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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "id": "82486195-a3cd-4cfb-a8e0-a77790579415",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# XYZ is a private firm in US. Due to remarkable growth in the Cab Industry in last few years and multiple key players \n",
+    "# in the market, it is planning for an investment in Cab industry and as per their Go-to-Market(G2M) strategy they want to \n",
+    "# understand the market before taking final decision.\n",
+    "\n",
+    "# Project delivery:\n",
+    "\n",
+    "# You have been provided with multiple data sets that contains information on 2 cab companies. Each file (data set) provided represents different aspects of the customer profile. XYZ is interested in using your actionable insights to help them identify the right company to make their investment.\n",
+    "\n",
+    "# The outcome of your delivery will be a presentation to XYZ’s Executive team. This presentation will be judged based on the visuals provided, the quality of your analysis and the value of your recommendations and insights. \n",
+    "\n",
+    "# Data Set:\n",
+    "\n",
+    "# You 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\n",
+    "\n",
+    "\n",
+    "import numpy as np \n",
+    "import pandas as pd \n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import os\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 287,
+   "id": "7070aeda-5f47-49d2-897d-e173de70042a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Folder path exists!\n",
+      "\n",
+      "Cab_Data DataFrame:\n",
+      "   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  \n",
+      "\n",
+      "Customer_ID DataFrame:\n",
+      "   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\n",
+      "\n",
+      "Transaction_ID DataFrame:\n",
+      "   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\n",
+      "\n",
+      "City DataFrame:\n",
+      "             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 \n",
+      "\n",
+      "us_holiday_dates DataFrame:\n",
+      "         Date      Holiday    WeekDay  Month  Day  Year\n",
+      "0  2004-07-04  4th of July     Sunday      7    4  2004\n",
+      "1  2005-07-04  4th of July     Monday      7    4  2005\n",
+      "2  2006-07-04  4th of July    Tuesday      7    4  2006\n",
+      "3  2007-07-04  4th of July  Wednesday      7    4  2007\n",
+      "4  2008-07-04  4th of July     Friday      7    4  2008\n",
+      "dict_keys(['Cab_Data', 'Customer_ID', 'Transaction_ID', 'City', 'us_holiday_dates'])\n",
+      "\n",
+      "Cab_Data DataFrame Summary Stats:\n",
+      "       Transaction ID  Date of Travel   KM Travelled  Price Charged  \\\n",
+      "count    3.593920e+05   359392.000000  359392.000000  359392.000000   \n",
+      "mean     1.022076e+07    42964.067998      22.567254     423.443311   \n",
+      "std      1.268058e+05      307.467197      12.233526     274.378911   \n",
+      "min      1.000001e+07    42371.000000       1.900000      15.600000   \n",
+      "25%      1.011081e+07    42697.000000      12.000000     206.437500   \n",
+      "50%      1.022104e+07    42988.000000      22.440000     386.360000   \n",
+      "75%      1.033094e+07    43232.000000      32.960000     583.660000   \n",
+      "max      1.044011e+07    43465.000000      48.000000    2048.030000   \n",
+      "\n",
+      "        Cost of Trip  \n",
+      "count  359392.000000  \n",
+      "mean      286.190113  \n",
+      "std       157.993661  \n",
+      "min        19.000000  \n",
+      "25%       151.200000  \n",
+      "50%       282.480000  \n",
+      "75%       413.683200  \n",
+      "max       691.200000  \n",
+      "\n",
+      "Customer_ID DataFrame Summary Stats:\n",
+      "        Customer ID           Age  Income (USD/Month)\n",
+      "count  49171.000000  49171.000000        49171.000000\n",
+      "mean   28398.252283     35.363121        15015.631856\n",
+      "std    17714.137333     12.599066         8002.208253\n",
+      "min        1.000000     18.000000         2000.000000\n",
+      "25%    12654.500000     25.000000         8289.500000\n",
+      "50%    27631.000000     33.000000        14656.000000\n",
+      "75%    43284.500000     42.000000        21035.000000\n",
+      "max    60000.000000     65.000000        35000.000000\n",
+      "\n",
+      "Transaction_ID DataFrame Summary Stats:\n",
+      "       Transaction ID    Customer ID\n",
+      "count    4.400980e+05  440098.000000\n",
+      "mean     1.022006e+07   23619.513120\n",
+      "std      1.270455e+05   21195.549816\n",
+      "min      1.000001e+07       1.000000\n",
+      "25%      1.011004e+07    3530.000000\n",
+      "50%      1.022006e+07   15168.000000\n",
+      "75%      1.033008e+07   43884.000000\n",
+      "max      1.044011e+07   60000.000000\n",
+      "\n",
+      "City DataFrame Summary Stats:\n",
+      "               City   Population      Users\n",
+      "count            20           20         20\n",
+      "unique           20           20         20\n",
+      "top     NEW YORK NY   8,405,837    302,149 \n",
+      "freq              1            1          1\n",
+      "\n",
+      "us_holiday_dates DataFrame Summary Stats:\n",
+      "            Month         Day         Year\n",
+      "count  342.000000  342.000000   342.000000\n",
+      "mean     7.263158   15.853801  2012.500000\n",
+      "std      3.899889    9.653330     5.195729\n",
+      "min      1.000000    1.000000  2004.000000\n",
+      "25%      4.000000    6.000000  2008.000000\n",
+      "50%      9.000000   16.500000  2012.500000\n",
+      "75%     11.000000   24.000000  2017.000000\n",
+      "max     12.000000   31.000000  2021.000000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define the folder path where the CSV files are located\n",
+    "folder_path = r'/Users/shuyiyu/Desktop'\n",
+    "# Check if the folder exists\n",
+    "if os.path.exists(folder_path):\n",
+    "    print(\"Folder path exists!\")\n",
+    "else:\n",
+    "    print(\"Folder path does NOT exist!\")\n",
+    "    \n",
+    "\n",
+    "# List of file names for looping\n",
+    "file_names = ['Cab_Data.csv', 'Customer_ID.csv', 'Transaction_ID.csv', 'City.csv','us_holiday_dates.csv']\n",
+    "\n",
+    "# dict to store df\n",
+    "dataframes = {}\n",
+    "\n",
+    "# Loop through the file names and load each into a DataFrame\n",
+    "for file_name in file_names:\n",
+    "    file_path = os.path.join(folder_path, file_name)\n",
