pA7tvwAHtb94ZmbW31o54e5p4AOSdgTeC2wAPA7cGBFX9lH5zMysn5U2PUlaR9KFkj5RSYuIKyLiqIj4RkQclbLpQkkNb1okaRVJf5P0d0l3Sjoyp28k6UZJ90v6jaSVcvrKeXp2nj+mV+/WzMxa1qiP4tukK8OW1RiuBDaiuaanV4GPRsTmwBbAjpLGAz8CToiIscCzwJScfwrwbES8DTgh5zMzsw5qFCg+B/w833OipjzvF8CkRhuL5KU8uWJ+BOn8iwty+nRg1/x6Up4mz5+Qb5RkZmYd0ihQ/AtwVxPruRsY08wGJQ2RdBswF7gK+AfwXERU7pDXDYzMr0cCjwLk+c9TY4iupKmSuiR1zZs3r5limJlZkxoFivmk+2E3snrO21BELIqILYBRpJP4NqmVLT/Xqj0sVbuJiGkRMS4ixg0fPryZYpiZWZMaBYpbgE81sZ5JOW/TIuI54FpgPDBMUmUE1ihgTn7dDYwGyPPXBJ5pZTtmZtY7jQLFycAUSZPrZZC0F7APtW9sVJ13uKRh+fWqwMdIzVbXALvlbJNJ52sAzMjT5PlXl/WXmJlZ+5WeRxERv5V0InCGpP2BK4BHSM0/GwKfAMaRRixd1MT2NgCmSxpCClLnRcSlku4CzpX0fdKFB0/P+U8Hfi1pNqkmsUfL79DMzHql4Ql3EXGQpGtJQ2UPBlbOs14lnZE9KSIubWZjETEL2LJG+gOk/orq9AXA7s2s28zM+kZTZ2ZHxCXAJbmfoDLq6OnCSCUzM1tOtXKtp8oQ1Sf7qCxmZjYA+Z7ZZmZWyoHCzMxKOVCYmVkpBwozMyvlQGFmZqUcKMzMrJQDhZmZlXKgMDOzUg4UZmZWyoHCzMxKOVCYmVkpBwozMyvlQGFmZqUcKMzMrJQDhZmZlXKgMDOzUg4UZmZWyoHCzMxKdTRQSBot6RpJd0u6U9IBOX1tSVdJuj8/r5XTJekkSbMlzZK0VSfLa2Zmna9RLAQOiohNgPHAfpI2BQ4DZkbEWGBmngaYCIzNj6nAKR0ur5nZoNfRQBERj0fELfn1i8DdwEhgEjA9Z5sO7JpfTwLOjOQGYJikDTpZZjOzwa7f+igkjQG2BG4ERkTE45CCCbBezjYSeLSwWHdOq17XVEldkrrmzZvXl8U2Mxt0+iVQSFoduBD4dkS8UJa1RloslRAxLSLGRcS44cOHt6uYZmZGPwQKSSuSgsRZEfHbnPxkpUkpP8/N6d3A6MLio4A5nSqrmZl1ftSTgNOBuyPifwuzZgCT8+vJwMWF9L3y6KfxwPOVJiozM+uMoR3e3nbAl4DbJd2W0/4N+CFwnqQpwCPA7nneZcBOwGzgFWCfzhbXzMw6Gigi4jpq9zsATKiRP4D9+rRQZmZWymdmm5lZKQcKMzMr5UBhZmalHCjMzKyUA4WZmZVyoDAzs1IOFGZmVsqBwszMSjlQmJlZKQcKMzMr5UBhZmalHCjMzKyUA4WZmZVyoDAzs1IOFGZmVsqBwszMSjlQmJlZKQcKMzMr5UBhZmalHCjMzKxURwOFpF9KmivpjkLa2pKuknR/fl4rp0vSSZJmS5olaatOltXMzJJO1yh+BexYlXYYMDMixgIz8zTARGBsfkwFTulQGc3MrKCjgSIi/gw8U5U8CZieX08Hdi2knxnJDcAwSRt0pqRmZlYxEPooRkTE4wD5eb2cPhJ4tJCvO6eZmVkHDYRAUY9qpEXNjNJUSV2SuubNm9fHxTIzG1wGQqB4stKklJ/n5vRuYHQh3yhgTq0VRMS0iBgXEeOGDx/ep4U1MxtsBkKgmAFMzq8nAxcX0vfKo5/GA89XmqjMzKxzhnZyY5LOAT4CrCupG/ge8EPgPElTgEeA3XP2y4CdgNnAK8A+nSyrmZklHQ0UEfGFOrMm1MgbwH59WyIzM2tkIDQ9mZnZAOZAYWZmpRwozMyslAOFmZmVcqAwM7NSDhRmZlbKgcLMzEo5UJiZWSkHCjMzK+VAYWZmpRwozMyslAOFmZmVcqAwM7NSDhRmZlbKgcLMzEo5UJiZWSkHCjMzK+VAYWZmpRwozMyslAOFmZmVcqAwM7NSAz5QSNpR0r2SZks6rL/LY2Y22AzoQCFpCHAyMBHYFPiCpE37t1RmZoPLgA4UwDbA7Ih4ICJeA84FJvVzmczMBpWh/V2ABkYCjxamu4H3VWeSNBWYmidfknRvB8rWG+sCT/XpFqQ+Xb31SN9/74CO8Hc/AHXku4defff/Um/GQA8Utd51LJUQMQ2Y1vfFaQ9JXRExrr/LYZ3l733wWta/+4He9NQNjC5MjwLm9FNZzMwGpYEeKG4CxkraSNJKwB7AjH4uk5nZoDKgm54iYqGk/YE/AEOAX0bEnf1crHZYZprJrK38vQ9ey/R3r4ilmvzNzMzeMNCbnszMrJ85UJiZWSkHijaSFJKOL0wfLOmIwvReku6QdKekuyQdnNN/JelBSbdJ+rukCf1Q/EFP0qLCd3CLpPd3cNtjJN3Rqe1ZY3l//nVheqikeZIubdP6j6j8B7SwzF/bse1WOVC016vAZyStWz1D0kTg28AOEfEuYCvg+UKWQyJii5zn550orC1lfkRsERGbA4cDR/d3geqRNKAHoiwnXgY2k7Rqnv448Fg/loeI6NjBS5EDRXstJI1uOLDGvMOBgyNiDkBELIiIU2vku550Rrr1rzWAZwEkrS5pZq5l3C5pUk4fI+luSafmWuKVlVe804gAAAZaSURBVD8VSe+VNEvS9ZKOrdQW8jL/l9dVs9YiaRVJZ+Rt3Spp+5y+t6TzJV0CXJnTDpF0U97WkZ35aAaVy4Gd8+svAOdUZlTXCHJrwZj8uEfSaTntLEkfk/QXSfdL2qaw/s0lXZ3Tv5rXU/P3lue91LdvtzYHivY7GdhT0ppV6ZsBNzex/I7A79peKmvGqrnp6R7gNOConL4A+HREbAVsDxwvvXGNlLHAybmW+Bzw2Zx+BrBvRGwLLCpsYy7w8byuzwMn1SjHfgAR8W7Sn9N0SavkedsCkyPio5J2yNvfBtgC2FrSh3r3EViVc4E98uf/HuDGJpd7G3BiXuadwBeBDwAHA/9WyPceUiDaFvgvSW+h/PfWL1x9bbOIeEHSmcC3gPktLHqspGOA9YDxfVI4a2R+bv5D0rbAmZI2I11K5gf5T/h1Uo1vRF7mwYi4Lb++GRgjaRjw5oiotCefDeySX68I/FTSFqQA8vYa5fgA8BOAiLhH0sOFfFdFxDP59Q75cWueXp0UOP7c0w/AlhQRsySNIQXsy1pY9MGIuB1A0p3AzIgISbcDYwr5Lo6I+cB8SdeQgv7vqf17e6KXb6fHHCj6xo+BW0hHlRV3AlsDV9dZ5hDgt6QAMz3ntX4SEdfnvqbhwE75eeuI+Kekh4DKEf6rhcUWAatSfmW2A4Engc1JNfoFNfKULf9yVb6jI+IXJfmt92YAxwEfAdYppC9kyVaZVQqvi7+L1wvTr7Pk/271iWwB7En931u/cNNTH8hHfOcBUwrJRwPHSFofQNLKkr5VtdzrpOrqCpI+0any2tIkvZN0NYCngTWBuXmn3Z6Sq2wCRMSzwIuSKjXDPQqz1wQez9/1l/I2qv2Z9GeBpLcDGwK1roj8B+DLklbPeUdKWq/Jt2jN+yXw35UaQsFDpEEpSNoK2KgH656U+6TWIQWim2jx99YJrlH0neOB/SsTEXGZpBHAH3N7Y5B+gEvI1dPvA98l/RFY56wqqdKMJFJfwCJJZwGXSOoCbgPuaWJdU4BTJb0MXMviEW4/Ay6UtDtwDUvWECjk+XluplgI7B0Rr1Y3U0fElZI2Aa7P814C/pXUD2JtEhHdpAO4ahcCe+XfzE3AfT1Y/d9ITU0bAkdFxJwe/t76lC/hYdYHJK0eES/l14cBG0TEAf1cLLMecY3CrG/sLOlw0j72MLB3/xbHrOdcozAzs1LuzDYzs1IOFGZmVsqBwszMSjlQmDVB0mfyNXmek/SqpPskfV/SuvnaPiFpl0L+70r6SI31hNJdG82WGQ4UZg0oXTr+fOAB0klyOwAnAJ8ETgUeJ12r57rCYt8lnUBVbdu8LrNlhofHmpWQ9EngO8CUiCieIPknSdNIl41/FbihmfVFRFP5zAYS1yjMyh0I3FIVJACIiEURcXl101O+Ns86wPdyelSaoWo1PUmaJKlL0gJJT0g6RtKKhfmjJJ0naa6k+ZL+IekozDrENQqzOvKf9ftJl2NpxadJl+e4gHS5coC76mzjc6R7HPyCdPnpt5KuC7YC6ZLUAGeSLjY4lXQp841Jl6426wgHCrP61gFWBh5pZaGIuFXSQqC7rKkpX/PrWODMiPhGIf1V4GRJR0fE06RLT38hIi7JWa5t7W2Y9Y6bnswa66vLF1SuDHue0v2Yhyrd4vRq0mWlN8v5bgOOVrrD3YZ9VBazuhwozOp7mnQfgb76c67cW/0y4J+Fx4M5fXR+/jzQRRpp9XC+C9+EPiqT2VLc9GRWR74fwF+ATwD/0QebqNypbiqL71JX9GAux2PA3pJWIDVDHQHMkLRhbpoy61OuUZiV+zEwTtLk6hmSVpC0Y53lXqPxXcnuBR4DxkREV43HEkEgIl7PfR5HAqsxAG5oY4ODaxRmJSLiEkn/C5wuaTvgYtINgt4J7Eu6y9mBNRa9h3Sp8Sty/nsj4sWqdb8u6SDg15LWAC4nBZiNgV2B3Uj32P4DaeTTfaTO9YNI90++u73v1qw2BwqzBiLiIEl/Jd2x8GzSUNWHWHwv5Vo1h0OAk0l3L1sN2J4ao5Ui4jeSXiANjf0y6b7bDwCXkoLGIuB24ABSn8UrpJP7doiI+e16j2ZlfD8KMzMr5T4KMzMr5UBhZmalHCjMzKyUA4WZmZVyoDAzs1IOFGZmVsqBwszMSjlQmJlZqf8PkSvaxcnjAPAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mLocation  Investment\n",
      "NCR          709\n",
      "Bangalore    637\n",
      "Mumbai       449\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = pd.read_csv(\"startup_funding.csv\")\n",
    "df = data.copy()\n",
    "\n",
    "df = df[pd.notnull(df['CityLocation'])]\n",
    "df.CityLocation.replace(\"bangalore\",\"Bangalore\", inplace = True)\n",
    "\n",
    "jay = {}\n",
    "for i in df['CityLocation']:\n",
    "    temp = i.split('/')\n",
    "    for j in temp:\n",
