1. Project Case Study (Part - I).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of Fundings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the trend of investments over the years. To check the trend, find -\n",
    "Total number of fundings done in each year.\n",
    "Plot a line graph between year and number of fundings. Take year on x-axis and number of fundings on y-axis.\n",
    "Print year-wise total number of fundings also. Print years in ascending order.\n",
    "Note :\n",
    "There is some error in the 'Date' feature. Make sure to handle that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": [
      "2015 936\n",
      "2016 993\n",
      "2017 443\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "df=pd.read_csv(\"Datasets/startup_funding.csv\")\n",
    "date=df[\"Date\"].values\n",
    "date_set=set()\n",
    "for i in date:\n",
    "    li=i.split(\"/\")\n",
    "    if len(li)!=3:\n",
    "        date_set.add(i)\n",
    "#print(date_set)\n",
    "df[\"Date\"].replace(\"12/05.2015\",\"12/05/2015\",inplace=True)\n",
    "df[\"Date\"].replace(\"13/04.2015\",\"13/04/2015\",inplace=True)\n",
    "df[\"Date\"].replace(\"15/01.2015\",\"15/01/2015\",inplace=True)\n",
    "df[\"Date\"].replace(\"22/01//2015\",\"22/01/2015\",inplace=True)\n",
    "\n",
    "def f(s):\n",
    "    li=s.split(\"/\")\n",
    "    d=int(float(li[-1]))\n",
    "    return d\n",
    "df[\"Year\"]=df[\"Date\"].apply(f)\n",
    "z=df[\"Year\"].value_counts().sort_index(ascending=True)\n",
    "years=z.index\n",
    "counts=z.values\n",
    "li=[]\n",
    "for i in years:\n",
    "    li.append(str(i))\n",
    "years=li\n",
    "    \n",
    "plt.plot(years,counts)\n",
    "plt.xticks(rotation=40)\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Fundings\")\n",
    "plt.show()\n",
    "for i in range(len(years)):\n",
    "    print(years[i],counts[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top Indian Cities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Problem Statement :\n",
    "Find out which cities are generally chosen for starting a startup.\n",
    "Find top 10 Indian cities which have most number of startups ?\n",
    "Plot a pie chart and visualise it.\n",
    "Print the city name and number of startups in that city also.\n",
    "Note :\n",
    "Take city name \"Delhi\" as \"New Delhi\".\n",
    "Check the case-sensitiveness of cities also. That means - at some place, instead of \"Bangalore\", \"bangalore\" is given. Take city name as \"Bangalore\".\n",
    "For few startups multiple locations are given, one Indian and one Foreign. Count those startups in Indian startup also. Indian city name is first.\n",
    "Print the city in descending order with respect to the number of startups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bangalore 635\n",
      "Mumbai 449\n",
      "New Delhi 389\n",
      "Gurgaon 241\n",
      "Pune 91\n",
      "Noida 79\n",
      "Hyderabad 77\n",
      "Chennai 67\n",
      "Ahmedabad 35\n",
      "Jaipur 25\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "def f(s):\n",
    "    return s.split('/')[0].strip()\n",
    "df=pd.read_csv(\"Datasets/startup_funding.csv\")\n",
    "df[\"CityLocation\"].dropna(inplace = True)\n",
    "df[\"CityLocation\"]=df[\"CityLocation\"].apply(f)\n",
    "df.replace(\"Delhi\",\"New Delhi\",inplace=True)\n",
    "df.replace(\"bangalore\",\"Bangalore\",inplace=True)\n",
    "z=df[\"CityLocation\"].value_counts().sort_values(ascending=False)[0:10]\n",
    "cities=z.index\n",
    "startups=z.values\n",
    "plt.pie(startups,labels=cities,autopct=\"%.2f%%\")\n",
    "plt.axis(\"equal\")\n",
    "plt.show()\n",
    "for i in range(len(cities)):\n",
    "    print(cities[i],startups[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Funding amount"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Problem Statement :\n",
