From fabd69874e3a447a765c04580900a000cb743bd5 Mon Sep 17 00:00:00 2001
From: David Ortiz <dortizrom@gmail.com>
Date: Mon, 13 Jun 2022 22:34:52 -0500
Subject: [PATCH 1/3] EDA G2M Case Study

---
 Data Intake Report_G2M case study.docx        |  Bin 0 -> 16818 bytes
 ...nsight for Cab Investment Case Study.ipynb | 1786 +++++++++++++++++
 2 files changed, 1786 insertions(+)
 create mode 100644 Data Intake Report_G2M case study.docx
 create mode 100644 G2M insight for Cab Investment Case Study.ipynb

diff --git a/Data Intake Report_G2M case study.docx b/Data Intake Report_G2M case study.docx
new file mode 100644
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diff --git a/G2M insight for Cab Investment Case Study.ipynb b/G2M insight for Cab Investment Case Study.ipynb
new file mode 100644
index 00000000..abf5c66c
--- /dev/null
+++ b/G2M insight for Cab Investment Case Study.ipynb	
@@ -0,0 +1,1786 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "63b21722",
+   "metadata": {},
+   "source": [
+    "# Go-to-Market(G2M) insight for Cab Investment firm\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "**The Client**\n",
+    "\n",
+    "XYZ is a private firm in US. Due to remarkable growth in the Cab Industry in last few years and multiple key players in the market, it is planning for an investment in Cab industry and as per their Go-to-Market(G2M) strategy they want to understand the market before taking final decision.\n",
+    "\n",
+    "**Data Set:**\n",
+    "\n",
+    "We have been provided 4 individual data sets. Time period of data is from 31/01/2016 to 31/12/2018.\n",
+    "\n",
+    "Below are the list of datasets which are provided for the analysis:\n",
+    "\n",
+    "**Cab_Data.csv** – This file includes details of transaction for 2 cab companies\n",
+    "\n",
+    "**Customer_ID.csv** – This is a mapping table that contains a unique identifier which links the customer’s demographic details\n",
+    "\n",
+    "**Transaction_ID.csv** – This is a mapping table that contains transaction to customer mapping and payment mode\n",
+    "\n",
+    "**City.csv** – This file contains list of US cities, their population and number of cab users"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71f56ea5",
+   "metadata": {},
+   "source": [
+    "## To decide which company is a better investment opportunity for XYZ we will try to respond the following questions:\n",
+    "\n",
+    "•\tWhich company has had more profit over the years?\n",
+    "\n",
+    "•\tWhich company has users with better income?\n",
+    "\n",
+    "•\tWhich company has more users by city?\n",
+    "\n",
+    "•\tWhich company has more ride throughout the years?\n",
+    "\n",
+    "•\tWhich company tend to retain more customers?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f003b9c",
+   "metadata": {},
+   "source": [
+    "## Exploratory Data Analysis (EDA)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "c346aa0e",
+   "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>Transaction ID</th>\n",
+       "      <th>Date of Travel</th>\n",
+       "      <th>Company</th>\n",
+       "      <th>City</th>\n",
+       "      <th>KM Travelled</th>\n",
+       "      <th>Price Charged</th>\n",
+       "      <th>Cost of Trip</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10000011</td>\n",
+       "      <td>42377</td>\n",
+       "      <td>Pink Cab</td>\n",
+       "      <td>ATLANTA GA</td>\n",
+       "      <td>30.45</td>\n",
+       "      <td>370.95</td>\n",
+       "      <td>313.635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10000012</td>\n",
+       "      <td>42375</td>\n",
+       "      <td>Pink Cab</td>\n",
+       "      <td>ATLANTA GA</td>\n",
+       "      <td>28.62</td>\n",
+       "      <td>358.52</td>\n",
+       "      <td>334.854</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10000013</td>\n",
+       "      <td>42371</td>\n",
+       "      <td>Pink Cab</td>\n",
+       "      <td>ATLANTA GA</td>\n",
+       "      <td>9.04</td>\n",
+       "      <td>125.20</td>\n",
+       "      <td>97.632</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10000014</td>\n",
+       "      <td>42376</td>\n",
+       "      <td>Pink Cab</td>\n",
+       "      <td>ATLANTA GA</td>\n",
+       "      <td>33.17</td>\n",
+       "      <td>377.40</td>\n",
+       "      <td>351.602</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10000015</td>\n",
+       "      <td>42372</td>\n",
+       "      <td>Pink Cab</td>\n",
+       "      <td>ATLANTA GA</td>\n",
+       "      <td>8.73</td>\n",
+       "      <td>114.62</td>\n",
+       "      <td>97.776</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Transaction ID  Date of Travel   Company        City  KM Travelled  \\\n",
+       "0        10000011           42377  Pink Cab  ATLANTA GA         30.45   \n",
+       "1        10000012           42375  Pink Cab  ATLANTA GA         28.62   \n",
+       "2        10000013           42371  Pink Cab  ATLANTA GA          9.04   \n",
