ANNA LAPUSHNER

Posted on Aug 13

you can use this FoodHub Case Study Code

#casestudy #univariate #multivariate #data

This was a really fun project. It was just rad to be given the opportunity to showcase this awesome stuff! This code has color and style, so if you're into getting jazzy on your presentations, be my guest and have at it!

code on and conquer..

https://drive.google.com/file/d/1XnIKpd-aE08_sPUJuphwSL4N7K4EHZnv/view?usp=sharing

https://colab.research.google.com/drive/1m5OBtEKyFDgk6wtbOJ-q9GvErzxyVBeb?usp=drive_link

Import necessary libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as stats
from scipy.stats import binom

%matplotlib inline

Connect to Google Drive to access necessary files

from google.colab import drive
drive.mount('/content/drive')

Define file path to access the data file

file_path = '/content/drive/MyDrive/2024_mit/Projects/Food Hub/foodhub_order.csv'

Read the data file into a pandas DataFrame

Use the standard name "df" to name the DataFrame

df = pd.read_csv(file_path)
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).

Use .head() to describe the data

df.head()
order_id customer_id restaurant_name cuisine_type cost_of_the_order day_of_the_week rating food_preparation_time delivery_time
0 1477147 337525 Hangawi Korean 30.75 Weekend Not given 25 20
1 1477685 358141 Blue Ribbon Sushi Izakaya Japanese 12.08 Weekend Not given 25 23
2 1477070 66393 Cafe Habana Mexican 12.23 Weekday 5 23 28
3 1477334 106968 Blue Ribbon Fried Chicken American 29.20 Weekend 3 25 15
4 1478249 76942 Dirty Bird to Go American 11.59 Weekday 4 25 24
Question 1: How many rows and columns are present in the data?

Use .shape to describe the data as (rows, columns)

df.shape
(1898, 9)
Question 2: What are the datatypes of the different columns in the dataset?

Use .info() to identify the datatypes in each column

df.info()

RangeIndex: 1898 entries, 0 to 1897
Data columns (total 9 columns):
# Column Non-Null Count Dtype

0 order_id 1898 non-null int64

1 customer_id 1898 non-null int64

2 restaurant_name 1898 non-null object
3 cuisine_type 1898 non-null object
4 cost_of_the_order 1898 non-null float64
5 day_of_the_week 1898 non-null object
6 rating 1898 non-null object
7 food_preparation_time 1898 non-null int64

8 delivery_time 1898 non-null int64

dtypes: float64(1), int64(4), object(4)
memory usage: 133.6+ KB
Question 3: Are there any missing values in the data? If yes, treat them using an appropriate method.

Use .isnull().sum() to determine if there are missing values in the data

df.isnull().sum()
0
order_id 0
customer_id 0
restaurant_name 0
cuisine_type 0
cost_of_the_order 0
day_of_the_week 0
rating 0
food_preparation_time 0
delivery_time 0
dtype: int64
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?

Use describe().T to get the statistical summary the numerical data

df.describe().T
count mean std min 25% 50% 75% max
order_id 1898.0 1.477496e+06 548.049724 1476547.00 1477021.25 1477495.50 1.477970e+06 1478444.00
customer_id 1898.0 1.711685e+05 113698.139743 1311.00 77787.75 128600.00 2.705250e+05 405334.00
cost_of_the_order 1898.0 1.649885e+01 7.483812 4.47 12.08 14.14 2.229750e+01 35.41
food_preparation_time 1898.0 2.737197e+01 4.632481 20.00 23.00 27.00 3.100000e+01 35.00
delivery_time 1898.0 2.416175e+01 4.972637 15.00 20.00 25.00 2.800000e+01 33.00

