Task-4.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "id": "7d181de9",
   "metadata": {},
   "source": [
    "Ques 4)\n",
    "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": "markdown",
   "id": "00057307",
   "metadata": {},
   "source": [
    "## Importing the Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "064e1844",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # To create n dimensional arrays\n",
    "import pandas as pd # To read and create dataframe for the dataset\n",
    "import matplotlib.pyplot as plt # To plot the graphs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a31b4b6",
   "metadata": {},
   "source": [
    "## Getting the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3a159f3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading the file\n",
    "dataset = pd.read_csv('startup_funding.csv', skipinitialspace = True, encoding = 'UTF-8')\n",
    "df = dataset.copy() # Just a another dataframe to so that we can get back our original dataframe if required"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "def1afb8",
   "metadata": {},
   "source": [
    "## Taking care of the Discrepancies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8881b37d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace function is used to replace data values with another in dataframe, inplace is used to make changes in \n",
    "# the original or not\n",
    "df.InvestmentType.replace('SeedFunding', 'Seed Funding', inplace = True)\n",
    "df.InvestmentType.replace('Crowd funding', 'Crowd Funding', inplace = True)\n",
    "df.InvestmentType.replace('PrivateEquity', 'Private Equity', inplace = True)\n",
    "df.StartupName.replace('Flipkart.com', 'Flipkart', inplace = True)\n",
    "df.StartupName.replace('Olacabs', 'Ola Cabs',inplace = True)\n",
    "df.StartupName.replace('OYO Rooms', 'Oyo', inplace = True)\n",
    "df.StartupName.replace('Oyo Rooms', 'Oyo', inplace = True)\n",
    "df.StartupName.replace('OyoRooms', 'Oyo', inplace = True)\n",
    "df.StartupName.replace('Oyorooms', 'Oyo', inplace = True)\n",
    "df.StartupName.replace('Paytm Marketplace', 'Paytm', inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf337a06",
   "metadata": {},
   "source": [
    "## Solution for the Problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1093bf90",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function f() is used to sort the list on the basis of frequency rather than string\n",
    "def f(x):\n",
    "    return x[1]\n",
    "# Since we need to find to the investors having investment type:- Seed Funding or Crowd Funding\n",
    "# We have two conditions and taking bitwise Or since both condition will return a boolean array.\n",
    "cond1 = df.InvestmentType == 'Seed Funding' # Condition 1\n",
    "cond2 = df.InvestmentType == 'Crowd Funding' # Condition 2\n",
    "investors = np.array(df.InvestorsName[(cond1)|(cond2)]) # List of all the investors\n",
    "companies = np.array(df.StartupName[(cond1)|(cond2)]) # List of all the companies\n",
    "# Since we need to get the list of investors with the list of companies for each investor\n",
    "# Dictionary is best data structure to handle this problem.\n",
    "invc = {}\n",
    "for i in range(len(investors)):\n",
    "    # Since each row in InvestorName contain more than one investor that's why we need\n",
    "    # to seperate each investor in using split() function based on the character to seperate\n",
    "    # as per my observation each investor has been seperated using ', ', ' and ' and ','.\n",
    "    name1 = str(investors[i]).split(', ')\n",
    "    for j in range(len(name1)):\n",
    "        name2 = name1[j].split(' and ')\n",
    "        for k in range(len(name2)):\n",
    "            name3 = name2[k].split(',')\n",
    "            for n in name3:\n",
    "                # As told above we need to ignore Undisclosed Investor or string is empty\n",
    "                if n == '' or n == ' ' or n == 'Undisclosed Investors' or n == 'Undisclosed investors':\n",
    "                    continue\n",
    "                if n not in invc:\n",
    "                    invc[n] = [companies[i]]\n",
    "                else:\n",
    "                    invc[n].append(companies[i])\n",
    "# Since we got the Dictionary of investors as key and the list of the companies they have invested as values.\n",
    "# The list may contain duplicate values and for getting unique values we can use set or np.unique() functionality\n",
    "# and since we need the no of companies we can use len() functionality to count.\n",
    "invf = []\n",
    "for i in invc:\n",
    "    invf.append([i, len(set(invc[i]))])\n",
    "# Sort() function used to sort the invfreq list into descending order using reverse = True\n",
    "# and key is set to function f which returns frequency of each investor so that\n",
    "# it can be sorted as per frequency rather than string.\n",
    "invf.sort(key = f, reverse = True)\n",
    "inv,comp = [],[]\n",
    "# Storing top 5 investors and their frequency\n",
    "for i in range(5):\n",
    "    comp.append(invf[i][0])\n",
    "    inv.append(invf[i][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d48ba4f9",
   "metadata": {},
   "source": [
    "## Plotting the Bar Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8e2f6fd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = ['red','green','blue','pink','brown'] # List of the colors for the bars\n",
    "# alpha is used to define opacity of the bar color.\n",
    "plt.bar(comp, inv, edgecolor = 'black', color = c, alpha = 0.7)\n",
    "# rotation parameter is used so that investors name in the x-axis\n",
    "# do not coincide\n",
    "plt.xticks(rotation = 75, fontsize = 12)\n",
    "plt.yticks(fontsize = 12)\n",
    "plt.ylim(0,38)\n",
    "for i in range(5):\n",
    "    # text() function is used to write text on the x-y plane of the graph\n",
    "    plt.text(i-0.1, inv[i]+0.5, inv[i], fontsize = 12, color = 'blue')\n",
    "plt.xlabel('<--- Investors --->', fontsize = 15)\n",
    "plt.ylabel('<-- No of Companies -->', fontsize = 15)\n",
    "plt.title('Top 5 Investors 2015-2017 who funded through Seed Funding or Crowd Funding', fontsize = 20)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}