+       "3        10000014           42376  Pink Cab  ATLANTA GA         33.17   \n",
+       "4        10000015           42372  Pink Cab  ATLANTA GA          8.73   \n",
+       "\n",
+       "   Price Charged  Cost of Trip  \n",
+       "0         370.95       313.635  \n",
+       "1         358.52       334.854  \n",
+       "2         125.20        97.632  \n",
+       "3         377.40       351.602  \n",
+       "4         114.62        97.776  "
+      ]
+     },
+     "metadata": {},
+     "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>City</th>\n",
+       "      <th>Population</th>\n",
+       "      <th>Users</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>NEW YORK NY</td>\n",
+       "      <td>8,405,837</td>\n",
+       "      <td>302,149</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>CHICAGO IL</td>\n",
+       "      <td>1,955,130</td>\n",
+       "      <td>164,468</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>LOS ANGELES CA</td>\n",
+       "      <td>1,595,037</td>\n",
+       "      <td>144,132</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>MIAMI FL</td>\n",
+       "      <td>1,339,155</td>\n",
+       "      <td>17,675</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>SILICON VALLEY</td>\n",
+       "      <td>1,177,609</td>\n",
+       "      <td>27,247</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             City   Population      Users\n",
+       "0     NEW YORK NY   8,405,837    302,149 \n",
+       "1      CHICAGO IL   1,955,130    164,468 \n",
+       "2  LOS ANGELES CA   1,595,037    144,132 \n",
+       "3        MIAMI FL   1,339,155     17,675 \n",
+       "4  SILICON VALLEY   1,177,609     27,247 "
+      ]
+     },
+     "metadata": {},
+     "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>Customer ID</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Income (USD/Month)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>29290</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>28</td>\n",
+       "      <td>10813</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>27703</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>27</td>\n",
+       "      <td>9237</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>28712</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>53</td>\n",
+       "      <td>11242</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>28020</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>23</td>\n",
+       "      <td>23327</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>27182</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>33</td>\n",
+       "      <td>8536</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Customer ID Gender  Age  Income (USD/Month)\n",
+       "0        29290   Male   28               10813\n",
+       "1        27703   Male   27                9237\n",
+       "2        28712   Male   53               11242\n",
+       "3        28020   Male   23               23327\n",
+       "4        27182   Male   33                8536"
+      ]
+     },
+     "metadata": {},
+     "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>Transaction ID</th>\n",
+       "      <th>Customer ID</th>\n",
+       "      <th>Payment_Mode</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10000011</td>\n",
+       "      <td>29290</td>\n",
+       "      <td>Card</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10000012</td>\n",
+       "      <td>27703</td>\n",
+       "      <td>Card</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10000013</td>\n",
+       "      <td>28712</td>\n",
+       "      <td>Cash</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10000014</td>\n",
+       "      <td>28020</td>\n",
+       "      <td>Cash</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10000015</td>\n",
+       "      <td>27182</td>\n",
+       "      <td>Card</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Transaction ID  Customer ID Payment_Mode\n",
+       "0        10000011        29290         Card\n",
+       "1        10000012        27703         Card\n",
+       "2        10000013        28712         Cash\n",
+       "3        10000014        28020         Cash\n",
+       "4        10000015        27182         Card"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from datetime import datetime\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "cab_data = pd.read_csv(\"Cab_Data.csv\")\n",
+    "city = pd.read_csv(\"City.csv\")\n",
+    "custumer = pd.read_csv(\"Customer_ID.csv\")\n",
+    "transaction = pd.read_csv(\"Transaction_ID.csv\")\n",
+    "\n",
+    "display(cab_data.head())\n",
+    "display(city.head())\n",
+    "display(custumer.head())\n",
+    "display(transaction.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "007a8982",
+   "metadata": {},
+   "source": [
+    "**Checking for missing values**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "0b361989",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Transaction ID    0\n",
+       "Date of Travel    0\n",