Use describe(include = 'all').T to get the statistical summary the numerical data

df.describe(include='all').T
count unique top freq mean std min 25% 50% 75% max
order_id 1898.0 NaN NaN NaN 1477495.5 548.049724 1476547.0 1477021.25 1477495.5 1477969.75 1478444.0
customer_id 1898.0 NaN NaN NaN 171168.478398 113698.139743 1311.0 77787.75 128600.0 270525.0 405334.0
restaurant_name 1898 178 Shake Shack 219 NaN NaN NaN NaN NaN NaN NaN
cuisine_type 1898 14 American 584 NaN NaN NaN NaN NaN NaN NaN
cost_of_the_order 1898.0 NaN NaN NaN 16.498851 7.483812 4.47 12.08 14.14 22.2975 35.41
day_of_the_week 1898 2 Weekend 1351 NaN NaN NaN NaN NaN NaN NaN
rating 1898 4 Not given 736 NaN NaN NaN NaN NaN NaN NaN
food_preparation_time 1898.0 NaN NaN NaN 27.37197 4.632481 20.0 23.0 27.0 31.0 35.0
delivery_time 1898.0 NaN NaN NaN 24.161749 4.972637 15.0 20.0 25.0 28.0 33.0

Calculate the IQR of the 'cost_of_the_order' column

Q1 = df['cost_of_the_order'].quantile(0.25)
Q3 = df['cost_of_the_order'].quantile(0.75)
IQR = Q3 - Q1

print("IQR of the mean cost of order:", IQR)
IQR of the mean cost of order: 10.2175

Use .min() to calculate the minimum time it takes for food to be prepared once an order is placed

df['food_preparation_time'].min()
print('Minimum food preparation time is =', round(df['food_preparation_time'].min(), 2), 'minutes')

Use .mean() to calculate the average time it takes for food to be prepared once an order is placed

df['food_preparation_time'].mean()
print('Average food preparation time is =', round(df['food_preparation_time'].mean(), 2), 'minutes')

Use .max() to calculate the maximum time it takes for food to be prepared once an order is placed

df['food_preparation_time'].max()
print('Maximum food preparation time is =', round(df['food_preparation_time'].max(), 2), 'minutes')
Minimum food preparation time is = 20 minutes
Average food preparation time is = 27.37 minutes
Maximum food preparation time is = 35 minutes

Calculate the IQR of the 'food_preparation_time' column

Q1 = df['food_preparation_time'].quantile(0.25)
Q3 = df['food_preparation_time'].quantile(0.75)
IQR = Q3 - Q1

print("IQR of the food preparation time:", IQR)
IQR of the food preparation time: 8.0

Calculate the IQR of the 'delivery_time' column

Q1 = df['delivery_time'].quantile(0.25)
Q3 = df['delivery_time'].quantile(0.75)
IQR = Q3 - Q1

print("IQR of the mean cost of delivery time:", IQR)
IQR of the mean cost of delivery time: 8.0

What restaurant has the longest food prep time

df.groupby('restaurant_name')['food_preparation_time'].mean().sort_values(ascending=False).head(1)
food_preparation_time
restaurant_name
Cipriani Le Specialita 35.0
dtype: float64

How many orders are from Cipriani Le Specialita

df[df['restaurant_name'] == 'Cipriani Le Specialita'].shape[0]
1

Find the food prep time of American restaurants

df[df['cuisine_type'] == 'American']['food_preparation_time'].mean()
27.440068493150687
Question 5: How many orders are not rated?

Use .value_counts() to get a count of all ratings

df['rating'].value_counts()
count
rating

Not given 736
5 588
4 386
3 188
dtype: int64

prompt: total number of rating value counts as not given

df['rating'].value_counts()['Not given']
print('Total number of rating value counts as not given =', df['rating'].value_counts()['Not given'])
Total number of rating value counts as not given = 736
Question 6: Explore all the variables and provide observations on their distributions. (Generally, histograms, boxplots, countplots, etc. are used for univariate exploration)
Order ID

Use .nunique() to check that a unique order ID is assigned to each order

df['order_id'].nunique()
1898
Customer ID

Use .nunique() to check if there are repeat customers

total_customers = df['customer_id'].nunique()

Calculate the percentage of customers who bought more than once

repeat_customers = df['customer_id'].value_counts()[df['customer_id'].value_counts() > 1].shape[0]

Calculate the percentage of customers who purchased more than once

percentage_repeat = (repeat_customers / total_customers) * 100

print(f"Percentage of customers who purchased more than once: {percentage_repeat:.2f}%")
Percentage of customers who purchased more than once: 34.67%

