Juan_Bertucci_PYF_Project_FullCode_v3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gc8x8QimJY_H"
   },
   "source": [
    "# Project Python Foundations: FoodHub Data Analysis\n",
    "\n",
    "**Marks: 60**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NqikjkWwJY_L"
   },
   "source": [
    "### Context\n",
    "\n",
    "The number of restaurants in New York is increasing day by day. Lots of students and busy professionals rely on those restaurants due to their hectic lifestyles. Online food delivery service is a great option for them. It provides them with good food from their favorite restaurants. A food aggregator company FoodHub offers access to multiple restaurants through a single smartphone app.\n",
    "\n",
    "The app allows the restaurants to receive a direct online order from a customer. The app assigns a delivery person from the company to pick up the order after it is confirmed by the restaurant. The delivery person then uses the map to reach the restaurant and waits for the food package. Once the food package is handed over to the delivery person, he/she confirms the pick-up in the app and travels to the customer's location to deliver the food. The delivery person confirms the drop-off in the app after delivering the food package to the customer. The customer can rate the order in the app. The food aggregator earns money by collecting a fixed margin of the delivery order from the restaurants.\n",
    "\n",
    "### Objective\n",
    "\n",
    "The food aggregator company has stored the data of the different orders made by the registered customers in their online portal. They want to analyze the data to get a fair idea about the demand of different restaurants which will help them in enhancing their customer experience. Suppose you are hired as a Data Scientist in this company and the Data Science team has shared some of the key questions that need to be answered. Perform the data analysis to find answers to these questions that will help the company to improve the business. \n",
    "\n",
    "### Data Description\n",
    "\n",
    "The data contains the different data related to a food order. The detailed data dictionary is given below.\n",
    "\n",
    "### Data Dictionary\n",
    "\n",
    "* order_id: Unique ID of the order\n",
    "* customer_id: ID of the customer who ordered the food\n",
    "* restaurant_name: Name of the restaurant\n",
    "* cuisine_type: Cuisine ordered by the customer\n",
    "* cost: Cost of the order\n",
    "* day_of_the_week: Indicates whether the order is placed on a weekday or weekend (The weekday is from Monday to Friday and the weekend is Saturday and Sunday)\n",
    "* rating: Rating given by the customer out of 5\n",
    "* food_preparation_time: Time (in minutes) taken by the restaurant to prepare the food. This is calculated by taking the difference between the timestamps of the restaurant's order confirmation and the delivery person's pick-up confirmation.\n",
    "* delivery_time: Time (in minutes) taken by the delivery person to deliver the food package. This is calculated by taking the difference between the timestamps of the delivery person's pick-up confirmation and drop-off information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cEtNUxNAJY_M"
   },
   "source": [
    "### Let us start by importing the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "u6PlpJAPJY_N"
   },
   "outputs": [],
   "source": [
    "# import libraries for data manipulation\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# import libraries for data visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Seaborn aesthethic settings. Source: https://seaborn.pydata.org/examples/ (seaborn example gallery)\n",
    "sns.set_theme(style='darkgrid')\n",
    "\n",
    "# \"Magic\" command to tell Python to actually display the graphs (sourced from cases and session notebooks)\n",
    "%matplotlib inline\n",
    "\n",
    "# Option to display values up to 2 decimal places (sourced from cases and session notebooks)\n",
    "pd.set_option(\"display.float_format\", lambda x: \"%.2f\" % x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AdracY0HJY_O"
   },
   "source": [
    "### Understanding the structure of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "WBh2ZGB9kFhU"
   },
   "outputs": [],
   "source": [
    "# uncomment and run the following lines for Google Colab\n",
    "# from google.colab import drive\n",
    "# drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "GF01IZDbJY_O",
    "scrolled": true
   },
   "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>order_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>restaurant_name</th>\n",
       "      <th>cuisine_type</th>\n",
       "      <th>cost_of_the_order</th>\n",
       "      <th>day_of_the_week</th>\n",
       "      <th>rating</th>\n",
       "      <th>food_preparation_time</th>\n",
       "      <th>delivery_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1477147</td>\n",
       "      <td>337525</td>\n",
       "      <td>Hangawi</td>\n",
       "      <td>Korean</td>\n",
       "      <td>30.75</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>Not given</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1477685</td>\n",
       "      <td>358141</td>\n",
       "      <td>Blue Ribbon Sushi Izakaya</td>\n",
       "      <td>Japanese</td>\n",
       "      <td>12.08</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>Not given</td>\n",
       "      <td>25</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1477070</td>\n",
       "      <td>66393</td>\n",
       "      <td>Cafe Habana</td>\n",
       "      <td>Mexican</td>\n",
       "      <td>12.23</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1477334</td>\n",
       "      <td>106968</td>\n",
       "      <td>Blue Ribbon Fried Chicken</td>\n",
       "      <td>American</td>\n",
       "      <td>29.20</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1478249</td>\n",
       "      <td>76942</td>\n",
       "      <td>Dirty Bird to Go</td>\n",
       "      <td>American</td>\n",
       "      <td>11.59</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  customer_id            restaurant_name cuisine_type  \\\n",
       "0   1477147       337525                    Hangawi       Korean   \n",
       "1   1477685       358141  Blue Ribbon Sushi Izakaya     Japanese   \n",
       "2   1477070        66393                Cafe Habana      Mexican   \n",
       "3   1477334       106968  Blue Ribbon Fried Chicken     American   \n",
       "4   1478249        76942           Dirty Bird to Go     American   \n",
       "\n",
       "   cost_of_the_order day_of_the_week     rating  food_preparation_time  \\\n",
       "0              30.75         Weekend  Not given                     25   \n",
       "1              12.08         Weekend  Not given                     25   \n",
       "2              12.23         Weekday          5                     23   \n",
       "3              29.20         Weekend          3                     25   \n",
       "4              11.59         Weekday          4                     25   \n",
       "\n",
       "   delivery_time  \n",
       "0             20  \n",
       "1             23  \n",
       "2             28  \n",
       "3             15  \n",
       "4             24  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the data\n",
    "# Comment: this notebook was run on Jupyter. If this code is run on Colab, the following file path has to be edited accordingly.\n",
    "df = pd.read_csv('foodhub_order.csv')\n",
    "# returns the first 5 rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J_Y9aJDJJY_P"
   },
   "source": [
    "#### Observations:\n",
    "\n",
    "The DataFrame has 9 columns as mentioned in the Data Dictionary. Data in each row corresponds to the order placed by a customer.\n",
    "\n",
    "**Extra observation:** We can already observe that there are records where the customer didn't leave a rating."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c3kenV4vJY_Q"
   },
   "source": [
    "### **Question 1:** How many rows and columns are present in the data? [0.5 mark]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "Yvz5i-PhJY_Q"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows:\t\t1898\n",
      "Columns:\t9\n"
     ]
    }
   ],
   "source": [
    "# Write your code here\n",
    "print(f'Rows:\\t\\t{df.shape[0]}', f'Columns:\\t{df.shape[1]}', sep = '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UsTNy3unJY_R"
   },
   "source": [
    "#### Observations:\n",
    "\n",
    "The loaded data set has 9 columns (fields) and 1898 rows (records)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jRXakVrUJY_R"
   },
   "source": [
    "### **Question 2:** What are the datatypes of the different columns in the dataset? (The info() function can be used) [0.5 mark]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "Kp70Z0BNJY_R"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1898 entries, 0 to 1897\n",
      "Data columns (total 9 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   order_id               1898 non-null   int64  \n",
      " 1   customer_id            1898 non-null   int64  \n",
      " 2   restaurant_name        1898 non-null   object \n",
      " 3   cuisine_type           1898 non-null   object \n",
      " 4   cost_of_the_order      1898 non-null   float64\n",
      " 5   day_of_the_week        1898 non-null   object \n",
      " 6   rating                 1898 non-null   object \n",
      " 7   food_preparation_time  1898 non-null   int64  \n",
      " 8   delivery_time          1898 non-null   int64  \n",
      "dtypes: float64(1), int64(4), object(4)\n",
      "memory usage: 133.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# Use info() to print a concise summary of the DataFrame\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WJes5GP2JY_S"
   },
   "source": [
    "#### Observations:\n",
    "\n",
    "- The data set has 9 fields (columns) of data, of which 5 are numerical.\n",
    "- order_id and customer_id are recorded as numerical (integers).\n",
    "- Order cost (cost_of_the_order) is registered as float.\n",
    "- Time variables (food_preparation_time and delivery_time) are considered as integer quantities of minutes.\n",
    "- The rest of the columns: restaurant_name, cuisine_type, day_of_the_week, and (counter-intuitively) rating, are registered as strings.\n",
    "- No null values among the rows. More analysis is needed to verify the \"sanity\" of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pF_92MN6JY_S"
   },
   "source": [
    "### **Question 3:** Are there any missing values in the data? If yes, treat them using an appropriate method. [1 mark]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "Zth-RR1PJY_S"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id                 0\n",
       "customer_id              0\n",
       "restaurant_name          0\n",
       "cuisine_type             0\n",
       "cost_of_the_order        0\n",
       "day_of_the_week          0\n",
       "rating                   0\n",
       "food_preparation_time    0\n",
       "delivery_time            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write your code here\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jDHT3vD1JY_S"
   },
   "source": [
    "#### Observations:\n",
    "- The standard method to detect null values returns no nulls.\n",
    "- This, *per se*, doesn't mean the data is reliable or that there are not invalid entries.\n",
    "\n",
    "**More in depth analysis is required:**\n",
    "\n",
    "**1. List of restaurant names:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\"'wichcraft\" '12 Chairs' '5 Napkin Burger' '67 Burger' 'Alidoro' 'Amma'\n",
      " \"Amy Ruth's\" 'Anjappar Chettinad' 'Asuka Sushi' 'Aurora' 'Balade'\n",
      " 'Balthazar Boulangerie' 'Barbounia' 'Bareburger' 'Benihana'\n",
      " 'Bhatti Indian Grill' \"Big Daddy's\" 'Big Wong Restaurant \\x8c_¤¾Ñ¼'\n",
      " 'Bistango' 'Blue Ribbon Brooklyn' 'Blue Ribbon Fried Chicken'\n",
      " 'Blue Ribbon Sushi' 'Blue Ribbon Sushi Bar & Grill'\n",
      " 'Blue Ribbon Sushi Izakaya' 'Boqueria' \"Bubby's \" 'Bukhara Grill'\n",
      " 'Burger Joint' 'Byblos Restaurant' 'Cafe Habana' 'Cafe Mogador'\n",
      " 'Cafe de La Esquina' 'Cafeteria' 'CafÌ© China' \"Carmine's\"\n",
      " 'Chipotle Mexican Grill $1.99 Delivery' 'Cho Dang Gol'\n",
      " 'Chola Eclectic Indian Cuisine' 'Chote Nawab' 'Cipriani Le Specialita'\n",
      " \"Coppola's East\" 'Crema Restaurante' 'Delicatessen' 'DespaÌ±a'\n",
      " \"Dickson's Farmstand Meats\" 'Dig Inn Seasonal Market'\n",
      " 'Dirty Bird To Go (archived)' 'Dirty Bird to Go'\n",
      " \"Don's Bogam BBQ & Wine Bar\" 'Donburi-ya' 'Dos Caminos'\n",
      " 'Dos Caminos Soho' 'DuMont Burger' 'El Parador Cafe'\n",
      " 'Empanada Mama (closed)' 'Emporio' 'Five Guys Burgers and Fries'\n",
      " 'Five Leaves' 'Frank Restaurant' 'Friend of a Farmer' 'Gaia Italian Cafe'\n",
      " 'Galli Restaurant' 'Go! Go! Curry!' 'Grand Sichuan International'\n",
      " 'Haandi' 'Hampton Chutney Co.' 'Han Dynasty' 'Hangawi'\n",
      " 'Haru Gramercy Park' 'Hatsuhana' 'Haveli Indian Restaurant' 'Hibino'\n",
      " 'Hill Country Fried Chicken' \"Hiroko's Place\" 'Hot Kitchen'\n",
      " 'Hummus Place' 'Hunan Manor' 'Il Bambino' 'Izakaya Ten' 'J. G. Melon'\n",
      " \"Jack's Wife Freda\" \"Joe's Shanghai \\x8e_À\\x8eü£¾÷´\" 'Junoon'\n",
      " 'Kambi Ramen House' 'Kanoyama' 'Klong' 'Kori Restaurant and Bar'\n",
      " \"L'Express\" 'La Follia' 'Lamarca Pasta' 'Lantern Thai Kitchen'\n",
      " 'Le Grainne Cafe' 'Le Zie 2000 Trattoria' 'Lucky Strike'\n",
      " \"Lucky's Famous Burgers\" \"Mamoun's Falafel\" 'Market Table' 'Melt Shop'\n",
      " 'Mira Sushi' 'Mission Cantina' 'Momoya' 'Moonstruck on Second'\n",
      " 'Nha Trang One' 'Nobu Next Door' 'Olea' 'Olive Garden' 'Osteria Morini'\n",
      " 'Otto Enoteca Pizzeria' \"P.J. Clarke's\" 'Parm' \"Paul & Jimmy's\"\n",
      " 'Pepe Giallo' 'Pepe Rosso To Go' 'Philippe Chow' 'Piccolo Angolo'\n",
      " 'Pinto Nouveau Thai Bistro' 'Pongsri Thai' 'Posto' 'Prosperity Dumpling'\n",
      " 'Pylos' 'Ravagh Persian Grill' 'RedFarm Broadway' 'RedFarm Hudson'\n",