+       "Company           0\n",
+       "City              0\n",
+       "KM Travelled      0\n",
+       "Price Charged     0\n",
+       "Cost of Trip      0\n",
+       "dtype: int64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "City          0\n",
+       "Population    0\n",
+       "Users         0\n",
+       "dtype: int64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Customer ID           0\n",
+       "Gender                0\n",
+       "Age                   0\n",
+       "Income (USD/Month)    0\n",
+       "dtype: int64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Transaction ID    0\n",
+       "Customer ID       0\n",
+       "Payment_Mode      0\n",
+       "dtype: int64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(cab_data.isna().sum())\n",
+    "display(city.isna().sum())\n",
+    "display(custumer.isna().sum())\n",
+    "display(transaction.isna().sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccd07989",
+   "metadata": {},
+   "source": [
+    "**Checking for duplicate values**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "8224fc19",
+   "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>Transaction ID</th>\n",
+       "      <th>Date of Travel</th>\n",
+       "      <th>Company</th>\n",
+       "      <th>City</th>\n",
+       "      <th>KM Travelled</th>\n",
+       "      <th>Price Charged</th>\n",
+       "      <th>Cost of Trip</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [Transaction ID, Date of Travel, Company, City, KM Travelled, Price Charged, Cost of Trip]\n",
+       "Index: []"
+      ]
+     },
+     "metadata": {},
+     "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>City</th>\n",
+       "      <th>Population</th>\n",
+       "      <th>Users</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [City, Population, Users]\n",
+       "Index: []"
+      ]
+     },
+     "metadata": {},
+     "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>Customer ID</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Income (USD/Month)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [Customer ID, Gender, Age, Income (USD/Month)]\n",
+       "Index: []"
+      ]
+     },
+     "metadata": {},
+     "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>Transaction ID</th>\n",
+       "      <th>Customer ID</th>\n",
+       "      <th>Payment_Mode</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [Transaction ID, Customer ID, Payment_Mode]\n",
+       "Index: []"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(cab_data[cab_data.duplicated()])\n",
+    "display(city[city.duplicated()])\n",
+    "display(custumer[custumer.duplicated()])\n",
+    "display(transaction[transaction.duplicated()])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc216051",
+   "metadata": {},
+   "source": [
+    "Since there are **no missing or duplicate values** in the tables, let's proceed to merge them all to begin the exploration and visualization that we will need for our analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "49358ab3",
+   "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>Transaction ID</th>\n",
+       "      <th>Date of Travel</th>\n",
+       "      <th>Company</th>\n",
+       "      <th>City</th>\n",
+       "      <th>KM Travelled</th>\n",
+       "      <th>Price Charged</th>\n",
+       "      <th>Cost of Trip</th>\n",
+       "      <th>Population</th>\n",
+       "      <th>Users</th>\n",
+       "      <th>Customer ID</th>\n",
+       "      <th>Payment_Mode</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Income (USD/Month)</th>\n",
+       "      <th>Profit</th>\n",
+       "      <th>Profit per KM</th>\n",
+       "      <th>Age Group</th>\n",
+       "      <th>Income Group</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10000011</td>\n",
+       "      <td>2016-01-08</td>\n",
+       "      <td>Pink Cab</td>\n",
+       "      <td>ATLANTA GA</td>\n",
+       "      <td>30.45</td>\n",
+       "      <td>370.95</td>\n",
+       "      <td>313.635</td>\n",
+       "      <td>814885</td>\n",
+       "      <td>24701</td>\n",
+       "      <td>29290</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>28</td>\n",
+       "      <td>10813</td>\n",
+       "      <td>57.315</td>\n",
+       "      <td>1.882266</td>\n",
+       "      <td>26-40</td>\n",
+       "      <td>Upper-middle class</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10000012</td>\n",
+       "      <td>2016-01-06</td>\n",
+       "      <td>Pink Cab</td>\n",
+       "      <td>ATLANTA GA</td>\n",
+       "      <td>28.62</td>\n",
+       "      <td>358.52</td>\n",
+       "      <td>334.854</td>\n",
+       "      <td>814885</td>\n",
+       "      <td>24701</td>\n",
+       "      <td>27703</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>27</td>\n",
+       "      <td>9237</td>\n",
+       "      <td>23.666</td>\n",
+       "      <td>0.826904</td>\n",
+       "      <td>26-40</td>\n",
+       "      <td>Upper-middle class</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10000013</td>\n",