    "Find out if cities play any role in receiving funding.\n",
    "Find top 10 Indian cities with most amount of fundings received. Find out percentage of funding each city has got (among top 10 Indian cities only).\n",
    "Print the city and percentage with 2 decimal place after rounding off.\n",
    "Note:\n",
    "Take city name \"Delhi\" as \"New Delhi\".\n",
    "Check the case-sensitiveness of cities also. That means - at some place, instead of \"Bangalore\", \"bangalore\" is given. Take city name as \"Bangalore\".\n",
    "For few startups multiple locations are given, one Indian and one Foreign. Count those startups in Indian startup also. Indian city name is first.\n",
    "Print the city in descending order with respect to the percentage of funding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bangalore 49.71\n",
      "New Delhi 16.63\n",
      "Mumbai 13.90\n",
      "Gurgaon 12.21\n",
      "Chennai 2.43\n",
      "Pune 2.16\n",
      "Hyderabad 1.15\n",
      "Noida 1.01\n",
      "Ahmedabad 0.58\n",
      "Jaipur 0.21\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "def f(s):\n",
    "    return s.split('/')[0].strip()\n",
    "def funding(rupee):\n",
    "    rupee=rupee.replace(\",\",\"\")\n",
    "    return int(float(rupee))\n",
    "df=pd.read_csv(\"Datasets/startup_funding.csv\")\n",
    "df[\"CityLocation\"].dropna(inplace = True)\n",
    "df[\"CityLocation\"]=df[\"CityLocation\"].apply(f)\n",
    "df.replace(\"Delhi\",\"New Delhi\",inplace=True)\n",
    "df.replace(\"bangalore\",\"Bangalore\",inplace=True)\n",
    "df[\"AmountInUSD\"].fillna(\"0\",inplace=True)\n",
    "df[\"AmountInUSD\"]=df[\"AmountInUSD\"].apply(funding)\n",
    "z=df.groupby(df[\"CityLocation\"])[\"AmountInUSD\"].sum().sort_values(ascending=False)[0:10]\n",
    "cities=z.index\n",
    "funding=z.values\n",
    "explode=[0,0,0,0,0.2,0.4,0.6,0.8,1,1.2]\n",
    "plt.pie(funding,labels=cities,autopct=\"%.2f%%\",explode=explode)\n",
    "plt.axis(\"equal\")\n",
    "plt.show()\n",
    "sum1=funding.sum()\n",
    "for i in range(len(cities)):\n",
    "    x=(funding[i]/sum1)*100\n",
    "    print(cities[i],\"%.2f\"%x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Investment Type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Problem Statement :\n",
    "There are 4 different type of investments. Find out percentage of amount funded for each investment type.\n",
    "Plot a pie chart to visualise.\n",
    "Print the investment type and percentage of amount funded with 2 decimal places after rounding off.\n",
    "Note :\n",
    "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.\n",
    "Print the investment type in descending order with respect to the percentage of the amount funded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Private Equity 98.15\n",
      "Seed Funding 1.81\n",
      "Debt Funding 0.04\n",
      "Crowd Funding 0.00\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "def funding(rupee):\n",
    "    rupee=rupee.replace(\",\",\"\")\n",
    "    return int(float(rupee))\n",
    "df=pd.read_csv(\"Datasets/startup_funding.csv\")\n",
    "df[\"AmountInUSD\"].fillna(\"0\",inplace=True)\n",
    "df[\"AmountInUSD\"]=df[\"AmountInUSD\"].apply(funding)\n",
    "#print(df[\"InvestmentType\"].value_counts())\n",
    "df.replace(\"SeedFunding\",\"Seed Funding\",inplace=True)\n",
    "df.replace(\"PrivateEquity\",\"Private Equity\",inplace=True)\n",
    "df.replace(\"Crowd funding\",\"Crowd Funding\",inplace=True)\n",
    "#print(df[\"InvestmentType\"].value_counts())\n",
    "z=df.groupby(df[\"InvestmentType\"])[\"AmountInUSD\"].sum().sort_values(ascending=False)\n",
    "investmenttype=z.index\n",
    "rupees=z.values\n",