      " 'Rohm Thai' 'Room Service' 'Rubirosa' 'Rye House' \"S'MAC\" 'Samurai Mama'\n",
      " \"Sarabeth's\" \"Sarabeth's East\" \"Sarabeth's Restaurant\" \"Sarabeth's West\"\n",
      " 'Saravanaa Bhavan' \"Schnipper's Quality Kitchen\"\n",
      " 'Serafina Fabulous Pizza' 'Shake Shack' 'Socarrat Paella Bar'\n",
      " 'Song Thai Restaurant & Bar' 'Spice Thai' 'Sushi Choshi' 'Sushi Samba'\n",
      " 'Sushi of Gari' 'Sushi of Gari 46' 'Sushi of Gari Tribeca' 'TAO'\n",
      " 'Tamarind TriBeCa' 'Tarallucci e Vino Restaurant' 'Taro Sushi'\n",
      " 'Terakawa Ramen' 'The Kati Roll Company' 'The Loop' 'The MasalaWala'\n",
      " 'The Meatball Shop' 'The Odeon' 'The Smile' \"Tony's Di Napoli\" 'Tortaria'\n",
      " 'Tres Carnes' 'UVA Wine Bar & Restaurant' 'V-Nam Cafe'\n",
      " \"Vanessa's Dumpling House\" \"Vanessa's Dumplings\" 'Vezzo Thin Crust Pizza'\n",
      " 'Wa Jeal' 'Waverly Diner' 'Westville Hudson' 'Wo Hop Restaurant'\n",
      " 'Woorijip' 'Xe May Sandwich Shop' \"Xi'an Famous Foods\" 'Yama 49'\n",
      " 'Yama Japanese Restaurant' 'Zero Otto Nove' 'brgr' 'da Umberto'\n",
      " 'ilili Restaurant' 'indikitch']\n"
     ]
    }
   ],
   "source": [
    "rest_lst = np.sort(df['restaurant_name'].unique())\n",
    "print (rest_lst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Extra Observations about the restaurant names column:**\n",
    "\n",
    "- A thorough observation of the restaurant list lets us know there are indeed problems and opportunities hidden in this field of data:\n",
    "\n",
    "a) Some names seem to be duplicated with \"alternative\" spellings, like 'Blue Ribbon Brooklyn' and 'Blue Ribbon Fried Chicken';\n",
    " 'Blue Ribbon Sushi', 'Blue Ribbon Sushi Bar & Grill', and 'Blue Ribbon Sushi Izakaya' (In fact the word \"izakaya\" is japanese that roughly translates as \"bar\" or \"pub\", so the last 3 names might very well be the same business); \"Vanessa's Dumpling House\" and \"Vanessa's Dumplings\", among others. However, before discarding, grouping, or renaming the apparent duplicates a little extra research is required. For the scope of this project, this list will not be modified.\n",
    "\n",
    "b) There might be special character issues with some names (maybe chinese and spanish).\n",
    "\n",
    "c) Some names include commentary like \"(closed)\"  which could be put in a separate column and enrich the data analysis with extra insights, like as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13 orders (0.68%) are from closed restaurants: Empanada Mama (closed)\n"
     ]
    }
   ],
   "source": [
    "df['Closed'] = [1 if df['restaurant_name'][idx].find('closed')!=-1 else 0 for idx in range(df.shape[0])] # new column with a dummy variable (1 = 'closed')\n",
    "\n",
    "closed_orders = df[df['Closed']==1].shape[0] # Count of orders from \"closed\" restaurants\n",
    "\n",
    "pct_cl_od = round(closed_orders/df.shape[0]*100,2) # % of orders from \"closed\" restaurants\n",
    "\n",
    "closed_rest_str = \", \".join(df[df['Closed']==1]['restaurant_name'].unique()) # if more than one, they will show up as a string separated by commas.\n",
    "\n",
    "print (f'{closed_orders} orders ({pct_cl_od}%) are from closed restaurants: {closed_rest_str}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Luckily the number of rows with this particular issue are very few, and won't affect the analysis in a significant way.\n",
    "\n",
    "There's also an \"archived\" for a repeated restaurant name which will be ignored for lack of better information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Cuisine Type & Day of the Week:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Korean', 'Japanese', 'Mexican', 'American', 'Indian', 'Italian',\n",
       "       'Mediterranean', 'Chinese', 'Middle Eastern', 'Thai', 'Southern',\n",
       "       'French', 'Spanish', 'Vietnamese'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cuisine_type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Weekend', 'Weekday'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['day_of_the_week'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No apparent issues with these two columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. Ratings:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Not given', '5', '3', '4'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rating'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see (again) that there are orders where no rating was given. Attributing a rating value to these cases could distort the analysis, but having a string value instead of a null value doesn't allow us to figure out the ratings distribution or statistical information for rated orders. The proposed solution is the following, where the original column will be conserved for categorical analysis purposes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>order_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>restaurant_name</th>\n",
       "      <th>cuisine_type</th>\n",
       "      <th>cost_of_the_order</th>\n",
       "      <th>day_of_the_week</th>\n",
       "      <th>rating</th>\n",
       "      <th>food_preparation_time</th>\n",
       "      <th>delivery_time</th>\n",
       "      <th>Closed</th>\n",
       "      <th>rating_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1477147</td>\n",
       "      <td>337525</td>\n",
       "      <td>Hangawi</td>\n",
       "      <td>Korean</td>\n",
       "      <td>30.75</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>Not given</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1477685</td>\n",
       "      <td>358141</td>\n",
       "      <td>Blue Ribbon Sushi Izakaya</td>\n",
       "      <td>Japanese</td>\n",
       "      <td>12.08</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>Not given</td>\n",
       "      <td>25</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1477070</td>\n",
       "      <td>66393</td>\n",
       "      <td>Cafe Habana</td>\n",
       "      <td>Mexican</td>\n",
       "      <td>12.23</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1477334</td>\n",
       "      <td>106968</td>\n",
       "      <td>Blue Ribbon Fried Chicken</td>\n",
       "      <td>American</td>\n",
       "      <td>29.20</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1478249</td>\n",
       "      <td>76942</td>\n",
       "      <td>Dirty Bird to Go</td>\n",
       "      <td>American</td>\n",
       "      <td>11.59</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  customer_id            restaurant_name cuisine_type  \\\n",
       "0   1477147       337525                    Hangawi       Korean   \n",
       "1   1477685       358141  Blue Ribbon Sushi Izakaya     Japanese   \n",
       "2   1477070        66393                Cafe Habana      Mexican   \n",
       "3   1477334       106968  Blue Ribbon Fried Chicken     American   \n",
       "4   1478249        76942           Dirty Bird to Go     American   \n",
       "\n",
       "   cost_of_the_order day_of_the_week     rating  food_preparation_time  \\\n",
       "0              30.75         Weekend  Not given                     25   \n",
       "1              12.08         Weekend  Not given                     25   \n",
       "2              12.23         Weekday          5                     23   \n",
       "3              29.20         Weekend          3                     25   \n",
       "4              11.59         Weekday          4                     25   \n",
       "\n",
       "   delivery_time  Closed  rating_value  \n",
       "0             20       0           NaN  \n",
       "1             23       0           NaN  \n",
       "2             28       0          5.00  \n",
       "3             15       0          3.00  \n",
       "4             24       0          4.00  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Every 'Not Given' value in the 'rating' column is turned into a null value.\n",
    "# The result is saved in a new column to avoid losing data.\n",
    "df['rating_value'] = df['rating'].replace(['Not given'],np.nan)\n",
    "df['rating_value'] = df['rating_value'].astype(float)\n",
    "\n",
    "# The 'object' type original column needs to be categorized properly to get a tidier analysis:\n",
    "rat_cat = ['Not given', '3', '4', '5']\n",
    "df.rating = pd.Categorical(df.rating, ordered=True, categories=rat_cat)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regarding other categorical variables, there doesn't seem to be a problem. No missing values for numerical variables either, so eventual issues with the sanity of the data will be assessed further after looking at the statistical summary and the univariate analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MnJo7MWrJY_S"
   },
   "source": [
    "### **Question 4:** Check the statistical summary of the data. What is the minimum, average, and maximum time it takes for food to be prepared once an order is placed? [2 marks]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "WprQBgOSJY_T"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>order_id</th>\n",
       "      <td>1898.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1477495.50</td>\n",
       "      <td>548.05</td>\n",
       "      <td>1476547.00</td>\n",
       "      <td>1477021.25</td>\n",
       "      <td>1477495.50</td>\n",
       "      <td>1477969.75</td>\n",
       "      <td>1478444.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <td>1898.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>171168.48</td>\n",
       "      <td>113698.14</td>\n",
       "      <td>1311.00</td>\n",
       "      <td>77787.75</td>\n",
       "      <td>128600.00</td>\n",
       "      <td>270525.00</td>\n",
       "      <td>405334.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restaurant_name</th>\n",
       "      <td>1898</td>\n",
       "      <td>178</td>\n",
       "      <td>Shake Shack</td>\n",
       "      <td>219</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cuisine_type</th>\n",
       "      <td>1898</td>\n",
       "      <td>14</td>\n",
       "      <td>American</td>\n",
       "      <td>584</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cost_of_the_order</th>\n",
       "      <td>1898.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.50</td>\n",
       "      <td>7.48</td>\n",
       "      <td>4.47</td>\n",
       "      <td>12.08</td>\n",
       "      <td>14.14</td>\n",
       "      <td>22.30</td>\n",
       "      <td>35.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_of_the_week</th>\n",
       "      <td>1898</td>\n",
       "      <td>2</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1351</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating</th>\n",
       "      <td>1898</td>\n",
       "      <td>4</td>\n",
       "      <td>Not given</td>\n",
       "      <td>736</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>food_preparation_time</th>\n",
       "      <td>1898.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.37</td>\n",
       "      <td>4.63</td>\n",
       "      <td>20.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>27.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>35.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delivery_time</th>\n",
       "      <td>1898.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.16</td>\n",
       "      <td>4.97</td>\n",
       "      <td>15.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>28.00</td>\n",
       "      <td>33.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Closed</th>\n",
       "      <td>1898.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating_value</th>\n",
       "      <td>1162.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.34</td>\n",
       "      <td>0.74</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count unique          top  freq       mean       std  \\\n",
       "order_id              1898.00    NaN          NaN   NaN 1477495.50    548.05   \n",
       "customer_id           1898.00    NaN          NaN   NaN  171168.48 113698.14   \n",
       "restaurant_name          1898    178  Shake Shack   219        NaN       NaN   \n",
       "cuisine_type             1898     14     American   584        NaN       NaN   \n",
       "cost_of_the_order     1898.00    NaN          NaN   NaN      16.50      7.48   \n",
       "day_of_the_week          1898      2      Weekend  1351        NaN       NaN   \n",
       "rating                   1898      4    Not given   736        NaN       NaN   \n",
       "food_preparation_time 1898.00    NaN          NaN   NaN      27.37      4.63   \n",
       "delivery_time         1898.00    NaN          NaN   NaN      24.16      4.97   \n",
       "Closed                1898.00    NaN          NaN   NaN       0.01      0.08   \n",
       "rating_value          1162.00    NaN          NaN   NaN       4.34      0.74   \n",
       "\n",
       "                             min        25%        50%        75%        max  \n",
       "order_id              1476547.00 1477021.25 1477495.50 1477969.75 1478444.00  \n",
       "customer_id              1311.00   77787.75  128600.00  270525.00  405334.00  \n",
       "restaurant_name              NaN        NaN        NaN        NaN        NaN  \n",
       "cuisine_type                 NaN        NaN        NaN        NaN        NaN  \n",
       "cost_of_the_order           4.47      12.08      14.14      22.30      35.41  \n",
       "day_of_the_week              NaN        NaN        NaN        NaN        NaN  \n",
       "rating                       NaN        NaN        NaN        NaN        NaN  \n",
       "food_preparation_time      20.00      23.00      27.00      31.00      35.00  \n",
       "delivery_time              15.00      20.00      25.00      28.00      33.00  \n",
       "Closed                      0.00       0.00       0.00       0.00       1.00  \n",
       "rating_value                3.00       4.00       5.00       5.00       5.00  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all').T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9YbSOR5wJY_T"
   },
   "source": [
    "#### Observations:\n",
    "\n",
    "\n",
    "\n",