+       "      <td>2016-01-02</td>\n",
+       "      <td>Pink Cab</td>\n",
+       "      <td>ATLANTA GA</td>\n",
+       "      <td>9.04</td>\n",
+       "      <td>125.20</td>\n",
+       "      <td>97.632</td>\n",
+       "      <td>814885</td>\n",
+       "      <td>24701</td>\n",
+       "      <td>28712</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>53</td>\n",
+       "      <td>11242</td>\n",
+       "      <td>27.568</td>\n",
+       "      <td>3.049558</td>\n",
+       "      <td>41-60</td>\n",
+       "      <td>Upper-middle class</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10000014</td>\n",
+       "      <td>2016-01-07</td>\n",
+       "      <td>Pink Cab</td>\n",
+       "      <td>ATLANTA GA</td>\n",
+       "      <td>33.17</td>\n",
+       "      <td>377.40</td>\n",
+       "      <td>351.602</td>\n",
+       "      <td>814885</td>\n",
+       "      <td>24701</td>\n",
+       "      <td>28020</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>23</td>\n",
+       "      <td>23327</td>\n",
+       "      <td>25.798</td>\n",
+       "      <td>0.777751</td>\n",
+       "      <td>18-25</td>\n",
+       "      <td>Upper-middle class</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10000015</td>\n",
+       "      <td>2016-01-03</td>\n",
+       "      <td>Pink Cab</td>\n",
+       "      <td>ATLANTA GA</td>\n",
+       "      <td>8.73</td>\n",
+       "      <td>114.62</td>\n",
+       "      <td>97.776</td>\n",
+       "      <td>814885</td>\n",
+       "      <td>24701</td>\n",
+       "      <td>27182</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>33</td>\n",
+       "      <td>8536</td>\n",
+       "      <td>16.844</td>\n",
+       "      <td>1.929439</td>\n",
+       "      <td>26-40</td>\n",
+       "      <td>Middle class</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Transaction ID Date of Travel   Company        City  KM Travelled  \\\n",
+       "0        10000011     2016-01-08  Pink Cab  ATLANTA GA         30.45   \n",
+       "1        10000012     2016-01-06  Pink Cab  ATLANTA GA         28.62   \n",
+       "2        10000013     2016-01-02  Pink Cab  ATLANTA GA          9.04   \n",
+       "3        10000014     2016-01-07  Pink Cab  ATLANTA GA         33.17   \n",
+       "4        10000015     2016-01-03  Pink Cab  ATLANTA GA          8.73   \n",
+       "\n",
+       "   Price Charged  Cost of Trip  Population  Users  Customer ID Payment_Mode  \\\n",
+       "0         370.95       313.635      814885  24701        29290         Card   \n",
+       "1         358.52       334.854      814885  24701        27703         Card   \n",
+       "2         125.20        97.632      814885  24701        28712         Cash   \n",
+       "3         377.40       351.602      814885  24701        28020         Cash   \n",
+       "4         114.62        97.776      814885  24701        27182         Card   \n",
+       "\n",
+       "  Gender  Age  Income (USD/Month)  Profit  Profit per KM Age Group  \\\n",
+       "0   Male   28               10813  57.315       1.882266     26-40   \n",
+       "1   Male   27                9237  23.666       0.826904     26-40   \n",
+       "2   Male   53               11242  27.568       3.049558     41-60   \n",
+       "3   Male   23               23327  25.798       0.777751     18-25   \n",
+       "4   Male   33                8536  16.844       1.929439     26-40   \n",
+       "\n",
+       "         Income Group  \n",
+       "0  Upper-middle class  \n",
+       "1  Upper-middle class  \n",
+       "2  Upper-middle class  \n",
+       "3  Upper-middle class  \n",
+       "4        Middle class  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 359392 entries, 0 to 359391\n",
+      "Data columns (total 18 columns):\n",
+      " #   Column              Non-Null Count   Dtype         \n",
+      "---  ------              --------------   -----         \n",
+      " 0   Transaction ID      359392 non-null  int64         \n",
+      " 1   Date of Travel      359392 non-null  datetime64[ns]\n",
+      " 2   Company             359392 non-null  object        \n",
+      " 3   City                359392 non-null  object        \n",
+      " 4   KM Travelled        359392 non-null  float64       \n",
+      " 5   Price Charged       359392 non-null  float64       \n",
+      " 6   Cost of Trip        359392 non-null  float64       \n",
+      " 7   Population          359392 non-null  int64         \n",
+      " 8   Users               359392 non-null  int64         \n",
+      " 9   Customer ID         359392 non-null  int64         \n",
+      " 10  Payment_Mode        359392 non-null  category      \n",
+      " 11  Gender              359392 non-null  category      \n",
+      " 12  Age                 359392 non-null  int64         \n",
+      " 13  Income (USD/Month)  359392 non-null  int64         \n",
+      " 14  Profit              359392 non-null  float64       \n",