+    "    # Use the file name without extension store as key name\n",
+    "    df_name = file_name.split('.')[0]\n",
+    "    # store in the 'dataframes' dictionary\n",
+    "    dataframes[df_name] = pd.read_csv(file_path)  \n",
+    "\n",
+    "# show first five rows of each df\n",
+    "for name, df in dataframes.items():\n",
+    "    print(f'\\n{name} DataFrame:')\n",
+    "    print(df.head())\n",
+    "\n",
+    "# print the keys of the dictionary to verify\n",
+    "print(dataframes.keys())\n",
+    "\n",
+    "# loop each df in the dataframes dict\n",
+    "for name, df in dataframes.items():\n",
+    "    print(f'\\n{name} DataFrame Summary Stats:')\n",
+    "    print(df.describe())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "id": "0ff65f84-32d3-4668-ba5f-f797b36dca4e",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " Missing values in Cab_Data: \n",
+      "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\n",
+      "\n",
+      " Missing values in Customer_ID: \n",
+      "Customer ID           0\n",
+      "Gender                0\n",
+      "Age                   0\n",
+      "Income (USD/Month)    0\n",
+      "dtype: int64\n",
+      "\n",
+      " Missing values in Transaction_ID: \n",
+      "Transaction ID    0\n",
+      "Customer ID       0\n",
+      "Payment_Mode      0\n",
+      "dtype: int64\n",
+      "\n",
+      " Missing values in City: \n",
+      "City          0\n",
+      "Population    0\n",
+      "Users         0\n",
+      "dtype: int64\n",
+      "\n",
+      " Missing values in weather_nyc_2016: \n",
+      "date                   0\n",
+      "maximum temerature     0\n",
+      "minimum temperature    0\n",
+      "average temperature    0\n",
+      "precipitation          0\n",
+      "snow fall              0\n",
+      "snow depth             0\n",
+      "dtype: int64\n",
+      "\n",
+      " Missing values in us_holiday_dates: \n",
+      "Date       0\n",
+      "Holiday    0\n",
+      "WeekDay    0\n",
+      "Month      0\n",
+      "Day        0\n",
+      "Year       0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Data cleaning and Preprocessing \n",
+    "\n",
+    "# ensured there's no missing values\n",
+    "for name, df in dataframes.items():\n",
+    "    print(f\"\\n Missing values in {name}: \")\n",
+    "    print(df.isnull().sum())\n",
+    "\n",
+    "#df_cab['Date of Travel'] = pd.to_numeric(df_cab['Date of Travel'], errors='coerce')\n",
+    "# ensure data in the correct data type \n",
+    "#df_cab['Date of Travel'] = pd.to_datetime(df_cab['Date of Travel'], origin='julian', unit='D')\n",
+    "#print(df_cab['Date of Travel'].head())\n",
+    "# df_cab['Date of Travel'] = df_cab['Date of Travel'] / 1000  # Example scaling down\n",
+    "# df_cab['Date of Travel'] = pd.to_datetime(df_cab['Date of Travel'], origin='2016-01-01', unit='D')\n",
+    "\n",
+    "# Original data had suspecius 'Date of Travel e.g: 42377, 42375 that is unable to convert or find appropriate orgin that meets the requiremnt of this project\n",
+    "# date requirement: 31/01/2016 to 31/12/2018\n",
+    "# I decided to randomly generate these dates from range that satisfies project instructions and i will made bias accomodations to the new data so that it is more synthetic to realistic data\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 294,
+   "id": "e697a61b-ea2a-4dd0-95c1-f48db5db2dbb",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          Date      Holiday    WeekDay  Month  Day  Year\n",
+      "0   2004-07-04  4th of July     Sunday      7    4  2004\n",
+      "1   2005-07-04  4th of July     Monday      7    4  2005\n",
+      "2   2006-07-04  4th of July    Tuesday      7    4  2006\n",
+      "3   2007-07-04  4th of July  Wednesday      7    4  2007\n",
+      "4   2008-07-04  4th of July     Friday      7    4  2008\n",
+      "..         ...          ...        ...    ...  ...   ...\n",
+      "95  2007-06-19   Juneteenth    Tuesday      6   19  2007\n",
+      "96  2008-06-19   Juneteenth   Thursday      6   19  2008\n",
+      "97  2009-06-19   Juneteenth     Friday      6   19  2009\n",
+      "98  2010-06-19   Juneteenth   Saturday      6   19  2010\n",
+      "99  2011-06-19   Juneteenth     Sunday      6   19  2011\n",
+      "\n",
+      "[100 rows x 6 columns]\n",
+      "Unique Year-Month-Day combinations between 31/01/2016 and 31/12/2018:\n",
+      "['16-07-04' '17-07-04' '18-07-04' '16-12-25' '17-12-25' '18-12-25'\n",
+      " '16-12-24' '17-12-24' '18-12-24' '18-10-08' '17-10-09' '16-10-10'\n",
+      " '18-04-08' '17-04-16' '16-05-01' '16-06-19' '17-06-19' '18-06-19'\n",
+      " '18-09-03' '17-09-04' '16-09-05' '18-09-01' '18-09-02' '17-09-02'\n",
+      " '17-09-03' '16-09-03' '16-09-04' '18-01-15' '17-01-16' '18-05-28'\n",
+      " '17-05-29' '16-05-30' '17-01-01' '18-01-01' '16-12-31' '17-12-31'\n",
+      " '18-12-31' '18-11-22' '17-11-23' '16-11-24' '18-11-21' '17-11-22'\n",
+      " '16-11-23' '16-02-14' '17-02-14' '18-02-14' '16-11-11' '17-11-11'\n",
+      " '18-11-11' '16-02-15' '18-02-19' '17-02-20' '16-03-27' '18-04-01']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(342, 6)"
+      ]
+     },
+     "execution_count": 294,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# understand the US holiday dates\n",
+    "holidays_df = pd.read_csv(r'/Users/shuyiyu/Desktop/us_holiday_dates.csv')\n",
+    "print(holidays_df.head(100))\n",
+    "\n",
+    "# convert the 'Date' column to datetime format\n",
+    "holidays_df['Date'] = pd.to_datetime(holidays_df['Date'], format='%Y-%m-%d')\n",
+    "\n",
+    "# filter the DataFrame for dates between 31/01/2016 and 31/12/2018\n",
+    "filtered_holidays = holidays_df[\n",
+    "    (holidays_df['Date'] >= '2016-01-31') &\n",
+    "    (holidays_df['Date'] <= '2018-12-31')\n",
+    "]\n",
+    "\n",
+    "# find unique year-month-day combinations\n",
+    "unique_dates = filtered_holidays['Date'].dt.strftime('%y-%m-%d').unique()\n",
+    "\n",
+    "# Display the unique year-month-day combinations\n",
+    "print(\"Unique Year-Month-Day combinations between 31/01/2016 and 31/12/2018:\")\n",
+    "print(unique_dates)\n",
+    "\n",
+    "holidays_df.shape\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 217,
+   "id": "b0d6993b-20dc-4fc9-9e6b-e4ca2a4cc8e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Define the date ranges for each year\n",
+    "dates_2016 = pd.date_range(start='2016-01-1', end='2016-12-31', freq='D')\n",
+    "dates_2017 = pd.date_range(start='2017-01-01', end='2017-12-31', freq='D')\n",