    "Food Preparation Time has a range between a minimum of 20 and a maximum of 35 minutes,  with an average value of 27.37 minutes. The mean (27 minutes) is very similar to the median, so the distribution of this variable doesn't appear to be skewed just by looking at this summary.\n",
    "\n",
    "Other information we can get from this statistical summary (not exhaustive):\n",
    "   - Cost of the Orders ranges between 4.47 and 35.41, dollars, about 50 % of the orders are under 14 dollars. The distribution of this variable looks right-skewed.\n",
    "   - Delivery Time ranges between 15 and 33 minutes, with a median of 25.\n",
    "   - Rating Values, where orders were rated, range between 3 and 5. At least 50% of the rating values are 5. Still, the most frequent value is rating not given.\n",
    "   - Notice that there is statistical info for ratings available (in the new column 'rating_value'), at least where the orders were indeed rated.\n",
    "   - The restaurant with the most orders is \"Shake Shack\".\n",
    "   - The most ordered cuisine type is American.\n",
    "   - A majority of orders are placed on weekends."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cw3l58z_JY_T"
   },
   "source": [
    "### **Question 5:** How many orders are not rated? [1 mark]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "hEzDUL9wgksi"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "736 orders were not rated, which is the 38.78% of the total.\n"
     ]
    }
   ],
   "source": [
    "# Write the code here\n",
    "NG_rat_rw = df[df['rating_value'].isnull()].shape[0] # Not rated orders have \"null\" (NaN) values in this new column\n",
    "NG_rat_rw_pc = round(NG_rat_rw/df.shape[0]*100,2)\n",
    "print (f'{NG_rat_rw} orders were not rated, which is the {NG_rat_rw_pc}% of the total.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5E0O_oviJY_T"
   },
   "source": [
    "#### Observations:\n",
    "\n",
    "We could figure out that 736 orders are not rated, almost 40%. It's a significant proportion! In some of the upcoming analyses, these cases could get attributed with some value, but the sensible action is to leave them as they are until further research is undertaken."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7msIfXE-JY_T"
   },
   "source": [
    "### Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-2xfI28YJY_T"
   },
   "source": [
    "### Univariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0RHZnugBJY_U"
   },
   "source": [
    "### **Question 6:** Explore all the variables and provide observations on their distributions. (Generally, histograms, boxplots, countplots, etc. are used for univariate exploration.) [9 marks]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's check out how many unique **order** and **customer** ID's are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "rU5ZqISjJY_U"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1898 unique order IDs and 1200 unique customer IDs, thats less than 2 orders per customer.\n"
     ]
    }
   ],
   "source": [
    "# Write the code here\n",
    "n_ord = df['order_id'].nunique()\n",
    "n_cust = df['customer_id'].nunique()\n",
    "print(f'There are {n_ord} unique order IDs and {n_cust} unique customer IDs, thats less than {int(n_ord/n_cust)+1} orders per customer.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no duplicate orders (rows). On the other side, customers are pretty atomized on average. Are there any over represented customers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Auxiliary data frame for agregated variables by customer ID:\n",
    "lst_cust = df['customer_id'].unique()\n",
    "ord_cust = [df[df['customer_id']==cust]['order_id'].count() for cust in lst_cust]\n",
    "\n",
    "agg_cust = pd.DataFrame({'customer': lst_cust, 'orders': ord_cust})\n",
    "agg_cust['orders'] = agg_cust['orders'].astype(int)\n",
    "\n",
    "plt.figure(figsize=(10,2))\n",
    "sns.boxplot(data = agg_cust, \n",
    "            x = 'orders', \n",
    "            color = 'red')\n",
    "plt.title('Orders by Customer')\n",
    "plt.xlabel('Number of Orders')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>customer</th>\n",
       "      <td>1200.00</td>\n",
       "      <td>177202.83</td>\n",
       "      <td>117661.96</td>\n",
       "      <td>1311.00</td>\n",
       "      <td>77543.00</td>\n",
       "      <td>133212.00</td>\n",
       "      <td>294304.00</td>\n",
       "      <td>405334.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orders</th>\n",
       "      <td>1200.00</td>\n",
       "      <td>1.58</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count      mean       std     min      25%       50%       75%  \\\n",
       "customer 1200.00 177202.83 117661.96 1311.00 77543.00 133212.00 294304.00   \n",
       "orders   1200.00      1.58      1.09    1.00     1.00      1.00      2.00   \n",
       "\n",
       "               max  \n",
       "customer 405334.00  \n",
       "orders       13.00  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_cust.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The auxiliary dataframe **\"agg_cust\"** helps understand the distribution of orders per customer. As the box plot shows, most customers placed just 1 to 3 orders (including the whisker of the plot), while there are a number of \"outlier\" customers, with 4 to 13 orders. The distribution of orders per customer is very right-skewed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Restaurant Names (restaurant_name):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Univariate analysis for the column 'restaurant_name', means analyzing the number of orders per restaurant. As there are too many for a clean analysis, aggregating by restaurant and filtering the top 80% of orders, Pareto-style seems like a good practice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "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>restaurant</th>\n",
       "      <th>orders</th>\n",
       "      <th>pct</th>\n",
       "      <th>pct_ac</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shake Shack</td>\n",
       "      <td>219</td>\n",
       "      <td>11.54</td>\n",
       "      <td>11.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Meatball Shop</td>\n",
       "      <td>132</td>\n",
       "      <td>6.95</td>\n",
       "      <td>18.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Blue Ribbon Sushi</td>\n",
       "      <td>119</td>\n",
       "      <td>6.27</td>\n",
       "      <td>24.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Blue Ribbon Fried Chicken</td>\n",
       "      <td>96</td>\n",
       "      <td>5.06</td>\n",
       "      <td>29.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Parm</td>\n",
       "      <td>68</td>\n",
       "      <td>3.58</td>\n",
       "      <td>33.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RedFarm Broadway</td>\n",
       "      <td>59</td>\n",
       "      <td>3.11</td>\n",
       "      <td>36.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RedFarm Hudson</td>\n",
       "      <td>55</td>\n",
       "      <td>2.90</td>\n",
       "      <td>39.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TAO</td>\n",
       "      <td>49</td>\n",
       "      <td>2.58</td>\n",
       "      <td>41.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Han Dynasty</td>\n",
       "      <td>46</td>\n",
       "      <td>2.42</td>\n",
       "      <td>44.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Blue Ribbon Sushi Bar &amp; Grill</td>\n",
       "      <td>44</td>\n",
       "      <td>2.32</td>\n",
       "      <td>46.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nobu Next Door</td>\n",
       "      <td>42</td>\n",
       "      <td>2.21</td>\n",
       "      <td>48.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rubirosa</td>\n",
       "      <td>37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>50.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Sushi of Gari 46</td>\n",
       "      <td>37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>52.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Momoya</td>\n",
       "      <td>30</td>\n",
       "      <td>1.58</td>\n",
       "      <td>54.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Five Guys Burgers and Fries</td>\n",
       "      <td>29</td>\n",
       "      <td>1.53</td>\n",
       "      <td>55.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Blue Ribbon Sushi Izakaya</td>\n",
       "      <td>29</td>\n",
       "      <td>1.53</td>\n",
       "      <td>57.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Bareburger</td>\n",
       "      <td>27</td>\n",
       "      <td>1.42</td>\n",
       "      <td>58.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Tamarind TriBeCa</td>\n",
       "      <td>27</td>\n",
       "      <td>1.42</td>\n",
       "      <td>60.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Jack's Wife Freda</td>\n",
       "      <td>25</td>\n",
       "      <td>1.32</td>\n",
       "      <td>61.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Sushi of Gari Tribeca</td>\n",
       "      <td>24</td>\n",
       "      <td>1.26</td>\n",
       "      <td>62.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Chipotle Mexican Grill $1.99 Delivery</td>\n",
       "      <td>23</td>\n",
       "      <td>1.21</td>\n",
       "      <td>64.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>The Smile</td>\n",
       "      <td>18</td>\n",
       "      <td>0.95</td>\n",
       "      <td>65.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ilili Restaurant</td>\n",
       "      <td>18</td>\n",
       "      <td>0.95</td>\n",
       "      <td>66.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Cafe Mogador</td>\n",
       "      <td>18</td>\n",
       "      <td>0.95</td>\n",
       "      <td>66.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Yama Japanese Restaurant</td>\n",
       "      <td>18</td>\n",
       "      <td>0.95</td>\n",
       "      <td>67.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>The Kati Roll Company</td>\n",
       "      <td>16</td>\n",
       "      <td>0.84</td>\n",
       "      <td>68.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Sushi of Gari</td>\n",
       "      <td>16</td>\n",
       "      <td>0.84</td>\n",
       "      <td>69.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Cafe Habana</td>\n",
       "      <td>16</td>\n",
       "      <td>0.84</td>\n",
       "      <td>70.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>P.J. Clarke's</td>\n",
       "      <td>15</td>\n",
       "      <td>0.79</td>\n",
       "      <td>71.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>J. G. Melon</td>\n",
       "      <td>15</td>\n",
       "      <td>0.79</td>\n",
       "      <td>72.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Westville Hudson</td>\n",
       "      <td>15</td>\n",
       "      <td>0.79</td>\n",
       "      <td>72.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Osteria Morini</td>\n",
       "      <td>14</td>\n",
       "      <td>0.74</td>\n",
       "      <td>73.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Hill Country Fried Chicken</td>\n",
       "      <td>14</td>\n",
       "      <td>0.74</td>\n",
       "      <td>74.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Empanada Mama (closed)</td>\n",
       "      <td>13</td>\n",
       "      <td>0.68</td>\n",
       "      <td>74.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Vanessa's Dumplings</td>\n",
       "      <td>13</td>\n",
       "      <td>0.68</td>\n",
       "      <td>75.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>12</td>\n",
       "      <td>0.63</td>\n",
       "      <td>76.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>S'MAC</td>\n",
       "      <td>12</td>\n",
       "      <td>0.63</td>\n",
       "      <td>76.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Otto Enoteca Pizzeria</td>\n",
       "      <td>12</td>\n",
       "      <td>0.63</td>\n",
       "      <td>77.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Melt Shop</td>\n",
       "      <td>11</td>\n",
       "      <td>0.58</td>\n",
       "      <td>78.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Sushi Samba</td>\n",
       "      <td>11</td>\n",
       "      <td>0.58</td>\n",
       "      <td>78.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Balthazar Boulangerie</td>\n",
       "      <td>10</td>\n",
       "      <td>0.53</td>\n",
       "      <td>79.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Boqueria</td>\n",
       "      <td>9</td>\n",
       "      <td>0.47</td>\n",
       "      <td>79.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               restaurant  orders   pct  pct_ac\n",
       "0                             Shake Shack     219 11.54   11.54\n",
       "1                       The Meatball Shop     132  6.95   18.49\n",
       "2                       Blue Ribbon Sushi     119  6.27   24.76\n",
       "3               Blue Ribbon Fried Chicken      96  5.06   29.82\n",
       "4                                    Parm      68  3.58   33.40\n",
       "5                        RedFarm Broadway      59  3.11   36.51\n",
       "6                          RedFarm Hudson      55  2.90   39.41\n",
       "7                                     TAO      49  2.58   41.99\n",
       "8                             Han Dynasty      46  2.42   44.42\n",
       "9           Blue Ribbon Sushi Bar & Grill      44  2.32   46.73\n",
       "10                         Nobu Next Door      42  2.21   48.95\n",
       "11                               Rubirosa      37  1.95   50.90\n",
       "12                       Sushi of Gari 46      37  1.95   52.85\n",
       "13                                 Momoya      30  1.58   54.43\n",
       "14            Five Guys Burgers and Fries      29  1.53   55.95\n",
       "15              Blue Ribbon Sushi Izakaya      29  1.53   57.48\n",
       "16                             Bareburger      27  1.42   58.90\n",
       "17                       Tamarind TriBeCa      27  1.42   60.33\n",
       "18                      Jack's Wife Freda      25  1.32   61.64\n",
       "19                  Sushi of Gari Tribeca      24  1.26   62.91\n",