+      " 15  Profit per KM       359392 non-null  float64       \n",
+      " 16  Age Group           359392 non-null  category      \n",
+      " 17  Income Group        359392 non-null  category      \n",
+      "dtypes: category(4), datetime64[ns](1), float64(5), int64(6), object(2)\n",
+      "memory usage: 42.5+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "datefromxls = lambda x: datetime.fromordinal(datetime(1900,1,1).toordinal() + x - 2)\n",
+    "cab_data['Date of Travel'] = cab_data['Date of Travel'].apply(datefromxls)\n",
+    "\n",
+    "# Merging tables\n",
+    "trans_per_custumer = transaction.merge(custumer, on = 'Customer ID', how = 'left')\n",
+    "cab_city_data = cab_data.merge(city, on = 'City', how = 'left')\n",
+    "data = cab_city_data.merge(trans_per_custumer, on = 'Transaction ID', how = 'left')\n",
+    "\n",
+    "# Establish the correct data type for some features\n",
+    "data['Population'] = data['Population'].str.replace(',','').astype('int64')\n",
+    "data['Users'] = data['Users'].str.replace(',','').astype('int64')\n",
+    "data['Payment_Mode'] = data['Payment_Mode'].astype('category')\n",
+    "data['Gender'] = data['Gender'].astype('category')\n",
+    "\n",
+    "# Defining new features for our analysis\n",
+    "\n",
+    "# Profit\n",
+    "data['Profit'] = data['Price Charged'] - data['Cost of Trip']\n",
+    "\n",
+    "# Profit per KM\n",
+    "data['Profit per KM'] = data['Profit'] / data['KM Travelled']\n",
+    "\n",
+    "# Age Group\n",
+    "def age_group(age):\n",
+    "    \n",
+    "    age_dict = {0:'18-25', \n",
+    "                1:'26-40', \n",
+    "                2:'41-60', \n",
+    "                3:'60+'}\n",
+    "    \n",
+    "    if (age >= 18 and age <= 25):\n",
+    "        return age_dict[0]\n",
+    "    elif (age > 25 and age <= 40):\n",
+    "        return age_dict[1]\n",
+    "    elif (age > 40 and age <= 60):\n",
+    "        return age_dict[2]\n",
+    "    elif (age > 60):\n",
+    "        return age_dict[3]\n",
+    "    \n",
+    "data['Age Group'] = data['Age'].apply(age_group)\n",
+    "data['Age Group'] = data['Age Group'].astype('category')\n",
+    "\n",
+    "# Income Group\n",
+    "def income_group(income):\n",
+    "    \n",
+    "    income_dict = {0:'Poor or near-poor', \n",
+    "                   1:'Lower-middle class', \n",
+    "                   2:'Middle class', \n",
+    "                   3:'Upper-middle class',\n",
+    "                   4:'Rich'}\n",
+    "    \n",
+    "    if (income <= 32048):\n",
+    "        return income_dict[0]\n",
+    "    elif (income > 32048 and income <= 53413):\n",
+    "        return income_dict[1]\n",
+    "    elif (income > 53413 and income <= 106827):\n",
+    "        return income_dict[2]\n",
+    "    elif (income > 106827 and income <= 373894):\n",
+    "        return income_dict[3]\n",
+    "    elif (income > 373894):\n",
+    "        return income_dict[4]\n",
+    "    \n",
+    "# The income grouping was decided with the help of this article:\n",
+    "#https://money.usnews.com/money/personal-finance/family-finance/articles/where-do-i-fall-in-the-american-economic-class-system\n",
+    "\n",
+    "data['Income Group'] = (data['Income (USD/Month)']*12).apply(income_group)\n",
+    "data['Income Group'] = data['Income Group'].astype('category')\n",
+    "\n",
+    "display(data.head())\n",
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06010acf",
+   "metadata": {},
+   "source": [
+    "## Which company has had more profit over the years?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "f458c015",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'Years')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "profit_df = data.groupby(['Company',data['Date of Travel'].dt.year])[['Profit']].sum().reset_index()\n",
+    "\n",
+    "\n",
+    "plt.rcParams['figure.figsize'] = [12, 8]\n",
+    "\n",
+    "ax = sns.lineplot(x = 'Date of Travel', y ='Profit', data = profit_df, hue = 'Company', palette = ['pink','yellow'])\n",
+    "\n",
+    "from matplotlib.ticker import FuncFormatter\n",
+    "\n",
+    "millions_formatter = lambda x, pos: f'{x / 1000000}'\n",
+    "\n",
+    "ax.yaxis.set_major_formatter(FuncFormatter(millions_formatter))\n",
+    "ax.set_ylabel('Profits (in millions)')\n",
+    "ax.set_xticks(profit_df['Date of Travel'].unique())\n",
+    "ax.set_title('Profits 2016 - 2018')\n",
+    "ax.set_xlabel('Years')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "b9ac46b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8.294262293256004"
+      ]
+     },
+     "metadata": {},
+     "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>Company</th>\n",
+       "      <th>Profit</th>\n",
+       "      <th>Profit per KM</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Pink Cab</td>\n",
+       "      <td>$5.3M</td>\n",
+       "      <td>2.769908</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Yellow Cab</td>\n",
+       "      <td>$44.0M</td>\n",