    "        temp1 = j.strip()\n",
    "        if temp1 in ['Delhi', 'New Delhi', 'Gurgaon', 'Noida']:\n",
    "            temp1 = 'NCR'\n",
    "        jay[temp1] = jay.get(temp1, 0) + 1\n",
    "        \n",
    "sorted_jay = dict(sorted(jay.items(), key = lambda x: x[1], reverse = True)[:3])\n",
    "\n",
    "x = [i for i in sorted_jay.keys()]\n",
    "y = [i for i in sorted_jay.values()]\n",
    "c = ['red', 'green', 'yellow']\n",
    "plt.bar(x, y, width = 0.4, color = c)\n",
    "plt.title('Top Cities in INDIA for Start - Ups', fontsize = 20, pad = 20)\n",
    "plt.xlabel('Cities', fontsize = 15)\n",
    "plt.ylabel('Counts', fontsize = 15)\n",
    "plt.show()\n",
    "\n",
    "print('\\033[1m'+'Location',' Investment')  # '\\033[1m' for bold letters\n",
    "print(pd.Series(sorted_jay))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. 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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mINVESTORS \t  Number of Investments\n",
      "Sequoia Capital         64\n",
      "Accel Partners          53\n",
      "Kalaari Capital         44\n",
      "SAIF Partners           41\n",
      "Indian Angel Network    40\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "data = pd.read_csv('startup_funding.csv', encoding = 'utf-8')\n",
    "df = data.copy()\n",
    "df = df[pd.notnull(df['InvestorsName'])]\n",
    "jay = {}\n",
    "for i in df['InvestorsName']:\n",
    "    temp = i.split(',')\n",
    "    for j in temp:\n",
    "        if j in ['', 'Undisclosed Investors', 'Undisclosed investors', 'undisclosed investors', 'Undisclosed Investor']:\n",
    "            continue\n",
    "        temp1 = j.strip()\n",
    "        jay[temp1] = jay.get(temp1, 0) + 1\n",
    "\n",
    "sorted_jay = dict(list(sorted(jay.items(), key = lambda x: x[1], reverse = True))[:5])\n",
    "\n",
    "x = [i for i in sorted_jay.keys()]\n",
    "y = [i for i in sorted_jay.values()]\n",
    "c = ['r', 'b', 'purple', 'g', 'orange']\n",
    "plt.bar(x, y, width = 0.4, color = c)\n",
    "plt.xticks(rotation = 20)\n",
    "plt.title('Top 5 Investors: Repeat investments in one company', fontsize = 20, pad = 20)\n",
    "plt.xlabel('INVESTORS', fontsize = 15)\n",
    "plt.ylabel('Number of Investments', fontsize = 15)\n",
    "plt.show()\n",
    "\n",
    "print('\\033[1m'+'INVESTORS', '\\t', ' Number of Investments')    # '\\033[1m' for bold letters\n",
    "print(pd.Series(sorted_jay))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. 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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mINVESTORS \t  Number of Investments\n",
      "Sequoia Capital         49\n",
      "Accel Partners          47\n",