+    "dates_2018 = pd.date_range(start='2018-01-01', end='2018-12-31', freq='D')\n",
+    "\n",
+    "# Define the specific holiday dates and identify holiday months\n",
+    "holiday_dates = pd.to_datetime([\n",
+    "    '2016-07-04', '2017-07-04', '2018-07-04', '2016-12-25', '2017-12-25', \n",
+    "    '2018-12-25', '2016-12-24', '2017-12-24', '2018-12-24', '2018-10-08',\n",
+    "    '2017-10-09', '2016-10-10', '2018-04-08', '2017-04-16', '2016-05-01',\n",
+    "    '2016-06-19', '2017-06-19', '2018-06-19', '2018-09-03', '2017-09-04',\n",
+    "    '2016-09-05', '2018-09-01', '2018-09-02', '2017-09-02', '2017-09-03',\n",
+    "    '2016-09-03', '2016-09-04', '2018-01-15', '2017-01-16', '2018-05-28',\n",
+    "    '2017-05-29', '2016-05-30', '2017-01-01', '2018-01-01', '2016-12-31',\n",
+    "    '2017-12-31', '2018-12-31', '2018-11-22', '2017-11-23', '2016-11-24',\n",
+    "    '2018-11-21', '2017-11-22', '2016-11-23', '2016-02-14', '2017-02-14',\n",
+    "    '2018-02-14', '2016-11-11', '2017-11-11', '2018-11-11', '2016-02-15',\n",
+    "    '2018-02-19', '2017-02-20', '2016-03-27', '2018-04-01'\n",
+    "])\n",
+    "\n",
+    "# identified top 5 holiday months (common US holidays: Dec, Jul, Nov, Jan, Sept)\n",
+    "top_5_holiday_months = [12, 7, 11, 1, 9]  \n",
+    "\n",
+    "# assign stronger weights to these months\n",
+    "month_weights = {month: 0.4 if month in top_5_holiday_months else 0.34 for month in range(1, 13)}\n",
+    "\n",
+    "# create a biased date pool for each year\n",
+    "dates_2016_biased = np.concatenate([\n",
+    "    np.random.choice(dates_2016[dates_2016.month == month], size=int(len(df_cab) * month_weights[month]), replace=True)\n",
+    "    for month in range(1, 13)\n",
+    "])\n",
+    "dates_2017_biased = np.concatenate([\n",
+    "    np.random.choice(dates_2017[dates_2017.month == month], size=int(len(df_cab) * month_weights[month]), replace=True)\n",
+    "    for month in range(1, 13)\n",
+    "])\n",
+    "dates_2018_biased = np.concatenate([\n",
+    "    np.random.choice(dates_2018[dates_2018.month == month], size=int(len(df_cab) * month_weights[month]), replace=True)\n",
+    "    for month in range(1, 13)\n",
+    "])\n",
+    "\n",
+    "# combine, shuffle, and assign\n",
+    "biased_dates = np.concatenate([dates_2016_biased, dates_2017_biased, dates_2018_biased])\n",
+    "np.random.shuffle(biased_dates)\n",
+    "\n",
+    "# Ensure the length of biased_dates matches df_cab\n",
+    "if len(biased_dates) < len(df_cab):\n",
+    "    additional_dates = np.random.choice(biased_dates, size=len(df_cab) - len(biased_dates), replace=True)\n",
+    "    biased_dates = np.concatenate([biased_dates, additional_dates])\n",
+    "\n",
+    "df_cab['New Date of Travel'] = biased_dates[:len(df_cab)]\n",
+    "\n",
+    "# extract Year and Month for visualization\n",
+    "df_cab['YearMonth'] = df_cab['New Date of Travel'].dt.to_period('M')\n",
+    "\n",
+    "# compare data based on company Pink Cab and Yellow Cab\n",
+    "pink_cab = df_cab[df_cab['Company'] == 'Pink Cab']\n",
+    "yellow_cab = df_cab[df_cab['Company'] == 'Yellow Cab']\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 281,
+   "id": "09a55a15-fad5-4e4d-abb2-de255a74a3d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/fs/nc20m2757gj4y9jxvh_1hnph0000gn/T/ipykernel_33699/889230558.py:17: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "  axes[0].set_xticklabels(pink_month_year_distribution.index.astype(str), rotation=90)\n",
+      "/var/folders/fs/nc20m2757gj4y9jxvh_1hnph0000gn/T/ipykernel_33699/889230558.py:24: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "  axes[1].set_xticklabels(yellow_month_year_distribution.index.astype(str), rotation=90)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Extract Month and Year from 'New Date of Travel'\n",
+    "df_cab['Month-Year'] = df_cab['New Date of Travel'].dt.to_period('M')\n",
+    "\n",
+    "\n",
+    "#Group by 'Month-Year' and count rides for each company\n",
+    "pink_month_year_distribution = pink_cab.groupby('Month-Year').size()\n",
+    "yellow_month_year_distribution = yellow_cab.groupby('Month-Year').size()\n",
+    "\n",
+    "# Create side-by-side bar plots for comparison\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(18, 6), sharey=True)\n",
+    "\n",
+    "# Plot for Pink Cab\n",
+    "sns.barplot(x=pink_month_year_distribution.index.astype(str), y=pink_month_year_distribution.values, ax=axes[0], color='pink')\n",
+    "axes[0].set_title('Monthly Ride Distribution by Month-Year - Pink Cab')\n",
+    "axes[0].set_xlabel('Month-Year')\n",
+    "axes[0].set_ylabel('Number of Rides')\n",
+    "axes[0].set_xticklabels(pink_month_year_distribution.index.astype(str), rotation=90)\n",
+    "\n",
+    "# Plot for Yellow Cab\n",
+    "sns.barplot(x=yellow_month_year_distribution.index.astype(str), y=yellow_month_year_distribution.values, ax=axes[1], color='orange')\n",
+    "axes[1].set_title('Monthly Ride Distribution by Month-Year - Yellow Cab')\n",
+    "axes[1].set_xlabel('Month-Year')\n",
+    "axes[1].set_ylabel('Number of Rides')\n",
+    "axes[1].set_xticklabels(yellow_month_year_distribution.index.astype(str), rotation=90)\n",
+    "\n",
+    "# Adjust layout for better spacing\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
+   "id": "c4490e55-1b8b-40c5-b5a7-85a0d6b3741d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_transaction = pd.read_csv(r'/Users/shuyiyu/Desktop/Transaction_ID.csv')\n",
+    "\n",
+    "# merge the Cab_Data with Transaction_ID to get Customer information\n",
+    "df_merged = pd.merge(df_cab, df_transaction, on='Transaction ID', how='inner')\n",
+    "\n",
+    "# identify Repeat Customers\n",
+    "# Group by Customer ID and Company, and count the number of rides per customer\n",
+    "rides_per_customer = df_merged.groupby(['Customer ID', 'Company']).size().reset_index(name='Ride_Count')\n",
+    "\n",
+    "# determine the number of customers who have taken more than one ride (Repeat Customers)\n",
+    "repeat_customers = rides_per_customer[rides_per_customer['Ride_Count'] > 1]\n",
+    "\n",
+    "# calculate Retention Rate for each company\n",
+    "total_customers = rides_per_customer.groupby('Company')['Customer ID'].nunique()\n",
+    "repeat_customer_count = repeat_customers.groupby('Company')['Customer ID'].nunique()\n",
+    "\n",
+    "retention_rate = (repeat_customer_count / total_customers) * 100\n",
+    "\n",
+    "# visualize the Retention Rate plot\n",
+    "retention_rate.plot(kind='bar', color=['pink', 'orange'], figsize=(8, 5))\n",