+       "      <td>7.105508</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Company  Profit  Profit per KM\n",
+       "0    Pink Cab   $5.3M       2.769908\n",
+       "1  Yellow Cab  $44.0M       7.105508"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "profit_table = data.groupby(['Company']).agg({'Profit':'sum', 'Profit per KM':'mean'}).reset_index()\n",
+    "ratio = profit_table.iloc[1,1] / profit_table.iloc[0,1]\n",
+    "display(ratio)\n",
+    "\n",
+    "format = lambda x:\"${:.1f}M\".format(x/1000000)\n",
+    "profit_table['Profit'] = profit_table['Profit'].apply(format)\n",
+    "display(profit_table)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0685700d",
+   "metadata": {},
+   "source": [
+    "Yellow Cab has earned **8.3** times the earnings of Pink Cab"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "909648f6",
+   "metadata": {},
+   "source": [
+    "## Which company has users with better income?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "60070d0f",
+   "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>Company</th>\n",
+       "      <th>Income Group</th>\n",
+       "      <th>Users</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Pink Cab</td>\n",
+       "      <td>Lower-middle class</td>\n",
+       "      <td>5971</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Pink Cab</td>\n",
+       "      <td>Middle class</td>\n",
+       "      <td>14743</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Pink Cab</td>\n",
+       "      <td>Poor or near-poor</td>\n",
+       "      <td>2309</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Pink Cab</td>\n",
+       "      <td>Rich</td>\n",
+       "      <td>3018</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Pink Cab</td>\n",
+       "      <td>Upper-middle class</td>\n",
+       "      <td>58670</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Yellow Cab</td>\n",
+       "      <td>Lower-middle class</td>\n",
+       "      <td>19545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Yellow Cab</td>\n",
+       "      <td>Middle class</td>\n",
+       "      <td>47278</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Yellow Cab</td>\n",
+       "      <td>Poor or near-poor</td>\n",
+       "      <td>7258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Yellow Cab</td>\n",
+       "      <td>Rich</td>\n",
+       "      <td>9702</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Yellow Cab</td>\n",
+       "      <td>Upper-middle class</td>\n",
+       "      <td>190898</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Company        Income Group   Users\n",
+       "0    Pink Cab  Lower-middle class    5971\n",
+       "1    Pink Cab        Middle class   14743\n",
+       "2    Pink Cab   Poor or near-poor    2309\n",
+       "3    Pink Cab                Rich    3018\n",
+       "4    Pink Cab  Upper-middle class   58670\n",
+       "5  Yellow Cab  Lower-middle class   19545\n",
+       "6  Yellow Cab        Middle class   47278\n",
+       "7  Yellow Cab   Poor or near-poor    7258\n",
+       "8  Yellow Cab                Rich    9702\n",
+       "9  Yellow Cab  Upper-middle class  190898"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Number of Users in each Income Group')"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "income_group_df = data.groupby(['Company', 'Income Group'])['Users'].count().reset_index()\n",
+    "display(income_group_df)\n",
+    "\n",
+    "ax = sns.barplot(x = 'Income Group', y = 'Users', data = income_group_df, hue = 'Company', palette = ['pink','yellow'], order = ['Poor or near-poor','Lower-middle class','Middle class', 'Upper-middle class','Rich'])\n",
+    "\n",
+    "thousands_formatter = lambda x, pos: f'{x / 1000}'\n",
+    "\n",
+    "ax.yaxis.set_major_formatter(FuncFormatter(thousands_formatter))\n",
+    "ax.set_ylabel('Number of Users (in thousands)')\n",
+    "ax.set_title('Number of Users in each Income Group')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae207a83",
+   "metadata": {},
+   "source": [
+    "In each economic group Yellow Cab has more users than Pink Cab"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68ae828a",
+   "metadata": {},
+   "source": [
+    "## Which company has more users by city?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "ee86b100",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Quantity of Users by City')"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "city_group_df = data.groupby(['Company', 'City'])['Users'].count().reset_index()\n",
+    "ax = sns.barplot(y = 'City', x = 'Users', data = city_group_df, hue = 'Company', palette = ['pink','yellow'], order = data.groupby(['City'])['Users'].count().reset_index().sort_values('Users', ascending=False).City)\n",
+    "ax.xaxis.set_major_formatter(FuncFormatter(thousands_formatter))\n",
+    "ax.set_xlabel('Users (in thousands)')\n",
+    "ax.set_title('Quantity of Users by City')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3697f15",
+   "metadata": {},
+   "source": [