      "Kalaari Capital         41\n",
      "Indian Angel Network    40\n",
      "Blume Ventures          36\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = pd.read_csv('startup_funding.csv', encoding = 'utf-8')\n",
    "df = data.copy()\n",
    "df = df[pd.notnull(df['InvestorsName'])]\n",
    "df = df[pd.notna(df['StartupName'])]\n",
    "df['StartupName'].replace('Ola Cabs', 'Ola', inplace = True)\n",
    "df['StartupName'].replace('Olacabs', 'Ola', inplace = True)\n",
    "df['StartupName'].replace('Oyo Rooms', 'Oyo', inplace = True)\n",
    "df['StartupName'].replace('Paytm Marketplace', 'Paytm', inplace = True)\n",
    "df['StartupName'].replace('Flipkart.com', 'Flipkart', inplace = True)\n",
    "df['StartupName'].replace('Oyorooms', 'Oyo', inplace = True)\n",
    "df['StartupName'].replace('OyoRooms', 'Oyo', inplace = True)\n",
    "\n",
    "a = list(df['StartupName'])\n",
    "b = []\n",
    "for i in df['InvestorsName']:\n",
    "    i = i.split(',')\n",
    "    c = []\n",
    "    for j in i:\n",
    "        c.append(j.strip())\n",
    "    b.append(c)\n",
    "    \n",
    "jay = {}\n",
    "for i in range(len(b)):\n",
    "    Investor = b[i]\n",
    "    Startup = a[i]\n",
    "    for j in Investor:\n",
    "        if j in jay:\n",
    "            jay[j].add(Startup)\n",
    "        else:\n",
    "            jay[j] = set()\n",
    "            jay[j].add(Startup)\n",
    "\n",
    "jay1 = {}\n",
    "for k,v in jay.items():\n",
    "    jay1[k] = len(v)\n",
    "\n",
    "sorted_jay1 = dict(list(sorted(jay1.items(), key = lambda x:x[1], reverse = True))[:6])\n",
    "del sorted_jay1['']\n",
    "\n",
    "x = [i for i in sorted_jay1.keys()]\n",
    "y = [i for i in sorted_jay1.values()]\n",
    "c = ['r', 'b', 'purple', 'g', 'orange']\n",
    "plt.bar(x, y, width = 0.4, color = c)\n",
    "plt.xticks(rotation = 20)\n",
    "plt.title('Top 5 Investors: Investments in Different Companies', fontsize = 20, pad = 20)\n",
    "plt.xlabel('INVESTORS', fontsize = 15)\n",
    "plt.ylabel('Number of Investments', fontsize = 15)\n",
    "plt.show()\n",
    "\n",
    "print('\\033[1m'+'INVESTORS', '\\t', ' Number of Investments')    # '\\033[1m' for bold letters\n",
    "print(pd.Series(sorted_jay1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. 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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mINVESTORS \t Number of Investments\n",
      "Indian Angel Network    33\n",
      "Rajan Anandan           23\n",
      "LetsVenture             16\n",
      "Anupam Mittal           16\n",
      "Kunal Shah              14\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = pd.read_csv('startup_funding.csv', encoding = 'utf-8')\n",