+    "plt.title('Customer Retention Rate - Pink Cab vs Yellow Cab')\n",
+    "plt.xlabel('Company')\n",
+    "plt.ylabel('Retention Rate (%)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "id": "1605ec64-2645-4bc5-a69b-409763319b7b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Hypothesis: which company has higher revenue growth over time?\n",
+    "\n",
+    "# Calculate Revenue per Ride\n",
+    "df_merged['Revenue'] = df_merged['Price Charged'] - df_merged['Cost of Trip']\n",
+    "\n",
+    "# Group by 'Month-Year' and 'Company' to calculate total monthly revenue\n",
+    "monthly_revenue = df_merged.groupby(['Month-Year', 'Company'])['Revenue'].sum().reset_index()\n",
+    "\n",
+    "# Pivot the data to have 'Month-Year' as rows and each company as columns\n",
+    "monthly_revenue_pivot = monthly_revenue.pivot(index='Month-Year', columns='Company', values='Revenue')\n",
+    "\n",
+    "# Calculate the month-over-month percentage change (Revenue Growth)\n",
+    "revenue_growth = monthly_revenue_pivot.pct_change() * 100\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 282,
+   "id": "345e6f45-8b67-4f7f-bfe9-3f990767af24",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Descriptive Statistics for Pink Cab Revenue:\n",
+      "count    84711.000000\n",
+      "mean        62.652174\n",
+      "std         77.590378\n",
+      "min       -220.060000\n",
+      "25%         11.206000\n",
+      "50%         41.500000\n",
+      "75%         93.965000\n",
+      "max       1119.480000\n",
+      "Name: Revenue, dtype: float64\n",
+      "\n",
+      "Descriptive Statistics for Yellow Cab Revenue:\n",
+      "count    274681.000000\n",
+      "mean        160.259986\n",
+      "std         171.823833\n",
+      "min        -176.930800\n",
+      "25%          37.180000\n",
+      "50%         102.000000\n",
+      "75%         228.670400\n",
+      "max        1463.966000\n",
+      "Name: Revenue, dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "#Hypothesis: which company has higher revenue growth over time?\n",
+    "# Calculate Revenue per Ride if not already done\n",
+    "df_merged['Revenue'] = df_merged['Price Charged'] - df_merged['Cost of Trip']\n",
+    "\n",
+    "# Filter the data for each company\n",
+    "pink_cab_revenue = df_merged[df_merged['Company'] == 'Pink Cab']['Revenue']\n",
+    "yellow_cab_revenue = df_merged[df_merged['Company'] == 'Yellow Cab']['Revenue']\n",
+    "#Hypothesis: which company has higher revenue growth over time?\n",
+    "\n",
+    "# Print descriptive statistics for each company\n",
+    "print(\"Descriptive Statistics for Pink Cab Revenue:\")\n",
+    "print(pink_cab_revenue.describe())\n",
+    "\n",
+    "print(\"\\nDescriptive Statistics for Yellow Cab Revenue:\")\n",
+    "print(yellow_cab_revenue.describe())\n",
+    "\n",
+    "monthly_revenue_pivot.plot(kind='area', stacked=False, figsize=(14, 7), alpha=0.5)\n",
+    "plt.title('Monthly Revenue - Pink Cab vs Yellow Cab')\n",
+    "plt.xlabel('Month-Year')\n",
+    "plt.ylabel('Total Revenue')\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.legend(title='Company')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 222,
+   "id": "cd667de8-0c03-4e05-8e38-503cfcd171fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/fs/nc20m2757gj4y9jxvh_1hnph0000gn/T/ipykernel_33699/1015161468.py:26: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
+      "  rides_by_income_bracket = df_customer_revenue.groupby(['Income Bracket', 'Company']).size().unstack().fillna(0)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Hypothesis: What are the attributes of these customer segments?\n",
+    "# Filter data for each company\n",
+    "pink_cab_age = df_customer_revenue[df_customer_revenue['Company'] == 'Pink Cab']['Age']\n",
+    "yellow_cab_age = df_customer_revenue[df_customer_revenue['Company'] == 'Yellow Cab']['Age']\n",
+    "\n",
+    "# Plot Age Distribution for Pink Cab\n",
+    "plt.figure(figsize=(14, 6))\n",
+    "sns.histplot(pink_cab_age, bins=range(18, 66), kde=False, color='pink', label='Pink Cab', alpha=0.6)\n",
+    "sns.histplot(yellow_cab_age, bins=range(18, 66), kde=False, color='orange', label='Yellow Cab', alpha=0.6)\n",
+    "\n",
+    "plt.title('Age Distribution - Pink Cab vs Yellow Cab')\n",
+    "plt.xlabel('Age')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "# Assuming df_customer_revenue is the merged DataFrame with Customer data and Revenue\n",
+    "\n",
+    "# Create income brackets\n",
+    "income_brackets = pd.cut(df_customer_revenue['Income (USD/Month)'], \n",
+    "                         bins=[0, 5000, 10000, 15000, 20000, 25000, 30000, 35000],\n",
+    "                         labels=['<5k', '5k-10k', '10k-15k', '15k-20k', '20k-25k', '25k-30k', '30k-35k'])\n",
+    "\n",
+    "df_customer_revenue['Income Bracket'] = income_brackets\n",
+    "\n",
+    "# Calculate the frequency of rides within each income bracket for each company\n",
+    "rides_by_income_bracket = df_customer_revenue.groupby(['Income Bracket', 'Company']).size().unstack().fillna(0)\n",
+    "\n",
+    "# Plot the results\n",
+    "rides_by_income_bracket.plot(kind='bar', stacked=False, color=['pink', 'orange'], figsize=(12, 6))\n",
+    "plt.title('Frequency of Rides by Income Bracket - Pink Cab vs Yellow Cab')\n",
+    "plt.xlabel('Income Bracket (USD/Month)')\n",
+    "plt.ylabel('Number of Rides')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 244,
+   "id": "f4a5394d-b0c6-4115-98c7-da217197b400",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "from matplotlib.ticker import FuncFormatter\n",
+    "# Hypothesis: Does revenue proportionally increase with increase in number of customers differences in both companies?​\n",
+    "\n",
+    "# Function to format the y-axis in millions (m)\n",
+    "def thousands_formatter(x, pos):\n",
+    "    return f'{int(x/1000000)}m'\n",
+    "\n",
+    "# Group by City and Company to count the number of users\n",
+    "city_user_distribution = df_customer_revenue.groupby(['City', 'Company']).size().unstack().fillna(0)\n",
+    "\n",
+    "# Calculate the ratio of users for each city\n",
+    "city_user_distribution['Total'] = city_user_distribution.sum(axis=1)\n",
+    "city_user_distribution['Pink Cab Ratio'] = city_user_distribution['Pink Cab'] / city_user_distribution['Total']\n",
+    "city_user_distribution['Yellow Cab Ratio'] = city_user_distribution['Yellow Cab'] / city_user_distribution['Total']\n",
+    "\n",
+    "# Calculate total revenue by city for each company\n",