    "plt.pie(rupees,labels=investmenttype,autopct=\"%.2f%%\",explode=[0,1,4,6])\n",
    "plt.show()\n",
    "\n",
    "totalsum=rupees.sum()\n",
    "for i in range(len(rupees)):\n",
    "    x=(rupees[i]/totalsum)*100\n",
    "    print(investmenttype[i],\"%.2f\"%x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top Industries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Problem Statement :\n",
    "Which type of companies got more easily funding. To answer this question, find -\n",
    "Top 5 industries and percentage of the total amount funded to that industry. (among top 5 only)\n",
    "Print the industry name and percentage of the amount funded with 2 decimal place after rounding off.\n",
    "Note :\n",
    "Ecommerce is the right word in IndustryVertical, so correct it.\n",
    "Print the industry in descending order with respect to the percentage of the amount funded. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ecommerce 40.53\n",
      "Consumer Internet 35.95\n",
      "Technology 10.45\n",
      "Online Marketplace 6.63\n",
      "E-Commerce & M-Commerce platform 6.44\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "def funding(rupee):\n",
    "    rupee=rupee.replace(\",\",\"\")\n",
    "    return int(float(rupee))\n",
    "df=pd.read_csv(\"Datasets/startup_funding.csv\")\n",
    "df[\"AmountInUSD\"].fillna(\"0\",inplace=True)\n",
    "df[\"AmountInUSD\"]=df[\"AmountInUSD\"].apply(funding)\n",
    "df[\"IndustryVertical\"].dropna(inplace=True)\n",
    "d=set()\n",
    "for i in df[\"IndustryVertical\"]:\n",
    "    if \"commerce\" in i.lower():\n",
    "        d.add(i)\n",
    "df.replace(\"ECommerce\",\"Ecommerce\",inplace=True)\n",
    "df.replace(\"eCommerce\",\"Ecommerce\",inplace=True)\n",
    "df.replace(\"ecommerce\",\"Ecommerce\",inplace=True)\n",
    "z=df.groupby(df[\"IndustryVertical\"])[\"AmountInUSD\"].sum().sort_values(ascending=False)[0:5]\n",
    "industry=z.index\n",
    "rupees=z.values\n",
    "plt.pie(rupees,labels=industry,autopct=\"%.2f%%\")\n",
    "plt.axis(\"equal\")\n",
    "plt.show()\n",
    "\n",
    "total=sum(rupees)\n",
    "for i in range(len(industry)):\n",
    "    x=(rupees[i]/total)*100\n",
    "    print(industry[i],\"%.2f\"%x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top startups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Problem Statement :\n",
    "Find top 5 startups with most amount of total funding.\n",
    "Print the startup name in descending order with respect to amount of funding.\n",
    "Note:\n",
    "Ola, Flipkart, Oyo, Paytm are important startups, so correct their names. There are many errors in startup names, ignore correcting all, just handle important ones. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Paytm\n",
      "Flipkart\n",
      "Ola\n",
      "Snapdeal\n",
      "Oyo\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "def funding(rupee):\n",
    "    rupee=rupee.replace(\",\",\"\")\n",
    "    return int(float(rupee))\n",
    "df=pd.read_csv(\"Datasets/startup_funding.csv\")\n",
    "df[\"AmountInUSD\"].fillna(\"0\",inplace=True)\n",
    "df[\"AmountInUSD\"]=df[\"AmountInUSD\"].apply(funding)\n",
    "s=set()\n",
    "df[\"StartupName\"].dropna(inplace=True)\n",
    "for i in df[\"StartupName\"]:\n",
    "    d=i.lower()\n",
    "    if ('ola' in d)or('flipkart' in d)or('oyo' in d)or('paytm' in d):\n",
    "        s.add(i)\n",
    "df[\"StartupName\"].replace(\"Olacabs\",\"Ola\",inplace=True)\n",
    "df[\"StartupName\"].replace(\"Ola Cabs\",\"Ola\",inplace=True)\n",
    "df[\"StartupName\"].replace(\"Oyorooms\",\"Oyo\",inplace=True)\n",
    "df[\"StartupName\"].replace(\"OyoRooms\",\"Oyo\",inplace=True)\n",
    "df[\"StartupName\"].replace(\"Oyo Rooms\",\"Oyo\",inplace=True)\n",
    "df[\"StartupName\"].replace(\"Paytm Marketplace\",\"Paytm\",inplace=True)\n",