+    "In the Top 5 cities with the largest number of users, it can be seen that Yellow Cab has more presence than Pink Cab."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19332807",
+   "metadata": {},
+   "source": [
+    "## Which company has more ride throughout the years?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "c28d3bb9",
+   "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>Company</th>\n",
+       "      <th>Date of Travel</th>\n",
+       "      <th>Customer ID</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Pink Cab</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>25080</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Pink Cab</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>30321</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Pink Cab</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>29310</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Yellow Cab</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>82239</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Yellow Cab</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>98189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Yellow Cab</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>94253</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Company  Date of Travel  Customer ID\n",
+       "0    Pink Cab            2016        25080\n",
+       "1    Pink Cab            2017        30321\n",
+       "2    Pink Cab            2018        29310\n",
+       "3  Yellow Cab            2016        82239\n",
+       "4  Yellow Cab            2017        98189\n",
+       "5  Yellow Cab            2018        94253"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.axis.XTick at 0x1b0c85eab80>,\n",
+       " <matplotlib.axis.XTick at 0x1b0c85eab50>,\n",
+       " <matplotlib.axis.XTick at 0x1b0c859f4c0>]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rides_df = data.groupby(['Company', data['Date of Travel'].dt.year])[['Customer ID']].count().reset_index()\n",
+    "display(rides_df)\n",
+    "ax = sns.lineplot(x = 'Date of Travel', y = 'Customer ID', data = rides_df, hue = 'Company', palette = ['pink','yellow'])\n",
+    "\n",
+    "ax.yaxis.set_major_formatter(FuncFormatter(thousands_formatter))\n",
+    "ax.set_title('Quantity of Rides 2016 - 2018')\n",
+    "ax.set_ylabel('Number of rides (in thousands)')\n",
+    "ax.set_xlabel('Years')\n",
+    "ax.set_xticks(rides_df['Date of Travel'].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "55dbcaac",
+   "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>Company</th>\n",
+       "      <th>Number of rides</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Pink Cab</td>\n",
+       "      <td>84711</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Yellow Cab</td>\n",
+       "      <td>274681</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Company  Number of rides\n",
+       "0    Pink Cab            84711\n",
+       "1  Yellow Cab           274681"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "3.2425659005323983"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rides_df = data.groupby(['Company'])[['Customer ID']].count().reset_index()\n",
+    "rides_df.columns = ['Company','Number of rides']\n",
+    "display(rides_df)\n",
+    "ratio = rides_df.iloc[1,1] / rides_df.iloc[0,1]\n",
+    "ratio"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2351876d",
+   "metadata": {},
+   "source": [
+    "Yellow Cab has had **3.25** more trips than Pink Cab in the period from 2016 to 2018."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f61ed5e",
+   "metadata": {},
+   "source": [
+    "## Which company tend to retain more customers?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "cd832a80",
+   "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>Company</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Customer ID</th>\n",
+       "      <th>Number of rides</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Pink Cab</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Pink Cab</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Pink Cab</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Pink Cab</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Pink Cab</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>134895</th>\n",
+       "      <td>Yellow Cab</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>59996</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>134896</th>\n",
+       "      <td>Yellow Cab</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>59997</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>134897</th>\n",
+       "      <td>Yellow Cab</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>59998</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>134898</th>\n",
+       "      <td>Yellow Cab</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>59999</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>134899</th>\n",