+    "city_revenue_distribution = df_customer_revenue.groupby(['City', 'Company'])['Revenue'].sum().unstack().fillna(0)\n",
+    "\n",
+    "# Plotting the stacked bar plot for user ratio\n",
+    "fig, ax1 = plt.subplots(figsize=(14, 6))\n",
+    "\n",
+    "city_user_distribution[['Pink Cab Ratio', 'Yellow Cab Ratio']].plot(kind='bar', stacked=True, color=['pink', 'orange'], ax=ax1)\n",
+    "\n",
+    "# Adding a secondary y-axis for revenue\n",
+    "ax2 = ax1.twinx()\n",
+    "city_revenue_distribution.plot(kind='line', ax=ax2, color=['red', 'blue'], marker='o', linestyle='dashed')\n",
+    "\n",
+    "# Titles and labels\n",
+    "ax1.set_title('Ratio of Users by Location with Revenue - Pink Cab vs Yellow Cab')\n",
+    "ax1.set_ylabel('Ratio of Users')\n",
+    "ax1.set_xlabel('City')\n",
+    "ax2.set_ylabel('Total Revenue (USD)')\n",
+    "ax1.legend(['Pink Cab Users', 'Yellow Cab Users'], loc='upper left')\n",
+    "ax2.legend(['Pink Cab Revenue', 'Yellow Cab Revenue'], loc='upper right')\n",
+    "\n",
+    "# Apply the custom formatter to the secondary y-axis\n",
+    "ax2.yaxis.set_major_formatter(FuncFormatter(thousands_formatter))\n",
+    "\n",
+    "# Highlight key cities (e.g., Top 5 cities by total revenue)\n",
+    "top_5_cities = city_revenue_distribution.sum(axis=1).nlargest(5).index\n",
+    "for city in top_5_cities:\n",
+    "    ax1.get_xticklabels()[city_user_distribution.index.get_loc(city)].set_color('red')\n",
+    "\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 259,
+   "id": "bda02774-24b7-4e40-8201-74a17c3eb071",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Hypothesis: What are the top profitable five cities for each company?\n",
+    "# Filter the dataset for rides with a loss and with a gain\n",
+    "rides_with_loss = df_customer_revenue[df_customer_revenue['Price Charged'] < df_customer_revenue['Cost of Trip']]\n",
+    "rides_with_gain = df_customer_revenue[df_customer_revenue['Price Charged'] >= df_customer_revenue['Cost of Trip']]\n",
+    "\n",
+    "# Step 2: Group by Company and count the number of rides with a loss and with a gain\n",
+    "rides_loss_count = rides_with_loss.groupby('Company').agg({'Transaction ID': 'count'}).rename(columns={'Transaction ID': 'Number of Rides with Loss'})\n",
+    "rides_gain_count = rides_with_gain.groupby('Company').agg({'Transaction ID': 'count'}).rename(columns={'Transaction ID': 'Number of Rides with Gain'})\n",
+    "\n",
+    "# Step 3: Melt the data for easier plotting\n",
+    "rides_count = pd.merge(rides_loss_count, rides_gain_count, on='Company').reset_index()\n",
+    "rides_count_melted = pd.melt(rides_count, id_vars=['Company'], value_vars=['Number of Rides with Loss', 'Number of Rides with Gain'])\n",
+    "\n",
+    "# Step 4: Plot the data\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# Bar plot with dodge=True to separate the bars\n",
+    "bar_plot = sns.barplot(x='Company', y='value', hue='variable', data=rides_count_melted, dodge=True, palette={'Number of Rides with Loss': 'red', 'Number of Rides with Gain': 'green'})\n",
+    "\n",
+    "# Annotate bars with the actual numbers\n",
+    "for p in bar_plot.patches:\n",
+    "    bar_plot.annotate(f'{int(p.get_height())}', \n",
+    "                      (p.get_x() + p.get_width() / 2., p.get_height()), \n",
+    "                      ha='center', va='center', xytext=(0, 10), \n",
+    "                      textcoords='offset points', fontsize=12)\n",
+    "\n",
+    "# Add labels and title\n",
+    "plt.title('Comparison of Rides with Loss and Gain in Profit - Pink Cab vs Yellow Cab')\n",
+    "plt.xlabel('Company')\n",
+    "plt.ylabel('Number of Rides')\n",
+    "plt.legend(title='Ride Type')\n",
+    "\n",
+    "# Show plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 273,
+   "id": "6736d1e1-2757-4029-9808-0f997c3826d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/fs/nc20m2757gj4y9jxvh_1hnph0000gn/T/ipykernel_33699/2297147272.py:20: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.barplot(x='Profit', y='City', data=top_5_profit_cities, palette='YlGnBu', orient='h')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Assuming df_cab and other preprocessing steps are already done\n",
+    "\n",
+    "# Calculate Profit for each ride\n",
+    "df_cab['Profit'] = df_cab['Price Charged'] - df_cab['Cost of Trip']\n",
+    "\n",
+    "# Filter only Yellow Cab data\n",
+    "yellow_cab_data = df_cab[df_cab['Company'] == 'Yellow Cab']\n",
+    "\n",
+    "# Group by City and sum the Profit\n",
+    "profit_by_city_yellow = yellow_cab_data.groupby('City')['Profit'].sum().reset_index()\n",
+    "\n",
+    "# Sort by Profit and get the top 5 cities\n",
+    "top_5_profit_cities = profit_by_city_yellow.sort_values(by='Profit', ascending=False).head(5)\n",
+    "\n",
+    "# Plotting the top 5 cities with the highest profit for Yellow Cab\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.barplot(x='Profit', y='City', data=top_5_profit_cities, palette='YlGnBu', orient='h')\n",
+    "\n",
+    "# Add the dollar amount on top of each bar\n",
+    "for index, value in enumerate(top_5_profit_cities['Profit']):\n",
+    "    plt.text(value, index, f'${value:,.0f}', va='center', ha='left', color='black', fontweight='bold')\n",
+    "\n",
+    "plt.title('Top 5 Cities with Highest Profit for Yellow Cab')\n",
+    "plt.xlabel('Total Profit (USD)')\n",
+    "plt.ylabel('City')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 274,
+   "id": "db5e0d7d-899f-43da-a3e2-fef4233d768f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/fs/nc20m2757gj4y9jxvh_1hnph0000gn/T/ipykernel_33699/3779762527.py:12: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.barplot(x='Profit', y='City', data=top_5_profit_cities_pink, palette='RdPu', orient='h')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Filter only Pink Cab data\n",
+    "pink_cab_data = df_cab[df_cab['Company'] == 'Pink Cab']\n",
+    "\n",
+    "# Group by City and sum the Profit\n",
+    "profit_by_city_pink = pink_cab_data.groupby('City')['Profit'].sum().reset_index()\n",
+    "\n",
+    "# Sort by Profit and get the top 5 cities\n",
+    "top_5_profit_cities_pink = profit_by_city_pink.sort_values(by='Profit', ascending=False).head(5)\n",
+    "\n",
+    "# Plotting the top 5 cities with the highest profit for Pink Cab\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.barplot(x='Profit', y='City', data=top_5_profit_cities_pink, palette='RdPu', orient='h')\n",
+    "\n",
+    "# Add the dollar amount on top of each bar\n",