       "20  Chipotle Mexican Grill $1.99 Delivery      23  1.21   64.12\n",
       "21                              The Smile      18  0.95   65.07\n",
       "22                       ilili Restaurant      18  0.95   66.02\n",
       "23                           Cafe Mogador      18  0.95   66.97\n",
       "24               Yama Japanese Restaurant      18  0.95   67.91\n",
       "25                  The Kati Roll Company      16  0.84   68.76\n",
       "26                          Sushi of Gari      16  0.84   69.60\n",
       "27                            Cafe Habana      16  0.84   70.44\n",
       "28                          P.J. Clarke's      15  0.79   71.23\n",
       "29                            J. G. Melon      15  0.79   72.02\n",
       "30                       Westville Hudson      15  0.79   72.81\n",
       "31                         Osteria Morini      14  0.74   73.55\n",
       "32             Hill Country Fried Chicken      14  0.74   74.29\n",
       "33                 Empanada Mama (closed)      13  0.68   74.97\n",
       "34                    Vanessa's Dumplings      13  0.68   75.66\n",
       "35                           Burger Joint      12  0.63   76.29\n",
       "36                                  S'MAC      12  0.63   76.92\n",
       "37                  Otto Enoteca Pizzeria      12  0.63   77.56\n",
       "38                              Melt Shop      11  0.58   78.13\n",
       "39                            Sushi Samba      11  0.58   78.71\n",
       "40                  Balthazar Boulangerie      10  0.53   79.24\n",
       "41                               Boqueria       9  0.47   79.72"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Building an auxiliary Data Frame to study the performance of each restaurant\n",
    "rest_lst = df['restaurant_name'].unique()\n",
    "orders_rest = [df[df['restaurant_name']==rest]['order_id'].count() for rest in rest_lst]\n",
    "agg_rest = pd.DataFrame({'restaurant': rest_lst, 'orders': orders_rest})\n",
    "\n",
    "agg_rest = agg_rest.sort_values(by = 'orders', ascending = False).reset_index() # sorting from highest to lowest order qty\n",
    "agg_rest.drop('index', axis=1, inplace=True) # dropping the old index\n",
    "agg_rest['orders'] = agg_rest['orders'].astype(int)\n",
    "\n",
    "# Filling new columns to help determine normalized performance and accumulated % of orders (to filter the top 80% later)\n",
    "agg_rest['pct'] = np.zeros(len(rest_lst))\n",
    "agg_rest['pct_ac'] = np.zeros(len(rest_lst)) # accumulated % of orders\n",
    "\n",
    "par_tot = 0\n",
    "\n",
    "for indx in range(len(rest_lst)):\n",
    "    par_tot += agg_rest.loc[indx,'orders']\n",
    "    agg_rest.loc[indx,'pct'] = agg_rest.loc[indx,'orders'] / df.shape[0] * 100\n",
    "    agg_rest.loc[indx,'pct_ac'] = par_tot / df.shape[0] * 100\n",
    "agg_rest[agg_rest['pct_ac']<=80] # selection of top restaurants by quantity of orders, for 80% of orders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 178 unique values for the column restaurant_name, of which 42 took about 80% of the orders.\n"
     ]
    }
   ],
   "source": [
    "top_80 = agg_rest[agg_rest['pct_ac']<=80].shape[0]\n",
    "print (f'There are {len(rest_lst)} unique values for the column restaurant_name, of which {top_80} took about 80% of the orders.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>restaurant</th>\n",
       "      <th>orders</th>\n",
       "      <th>pct</th>\n",
       "      <th>pct_ac</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Rohm Thai</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>99.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Dig Inn Seasonal Market</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>99.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Socarrat Paella Bar</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>99.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Alidoro</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>99.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Cipriani Le Specialita</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>99.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Sushi Choshi</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>99.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Dos Caminos Soho</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>99.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>La Follia</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>99.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>Philippe Chow</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>99.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>'wichcraft</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  restaurant  orders  pct  pct_ac\n",
       "168                Rohm Thai       1 0.05   99.53\n",
       "169  Dig Inn Seasonal Market       1 0.05   99.58\n",
       "170      Socarrat Paella Bar       1 0.05   99.63\n",
       "171                  Alidoro       1 0.05   99.68\n",
       "172   Cipriani Le Specialita       1 0.05   99.74\n",
       "173             Sushi Choshi       1 0.05   99.79\n",
       "174         Dos Caminos Soho       1 0.05   99.84\n",
       "175                La Follia       1 0.05   99.89\n",
       "176            Philippe Chow       1 0.05   99.95\n",
       "177               'wichcraft       1 0.05  100.00"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_rest.tail(10) # Taking a look at the ones with the lowest quantity of orders taken"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be seen that 42 restaurants account for about 80% of orders, with only 4 restaurants having more than 90 orders each. The bottom 10 restaurants have only 1 order each.\n",
    "\n",
    "Let's do some visualizations on the quantity of orders:\n",
    "1. Bar plot for the number of orders by each of the top restaurants that make up to 50% of total orders;\n",
    "2. Histogram for the new variable orders per restaurant;\n",
    "3. Box plot for the same new variable;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "sns.barplot(data = agg_rest[agg_rest['pct_ac']<=50], \n",
    "            y = 'restaurant', \n",
    "            x = 'orders', \n",
    "            palette = 'colorblind')\n",
    "plt.title('Orders by Restaurant, best performing venues up to 50% of orders')\n",
    "plt.ylabel('')\n",
    "plt.xlabel('Number of Orders')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(10,2))\n",
    "sns.histplot(data = agg_rest, \n",
    "             x = 'orders', \n",
    "             color = 'green')\n",
    "plt.xlim (0, 70) # limit to better visualize the main part of the distribution, that has a very long right tail\n",
    "plt.title('Orders by Restaurant (clipped @ 70 orders)')\n",
    "plt.ylabel('')\n",
    "plt.xlabel('Number of Orders')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(10,2))\n",
    "sns.boxplot(data = agg_rest, \n",
    "            x = 'orders', \n",
    "            color = 'orange')\n",
    "plt.xlim (0, 70)\n",
    "plt.title('Orders by Restaurant (clipped @ 70 orders)')\n",
    "plt.ylabel(' ')\n",
    "plt.xlabel('Number of Orders')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>restaurant</th>\n",
       "      <td>178</td>\n",
       "      <td>178</td>\n",
       "      <td>Shake Shack</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orders</th>\n",
       "      <td>178.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.66</td>\n",
       "      <td>24.05</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>8.75</td>\n",
       "      <td>219.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct</th>\n",
       "      <td>178.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.27</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.46</td>\n",
       "      <td>11.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_ac</th>\n",
       "      <td>178.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85.64</td>\n",
       "      <td>17.63</td>\n",
       "      <td>11.54</td>\n",
       "      <td>81.24</td>\n",
       "      <td>92.49</td>\n",
       "      <td>97.67</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count unique          top freq  mean   std   min   25%   50%  \\\n",
       "restaurant    178    178  Shake Shack    1   NaN   NaN   NaN   NaN   NaN   \n",
       "orders     178.00    NaN          NaN  NaN 10.66 24.05  1.00  1.00  3.00   \n",
       "pct        178.00    NaN          NaN  NaN  0.56  1.27  0.05  0.05  0.16   \n",
       "pct_ac     178.00    NaN          NaN  NaN 85.64 17.63 11.54 81.24 92.49   \n",
       "\n",
       "             75%    max  \n",
       "restaurant   NaN    NaN  \n",
       "orders      8.75 219.00  \n",
       "pct         0.46  11.54  \n",
       "pct_ac     97.67 100.00  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_rest.describe(include = 'all').T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A new variable, **orders per restaurant**, was created with the goal of getting a better understanding of the categorical variable 'restaurant_name'. As a byproduct, an interesting insight was found: orders per restaurant is extremely right-skewed, with about 75% of restaurants having less than 9 orders each, and half of them having no more than 3 orders."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cuisine type (cuisine_type):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 14 unique cuisine types in this data set: Korean, Japanese, Mexican, American, Indian, Italian, Mediterranean, Chinese, Middle Eastern, Thai, Southern, French, Spanish, Vietnamese.\n"
     ]
    }
   ],
   "source": [
    "cuis_txt = \", \".join(df.cuisine_type.unique())\n",
    "print(f'There are {df.cuisine_type.nunique()} unique cuisine types in this data set: {cuis_txt}.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.countplot(data = df, \n",
    "              y = 'cuisine_type', \n",
    "              palette = 'colorblind', \n",
    "              order = df.cuisine_type.value_counts().index)\n",
    "plt.title('Orders by Cuisine Type')\n",
    "plt.ylabel('')\n",
    "plt.xlabel('Number of Orders')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "American    584\n",
      "Japanese    470\n",
      "Italian     298\n",
      "Chinese     215\n",
      "Name: cuisine_type, dtype: int64 \n",
      "\n",
      "Total: 1567 \n",
      "\n",
      "American   30.77\n",
      "Japanese   24.76\n",
      "Italian    15.70\n",
      "Chinese    11.33\n",
      "Name: cuisine_type, dtype: float64 \n",
      "\n",
      "Total: 82.6%\n"
     ]
    }
   ],
   "source": [
    "print(df['cuisine_type'].value_counts().head(4), '\\n')\n",
    "print('Total:', sum(df['cuisine_type'].value_counts().head(4)), '\\n')\n",
    "print(df['cuisine_type'].value_counts(normalize=True).head(4)*100, '\\n')\n",
    "print('Total:', str(round(sum(df['cuisine_type'].value_counts(normalize=True).head(4)*100),1))+\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the preceding bar plot shows, most orders are concentrated among a few cuisine types, with the top 4 being:\n",
    "   - American (584 orders, about 31%),\n",
    "   - Japanese (470 orders, about 25%),\n",
    "   - Italian (298 orders, about 16%) and\n",
    "   - Chinese (215 orders, about 11%).\n",
    "\n",
    "These 4 cuisine types took 1567 orders, encompassing almost 83% of the 1898 total orders."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cost of Orders (cost_of_the_order):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,3))\n",
    "sns.histplot(data = df, \n",
    "             x = 'cost_of_the_order', \n",
    "             color = 'green', \n",
    "             binwidth = 3,\n",
    "             kde = True)\n",
    "plt.title('Cost of Orders')\n",
    "plt.ylabel('Number of Orders')\n",
    "plt.xlabel('Cost of the Order')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(8,2))\n",
    "sns.boxplot(data = df, \n",
    "            x = 'cost_of_the_order', \n",
    "            color = 'orange')\n",
    "plt.title('Cost of Orders')\n",
    "plt.ylabel(' ')\n",
    "plt.xlabel('Cost of the Order')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of this variable looks right-skewed. About 50 % of the orders are under the median of 14 dollars, while the average cost is 16.50 (from stats summary). Also, 50% of orders range between about 12 and 22 dollars. No outlier values observed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Day of the Week (day_of_the_week):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,2))\n",
    "sns.countplot(data = df, \n",
    "              y = 'day_of_the_week', \n",
    "              palette = 'colorblind')\n",
    "plt.title('Orders by Day of the Week')\n",
    "plt.ylabel('')\n",
    "plt.xlabel('Number of Orders')\n",
    "plt.show()\n",
    "\n",
    "labels = df['day_of_the_week'].unique()\n",
    "data = [df[df['day_of_the_week']==lab]['day_of_the_week'].count() for lab in labels]\n",
    "expl = [0, 0.1]\n",
    "palette_color = sns.color_palette('colorblind')\n",
    "plt.pie(data, \n",
    "        labels=labels, \n",
    "        colors=palette_color, \n",
    "        explode = expl)\n",
    "plt.show() # A pie chart is added to better visualize proportions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Weekend   0.71\n",
       "Weekday   0.29\n",
       "Name: day_of_the_week, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.day_of_the_week.value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the visuals and results above it can be concluded that almost 3/4 (71%) of the orders are placed on weekends."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Order ratings (rating and auxiliary column rating_value):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data = df, \n",
    "              x = 'rating', \n",
    "              palette = 'colorblind')\n",
    "plt.title('Categorical Analysis of Ratings (counts)')\n",
    "plt.ylabel('Number of Orders')\n",
    "plt.xlabel('Rating')\n",
    "plt.show()\n",
    "\n",
    "labels = rat_cat\n",
    "data = [df[df['rating']==lab]['rating'].count() for lab in labels]\n",
    "expl = [0.1, 0.05, 0.05, 0.05]\n",
    "palette_color = sns.color_palette('colorblind')\n",
    "plt.pie(data, \n",
    "        labels=labels, \n",