    "df[\"StartupName\"].replace(\"OYO Rooms\",\"Oyo\",inplace=True)\n",
    "df[\"StartupName\"].replace(\"Flipkart.com\",\"Flipkart\",inplace=True)\n",
    "z=df.groupby(df[\"StartupName\"])[\"AmountInUSD\"].sum().sort_values(ascending=False)[:5]\n",
    "for i in z.index:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Funding rounds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Problem Statement :\n",
    "Find the top 5 startups who received the most number of funding rounds. That means, startups which got fundings maximum number of times.\n",
    "Print the startup name in descending order with respect to the number of funding round as integer value.\n",
    "Note:\n",
    "Ola, Flipkart, Oyo, Paytm are important startups, so correct their names. There are many errors in startup names, ignore correcting all, just handle important ones. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ola 9\n",
      "Swiggy 7\n",
      "Oyo 6\n",
      "UrbanClap 6\n",
      "Paytm 6\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "def funding(rupee):\n",
    "    rupee=rupee.replace(\",\",\"\")\n",
    "    return int(float(rupee))\n",
    "df=pd.read_csv(\"Datasets/startup_funding.csv\")\n",
    "df[\"AmountInUSD\"].fillna(\"0\",inplace=True)\n",
    "df[\"AmountInUSD\"]=df[\"AmountInUSD\"].apply(funding)\n",
    "s=set()\n",
    "df[\"StartupName\"].dropna(inplace=True)\n",
    "for i in df[\"StartupName\"]:\n",
    "    d=i.lower()\n",
    "    if ('ola' in d)or('flipkart' in d)or('oyo' in d)or('paytm' in d):\n",
    "        s.add(i)\n",
    "df[\"StartupName\"].replace(\"Olacabs\",\"Ola\",inplace=True)\n",
    "df[\"StartupName\"].replace(\"Ola Cabs\",\"Ola\",inplace=True)\n",
    "df[\"StartupName\"].replace(\"Oyorooms\",\"Oyo\",inplace=True)\n",
    "df[\"StartupName\"].replace(\"OyoRooms\",\"Oyo\",inplace=True)\n",
    "df[\"StartupName\"].replace(\"Oyo Rooms\",\"Oyo\",inplace=True)\n",
    "df[\"StartupName\"].replace(\"Paytm Marketplace\",\"Paytm\",inplace=True)\n",
    "df[\"StartupName\"].replace(\"OYO Rooms\",\"Oyo\",inplace=True)\n",
    "df[\"StartupName\"].replace(\"Flipkart.com\",\"Flipkart\",inplace=True)\n",
    "z=df[\"StartupName\"].value_counts().sort_values(ascending=False)[:5]\n",
    "startup=z.index\n",
    "rounds=z.values\n",
    "for i in range(len(rounds)):\n",
    "    print(startup[i],rounds[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top Investor\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Problem Statement :\n",
    "Find the Investors who have invested maximum number of times.\n",
    "Print the investor name and number of times invested as integer value.\n",
    "Note:\n",
    "In startup, multiple investors might have invested. So consider each investor for that startup.\n",
    "Ignore the undisclosed investors. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequoia Capital 64\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "def makedict(array):\n",
    "    d={}\n",
    "    for string in array:\n",
    "        li=string.strip().split(',')\n",
    "        for i in li:\n",
    "            z=i.strip()\n",
    "            d[z]=d.get(z,0)+1\n",
    "    return d\n",
    "        \n",
    "df=pd.read_csv(\"Datasets/startup_funding.csv\")\n",
    "df[\"InvestorsName\"].dropna(inplace=True)\n",
    "dictionary=makedict(df[\"InvestorsName\"])\n",
    "data=[]\n",
    "z=list(dictionary.values())\n",
    "y=list(dictionary.keys())\n",
    "for i in range(len(z)):\n",
    "    data.append([z[i],y[i]])\n",
    "df2=pd.DataFrame(data,columns=[\"Frequency\",\"Investors\"])\n",
    "coun=df2[\"Frequency\"].argmax()\n",
    "top=df2.iloc[coun].Investors\n",
    "count=df2.iloc[coun].Frequency\n",
    "print(top,count)"
   ]
  }
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