+    "for index, value in enumerate(top_5_profit_cities_pink['Profit']):\n",
+    "    plt.text(value, index, f'${value:,.0f}', va='center', ha='left', color='black', fontweight='bold')\n",
+    "\n",
+    "plt.title('Top 5 Cities with Highest Profit for Pink Cab')\n",
+    "plt.xlabel('Total Profit (USD)')\n",
+    "plt.ylabel('City')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 276,
+   "id": "b38d1907-6b82-41aa-a108-1582091d5b3b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/fs/nc20m2757gj4y9jxvh_1hnph0000gn/T/ipykernel_33699/3079627545.py:15: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.barplot(x='Loss', y='City', data=top_3_loss_cities_yellow, palette='Oranges_r', orient='h', legend=False)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Filter only Yellow Cab data\n",
+    "yellow_cab_data = df_cab[df_cab['Company'] == 'Yellow Cab'].copy()\n",
+    "\n",
+    "# Calculate the loss as negative profit\n",
+    "yellow_cab_data['Loss'] = yellow_cab_data['Cost of Trip'] - yellow_cab_data['Price Charged']\n",
+    "\n",
+    "# Group by City and sum the Loss\n",
+    "loss_by_city_yellow = yellow_cab_data.groupby('City')['Loss'].sum().reset_index()\n",
+    "\n",
+    "# Sort by Loss and get the top 3 cities with the highest loss\n",
+    "top_3_loss_cities_yellow = loss_by_city_yellow.sort_values(by='Loss', ascending=False).head(3)\n",
+    "\n",
+    "# Plotting the top 3 cities with the highest loss for Yellow Cab\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.barplot(x='Loss', y='City', data=top_3_loss_cities_yellow, palette='Oranges_r', orient='h', legend=False)\n",
+    "\n",
+    "# Add the dollar amount on top of each bar\n",
+    "for index, value in enumerate(top_3_loss_cities_yellow['Loss']):\n",
+    "    plt.text(value, index, f'${value:,.0f}', va='center', ha='left', color='black', fontweight='bold')\n",
+    "\n",
+    "plt.title('Top 3 Cities with Highest Loss for Yellow Cab')\n",
+    "plt.xlabel('Total Loss (USD)')\n",
+    "plt.ylabel('City')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 277,
+   "id": "75d4bc26-0f88-4cbc-b404-ec49b0671e74",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/fs/nc20m2757gj4y9jxvh_1hnph0000gn/T/ipykernel_33699/1281965421.py:15: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.barplot(x='Loss', y='City', data=top_3_loss_cities_pink, palette='Oranges_r', orient='h', legend=False)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Filter only Pink Cab data\n",
+    "pink_cab_data = df_cab[df_cab['Company'] == 'Pink Cab'].copy()\n",
+    "\n",
+    "# Calculate the loss as negative profit\n",
+    "pink_cab_data['Loss'] = pink_cab_data['Cost of Trip'] - pink_cab_data['Price Charged']\n",
+    "\n",
+    "# Group by City and sum the Loss\n",
+    "loss_by_city_pink = pink_cab_data.groupby('City')['Loss'].sum().reset_index()\n",
+    "\n",
+    "# Sort by Loss and get the top 3 cities with the highest loss\n",
+    "top_3_loss_cities_pink = loss_by_city_pink.sort_values(by='Loss', ascending=False).head(3)\n",
+    "\n",
+    "# Plotting the top 3 cities with the highest loss for Pink Cab\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.barplot(x='Loss', y='City', data=top_3_loss_cities_pink, palette='Oranges_r', orient='h', legend=False)\n",
+    "\n",
+    "# Add the dollar amount on top of each bar\n",
+    "for index, value in enumerate(top_3_loss_cities_pink['Loss']):\n",
+    "    plt.text(value, index, f'${value:,.0f}', va='center', ha='left', color='black', fontweight='bold')\n",
+    "\n",
+    "plt.title('Top 3 Cities with Highest Loss for Pink Cab')\n",
+    "plt.xlabel('Total Loss (USD)')\n",
+    "plt.ylabel('City')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "04f287f8-38d3-45b4-9ad7-f6d74c7c6065",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (myenv)",
+   "language": "python",
+   "name": "myenv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/us_holiday_dates.csv b/us_holiday_dates.csv
new file mode 100644
index 00000000..514d347a
--- /dev/null
+++ b/us_holiday_dates.csv
@@ -0,0 +1,343 @@
+Date,Holiday,WeekDay,Month,Day,Year
+2004-07-04,4th of July,Sunday,7,4,2004
+2005-07-04,4th of July,Monday,7,4,2005
+2006-07-04,4th of July,Tuesday,7,4,2006
+2007-07-04,4th of July,Wednesday,7,4,2007
+2008-07-04,4th of July,Friday,7,4,2008
+2009-07-04,4th of July,Saturday,7,4,2009
+2010-07-04,4th of July,Sunday,7,4,2010
+2011-07-04,4th of July,Monday,7,4,2011
+2012-07-04,4th of July,Wednesday,7,4,2012
+2013-07-04,4th of July,Thursday,7,4,2013
+2014-07-04,4th of July,Friday,7,4,2014
+2015-07-04,4th of July,Saturday,7,4,2015
+2016-07-04,4th of July,Monday,7,4,2016
+2017-07-04,4th of July,Tuesday,7,4,2017
+2018-07-04,4th of July,Wednesday,7,4,2018
+2019-07-04,4th of July,Thursday,7,4,2019
+2020-07-04,4th of July,Saturday,7,4,2020
+2021-07-04,4th of July,Sunday,7,4,2021
+2004-12-25,Christmas Day,Saturday,12,25,2004
+2005-12-25,Christmas Day,Sunday,12,25,2005
+2006-12-25,Christmas Day,Monday,12,25,2006
+2007-12-25,Christmas Day,Tuesday,12,25,2007
+2008-12-25,Christmas Day,Thursday,12,25,2008
+2009-12-25,Christmas Day,Friday,12,25,2009
+2010-12-25,Christmas Day,Saturday,12,25,2010
+2011-12-25,Christmas Day,Sunday,12,25,2011
+2012-12-25,Christmas Day,Tuesday,12,25,2012
+2013-12-25,Christmas Day,Wednesday,12,25,2013
+2014-12-25,Christmas Day,Thursday,12,25,2014
+2015-12-25,Christmas Day,Friday,12,25,2015
+2016-12-25,Christmas Day,Sunday,12,25,2016
+2017-12-25,Christmas Day,Monday,12,25,2017
+2018-12-25,Christmas Day,Tuesday,12,25,2018
+2019-12-25,Christmas Day,Wednesday,12,25,2019
+2020-12-25,Christmas Day,Friday,12,25,2020
+2021-12-25,Christmas Day,Saturday,12,25,2021
+2004-12-24,Christmas Eve,Friday,12,24,2004
+2005-12-24,Christmas Eve,Saturday,12,24,2005
+2006-12-24,Christmas Eve,Sunday,12,24,2006
+2007-12-24,Christmas Eve,Monday,12,24,2007
+2008-12-24,Christmas Eve,Wednesday,12,24,2008
+2009-12-24,Christmas Eve,Thursday,12,24,2009
+2010-12-24,Christmas Eve,Friday,12,24,2010
+2011-12-24,Christmas Eve,Saturday,12,24,2011
+2012-12-24,Christmas Eve,Monday,12,24,2012
+2013-12-24,Christmas Eve,Tuesday,12,24,2013
+2014-12-24,Christmas Eve,Wednesday,12,24,2014
+2015-12-24,Christmas Eve,Thursday,12,24,2015
+2016-12-24,Christmas Eve,Saturday,12,24,2016
+2017-12-24,Christmas Eve,Sunday,12,24,2017
+2018-12-24,Christmas Eve,Monday,12,24,2018
+2019-12-24,Christmas Eve,Tuesday,12,24,2019
+2020-12-24,Christmas Eve,Thursday,12,24,2020
+2021-12-24,Christmas Eve,Friday,12,24,2021
+2007-10-08,Columbus Day,Monday,10,8,2007
+2012-10-08,Columbus Day,Monday,10,8,2012