    "        colors=palette_color, \n",
    "        explode = expl)\n",
    "plt.show() # A pie chart is added to better visualize proportions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Not given   0.39\n",
       "5           0.31\n",
       "4           0.20\n",
       "3           0.10\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rating.value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization of auxiliary rating values column (only given ratings)\n",
    "plt.figure(figsize=(8,3))\n",
    "sns.histplot(data = df, \n",
    "             x = 'rating_value', \n",
    "             color = 'green',\n",
    "             bins = 5)\n",
    "plt.title('Given Ratings, Numerical')\n",
    "plt.ylabel('Number of Orders')\n",
    "plt.xlabel('Rating value')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(8,2))\n",
    "sns.boxplot(data = df, x = 'rating_value', color = 'orange')\n",
    "plt.title('Given Ratings, Numerical')\n",
    "plt.ylabel(' ')\n",
    "plt.xlabel('Rating value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From these visualizations and results, the following insights can be obtained:\n",
    "- A substantial proportion of the orders received no rating from the customer. At this point it can't be deducted if this means that the customer didn't want to rate the service, that the customer was displeased, or any other situation. This issue will be reassessed on the occasion of the multivariate analysis.\n",
    "- Among the ratings given, about half were top rated (5).\n",
    "- There are only \"mediocre\" to \"very good\" (our interpretation) ratings. No \"bad\" or \"poor\" ones (1 or 2), which could mean many of the \"not given\" ratings could indeed be situations where the customer was not pleased with the food or the service.\n",
    "- From this outlook, it seems that customers are almost binary in their judgement of these type of services: it's either very good, or very bad, with relatively few nuances.\n",
    "- The visualization of the numerical values is shown for reference, it adds just a little extra insights at this point. The position of the Q1 at rating = 4 means at least 75% of given ratings are 4 or 5. More usefulness is expected from this numerical variable during multivariate analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A new column with an estimated \"bad\" value for ratings not given, will be created, to (informally) test if there's a relationship between variables that allows us to confirm the assumption that a significative quantity of orders with no ratings given are indeed bad experiences (will be used for the multivariate analysis)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# new column in the df with \"Not Given\" ratings = 1.5\n",
    "df['rating_value_est'] = df['rating'].replace(['Not given'], 1.5)\n",
    "df['rating_value_est'] = df['rating_value_est'].astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Food Preparation Time (food_preparation_time):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IiAxBIQn/fOBEd78dwMz2Ay4A3lXKwGpNLpfj9Y7Xee6NZ3lpzUKWtr3Wm9wnN0xmSsNUZk+cw+SGKUyqn0Rz3Tiakk3UJQp7hv+8Q75JMt7/153L5ehId7C2ay2rOlaxsn0lKztWbvB6g3kdK3m9fRkLVjsr21eyunP1gOWm4immNk1lStOU8Luxz++8+T2vJ9RPKPggQf0LjIz6gRCpHYUk/JaeZA/g7reZWVMJY6opz77+LHcvupPnVjorO98AYEbzTHbZZD4zx23KjHEzaUw2jricZDw5wuf9k0xKtDKpuZUtmzd8J5vL0pHuoD3dTke6nQ/tdCw/feDntKc7aE+30ZFup72rnQVtz/N4+one5QZqVYgRozHZRGOyMfykmmjKn45eNyWb+NZBXyfXXU9TSn+Sw5HfD0QulyNHjmwuS4wY8Vi8aK0z6gdCpPIKSfg5M9si6mYXM5sFZEoaVZXL5rLc9q+/8n+P/4w7XrmNGDE2b9mCnTfZlTmT5tCcGmxk4tEnHov33gsAcIQdwd3PD/40Zy6XozPTSXu6nfZ0W+9BQPsGP+Fyw4r211k0wEHCZc9cAkBjspHW5lamNk1986dx6lvnNU2ltamVyY2TSSUKH+W5s7uLNas6h/bBlEAmm2Fd91rWdq1lXfc61nVt+Lq/99Z3r+udt7ZrDeu619GV6SSdzZAlQ2e6k2wu2+8BWE/ij8cSxGMx4rEEiVg8unxUR12irvd3XTxFKlFHXbyO+kQ9DclGGpINNCYbeWzJY7yt+W1MbpxMY2rkB7CDGS3flchoU0jC/wbwgJn9jXBR+CDgEyWNqsLS2TStrS0l2e6l/7iUb9/7bZ5/43mmj5vON/f9Jq++sYzmVPPGN1BFYrEYDckGGpINTGJSQetkc9noIKGN9u5wQHDodofw60cv7z1AWNm2mlfXLOk9aOjKDLzjr0/U05h8s/WgIdlIfZTAUokUdfG6cE9Eoo4zdzud7rYYqXgdyXiCZDxJIpYkGU9G0ykSsQTZXJZMLkMmmyGTy5DOpsnk0mSyGdK5NO3pdtq622hLh/sn2rrX05Zu672Xoi3dRibRxYq1K/tN3G3ptoI+q/pEPeNS4xhX18K4VAstdS20Nray5YQtGZdqoT5ZTzKWpKW5ibsW3hPO5vPP6nOQJdtbn2wum/cT6tWV7aIr00VHup01XavpznT1zut78HClX9H7OhlL9h4MNCQaos++IcxLNFCfaOh9Lxw4hHn1iXoS8cRG6x5aE8qT8Eu1r+hLBzFSDBtN+O5+rZk9A+xHuGnvPHd/puSRVdDIm783lMvleHH1C9y16A5WdLzOtKbpHLbVEcyZaJz7nnPL0rVuNYjH4r3N+kQPDZz2ztPwJS8MuE46m85rOWijLa9Foed3W3cba7rWsLRtKd0DJKzrX/xTKatGIpagKdVMU7KJloZxNMSbaKlrYZOmTZg9cTbNUdIOSXwcLanx4XddC82pML+lroVxdeMYl2op+N6O1taWov/95XI5urPddGQ66Ei305Hu4Ngdj+b/Hrqod7o90977emXnSjrWh+8okxu88TAVT9GQaKAuUU8qngotDYk6UtENq8l4is/fkibbFSMRT5KMJUnEE9HB2ZsHavFYPBy8kCWXy5LL5Xqns9HrHDmy0YFbz082m91guq4+zt0L7yUXrZfLZcmSiy6PvDmv51JJz2WTXC4XXS6JEYuFZ2l6DrqIxej9L3q9y6bvpLsrS5x4b4tLIh4nEUuQiCWIxd58nYhH78cSxKP58Xj0XjTdu3y8z3KxOJOWjmP9uq6oVScqr/dgMP7mvFi8N54wP7bBvPzlE7EEDckGmpLNNKbC5bh4LF62e0gkKKgvfXd3wEscS1Va1raU21+5lVfW/otJ9ZM4YuujmDNxru5cL5NkPBnOcusKPwvL5XJkchm6s910Zbroznbzb7ufzuLly+nOdJPJpUln3zx77852h9fZTNi5RTvRnuSSiCdIRjvXhkRj72OQ+Y9E1ifqe/8mWltbWL587D75GovFQhN/oo7xdeMBOGa7Y3hw4SODrpfL5Ujn0nSmO+jIdEa/O+jM9JlOd9CZ7SKdCY+htnWvj76DML3gYae9e+B7REbizaSaIB5LkEok6Ux3Rsk5JMZYLP91T8tJ/uvwO8ebyb+/3+S9vvtf60NrSzbzllaknvl954124RHhJroy3aTiKZqi+3OaUk29LW9NqWbG141nQv0EmpLNw95v6h6SQIPnlEgmm+HB1+7ngSX3UZ9o4L2bH8gOU3csqElSKisWi5GMmut7bpicP3M+y+vKk4TL1Uw82sRiMVKxFKm6FOMYfv2/e+h5LF++tjcRpnNpMtl0dIklTGezGeLxBHHCJYx4LLbBmSl5028m+Lf2RF6KFpKB6jSUcnK5cLCTzW9dyDuAeLO1IWqFyGuV+Ox7Ps3yFWuiZfIu5dDzOvfmfLIbXO55c35ug/mZbJqOTAfro35DevoPySW7uXPhXXRlu+lIt/NGxxssWreI9n4uXSXjScbXTWBKwxSmNrYytbGV1qZWJtVP1glUgZTwS2BZ21JuXngDy9qXsd3kt7Pv5vsX5U57qZxyJ+FyXeap5jOfeCxOPBEnReE3Z1aLngSYiA39BGPulLls11q+1NDf33rPkz/r0+tZ07maNV2rWd25mtWdq3i9/XWeX7WgtwWnPlHPjOaZzGzZlJnjNmV684wBH0GudRv9VMzs1+5+cjmCGetyuRx/X/oQ9yy+k4ZEA0dufTRzJs2tdFhSBMW+r2Mw1ZyEZfQbDX/r+U/+tDa2vuX97mw3b7SvYFn7MpasW8zidYtZuPguIHRGNmvClmw9YTZbTdhaj+zmKeQwaJ6Zxdy9+BfEqkhXpoubX7qR51Y+y5xJxgGbH6Q/NBGREkjFU2zSPI1NmqfxjqlhHLf2dDuL1y3ixVUv8OLq51mwMtx2tsX4Wey8xY6ks+maP/MvpPavAk+Z2QPAup6Z7n5OyaIaY1Z2rOTa56/mjY4VvGfTfdl1k/m6piRSIbV6D0Sta0w2MnviHGZPnEMul2NZ21IWrHqOp1c8yfFXH099op5tJm/HTm/bmamNUysdbkUUkvDvj36kHy+tXsifX7yWGDE+MOc4Zk3YstIhidS00dAkLZUVi8V6WwD2mLEXh2x3AGff8Cmeev0J/rn8MWZPnMNu03Zn+rgZlQ61rAp5Dv/rZtYIzAaeAhrcvaDeP8xsPHAfcJi7v2Rm+xP64W8ErnT3c6Pl5gG/BMYDdwFnuvuo73z7mRVPc9NL1zOlYQpHzv4AE+snVjokERHJE4vFeO9W7+XQrQ6nPb0/jy59mMeWPcLzqxawWcvm7DnjPcxs2bTSYZbFW58z6cPMdgNeAG4AZgCvmNm7C1zvHmBuNN0IXAwcCWwL7Gpmh0SLXw6c5e5zgRhw+tCrUl6PLnuEGxZex4zmmXzITlSyFxEZ5RqTjewxcy/O2OET7LPpfqzseIPf+uX8+YU/DToIWLXYaMIHvgfsD6xw90XAScAPC1jvdOCThHsAAOYDC9x9YXT2fjlwrJltATS6+wPRcpcAxxZehfLK5XLcu/hubvvXX5k9cQ7HzP0g9RorXkRkzKhL1LHLtPl8dPsz2H36HrywegG/evJC7ll8F12ZrkqHVzKFJPwmd3+6Z8Ldb6SwSwGnufvdebNmAEvyppcAmw4yf9TJ5XLc8cqt3L/kXraf8g6O2Pqomr/rU0RkrKpL1LHHzL346PZnMHvSXB5Ych+/fvpiFq19pdKhlUQh2arbzCYRBs7BzGyYZcV7thGJAdlB5g/JlCnFHWGuubl+g+lcLsffFv6NR5Y9zG4zd+OgrQ8q2p34fcsqlXKVU61lVWOdyllWNdapnGVVY53KWdZg5TQ3t/LBScfy0qqX+JP/id/5b9h9093Zb8v9hnVSN1qfEimkJt8C7gSmm9lvgQOBM4ZR1iJget70NEJz/0Dzh2TFinVks8XpKqC1tYX16zccmerexXdz/5L72LF1J/actg9tbcVr9ulbVqmUq5xqLasa61TOsqqxTuUsqxrrVM6yCimnNTWdk7c9lTsX3c79i+7nudcXcOhWh/O2pk2GVFapx8KIx2PDOsndaJO+u18PHA38B3AvsKe7Xz3kkuBBQgPBbDNLACcAN7n7y0CHme0RLXcScNMwtl8yDy55oLcZf//ND9Qz9iIiVaouUc8BWxzMB+YcR2emgyueuYynVjxZ6bCKopBr+AApwtC43dHPkLl7B3AKcDXwNPAscFX09onA983sWWAc8KPhlFEKjy17hLsX38E2k7fjwFmHKNmLiNSALSdsxUnbncq05unctPB6bv3XLWSygw/fPNoV0pf+qcB5wF8ISf8/zeysQs/y3X1W3utbgR37WeafhLv4R5XnVjq3RnfjHzLr0H5HyxIRkerUnGrmODueOxfdziNL/86ytmUcsfX7aU4V956xcikkg30G2MndP+LuHwZ2A75W2rAqb/G6Rdz44p+Z3jyDQ7c6QsPaiojUoHgszr6bvZfDtjqCpW1L+c0zv+aNjhWVDmtYCkn4Xe7e+9icu/+LYTbrjxXPrXiOa5+/mnF1LRw1+xhS8dobXlNERN60zeTt+JCdQHc2zRXPXMar6xZXOqQhG7BJ38zeGb38p5n9BPgFkCFch7+39KFVxvK25Rz+p9AB4AfmHKcR70REBIBpzdM5cZuTuGrBlfz+ud9y+FZHsvXEOZUOq2CDXcPve43+0LzXOaAqR8v70aPns2TtEo6efSyTGiZVOhwRERlFJjZM4vhtTuKPC/7Atc//kQNnHdI7RO9oN2DCd/eaHPbt3+adzb/vdQ6/fPCSSociIiKjUHOqmQ/aCfzphT/yl5duJJfLsUPrW+5HH3UKuUt/GqEZf3L+fHf/QoliqqgZ42bSOnV09pIkIiKjQ12ijqNmH8O1z/+RW16+iRw5dmydV+mwBlXITXvXER6Zi/X5ERERqVnJeJL3zz6aLcdvxV9fvpl/Ln+s0iENqpCudevc/eiSRyIiIjLGJONJjpx9NNe9cA1/ffkvXP/c9ew2ae9Kh9WvQs7wHzGz7UseiYiIyBiUjCc5Yuuj2HWT3WipG72XhAs5w78X+IeZLSHv+Xt336pkUYmIiIwhyXiSvTfbl71n7V3ywXOGq5CE/3nCQDcvlDgWERERKZFCEv4qd/99ySMRERGRkikk4d9mZt8jdMTTO6Cwuz9asqhERESkqApJ+CdEvz+QNy8H6Bq+iIjIGLHRhF+rPe6JiIhUk0J62vtMf/Pd/YLihyMiIiKlUEiT/jvyXtcBewO3liYcERERKYVCmvRPzZ82sxnARSWLSERERIqukJ72NuDurwKzih+KiIiIlMpQr+HHgF2AZSWLSERERIpuqNfwc8C/CL3viYiIyBgx5Gv4IiIiMvYMmPDN7FeEM/r+5Nz9Y6UJSURERIptsDP8J/uZNxX4NPBSKYIRERGR0hgw4bv7+fnTZrY/cCnwG+CcEsclIiIiRVTIXfpJ4DzgFOBMd796pIWa2e3A24DuaNbHgRbgAqARuNLdzx1pOSIiIhIMmvDNbA7wW2AdsJO7LxppgWYWA+YCW7h7OprXCDihF79XgBvM7BB3v2mk5YmIiMjgN+2dCpwPnO/u/1XEMi36fYuZTQEuBJ4AFrj7wqjsy4FjASV8ERGRIhjsDP8iIAt8ycy+mDc/RrhLf/wwy5xE6Iv/bCAF3AF8B1iSt8wSYNOhbHTKlHHDDKd/zc31Rd3eaCirGutUzrKqsU7lLKsa61TOsqqxTuUsq5x1am1tKVtZQzFYwi/JsLjufj9wf8+0mV0EfAO4J2+xGOFgo2ArVqwjmx3oKcKhaW1tYf36zqJsqxDlKqsa61TOsqqxTuUsqxrrVM6yqrFO5SyrnHVavnxtSbcfj8eGdZI72F36L48oogGY2Z5Avbv3jLgXIzzmNz1vsWnAq6UoX0REpBYV0rVusU0EvmFm7yY06X8EOBP4vZnNBhYCJwAXVyA2ERGRqjTk0fJGyt2vB24AHgMeAS6OmvlPAa4GngaeBa4qd2wiIiLVqhJn+Lj7V4Gv9pl3K7BjJeIRERGpdmU/wxcREZHyU8IXERGpAUr4IiIiNUAJX0REpAYo4YuIiNQAJXwREZEaoIQvIiJSA5TwRUREaoASvoiISA1QwhcREakBSvgiIiI1QAlfRESkBijhi4iI1AAlfBERkRqghC8iIlIDlPBFRERqgBK+iIhIDVDCFxERqQFK+CIiIjVACV9ERKQGKOGLiIjUACV8ERGRGqCELyIiUgOU8EVERGpAstIB5DOzE4BzgRTwA3f/3wqHJCIiUhVGzRm+mc0E/gvYE5gHnGFm21U0KBERkSoxahI+sD9wm7u/4e7rgauAYyock4iISFUYTU36M4AledNLgPkFrJcAiMdjRQ1mUuPEom5vNJRVjXUqZ1nVWKdyllWNdSpnWdVYp3KWVc46FTsfDbL9xFDWi+VyueJHMwxm9hWgwd2/Gk2fDuzs7mduZNU9gbtLHZ+IiMgosxdwT6ELj6Yz/EWE4HtMA14tYL2/R+stATIliEtERGQ0SQDTCfmvYKPpDH8m4UhlPrAeuA84w90fqmhgIiIiVWDU3LTn7ouBrwC3A/8ArlCyFxERKY5Rc4YvIiIipTNqzvBFRESkdJTwRUREaoASvoiISA1QwhcREakBSvgiIiI1YDR1vFMRZvY14Lho8gZ3/4KZ7Q9cADQCV7r7uRULcJgGqNcZwDlADngY+Li7d1UqxqHqr055750FHOPu+1QituEa4HvaHfg+0AI8DnxkrH9PZnYg8F1ChyGPAqeNpToBmNk3CON75ICL3P2Csb6vGKBOY3o/Af3XK++9sbqv6O+7GtK+oqbP8KN/rAcCOxFG6NvZzI4HLgaOBLYFdjWzQyoW5DAMUK8vAp8H3g3sQPjuP1mpGIdqgDodFb23HfClykU3PAPU6SPAHwmdTr09WvRjlYlw6Ab5ni4CPuTu2wNNwMkVC3IYzGxvYD/Cv51dgLPNbEfG8L5igDoZY3g/AYPWayzvKwb6+xvSvqKmEz6hO97PunuXu3cDzwBzgQXuvtDd08DlwLGVDHIY+qtXA/AJd1/j7jngCWDzSgY5RP3VaXMzqwd+AfxHRaMbnv7qNAu4390fj5Y5G7imQvENR7/fE+HMfryZJQh/i+0VjHHI3P1OYN9on/A2QuvoRMbwvmKAOnUwtvcTA9Vr/VjeVwxQp3kMcV9R00367v5Uz2szm0Nohvwxbx21b9MyhzYiA9RrD3dfEM1rBc4CTqlIgMMwUJ2A8whnWQsrFNqwDVCn84F1ZvY7YBvgXuCzlYlw6Ab5nl4G7gDWEL6rqyoR30i4e7eZfR34HPAH+h/hc6ztK/rW6V/u/jKMzf1Ej37qtZjwb2tM7iug3zpNY4j7ilo/wwfAzN4O/JXQlPUi4RpJjxiQrURcI5Vfr7xkPxO4lXAN6I4Khjcsfb6rWcDm7v6rigY1Qn3qlAQOAr4M7Aw0MzabIPPrtBb4NrA9YcCPBwjXvcccd/8a0ApsRmgNHPP7ij51Oh3G/n4C3lKvM6iCfUWfOjUwxH1FzSd8M9uD8If9JXe/lDBq3/S8RQodtW9U6ademNk2hEGJLnX3b1YyvuHop07HA283s38AvwR2MbMrKxjikPVTp9eAB6Jm4gzwe8KAUmNGP3XaC3jS3V9w9yxwIbBPBUMcMjPbxszmAbh7G+Ha6T6M4X3FAHXaoQr2E/3VazfG8L5igDp9iSHuK2o64ZvZZsC1wAnu/rto9oPhLZsdXW88AbipQiEOS3/1MrMW4BbgXHc/v4LhDUt/dXL3j7r7tu4+DzgNeNjdP1i5KIdmgL+/Wwg3um0WTR8GPFKB8IZlgDo9Ccw3s02i6SMZ4rCeo8BWwIVmVm9mdYQ6/IKxva/or04PMob3E5H+6nXLWN5X0H+dzmCI+4qavoZPuBbSAFwQ3cQJ8HPCNauro/duZOxdb+yvXlcCmwCfNbOe6zzXuftYuYGl3+/K3X9euZBGbKC/v48DfzazBsLIkZ+rSHTDM1CdvgrcbmZp4HnCzmrMcPcbzWw+8BiQAa5299+Z2XLG6L6ivzoBUxnb+4kBv6sKhzUiA9TpMjN7gyHsKzRanoiISA2o6SZ9ERGRWqGELyIiUgOU8EVERGqAEr6IiEgNUMIXERGpAbX+WJ7IoMxsFvACoU/xHjHgh+5+cUWCGoCZHQrs5u7/YWZHAPu7+zlF2O5VwOxockfCc/UZYCVwN/C8u/96pOUMUPY84FPufuoQ1rkR+Jy7Pz3MMk8D6tz9p8Nc/9PAG6X6TESGSwlfZOPaow47gN5uR580s4fzBq4YDXYFJgO4+3XAdcXYqLsf0/PazHKEQTxeL8a2B2NmccIoe0cMZT13f98Ii96TcFAzXD8G/m5mt7j7ayOMRaRolPBFhsjdF5vZAmCumb2TMCRlM7Da3fc1s48BnyBcMlsBnOXuz5rZJYRR4uYRRry6BTgnGhTjo4QOd+oISfvb7v4zMzslf/uE3rR+BswBphD6qT+BMHLbmUDCzFYDCwhjfh9mZptG68witE5c6u7fjVovbiV0GLMbMAn4grsXPDpfVKcn3f17ZtZB6CN/f2Ac8J+E0ePeQehy9nB3X29m2wI/jOJPAD8aoLXkOGChuy+OynoJuIIwTOgk4H8IA/PsDHQDR7j7q9Fyx0Qx/BdhfIztgRRhbPd78+POrwehNecI4AAza3f3/zWzrwAfIHyfLxFGk3vVzI4GziX0n58hjFlxl7tnzOz3wBeBfy/0sxQpNV3DFxkiM9ud0MT9YDTr7cA+UbLfG/gIsJe770RISvkJdDfgAGC76OfjZjaOMGjJ+6J1PhitR9/tA4cAq9x9d3efS+ii9ix3f5DQo92V7v6VPiH/Brjd3d9BSJAfNrMPRe9tBfzF3ecT+ub+wQg+mnrgtWhblxL6LP90VM8JwJFmliT0Rvcld98Z2Bv4nJm9q5/tHQNc32deg7u/izDE6f8RLq3sCLxC/6O67QacH32uvwL+e7AKRAc71wHfj5L9yYQDlvlRK8+NUb0AvktI/rsQehLcJ29TtwBHD1aWSLnpDF9k4xqjQTcg/Jt5HTjR3V+Juo993N3XRO8fSjgYuC+va9lJZjY5en2Ju68DMLNfA+9395+Y2WHAodGQsvMIZ6c9erfv7leZ2YtmdnZUzj7A/QMFbmbNhCR/YLT+6uhs9hDCqHXdhCQG8CjRJYERuDr6/QLwRN7Z+cJo23OBrYGL8z6fRmCnKJ582/DWA5D87b/m7v/Mm+4v9pfd/R/R60cZ+lCvhxEGJHk4ijcBNEXv/Q64xsxuIIwMmH+QthDY3Mwa3L1jiGWKlIQSvsjGbXANvx/r8l4ngMvc/YvQex16BuEGN4B03rJxIBM1ud9POGO9h3AGfFh/2zezfyP0Q/8TQvP2G8CWg8QWJzTj952Xil53RSPYQRjqte+yQ9WZ97q7n/cThEsf83pmRIPqrO5n2RxvbYXc2Pb7au+zvVg/ryFcSulPAviOu/8sirWecDkBd/+KmV1MaLE5hTAWec9oZV1RGWNuuFypXmrSFymuvwDHm1nPsKlnEq6T9/hgNOJVA6Hp/8/ALsBy4FuEpuDDAKIR2Po6iNBKcBHgwOGEpAThYCKVv7C7ryWcOX8y2uYE4GTCGWklONBuZh+O4tmMcO185wGW3bpEcSwnfO6Y2QzCpYUe+Z/jX4DTzGx8NP0N4DIzS0b3CjRFAzh9gjC0bH203FaE+w+6ShS/yJAp4YsUkbvfAnwH+KuZPU64oe5od+8ZpaqN8CjbE9HvXxGS/CJCgnsG2JyQkGbzVt8jXPd/PFr/0bzlbgMOMrMf91nnROC9ZvYE8BBhLO1LRlzZYYgS4JGEJPo4oe5fdfd7+1n8KuDgEoXyY2C6mTnhO7gt772bgDPN7MuE6/XXAw+Y2VPADsAp7p4m3J9whZk9CvwB+Ki797RAHBzNExk1NFqeSJn0vTNcBhe1cDwCHNpzL8BYEMX9KHCguy+tdDwiPXSGLyKjkrtnCE8vDHpn/Sh0DvADJXsZbXSGLyIiUgN0hi8iIlIDlPBFRERqgBK+iIhIDVDCFxERqQFK+CIiIjVACV9ERKQG/H9PydMkjJ9DMAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,3)) # binwidth = 3\n",
    "sns.histplot(data = df, \n",
    "             x = 'food_preparation_time', \n",
    "             color = 'green',\n",
    "             binwidth = 1,\n",
    "             kde = True)\n",
    "plt.title('Food Preparation Time')\n",
    "plt.ylabel('Number of Orders')\n",
    "plt.xlabel('Preparation Time (minutes)')\n",
    "plt.xticks(np.arange(20, 37, 2))\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(8,2))\n",
    "sns.boxplot(data = df, x = 'food_preparation_time', color = 'orange')\n",
    "plt.title('Food Preparation Time')\n",
    "plt.ylabel(' ')\n",
    "plt.xlabel('Preparation Time (minutes)')\n",
    "plt.xticks(np.arange(20, 37, 2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen in the boxplot and the stats summary, about 50% of orders take between 23 and 31 minutes to be prepared. The median is 27 minutes and the mean is very close as per stats summary, at 27.37 minutes.\n",
    "\n",