    "df = data.copy()\n",
    "df = df[pd.notnull(df['InvestorsName'])]\n",
    "df = df[pd.notna(df['StartupName'])]\n",
    "df = df[pd.notnull(df['InvestmentType'])]\n",
    "\n",
    "df['StartupName'].replace('Ola Cabs', 'Ola', inplace = True)\n",
    "df['StartupName'].replace('Olacabs', 'Ola', inplace = True)\n",
    "df['StartupName'].replace('Oyo Rooms', 'Oyo', inplace = True)\n",
    "df['StartupName'].replace('Paytm Marketplace', 'Paytm', inplace = True)\n",
    "df['StartupName'].replace('Flipkart.com', 'Flipkart', inplace = True)\n",
    "df['StartupName'].replace('Oyorooms', 'Oyo', inplace = True)\n",
    "df['StartupName'].replace('OyoRooms', 'Oyo', inplace = True)\n",
    "\n",
    "df[df['InvestmentType'] == 'PrivateEquity'] = 'Private Equity'\n",
    "df[df['InvestmentType'] == 'SeedFunding'] = 'Seed Funding'\n",
    "df[df['InvestmentType'] == 'Crowd funding'] = 'Crowd Funding'\n",
    "df = df[(df['InvestmentType'] == 'Seed Funding') | (df['InvestmentType'] == 'Crowd Funding')]\n",
    "\n",
    "a = list(df['StartupName'])\n",
    "b = []\n",
    "for i in df['InvestorsName']:\n",
    "    i = i.split(',')\n",
    "    c = []\n",
    "    for j in i:\n",
    "        c.append(j.strip())\n",
    "    b.append(c)\n",
    "    \n",
    "jay = {}\n",
    "for i in range(len(b)):\n",
    "    Investor = b[i]\n",
    "    Startup = a[i]\n",
    "    for j in Investor:\n",
    "        if j in jay:\n",
    "            jay[j].add(Startup)\n",
    "        else:\n",
    "            jay[j] = set()\n",
    "            jay[j].add(Startup)\n",
    "\n",
    "jay1 = {}\n",
    "for k,v in jay.items():\n",
    "    jay1[k] = len(v)\n",
    "\n",
    "sorted_jay1 = dict(list(sorted(jay1.items(), key = lambda x:x[1], reverse = True))[:8])\n",
    "del sorted_jay1['']\n",
    "del sorted_jay1['Undisclosed Investors']\n",
    "del sorted_jay1['Undisclosed investors']\n",
    "\n",
    "x = [i for i in sorted_jay1.keys()]\n",
    "y = [i for i in sorted_jay1.values()]\n",
    "c = ['r', 'b', 'purple', 'g', 'orange']\n",
    "plt.bar(x, y, width = 0.4, color = c)\n",
    "plt.title('Top 5 Investors: Seed Funding & Crowd Funding', fontsize = 20, pad = 20)\n",
    "plt.xlabel('INVESTORS', fontsize = 15)\n",
    "plt.ylabel('Number of Investments', fontsize = 15)   \n",
    "plt.xticks(rotation = 20)\n",
    "plt.show()\n",
    "\n",
    "print('\\033[1m'+'INVESTORS', '\\t', 'Number of Investments')     # '\\033[1m' for bold letters\n",
    "print(pd.Series(sorted_jay1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. 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": 82,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mINVESTORS \t Number of Investments\n",