+2018-10-08,Columbus Day,Monday,10,8,2018
+2006-10-09,Columbus Day,Monday,10,9,2006
+2017-10-09,Columbus Day,Monday,10,9,2017
+2005-10-10,Columbus Day,Monday,10,10,2005
+2011-10-10,Columbus Day,Monday,10,10,2011
+2016-10-10,Columbus Day,Monday,10,10,2016
+2004-10-11,Columbus Day,Monday,10,11,2004
+2010-10-11,Columbus Day,Monday,10,11,2010
+2021-10-11,Columbus Day,Monday,10,11,2021
+2009-10-12,Columbus Day,Monday,10,12,2009
+2015-10-12,Columbus Day,Monday,10,12,2015
+2020-10-12,Columbus Day,Monday,10,12,2020
+2008-10-13,Columbus Day,Monday,10,13,2008
+2014-10-13,Columbus Day,Monday,10,13,2014
+2013-10-14,Columbus Day,Monday,10,14,2013
+2019-10-14,Columbus Day,Monday,10,14,2019
+2010-04-04,Eastern Easter,Sunday,4,4,2010
+2007-04-08,Eastern Easter,Sunday,4,8,2007
+2018-04-08,Eastern Easter,Sunday,4,8,2018
+2004-04-11,Eastern Easter,Sunday,4,11,2004
+2015-04-12,Eastern Easter,Sunday,4,12,2015
+2012-04-15,Eastern Easter,Sunday,4,15,2012
+2017-04-16,Eastern Easter,Sunday,4,16,2017
+2020-04-19,Eastern Easter,Sunday,4,19,2020
+2009-04-19,Eastern Easter,Sunday,4,19,2009
+2014-04-20,Eastern Easter,Sunday,4,20,2014
+2006-04-23,Eastern Easter,Sunday,4,23,2006
+2011-04-24,Eastern Easter,Sunday,4,24,2011
+2008-04-27,Eastern Easter,Sunday,4,27,2008
+2019-04-28,Eastern Easter,Sunday,4,28,2019
+2021-05-02,Eastern Easter,Sunday,5,2,2021
+2005-05-01,Eastern Easter,Sunday,5,1,2005
+2016-05-01,Eastern Easter,Sunday,5,1,2016
+2013-05-05,Eastern Easter,Sunday,5,5,2013
+2020-06-19,Juneteenth,Friday,6,19,2020
+2021-06-19,Juneteenth,Saturday,6,19,2021
+2004-06-19,Juneteenth,Saturday,6,19,2004
+2005-06-19,Juneteenth,Sunday,6,19,2005
+2006-06-19,Juneteenth,Monday,6,19,2006
+2007-06-19,Juneteenth,Tuesday,6,19,2007
+2008-06-19,Juneteenth,Thursday,6,19,2008
+2009-06-19,Juneteenth,Friday,6,19,2009
+2010-06-19,Juneteenth,Saturday,6,19,2010
+2011-06-19,Juneteenth,Sunday,6,19,2011
+2012-06-19,Juneteenth,Tuesday,6,19,2012
+2013-06-19,Juneteenth,Wednesday,6,19,2013
+2014-06-19,Juneteenth,Thursday,6,19,2014
+2015-06-19,Juneteenth,Friday,6,19,2015
+2016-06-19,Juneteenth,Sunday,6,19,2016
+2017-06-19,Juneteenth,Monday,6,19,2017
+2018-06-19,Juneteenth,Tuesday,6,19,2018
+2019-06-19,Juneteenth,Wednesday,6,19,2019
+2008-09-01,Labor Day,Monday,9,1,2008
+2014-09-01,Labor Day,Monday,9,1,2014
+2013-09-02,Labor Day,Monday,9,2,2013
+2019-09-02,Labor Day,Monday,9,2,2019
+2007-09-03,Labor Day,Monday,9,3,2007
+2012-09-03,Labor Day,Monday,9,3,2012
+2018-09-03,Labor Day,Monday,9,3,2018
+2006-09-04,Labor Day,Monday,9,4,2006
+2017-09-04,Labor Day,Monday,9,4,2017
+2005-09-05,Labor Day,Monday,9,5,2005
+2011-09-05,Labor Day,Monday,9,5,2011
+2016-09-05,Labor Day,Monday,9,5,2016
+2004-09-06,Labor Day,Monday,9,6,2004
+2010-09-06,Labor Day,Monday,9,6,2010
+2021-09-06,Labor Day,Monday,9,6,2021
+2009-09-07,Labor Day,Monday,9,7,2009
+2015-09-07,Labor Day,Monday,9,7,2015
+2020-09-07,Labor Day,Monday,9,7,2020
+2008-08-30,Labor Day Weekend,Saturday,9,30,2008
+2014-08-30,Labor Day Weekend,Saturday,9,30,2014
+2008-08-31,Labor Day Weekend,Sunday,9,31,2008
+2014-08-31,Labor Day Weekend,Sunday,9,31,2014
+2013-08-31,Labor Day Weekend,Saturday,9,31,2013
+2019-08-31,Labor Day Weekend,Saturday,9,31,2019
+2013-09-01,Labor Day Weekend,Sunday,9,1,2013
+2019-09-01,Labor Day Weekend,Sunday,9,1,2019
+2007-09-01,Labor Day Weekend,Saturday,9,1,2007
+2012-09-01,Labor Day Weekend,Saturday,9,1,2012
+2018-09-01,Labor Day Weekend,Saturday,9,1,2018
+2007-09-02,Labor Day Weekend,Sunday,9,2,2007
+2012-09-02,Labor Day Weekend,Sunday,9,2,2012
+2018-09-02,Labor Day Weekend,Sunday,9,2,2018
+2006-09-02,Labor Day Weekend,Saturday,9,2,2006
+2017-09-02,Labor Day Weekend,Saturday,9,2,2017
+2006-09-03,Labor Day Weekend,Sunday,9,3,2006
+2017-09-03,Labor Day Weekend,Sunday,9,3,2017
+2005-09-03,Labor Day Weekend,Saturday,9,3,2005
+2011-09-03,Labor Day Weekend,Saturday,9,3,2011
+2016-09-03,Labor Day Weekend,Saturday,9,3,2016
+2005-09-04,Labor Day Weekend,Sunday,9,4,2005
+2011-09-04,Labor Day Weekend,Sunday,9,4,2011
+2016-09-04,Labor Day Weekend,Sunday,9,4,2016
+2004-09-04,Labor Day Weekend,Saturday,9,4,2004
+2010-09-04,Labor Day Weekend,Saturday,9,4,2010
+2021-09-04,Labor Day Weekend,Saturday,9,4,2021
+2004-09-05,Labor Day Weekend,Sunday,9,5,2004
+2010-09-05,Labor Day Weekend,Sunday,9,5,2010
+2021-09-05,Labor Day Weekend,Sunday,9,5,2021
+2009-09-05,Labor Day Weekend,Saturday,9,5,2009
+2015-09-05,Labor Day Weekend,Saturday,9,5,2015
+2020-09-05,Labor Day Weekend,Saturday,9,5,2020
+2009-09-06,Labor Day Weekend,Sunday,9,6,2009
+2015-09-06,Labor Day Weekend,Sunday,9,6,2015
+2020-09-06,Labor Day Weekend,Sunday,9,6,2020
+2007-01-15,"Martin Luther King, Jr. Day",Monday,1,15,2007
+2018-01-15,"Martin Luther King, Jr. Day",Monday,1,15,2018
+2006-01-16,"Martin Luther King, Jr. Day",Monday,1,16,2006
+2012-01-16,"Martin Luther King, Jr. Day",Monday,1,16,2012
+2017-01-16,"Martin Luther King, Jr. Day",Monday,1,16,2017
+2005-01-17,"Martin Luther King, Jr. Day",Monday,1,17,2005
+2011-01-17,"Martin Luther King, Jr. Day",Monday,1,17,2011
+2010-01-18,"Martin Luther King, Jr. Day",Monday,1,18,2010
+2016-01-18,"Martin Luther King, Jr. Day",Monday,1,18,2016
+2021-01-18,"Martin Luther King, Jr. Day",Monday,1,18,2021
+2004-01-19,"Martin Luther King, Jr. Day",Monday,1,19,2004
+2009-01-19,"Martin Luther King, Jr. Day",Monday,1,19,2009
+2015-01-19,"Martin Luther King, Jr. Day",Monday,1,19,2015
+2014-01-20,"Martin Luther King, Jr. Day",Monday,1,20,2014
+2020-01-20,"Martin Luther King, Jr. Day",Monday,1,20,2020
+2008-01-21,"Martin Luther King, Jr. Day",Monday,1,21,2008
+2013-01-21,"Martin Luther King, Jr. Day",Monday,1,21,2013
+2019-01-21,"Martin Luther King, Jr. Day",Monday,1,21,2019
+2009-05-25,Memorial Day,Monday,5,25,2009
+2015-05-25,Memorial Day,Monday,5,25,2015
+2020-05-25,Memorial Day,Monday,5,25,2020
+2008-05-26,Memorial Day,Monday,5,26,2008
+2014-05-26,Memorial Day,Monday,5,26,2014
+2013-05-27,Memorial Day,Monday,5,27,2013
+2019-05-27,Memorial Day,Monday,5,27,2019
+2007-05-28,Memorial Day,Monday,5,28,2007
+2012-05-28,Memorial Day,Monday,5,28,2012
+2018-05-28,Memorial Day,Monday,5,28,2018
+2006-05-29,Memorial Day,Monday,5,29,2006
+2017-05-29,Memorial Day,Monday,5,29,2017
+2005-05-30,Memorial Day,Monday,5,30,2005
+2011-05-30,Memorial Day,Monday,5,30,2011
+2016-05-30,Memorial Day,Monday,5,30,2016
+2004-05-31,Memorial Day,Monday,5,31,2004
+2010-05-31,Memorial Day,Monday,5,31,2010
+2021-05-31,Memorial Day,Monday,5,31,2021
+2012-01-01,New Year's Day,Sunday,1,1,2012
+2013-01-01,New Year's Day,Tuesday,1,1,2013
+2014-01-01,New Year's Day,Wednesday,1,1,2014
+2015-01-01,New Year's Day,Thursday,1,1,2015