    "As a whole, the distribution looks almost uniform between 20 and 35 minutes, with an apparent \"mode\" that is product of a visualization glitch (34 and 35 min counted in the same bar). The kde curve helps detect this issue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Food Delivery Time (delivery_time):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,3))\n",
    "sns.histplot(data = df, \n",
    "             x = 'delivery_time', \n",
    "             color = 'green',\n",
    "             binwidth = 1,\n",
    "             kde = True)\n",
    "# plt.xlim (0, 80)\n",
    "plt.title('Food Delivery Time')\n",
    "plt.ylabel('Number of Orders')\n",
    "plt.xlabel('Delivery Time (minutes)')\n",
    "plt.xticks(np.arange(14, 35, 2))\n",
    "plt.show()\n",
    "plt.figure(figsize=(8,2))\n",
    "sns.boxplot(data = df, x = 'delivery_time', color = 'orange')\n",
    "# plt.xlim (0, 80)\n",
    "plt.title('Food Delivery Time')\n",
    "plt.ylabel(' ')\n",
    "plt.xlabel('Delivery Time (minutes)')\n",
    "plt.xticks(np.arange(14, 35, 2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of delivery times is interesting: the variable distributes uniformly up until the 24 minutes mark, when it goes up in an apparent ladder step. The reason for this will be discussed in the multivariate analysis.\n",
    "\n",
    "The distribution appears left skewed, with IQR between 20 and 28 minutes, a median of 25 minutes and an average of 24.16 minutes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K9CqA5H_JY_U"
   },
   "source": [
    "### **Question 7**: Which are the top 5 restaurants in terms of the number of orders received? [1 mark]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "PaJZ3wlOJY_U"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The top 5 restaurants are: Shake Shack, The Meatball Shop, Blue Ribbon Sushi, Blue Ribbon Fried Chicken, Parm; with a total of 634 orders.\n"
     ]
    }
   ],
   "source": [
    "# Write the code here\n",
    "top_5_txt = \", \".join(agg_rest.head(5)['restaurant'])\n",
    "top_5_ord = agg_rest.head(5)['orders'].sum()\n",
    "print(f'The top 5 restaurants are: {top_5_txt}; with a total of {top_5_ord} orders.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tnjhWgUfJY_U"
   },
   "source": [
    "#### Observations:\n",
    "\n",
    "The auxiliary data frame created before (and which was already sorted by order quantities) helps to answer the question. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gWPMqGq0JY_U"
   },
   "source": [
    "### **Question 8**: Which is the most popular cuisine on weekends? [1 mark]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "c-Xnus3VJY_U"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "American   0.31\n",
       "Name: cuisine_type, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write the code here\n",
    "df[df['day_of_the_week'] == 'Weekend']['cuisine_type'].value_counts(normalize = True).head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.countplot(data = df, \n",
    "              hue = 'day_of_the_week', \n",
    "              y = 'cuisine_type', \n",
    "              palette = 'colorblind', \n",
    "              order = df.cuisine_type.value_counts().index)\n",
    "plt.title('Orders by Cuisine Time and Day of the Week')\n",
    "plt.ylabel('')\n",
    "plt.xlabel('Number of Orders')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bpQ270TUJY_U"
   },
   "source": [
    "#### Observations:\n",
    "\n",
    "Both the code and visual analysis confirm that American cuisine is the most popular on weekends, with a 31% of placed orders. In fact the overall top 4 types remain unchanged regardless of the day of the week."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "88xgTqkxJY_U"
   },
   "source": [
    "### **Question 9**: What percentage of the orders cost more than 20 dollars? [2 marks]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "OTpHyTdzJY_U"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The proportion of orders costing more than 20 dollars is 29.24%, for a total of 555 orders.\n"
     ]
    }
   ],
   "source": [
    "# Write the code here\n",
    "od_over_20 = df[df['cost_of_the_order']>20].shape[0]\n",
    "pc_over_20 = round(od_over_20/df.shape[0]*100, 2)\n",
    "print(f'The proportion of orders costing more than 20 dollars is {pc_over_20}%, for a total of {od_over_20} orders.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "77KNAC5zJY_U"
   },
   "source": [
    "#### Observations:\n",
    "\n",
    "Almost 30% of orders amount to 20 dollars or more. It must be remembered that the distribution is right-skewed, so the bulk of orders are on the low cost side."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HFyHn7hsJY_V"
   },
   "source": [
    "### **Question 10**: What is the mean order delivery time? [1 mark]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "cyFL13hNJY_V"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean order delivery time is 24.16 minutes\n"
     ]
    }
   ],
   "source": [
    "# Write the code here\n",
    "print(f'The mean order delivery time is {round(df.delivery_time.mean(), 2)} minutes')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F35SRV-HJY_V"
   },
   "source": [
    "#### Observations:\n",
    "\n",
    "The answer is printed above. Informed insights about this variable are possible after the multivariate analysis below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "48IjaOuDJY_V"
   },
   "source": [
    "### **Question 11:** The company has decided to give 20% discount vouchers to the top 3 most frequent customers. Find the IDs of these customers and the number of orders they placed. [1 mark]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "Hz8-DE4Zge0j"
   },
   "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>customer</th>\n",
       "      <th>orders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>52832</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>47440</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>83287</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     customer  orders\n",
       "226     52832      13\n",
       "242     47440      10\n",
       "99      83287       9"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write the code here\n",
    "agg_cust.sort_values(by='orders', ascending=False).head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9tWVwfX6JY_V"
   },
   "source": [
    "#### Observations:\n",
    "\n",
    "The auxiliary data frame created before helps to answer this question. The requested IDs (52832, 47440 and 83287) and their respective number of orders are shown in the header displayed above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AfIc2jPaJY_V"
   },
   "source": [
    "### Multivariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iqiS0RT_JY_V"
   },
   "source": [
    "### **Question 12**: Perform a multivariate analysis to explore relationships between the important variables in the dataset. (It is a good idea to explore relations between numerical variables as well as relations between numerical and categorical variables) [10 marks]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Finding relationships between numerical variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "3qt4MCNTJY_V"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Write the code here\n",
    "# First, it's convenient to try a correlation heatmap for numerical variables:\n",
    "num_vars = ['cost_of_the_order', 'food_preparation_time', 'delivery_time', 'rating_value_est']\n",
    "\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.heatmap(df[num_vars].corr(), \n",
    "            annot=True, \n",
    "            vmin=-1, \n",
    "            vmax=1, \n",
    "            fmt=\".2f\", \n",
    "            cmap=\"Spectral\")\n",
    "plt.title('Correlation Heatmap for Numerical Variables')\n",
    "plt.xticks(rotation = 45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 822.8x720 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A pairplot helps to consolidate all the needed scatterplots to confirm the lack of correlations between numerical variables.\n",
    "sel_vars = ['cost_of_the_order', 'day_of_the_week', 'food_preparation_time', 'delivery_time', 'rating_value_est']\n",
    "sns.pairplot(df[sel_vars], \n",
    "             hue = 'day_of_the_week', \n",
    "             corner = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The heatmap and the pairplot don't show any significant correlation between the numerical variables (unless we introduce a categorical distinction). Using the day of the week as a categorical distinction helps hint a relationship between delivery time and this variable. This wasn't tried with the cuisine types, it would've been visually confusing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cost of the Orders vs Categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 2))\n",
    "sns.boxplot(data = df, \n",
    "            x = 'cost_of_the_order', \n",
    "            y = 'day_of_the_week', \n",
    "            palette = 'colorblind', \n",
    "           )\n",
    "plt.title('Cost of Orders by Day of the Week')\n",
    "plt.ylabel('')\n",
    "plt.xlabel('Order Cost (dollars)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Almost no difference in the cost of the order between days of the week. What if we introduce cuisine type into the analysis?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "sns.boxplot(data = df, \n",
    "            x = 'cost_of_the_order', \n",
    "            y = 'cuisine_type', \n",
    "            palette = 'colorblind', \n",
    "            order = df.cuisine_type.value_counts().index\n",
    "           )\n",
    "plt.title('Cost of Orders by Cuisine Type')\n",
    "plt.ylabel('')\n",
    "plt.xlabel('Order Cost (dollars)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The boxplot allows to observe that most cuisine types have similar ranges for their cost orders, with some nuances:\n",
    "*  The 4 best-performing cuisine types have a very similar median cost order, just below 15 dollars, and a bit lower than other competing cuisine types (Mexican, Indian and Middle-Eastern). Given that the 5th most ordered cuisine type (Mexican) is way far below the 4th place in quantity of orders, exploring the effect of prices on this (demand elasticity) seems worthy.\n",
    "*  Mediterranean cuisine orders are distributed with a greater right-skew than the rest, as the median cost of orders is almost the same as the most popular cuisine types, but with a long right tail that includes some outliers.\n",
    "*  Southern cuisine orders cost distribution have a very large IQR compared with other cuisine types.\n",
    "*  Median cost of orders of Thai and french cuisines appear noticeably higher than the rest.\n",
    "*  Korean and Vietnamese cuisine: most of their orders seem to be less costly than most other cuisine types, than includes both the median cost and the relative position of their IQR's.\n",
    "\n",
    "Finally, it's important to mention that the apparent variability of worse performing cuisine types could be caused by the low quantities involved. It must be remembered that after the top 4 cuisine types, the quantity of orders drop dramatically.\n",
    "\n",
    "Weekday and cuisine type could be put together in a boxplot with hue. The analysis was tried but it didn't add any meaningful insight, only showing significative differences between weekends and week days for cuisine types with low order quantities (can be seen in the Appendix)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Food Preparation Time vs Categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 2))\n",
    "sns.boxplot(data = df, \n",
    "            x = 'food_preparation_time', \n",
    "            y = 'day_of_the_week', \n",
    "            palette = 'colorblind', \n",
    "           )\n",
    "plt.title('Food Preparation Time by Day of the Week')\n",
    "plt.ylabel('')\n",
    "plt.xlabel('Food Preparation Time (minutes)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The former graph confirms there is no relationship between day of the week and preparation time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "sns.boxplot(data = df, \n",
    "            x = 'food_preparation_time', \n",
    "            y = 'cuisine_type', \n",
    "            palette = 'colorblind', \n",
    "            order = df.cuisine_type.value_counts().index\n",
    "           )\n",
    "plt.title('Food Preparation Time by Cuisine Type')\n",
    "plt.ylabel('')\n",
    "plt.xlabel('Food Preparation Time (minutes)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No big differences in preparation times between cuisines, most have median times between 26 and 28 minutes; save from a few examples with very little order quantities: French, Korean and Vietnamese seem to have slightly shorter median prep times.\n",
    "\n",
    "As seen before, the preparation time distributes almost uniformly along its range. More research is needed in order to explain the spread of preparation  time and improve (reduce) its variability, in order to give better waiting time estimates to the customer at the time of ordering.\n",
    "\n",
    "The range of preparation time is limited, and it doesn't seem to have any relationship with the cost of the order or ratings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Delivery Time vs Categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 3))\n",
    "sns.histplot(data = df, \n",
    "            x = 'delivery_time', \n",
    "            hue = 'day_of_the_week', \n",
    "            palette = 'colorblind', \n",
    "            binwidth = 1\n",
    "           )\n",
    "plt.title('Delivery Time - Weekends and Weekdays')\n",
    "plt.xlabel('Delivery Time (minutes)')\n",
    "plt.show()\n",
    "\n",
    "sns.boxplot(data = df, \n",
    "            x = 'delivery_time', \n",
    "            y = 'day_of_the_week', \n",
    "            palette = 'colorblind', \n",
    "        )\n",
    "plt.title('Delivery Time - Weekends and Weekdays')\n",
    "plt.ylabel('')\n",
    "plt.xlabel('Delivery Time (minutes)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a clear difference between weekends and week days in terms of delivery times. In fact, more than half of the week day orders take longer to deliver than 75% of weekend ones!\n",
    "\n",
