Task-1.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "id": "44cde33c",
   "metadata": {},
   "source": [
    "Ques 1) \n",
    "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": "markdown",
   "id": "563d9615",
   "metadata": {},
   "source": [
    "## Importing the Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eebc582d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # To create n dimensional arrays\n",
    "import pandas as pd # To read and create a dataframe for the dataset\n",
    "import matplotlib.pyplot as plt # To plot the graphs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d06eceb",
   "metadata": {},
   "source": [
    "## Getting the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1b927f8c",
   "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, so that we can get back to our original dataframe if required"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3563afb8",
   "metadata": {},
   "source": [
    "## Taking care of the Discrepancies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9caff100",
   "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.CityLocation.replace('bangalore', 'Bangalore', inplace = True) \n",
    "df.CityLocation.replace('Delhi', 'New Delhi', inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29bf4948",
   "metadata": {},
   "source": [
    "## Solution for the Problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c1ffde0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gathering all the cities from the dataset and putting into the numpy array\n",
    "city_data = np.array(df.CityLocation) \n",
    "cities = []\n",
    "for c in city_data:\n",
    "    if c != np.nan: # some rows in dataset are empty\n",
    "        # City in city_data may contain more than one city hence using split() function to seperate it out\n",
    "        x = str(c).split('/')\n",
    "        # Each string may contain an extra space character in the end or at beginning and hence that character\n",
    "        # not required\n",
    "        for k in x:\n",
    "            if k[0] == ' ':\n",
    "                k = k[1:]\n",
    "            if k[-1] == ' ':\n",
    "                k = k[:-1]\n",
    "            cities.append(k)\n",
    "cities = np.array(cities) # Numpy array of all cities\n",
    "# Getting frequency of (City given above) string occured in cities\n",
    "bn = len(np.where(cities == 'Bangalore')[0])\n",
    "mum = len(np.where(cities == 'Mumbai')[0])\n",
    "# Since NCR consist of three Cities :- New Delhi, Gurgaom, Noida\n",
    "# We need to take care of all of them\n",
    "ncr = len(np.where((cities == 'New Delhi')|(cities == 'Gurgaon')|(cities == 'Noida'))[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceed61c6",
   "metadata": {},
   "source": [
    "## Plotting the Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5c989949",
   "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": [
    "cities = ['Bangalore', 'Mumbai', 'NCR']\n",
    "values = [bn,mum,ncr]\n",
    "plt.bar(cities, values, color = ['Red', 'Green', 'Blue'], edgecolor = 'black')\n",
    "plt.xlabel('<--- City --->', fontsize = 17)\n",
    "plt.ylabel('<--- No of Fundings --->', fontsize = 17)\n",
    "plt.ylim(0, 780) # ylim is used to describe the limited of y-axis.\n",
    "plt.yticks(fontsize = 12)\n",
    "plt.xticks(rotation = 0, fontsize = 15)\n",
    "for i in range(len(cities)):\n",
    "    plt.text(i-0.1, values[i]+20, values[i], fontsize = 12)\n",
    "plt.title('No of Fundings Among the Cities', fontsize = 18)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}