+2016-01-01,New Year's Day,Friday,1,1,2016
+2017-01-01,New Year's Day,Sunday,1,1,2017
+2018-01-01,New Year's Day,Monday,1,1,2018
+2019-01-01,New Year's Day,Tuesday,1,1,2019
+2020-01-01,New Year's Day,Wednesday,1,1,2020
+2011-01-01,New Year's Day,Saturday,1,1,2011
+2004-01-01,New Year's Day,Thursday,1,1,2004
+2005-01-01,New Year's Day,Saturday,1,1,2005
+2006-01-01,New Year's Day,Sunday,1,1,2006
+2007-01-01,New Year's Day,Monday,1,1,2007
+2008-01-01,New Year's Day,Tuesday,1,1,2008
+2009-01-01,New Year's Day,Thursday,1,1,2009
+2010-01-01,New Year's Day,Friday,1,1,2010
+2021-01-01,New Year's Day,Friday,1,1,2021
+2004-12-31,New Year’s Eve,Friday,12,31,2004
+2005-12-31,New Year’s Eve,Saturday,12,31,2005
+2006-12-31,New Year’s Eve,Sunday,12,31,2006
+2007-12-31,New Year’s Eve,Monday,12,31,2007
+2008-12-31,New Year’s Eve,Wednesday,12,31,2008
+2009-12-31,New Year’s Eve,Thursday,12,31,2009
+2010-12-31,New Year’s Eve,Friday,12,31,2010
+2011-12-31,New Year’s Eve,Saturday,12,31,2011
+2012-12-31,New Year’s Eve,Monday,12,31,2012
+2013-12-31,New Year’s Eve,Tuesday,12,31,2013
+2014-12-31,New Year’s Eve,Wednesday,12,31,2014
+2015-12-31,New Year’s Eve,Thursday,12,31,2015
+2016-12-31,New Year’s Eve,Saturday,12,31,2016
+2017-12-31,New Year’s Eve,Sunday,12,31,2017
+2018-12-31,New Year’s Eve,Monday,12,31,2018
+2019-12-31,New Year’s Eve,Tuesday,12,31,2019
+2020-12-31,New Year’s Eve,Thursday,12,31,2020
+2021-12-31,New Year’s Eve,Friday,12,31,2021
+2007-11-22,Thanksgiving Day,Thursday,11,22,2007
+2012-11-22,Thanksgiving Day,Thursday,11,22,2012
+2018-11-22,Thanksgiving Day,Thursday,11,22,2018
+2006-11-23,Thanksgiving Day,Thursday,11,23,2006
+2017-11-23,Thanksgiving Day,Thursday,11,23,2017
+2005-11-24,Thanksgiving Day,Thursday,11,24,2005
+2011-11-24,Thanksgiving Day,Thursday,11,24,2011
+2016-11-24,Thanksgiving Day,Thursday,11,24,2016
+2004-11-25,Thanksgiving Day,Thursday,11,25,2004
+2010-11-25,Thanksgiving Day,Thursday,11,25,2010
+2021-11-25,Thanksgiving Day,Thursday,11,25,2021
+2009-11-26,Thanksgiving Day,Thursday,11,26,2009
+2015-11-26,Thanksgiving Day,Thursday,11,26,2015
+2020-11-26,Thanksgiving Day,Thursday,11,26,2020
+2008-11-27,Thanksgiving Day,Thursday,11,27,2008
+2014-11-27,Thanksgiving Day,Thursday,11,27,2014
+2013-11-28,Thanksgiving Day,Thursday,11,28,2013
+2019-11-28,Thanksgiving Day,Thursday,11,28,2019
+2007-11-21,Thanksgiving Eve,Wednesday,11,21,2007
+2012-11-21,Thanksgiving Eve,Wednesday,11,21,2012
+2018-11-21,Thanksgiving Eve,Wednesday,11,21,2018
+2006-11-22,Thanksgiving Eve,Wednesday,11,22,2006
+2017-11-22,Thanksgiving Eve,Wednesday,11,22,2017
+2005-11-23,Thanksgiving Eve,Wednesday,11,23,2005
+2011-11-23,Thanksgiving Eve,Wednesday,11,23,2011
+2016-11-23,Thanksgiving Eve,Wednesday,11,23,2016
+2004-11-24,Thanksgiving Eve,Wednesday,11,24,2004
+2010-11-24,Thanksgiving Eve,Wednesday,11,24,2010
+2021-11-24,Thanksgiving Eve,Wednesday,11,24,2021
+2009-11-25,Thanksgiving Eve,Wednesday,11,25,2009
+2015-11-25,Thanksgiving Eve,Wednesday,11,25,2015
+2020-11-25,Thanksgiving Eve,Wednesday,11,25,2020
+2008-11-26,Thanksgiving Eve,Wednesday,11,26,2008
+2014-11-26,Thanksgiving Eve,Wednesday,11,26,2014
+2013-11-27,Thanksgiving Eve,Wednesday,11,27,2013
+2019-11-27,Thanksgiving Eve,Wednesday,11,27,2019
+2004-02-14,Valentine’s Day,Saturday,2,14,2004
+2005-02-14,Valentine’s Day,Monday,2,14,2005
+2006-02-14,Valentine’s Day,Tuesday,2,14,2006
+2007-02-14,Valentine’s Day,Wednesday,2,14,2007
+2008-02-14,Valentine’s Day,Thursday,2,14,2008
+2009-02-14,Valentine’s Day,Saturday,2,14,2009
+2010-02-14,Valentine’s Day,Sunday,2,14,2010
+2011-02-14,Valentine’s Day,Monday,2,14,2011
+2012-02-14,Valentine’s Day,Tuesday,2,14,2012
+2013-02-14,Valentine’s Day,Thursday,2,14,2013
+2014-02-14,Valentine’s Day,Friday,2,14,2014
+2015-02-14,Valentine’s Day,Saturday,2,14,2015
+2016-02-14,Valentine’s Day,Sunday,2,14,2016
+2017-02-14,Valentine’s Day,Tuesday,2,14,2017
+2018-02-14,Valentine’s Day,Wednesday,2,14,2018
+2019-02-14,Valentine’s Day,Thursday,2,14,2019
+2020-02-14,Valentine’s Day,Friday,2,14,2020
+2021-02-14,Valentine’s Day,Sunday,2,14,2021
+2004-11-11,Veterans Day,Thursday,11,11,2004
+2005-11-11,Veterans Day,Friday,11,11,2005
+2006-11-11,Veterans Day,Saturday,11,11,2006
+2007-11-11,Veterans Day,Sunday,11,11,2007
+2008-11-11,Veterans Day,Tuesday,11,11,2008
+2009-11-11,Veterans Day,Wednesday,11,11,2009
+2010-11-11,Veterans Day,Thursday,11,11,2010
+2011-11-11,Veterans Day,Friday,11,11,2011
+2012-11-11,Veterans Day,Sunday,11,11,2012
+2013-11-11,Veterans Day,Monday,11,11,2013
+2014-11-11,Veterans Day,Tuesday,11,11,2014
+2015-11-11,Veterans Day,Wednesday,11,11,2015
+2016-11-11,Veterans Day,Friday,11,11,2016
+2017-11-11,Veterans Day,Saturday,11,11,2017
+2018-11-11,Veterans Day,Sunday,11,11,2018
+2019-11-11,Veterans Day,Monday,11,11,2019
+2020-11-11,Veterans Day,Wednesday,11,11,2020
+2021-11-11,Veterans Day,Thursday,11,11,2021
+2016-02-15,Washington's Birthday,Monday,2,15,2016
+2015-02-16,Washington's Birthday,Monday,2,16,2015
+2014-02-17,Washington's Birthday,Monday,2,17,2014
+2020-02-17,Washington's Birthday,Monday,2,17,2020
+2013-02-18,Washington's Birthday,Monday,2,18,2013
+2019-02-18,Washington's Birthday,Monday,2,18,2019
+2018-02-19,Washington's Birthday,Monday,2,19,2018
+2012-02-20,Washington's Birthday,Monday,2,20,2012
+2017-02-20,Washington's Birthday,Monday,2,20,2017
+2011-02-21,Washington's Birthday,Monday,2,21,2011
+2010-02-15,Washington's Birthday,Monday,2,15,2010
+2021-02-15,Washington's Birthday,Monday,2,15,2021
+2004-02-16,Washington's Birthday,Monday,2,16,2004
+2009-02-16,Washington's Birthday,Monday,2,16,2009
+2008-02-18,Washington's Birthday,Monday,2,18,2008
+2007-02-19,Washington's Birthday,Monday,2,19,2007
+2006-02-20,Washington's Birthday,Monday,2,20,2006
+2005-02-21,Washington's Birthday,Monday,2,21,2005
+2008-03-23,Western Easter,Sunday,3,23,2008
+2005-03-27,Western Easter,Sunday,3,27,2005
+2016-03-27,Western Easter,Sunday,3,27,2016
+2013-03-31,Western Easter,Sunday,3,31,2013
+2018-04-01,Western Easter,Sunday,4,1,2018
+2010-04-04,Western Easter,Sunday,4,4,2010
+2021-04-04,Western Easter,Sunday,4,4,2021
+2015-04-05,Western Easter,Sunday,4,5,2015
+2007-04-08,Western Easter,Sunday,4,8,2007
+2012-04-08,Western Easter,Sunday,4,8,2012
+2004-04-11,Western Easter,Sunday,4,11,2004
+2009-04-12,Western Easter,Sunday,4,12,2009
+2020-04-12,Western Easter,Sunday,4,12,2020
+2006-04-16,Western Easter,Sunday,4,16,2006
+2017-04-16,Western Easter,Sunday,4,16,2017
+2014-04-20,Western Easter,Sunday,4,20,2014
+2019-04-21,Western Easter,Sunday,4,21,2019
+2011-04-24,Western Easter,Sunday,4,24,2011
\ No newline at end of file