    "As the histogram shows, the \"ladder\" shape observed in the univariate analysis was due to the addition of weekends and weekdays in the same graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 3))\n",
    "sns.histplot(data = df[df['day_of_the_week']=='Weekend'], \n",
    "            x = 'delivery_time', \n",
    "            color = 'green', \n",
    "            binwidth = 1,\n",
    "            kde = True\n",
    "           )\n",
    "plt.title('Delivery Time - Weekends')\n",
    "plt.xlabel('Delivery Time (minutes)')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(8, 3))\n",
    "sns.histplot(data = df[df['day_of_the_week']=='Weekday'], \n",
    "            x = 'delivery_time', \n",
    "            color = 'orange', \n",
    "            binwidth = 1,\n",
    "            kde = True\n",
    "           )\n",
    "plt.title('Delivery Time - Week days')\n",
    "plt.xlabel('Delivery Time (minutes)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Separate histograms for each category shows that the delivery time is distributed almost uniformly for each of them.\n",
    "\n",
    "Differences between both categories could be due to less availability of delivery people or more traffic on weekdays.\n",
    "\n",
    "More date/time and geographical information is needed to improve the analysis and eventually help make accurate predictions about waiting times for customers at the time of ordering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Other categorical Analysis:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A new auxiliary data frame that aggregates data by cuisine type is created to help with further analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>cuisine</th>\n",
       "      <th>orders</th>\n",
       "      <th>null_ratings</th>\n",
       "      <th>pc_nul_ratings</th>\n",
       "      <th>rest_n</th>\n",
       "      <th>ord_per_rest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>American</td>\n",
       "      <td>584</td>\n",
       "      <td>216</td>\n",
       "      <td>0.37</td>\n",
       "      <td>41</td>\n",
       "      <td>14.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Japanese</td>\n",
       "      <td>470</td>\n",
       "      <td>197</td>\n",
       "      <td>0.42</td>\n",
       "      <td>29</td>\n",
       "      <td>16.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Italian</td>\n",
       "      <td>298</td>\n",
       "      <td>126</td>\n",
       "      <td>0.42</td>\n",
       "      <td>31</td>\n",
       "      <td>9.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chinese</td>\n",
       "      <td>215</td>\n",
       "      <td>82</td>\n",
       "      <td>0.38</td>\n",
       "      <td>16</td>\n",
       "      <td>13.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mexican</td>\n",
       "      <td>77</td>\n",
       "      <td>29</td>\n",
       "      <td>0.38</td>\n",
       "      <td>11</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    cuisine  orders  null_ratings  pc_nul_ratings  rest_n  ord_per_rest\n",
       "0  American     584           216            0.37      41         14.24\n",
       "1  Japanese     470           197            0.42      29         16.21\n",
       "2   Italian     298           126            0.42      31          9.61\n",
       "3   Chinese     215            82            0.38      16         13.44\n",
       "4   Mexican      77            29            0.38      11          7.00"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# aggregated data frame for cuisine types, for ratings and restaurant performance evaluation\n",
    "cuis_list = df['cuisine_type'].unique()\n",
    "ord_by_cuis = [df[df['cuisine_type']==cuis]['cuisine_type'].count() for cuis in cuis_list]\n",
    "nul_rat = [df[df['cuisine_type']==cuis]['rating_value'].isnull().sum() for cuis in cuis_list]\n",
    "\n",
    "agg_ct = pd.DataFrame({'cuisine':cuis_list,\n",
    "                      'orders':ord_by_cuis,\n",
    "                      'null_ratings':nul_rat}) # auxiliary dataframe\n",
    "\n",
    "agg_ct['pc_nul_ratings'] = agg_ct['null_ratings'] / agg_ct['orders'] # proportion of not given ratings\n",
    "agg_ct = agg_ct.sort_values(by='orders', ascending = False).reset_index()\n",
    "agg_ct.drop('index', axis = 1, inplace = True)\n",
    "\n",
    "cuis_ord = agg_ct.loc[:,'cuisine'] # auxiliary vector to add more columns\n",
    "\n",
    "agg_ct['rest_n'] = [df[df['cuisine_type']==cuis]['restaurant_name'].nunique() for cuis in cuis_ord] # number of restaurants\n",
    "agg_ct['ord_per_rest'] = agg_ct['orders'] / agg_ct['rest_n'] # orders per restaurant ratio\n",
    "agg_ct.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's visualize the average rating by cuisine type, not considering ratings not given:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.barplot(data = df, \n",
    "            x = 'rating_value', # column with 'Not Given' = null\n",
    "            y = 'cuisine_type', \n",
    "            palette = 'colorblind', \n",
    "            order = df.cuisine_type.value_counts().index\n",
    "           )\n",
    "plt.title('Average Ratings (Not Given = not considered) by Cuisine Type')\n",
    "plt.ylabel('')\n",
    "plt.xlabel('Average Rating')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be seen, there are no great differences in average ratings. Nevertheless, Indian, Thai, and Spanish cuisines might have higher mean ratings. But the 'Not Given' values could be distorting the comparison:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.barplot(data=agg_ct, \n",
    "            x = 'pc_nul_ratings', \n",
    "            y = 'cuisine', \n",
    "            palette = 'colorblind')\n",
    "plt.title('Proportion of Ratings not Given by Cuisine Type')\n",
    "plt.ylabel('')\n",
    "plt.xlabel('Proportion of Ratings not Given')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be seen from the former figure, there are differences by cuisine type in the proportion of ratings not given. This could affect the ratings comparative analysis. What would happen if we \"punish\" every cuisine type by using an estimated rating of 1.5 points when there is no rating?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.barplot(data = df, \n",
    "            x = 'rating_value_est', # column with 'Not Given' = 1.5\n",
    "            y = 'cuisine_type', \n",
    "            palette = 'colorblind', \n",
    "            order = df.cuisine_type.value_counts().index\n",
    "           )\n",
    "plt.title('Average Ratings (Not Given = 1.5 points) by Cuisine Type')\n",
    "plt.ylabel('')\n",
    "plt.xlabel('Average Rating')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could say that Indian and Southern cuisines look promising. Their performance could be very well due to both cuisines being a niche taste, lack of advertising effort, and/or other reasons.\n",
    "\n",
    "Thai and French cuisines, among others, could have lower mean rating scores, but the low quantity and quality of the data doesn't allow for solid conclusions. In fact cuisines like Thai and Spanish may be very well received by the customers, more research is needed about the nature of the missing ratings to be able to make any solid conclusion about this variable. Also, the lower half of the graph shows cuisine types with very low quantity of orders, which could cause misleading results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What's driving cuisines' relative performance?\n",
    "\n",
    "This analysis seeks to discover explanations for the unequal number of orders among different cuisine types, since ratings didn't deliver the kind of conclusions needed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.barplot(data=agg_ct, \n",
    "            x = 'rest_n', \n",
    "            y = 'cuisine', \n",
    "            palette = 'colorblind')\n",
    "plt.title('Number of Restaurants by Cuisine Type')\n",
    "plt.ylabel('')\n",
    "plt.xlabel('Number of Restaurants')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.barplot(data=agg_ct, \n",
    "            x = 'ord_per_rest', \n",
    "            y = 'cuisine', \n",
    "            palette = 'colorblind')\n",
    "plt.title('Number of Orders per Restaurants, by Cuisine Type')\n",
    "plt.ylabel('')\n",
    "plt.xlabel('Number of Orders per Restaurant')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Some observations:**\n",
    "\n",
    "The cuisine types with the most quantity of restaurants are American, Japanes and Italian, which explains a lot of their relative presence in terms of order quantities.\n",
    "\n",
    "While there are much more restaurants serving American food than other types, Japanese restaurants enjoy the highest order per restaurant ratio, followed by American and Chinese restaurants.\n",
    "\n",
    "Indian restaurants, given their apparently good ratings, could have room for growth.\n",
    "\n",
    "Thai, Korean, Spanish and Vietnamese restaurants apparently have very low turnovers which merits more research.\n",
    "\n",
    "More research is needed to determine if there are restaurants with idle capacity that could be used to improve performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Orders by Cuisine, weekends vs week days:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.countplot(data = df, \n",
    "              hue = 'day_of_the_week', \n",
    "              y = 'cuisine_type', \n",
    "              palette = 'colorblind', \n",
    "              order = df.cuisine_type.value_counts().index)\n",
    "plt.title('Orders by Cuisine Time and Day of the Week')\n",
    "plt.ylabel('')\n",
    "plt.xlabel('Number of Orders')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen before, the ranking of orders by cuisine type doesn't vary much on weekends vs week days."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YLkMf09YJY_V"
   },
   "source": [
    "### **Question 13:** The company wants to provide a promotional offer in the advertisement of the restaurants. The condition to get the offer is that the restaurants must have a rating count of more than 50 and the average rating should be greater than 4. Find the restaurants fulfilling the criteria to get the promotional offer. [3 marks]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "0M_QDvbYJY_V"
   },
   "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>restaurant</th>\n",
       "      <th>orders</th>\n",
       "      <th>pct</th>\n",
       "      <th>pct_ac</th>\n",
       "      <th>rat_count</th>\n",
       "      <th>rat_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Meatball Shop</td>\n",
       "      <td>132</td>\n",
       "      <td>6.95</td>\n",
       "      <td>18.49</td>\n",
       "      <td>84</td>\n",
       "      <td>4.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Blue Ribbon Fried Chicken</td>\n",
       "      <td>96</td>\n",
       "      <td>5.06</td>\n",
       "      <td>29.82</td>\n",
       "      <td>64</td>\n",
       "      <td>4.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shake Shack</td>\n",
       "      <td>219</td>\n",
       "      <td>11.54</td>\n",
       "      <td>11.54</td>\n",
       "      <td>133</td>\n",
       "      <td>4.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Blue Ribbon Sushi</td>\n",
       "      <td>119</td>\n",
       "      <td>6.27</td>\n",
       "      <td>24.76</td>\n",
       "      <td>73</td>\n",
       "      <td>4.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  restaurant  orders   pct  pct_ac  rat_count  rat_avg\n",
       "1          The Meatball Shop     132  6.95   18.49         84     4.51\n",
       "3  Blue Ribbon Fried Chicken      96  5.06   29.82         64     4.33\n",
       "0                Shake Shack     219 11.54   11.54        133     4.28\n",
       "2          Blue Ribbon Sushi     119  6.27   24.76         73     4.22"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The approach will be based on the new agg_rest data frame created before\n",
    "# Crating new columns: ratings count, and ratings average.\n",
    "rest_lst_ord = agg_rest.loc[:,'restaurant'] \n",
    "agg_rest['rat_count'] = [df[df['restaurant_name'] == rest]['rating_value'].count() for rest in rest_lst_ord]\n",
    "agg_rest['rat_avg'] = [df[df['restaurant_name'] == rest]['rating_value'].mean() for rest in rest_lst_ord]\n",
    "\n",
    "# Filtering the desired outcome and ordering by ratings average (descending)\n",
    "agg_rest[(agg_rest['rat_count'] > 50) & (agg_rest['rat_avg'] > 4)].sort_values(by = 'rat_avg', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "10uryUMhJY_V"
   },
   "source": [
    "#### Observations:\n",
    "\n",
    "The former table lists the 4 restaurants that satisfy both conditions, ordered by descending ratings average: The Meatball Shop, Blue Ribbon Fried Chicken, Shake Shack, and Blue Ribbon Sushi.\n",
    "\n",
    "These kinds of offers could incentivize restaurants to ask customers for their rating score, improving the quality of the data gathering.\n",
    "\n",
    "Still, in our opinion the value of the score itself is not very indicative of the quality of the customer experience (about 50% of rated orders get 5 points), unless the definition of each score is better defined for the customer before they send their input."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1gvWBkRUJY_V"
   },
   "source": [
    "### **Question 14:** The company charges the restaurant 25% on the orders having cost greater than 20 dollars and 15% on the orders having cost greater than 5 dollars. Find the net revenue generated by the company across all orders. [3 marks]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "id": "rEjbnTNEJY_W",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The net revenue generated by FoodHub from the orders listed in the data set is 6166.3 dollars.\n",
      "\n",
      "On average, that means about 3.25 dollars per order, or 19.7% of the mean order cost.\n"
     ]
    }
   ],
   "source": [
    "# a function seems to be the simplest way to calculate the amount (fee) FoodHub charges the restaurants for each order\n",
    "def fee (cost):\n",
    "    if cost > 20:\n",
    "        return cost * 0.25\n",
    "    elif cost > 5:\n",