+       "      <td>Yellow Cab</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>60000</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>134900 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Company  Year  Customer ID  Number of rides\n",
+       "0         Pink Cab  2016            1                1\n",
+       "1         Pink Cab  2016            2                2\n",
+       "2         Pink Cab  2016            3                2\n",
+       "3         Pink Cab  2016            5                2\n",
+       "4         Pink Cab  2016            6                1\n",
+       "...            ...   ...          ...              ...\n",
+       "134895  Yellow Cab  2018        59996                3\n",
+       "134896  Yellow Cab  2018        59997                4\n",
+       "134897  Yellow Cab  2018        59998                2\n",
+       "134898  Yellow Cab  2018        59999                4\n",
+       "134899  Yellow Cab  2018        60000                7\n",
+       "\n",
+       "[134900 rows x 4 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.axis.XTick at 0x1b0c86a09a0>,\n",
+       " <matplotlib.axis.XTick at 0x1b0c86a0970>,\n",
+       " <matplotlib.axis.XTick at 0x1b0c86e6580>]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 864x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "retention_df = data.groupby(['Company',data['Date of Travel'].dt.year,'Customer ID'])[['Payment_Mode']].count().reset_index()\n",
+    "retention_df.columns = ['Company','Year','Customer ID', 'Number of rides']\n",
+    "display(retention_df)\n",
+    "\n",
+    "sporadic_customers = retention_df[retention_df['Number of rides'] <= 5]\n",
+    "loyal_customers = retention_df[retention_df['Number of rides'] > 5]\n",
+    "\n",
+    "fig, ax = plt.subplots(1,2)\n",
+    "\n",
+    "sns.lineplot(x = 'Year', y = 'Number of rides', data = sporadic_customers, hue = 'Company', palette = ['pink', 'yellow'], ax = ax[0])\n",
+    "sns.lineplot(x = 'Year', y = 'Number of rides', data = loyal_customers, hue = 'Company', palette = ['pink', 'yellow'], ax = ax[1])\n",
+    "ax[0].set_title('Sporadic Customers (5 rides or less)')\n",
+    "ax[1].set_title('Loyal Custumers (more than 5 rides)')\n",
+    "ax[0].set_xticks(sporadic_customers['Year'].unique())\n",
+    "ax[1].set_xticks(sporadic_customers['Year'].unique())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd9167c8",
+   "metadata": {},
+   "source": [
+    "We categories the customers as sporadics and loyal based on the number of rides they take in each company every year (5 rides or less is consider a sporadic customer in our analysis), and we found that Yellow Cab is doing a better job than Pink Cab in customer retention."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89c5e747",
+   "metadata": {},
+   "source": [
+    "## Recomendation\n",
+    "\n",
+    "Based on the questions previously analyzed and answered, we can conclude that **Yellow Cab** is a better option to invest than Pink Cab.\n",
+    "\n",
+    "**Profit through the years:** Yellow Cab has earned **8.3** times the earnings of Pink Cab in the period from 2016 to 2018.\n",
+    "\n",
+    "**User income profile:** In each economic group (Poor or near-poor, Lower-middle class, Middle class, Upper-middle class, Rich) Yellow Cab has more users than Pink Cab.\n",
+    "\n",
+    "**Users by city:** In the Top 5 cities with the largest number of users Yellow Cab has more presence than Pink Cab.\n",
+    "\n",
+    "**Volume of rides:** Yellow Cab has had **3.25** more trips than Pink Cab in the period from 2016 to 2018.\n",
+    "\n",
+    "**Customer retention:** We categories the customers as sporadics and loyal based on the number of rides they take in each company every year (5 rides or less is consider a sporadic customer in our analysis), and we found that Yellow Cab is doing a better job than Pink Cab in customer retention."
+   ]
+  }
+ ],
+ "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
+}

From 5c2e3e00ae4127410b474bc065d035e4cff72c38 Mon Sep 17 00:00:00 2001
From: David Ortiz <dortizrom@gmail.com>
Date: Mon, 13 Jun 2022 22:45:28 -0500
Subject: [PATCH 2/3] EDA G2M Case Study

---
 Data Intake Report_G2M case study.pdf | Bin 0 -> 26656 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 Data Intake Report_G2M case study.pdf

diff --git a/Data Intake Report_G2M case study.pdf b/Data Intake Report_G2M case study.pdf
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From 01c9aa4061437374a2efaeb9465cc70f895f23db Mon Sep 17 00:00:00 2001
From: David Ortiz <94481436+dortizrom@users.noreply.github.com>
Date: Mon, 13 Jun 2022 22:46:25 -0500
Subject: [PATCH 3/3] Delete Data Intake Report_G2M case study.docx

---
 Data Intake Report_G2M case study.docx | Bin 16818 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 Data Intake Report_G2M case study.docx

diff --git a/Data Intake Report_G2M case study.docx b/Data Intake Report_G2M case study.docx
deleted file mode 100644
index b16b705f4f84b98ca7a62900bcbc5a9f6c073fc9..0000000000000000000000000000000000000000
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