      "Sequoia Capital    46\n",
      "Accel Partners     43\n",
      "Kalaari Capital    35\n",
      "Blume Ventures     26\n",
      "SAIF Partners      24\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = pd.read_csv('startup_funding.csv', encoding = 'utf-8')\n",
    "df = data.copy()\n",
    "df = df[pd.notnull(df['InvestorsName'])]\n",
    "df = df[pd.notna(df['StartupName'])]\n",
    "df = df[pd.notnull(df['InvestmentType'])]\n",
    "\n",
    "df['StartupName'].replace('Ola Cabs', 'Ola', inplace = True)\n",
    "df['StartupName'].replace('Olacabs', 'Ola', inplace = True)\n",
    "df['StartupName'].replace('Oyo Rooms', 'Oyo', inplace = True)\n",
    "df['StartupName'].replace('Paytm Marketplace', 'Paytm', inplace = True)\n",
    "df['StartupName'].replace('Flipkart.com', 'Flipkart', inplace = True)\n",
    "df['StartupName'].replace('Oyorooms', 'Oyo', inplace = True)\n",
    "df['StartupName'].replace('OyoRooms', 'Oyo', inplace = True)\n",
    "\n",
    "df[df['InvestmentType'] == 'PrivateEquity'] = 'Private Equity'\n",
    "df[df['InvestmentType'] == 'SeedFunding'] = 'Seed Funding'\n",
    "df[df['InvestmentType'] == 'Crowd funding'] = 'Crowd Funding'\n",
    "df = df[(df['InvestmentType'] == 'Private Equity')]\n",
    "\n",
    "a = list(df['StartupName'])\n",
    "b = []\n",
    "for i in df['InvestorsName']:\n",
    "    i = i.split(',')\n",
    "    c = []\n",
    "    for j in i:\n",
    "        c.append(j.strip())\n",
    "    b.append(c)\n",
    "    \n",
    "jay = {}\n",
    "for i in range(len(b)):\n",
    "    Investor = b[i]\n",
    "    Startup = a[i]\n",
    "    for j in Investor:\n",
    "        if j in jay:\n",
    "            jay[j].add(Startup)\n",
    "        else:\n",
    "            jay[j] = set()\n",
    "            jay[j].add(Startup)\n",
    "\n",
    "jay1 = {}\n",
    "for k,v in jay.items():\n",
    "    jay1[k] = len(v)\n",
    "\n",
    "sorted_jay1 = dict(list(sorted(jay1.items(), key = lambda x:x[1], reverse = True))[:5])\n",
    "\n",
    "x = [i for i in sorted_jay1.keys()]\n",
    "y = [i for i in sorted_jay1.values()]\n",
    "c = ['r', 'b', 'purple', 'g', 'orange']\n",
    "plt.bar(x, y, width = 0.4, color = c)\n",
    "plt.title('Top 5 Investors: Investments in different startups and their investment type is Private Equity', fontsize = 20, pad = 20)\n",
    "plt.xlabel('INVESTORS', fontsize = 15)\n",
    "plt.ylabel('Number of Investments', fontsize = 15)\n",
    "plt.xticks(rotation = 20)\n",
    "plt.show()\n",
    "\n",
    "print('\\033[1m'+'INVESTORS', '\\t', 'Number of Investments')       # '\\033[1m' for bold letters\n",
    "print(pd.Series(sorted_jay1))"
   ]
  }
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