    "        return cost * 0.15\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "df['fee'] = [fee(df.loc[x,'cost_of_the_order']) for x in range(df.shape[0])]\n",
    "total_fee = df['fee'].sum()\n",
    "print(f'The net revenue generated by FoodHub from the orders listed in the data set is {round(total_fee, 2)} dollars.')\n",
    "mean_pc_ord = total_fee / df.cost_of_the_order.sum()\n",
    "print(f'\\nOn average, that means about {round(total_fee/df.shape[0], 2)} dollars per order, or {round(mean_pc_ord*100, 1)}% of the mean order cost.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XHCEJgH0JY_W"
   },
   "source": [
    "#### Observations:\n",
    "\n",
    "It's interesting that FoodHub earns about 20% of the cost of the orders on average. With more research about the effects of the fee structure on order amounts and quantities, an optimization algorithm could be used to verify if the fee structure is indeed optimal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pRlVW-oeJY_W"
   },
   "source": [
    "### **Question 15:** The company wants to analyze the total time required to deliver the food. What percentage of orders take more than 60 minutes to get delivered from the time the order is placed? (The food has to be prepared and then delivered.) [2 marks]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "id": "zxoTrxfLJY_W"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.5% of orders take more than 60 minutes to get delivered.\n",
      "\n",
      "The proportion for week days is 14.8%\n"
     ]
    }
   ],
   "source": [
    "# Write the code here\n",
    "df['wait_time'] = df['food_preparation_time'] + df['delivery_time']\n",
    "\n",
    "# number of orders that mtch the condition, also on weekends as additional info\n",
    "over_60 = df[df['wait_time'] > 60].shape[0]\n",
    "over_60_wend = df[(df['wait_time'] > 60) & (df['day_of_the_week']=='Weekend')].shape[0]\n",
    "\n",
    "# percent indicators\n",
    "pc_over_60 = over_60/df.shape[0]\n",
    "pc_over_60_wday = over_60_wend/df[df['day_of_the_week']=='Weekday'].shape[0]\n",
    "\n",
    "print(f'{round(pc_over_60*100, 1)}% of orders take more than 60 minutes to get delivered.')\n",
    "print(f'\\nThe proportion for week days is {round(pc_over_60_wday*100, 1)}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iqzBXn2yJY_W"
   },
   "source": [
    "#### Observations:\n",
    "\n",
    "For about 11% of orders the customer has to wait more than 60 minutes. Only considering week days, the proportion goes up to almost 15%.\n",
    "\n",
    "One hour of waiting could be very well a psychological boundary that discourages orders. There is little information about what drives preparation times, but delivery times are clearly the source of the difference between weekends and week days in terms of waiting time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bUd6cYpPJY_W"
   },
   "source": [
    "### **Question 16:** The company wants to analyze the delivery time of the orders on weekdays and weekends. How does the mean delivery time vary during weekdays and weekends? [2 marks]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "id": "pIU5xZZ6JY_W"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average delivery time on weekends is about 22.5 minutes, while on weekdays is 28.3 minutes. Thats a 26.1% increase.\n"
     ]
    }
   ],
   "source": [
    "# averages for delivery time, on weekdays and weekends\n",
    "wday_deltime_avg = df[df['day_of_the_week']=='Weekday']['delivery_time'].mean()\n",
    "wend_deltime_avg = df[df['day_of_the_week']=='Weekend']['delivery_time'].mean()\n",
    "\n",
    "print(f'Average delivery time on weekends is about {round(wend_deltime_avg, 1)} minutes,',\n",
    "      f'while on weekdays is {round(wday_deltime_avg, 1)} minutes.', \n",
    "      f'Thats a {round((wday_deltime_avg/wend_deltime_avg-1)*100, 1)}% increase.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8H-ODxbVJY_W"
   },
   "source": [
    "#### Observations:\n",
    "\n",
    "As seen in the multivariate analysis, there's a substantial increase of delivery times on week days, which can be caused by traffic or less availability of delivery people. Geographical location of the customers (which is unknown) could also influence this. Maybe the business district is farther from the restaurants on average and most week day orders are lunch orders from offices? Detailed date-time and geographical information could help answer this question."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "17roJywaJY_W"
   },
   "source": [
    "### Conclusion and Recommendations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZdX2Wz4FJY_W"
   },
   "source": [
    "### **Question 17:** What are your conclusions from the analysis? What recommendations would you like to share to help improve the business? (You can use cuisine type and feedback ratings to drive your business recommendations.) [6 marks]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VTugVsL4Ddz-"
   },
   "source": [
    "### Conclusions:\n",
    "\n",
    "After the analysis of a dataset containing 1,898 orders placed through FoodHub, the following insights can be drawn:\n",
    "\n",
    "*  The restaurants list might need some cleaning of repeated and closed locals. More research is needed to take advantage of the opportunities hidden in this list\n",
    "*  75% of customers placed 2 orders or less, while some \"outlier\" customers placed 4 to 13 orders each. Among the latter, customers N° 52832, 47440 and 83287 are the top 3.\n",
    "*  80% of orders were taken by only 42 restaurants out of 178, with the top 4 restaurants having more than 90 orders each. On the other hand, half the restaurants took only 3 orders each or less. The top 5 restaurants are: Shake Shack, The Meatball Shop, Blue Ribbon Sushi, Blue Ribbon Fried Chicken, and Parm.\n",
    "*  Most orders (83%) are concentrated in a few cuisine types: American (31%), Japanese (25%), Italian (16%) and Chinese (11%).\n",
    "*  Almost three-quarters of the orders are placed on weekends, following almost the same order of preference in regards to cuisine types.\n",
    "*  The cost of orders varies between 4.47 to 35.41 dollars. Half of the orders  cost less than 14 dollars. The top 4 cuisine types (American, Japanese, Italian and Chinese) have slightly lower median order costs than the following 3 (Mexican, Indian, Middle-Eastern). Thai and French median order costs are noticeably higher, while Korean and Vietnamese orders seem to be cheaper.\n",
    "*  Food preparation time varies between 20 and 35 minutes, with an average of about 27 minutes. The distribution of this variable is almost uniform, with little difference between days of the week or cuisine types.\n",
    "*  Delivery times vary between 15 and 33 minutes. There's a substantial (26%) increase of delivery times on week days, which can be caused by traffic, less availability of delivery people, or geographical considerations. Delivery time follows an almost uniform distribution for both weekends and week days (but logically not for the whole number of orders).\n",
    "*  For about 11% of the orders the customer has to wait more than 60 minutes for their order. On week days, it’s almost 15% (clearly lower for weekends). One hour of wait time could be very well a psychological boundary that discourages orders and could be acted upon.\n",
    "*  Ratings information doesn’t help much to understand the quality of the customer experience, because of the large number of orders without a rating given (almost 40%). Also, the ratings score is slanted towards “perfect” scores, with about half the given ratings being top scores. Maybe customers just find the ratings interface annoying. The limited analysis that was possible to undertake suggests that Indian and Southern cuisines have higher mean ratings than the rest. Thai and French cuisines, among others, could have lower mean rating scores, but the low quantity and quality of the data doesn't allow for solid conclusions.\n",
    "*  The restaurants The Meatball Shop, Blue Ribbon Fried Chicken, Shake Shack, and Blue Ribbon Sushi have both more than 50 ratings each, and average ratings over 4 points. Promotional offers could incentivize restaurants to ask customers for their rating score, improving the quality of the data gathering.\n",
    "*  The cuisine types with the most quantity of restaurants are American, Japanes and Italian, which explains a lot about their relative higher order quantities.\n",
    "*  Japanese, American and Chinese restaurants have the highest turnover in terms of orders per restaurant. Thai, Korean, Spanish and Vietnamese restaurants apparently have very low turnovers. Indian restaurants seem to have potential, they got good average ratings, and they seem to have relatively low orders per restaurant.\n",
    "*  FoodHub earned 6,166.3 dollars from the orders listed in the dataset, that’s about 20% of the cost of the orders on average, or 3.25 dollars per order.\n",
    "*  No significant correlation was detected in this dataset between any of the numerical variables: order cost, preparation time, delivery time or ratings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PWnsI0i_guWJ"
   },
   "source": [
    "### Recommendations:\n",
    "\n",
    "*  Suggest to restaurants, as a way to increase sales on week days, to reduce preparation times and thus, waiting times, by proposing weekday simplified menus with convenient lunch combos for businesses, to streamline their kitchens. The increased sales could help restaurants’ use of idle capacity and improve FoodHub’s earnings.\n",
    "*  Help restaurants in the Mexican, Indian and Mediterranean cuisines (among others with potential) increase their sales through special promotions and events, like for example “Curry Friday Night” as a way to introduce more exotic flavors to customers. Suggest they reduce their average prices a little, maybe creating standardized combos and simplifying their menus, focusing on signature dishes.\n",
    "*  The high proportion of orders not rated, and the distribution of the ratings doesn't help understand the customer experience nor take any action about it. Maybe the rating scale could be better defined for the customer so they add more nuances to their ratings and opt less frequently for max ratings unless the experience was remarkable.Some rating interfaces often ask for written details or ask extra questions when the rating is not \"perfect\". That could discourage an honest appraisal of the experience. Finally, some kind of incentive for giving a rating every time could be figured out, like (as examples) loyalty coupons and periodic free meal lottos. Persevering in promotional advertising offers could incentivize restaurants to help ask customers for their rating score, improving the quality of the data gathering.\n",
    "*  **The most important thing is to protect and reward success**, as a big proportion of orders, and thus FoodHub’s income, is concentrated in just a few restaurants. Keep regular communication with top restaurants and get constant feedback about the delivery service, the ordering app, coupons, promotional material, advertising, and other concerns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Suggested further research and analysis:\n",
    "*  Adding detailed date/time information to the orders could bring new and important insights to the analysis, like identifying orders placed during rush hours. This could improve the prediction of total waiting times (after a ML algorythm is run, which is outside the scope of this analysis).\n",
    "*  Also, adding location information of restaurants and customers could help get a better understanding of geographical differences between weekend and weekday orders, and delivery times.\n",
    "*  Update and clean the restaurant list if needed. Get more information about common franchises, locals with the same owner, same brand with multiple locations, etc.\n",
    "*  With more research about the effects of the fee structure on order amounts and quantities, an optimization algorithm could be used to search for ways to improve the fee structure, increasing earnings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### APPENDIX\n",
    "\n",
    "Other visualizations excluded to avoid clutter and superfluous information:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cost of Orders vs cuisine and weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 12))\n",
    "sns.boxplot(data = df, \n",
    "            x = 'cost_of_the_order', \n",
    "            y = 'cuisine_type', \n",
    "            hue = 'day_of_the_week', \n",
    "            palette = 'colorblind', \n",
    "            order = df.cuisine_type.value_counts().index\n",
    "           )\n",
    "plt.legend(loc=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prep time vs cuisine and weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 12))\n",
    "sns.boxplot(data = df, \n",
    "            x = 'food_preparation_time', \n",
    "            y = 'cuisine_type', \n",
    "            hue = 'day_of_the_week', \n",
    "            palette = 'colorblind', \n",
    "            order = df.cuisine_type.value_counts().index\n",
    "           )\n",
    "plt.legend(loc=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Delivery time vs cuisine and weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 12))\n",
    "sns.boxplot(data = df, \n",
    "            x = 'delivery_time', \n",
    "            y = 'cuisine_type', \n",
    "            hue = 'day_of_the_week', \n",
    "            palette = 'colorblind', \n",
    "            order = df.cuisine_type.value_counts().index\n",
    "           )\n",
    "plt.legend(loc=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These visualizations didn't add substantive additional insights. The difference between weekday and weekend in delivery times was already discovered, and the difference between cuisines didn't need to be classified by day of the week."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tSoucKKbg9Hc"
   },
   "source": [
    "---"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "PYF_Project_LearnerNotebook_FullCode.ipynb",
   "provenance": []
  },
  "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}