Calculate the number of customers who bought more than twice

repeat_customers_more_than_twice = df['customer_id'].value_counts()[df['customer_id'].value_counts() > 2].shape[0]

Calculate the percentage of customers who purchased more than twice

percentage_repeat_more_than_twice = (repeat_customers_more_than_twice / total_customers) * 100

print(f"Percentage of customers who purchased more than twice: {percentage_repeat_more_than_twice:.2f}%")
Percentage of customers who purchased more than twice: 12.42%
Restaurant name

Identify number of unique restaurant names

df['restaurant_name'].nunique()
178

Print list of restaurant names as a vertical list

for restaurant in df['restaurant_name'].unique():
print(restaurant)
Hangawi
Blue Ribbon Sushi Izakaya
Cafe Habana
Blue Ribbon Fried Chicken
Dirty Bird to Go
Tamarind TriBeCa
The Meatball Shop
Barbounia
Anjappar Chettinad
Bukhara Grill
Big Wong Restaurant ¤¾Ñ¼
Empanada Mama (closed)
Pylos
Lucky's Famous Burgers
Shake Shack
Sushi of Gari
RedFarm Hudson
Blue Ribbon Sushi
Five Guys Burgers and Fries
Tortaria
Cafe Mogador
Otto Enoteca Pizzeria
Vezzo Thin Crust Pizza
Sushi of Gari 46
The Kati Roll Company
Klong
5 Napkin Burger
TAO
Parm
Sushi Samba
Haru Gramercy Park
Chipotle Mexican Grill $1.99 Delivery
RedFarm Broadway
Cafeteria
DuMont Burger
Sarabeth's East
Hill Country Fried Chicken
Bistango
Jack's Wife Freda
Mamoun's Falafel
Prosperity Dumpling
Blue Ribbon Sushi Bar & Grill
Westville Hudson
Blue Ribbon Brooklyn
Nobu Next Door
Osteria Morini
Haandi
Benihana
Han Dynasty
Chote Nawab
Mission Cantina
Xi'an Famous Foods
Rubirosa
Joe's Shanghai Àü£¾÷´
Bareburger
The Odeon
Pongsri Thai
Yama Japanese Restaurant
Momoya
Balthazar Boulangerie
CafÌ© China
Boqueria
Song Thai Restaurant & Bar
Five Leaves
Pinto Nouveau Thai Bistro
Amy Ruth's
Pepe Giallo
indikitch
Yama 49
Piccolo Angolo
Pepe Rosso To Go
L'Express
Amma
Delicatessen
S'MAC
Vanessa's Dumplings
Bhatti Indian Grill
Taro Sushi
Donburi-ya
Hatsuhana
Samurai Mama
Waverly Diner
Tarallucci e Vino Restaurant
P.J. Clarke's
Lantern Thai Kitchen
ilili Restaurant
The Smile
Vanessa's Dumpling House
Bubby's
Woorijip
Dirty Bird To Go (archived)
Haveli Indian Restaurant
Dos Caminos
da Umberto
Sushi of Gari Tribeca
Burger Joint
Room Service
Sarabeth's Restaurant
Xe May Sandwich Shop
Hibino
Mira Sushi
Melt Shop
J. G. Melon
Hummus Place
Saravanaa Bhavan
Friend of a Farmer
The Loop
Balade
Posto
Terakawa Ramen
Kambi Ramen House
Wo Hop Restaurant
Spice Thai
Dickson's Farmstand Meats
UVA Wine Bar & Restaurant
Serafina Fabulous Pizza
Gaia Italian Cafe
Chola Eclectic Indian Cuisine
Hot Kitchen
Junoon
Ravagh Persian Grill
Rohm Thai
Dig Inn Seasonal Market
Olea
Cho Dang Gol
El Parador Cafe
Socarrat Paella Bar
Don's Bogam BBQ & Wine Bar
Alidoro
Tony's Di Napoli
Cipriani Le Specialita
Sushi Choshi
Kanoyama
V-Nam Cafe
Zero Otto Nove
Dos Caminos Soho
Go! Go! Curry!
La Follia
Izakaya Ten
12 Chairs
Philippe Chow
The MasalaWala
brgr
Carmine's
Asuka Sushi
Aurora
Sarabeth's
Crema Restaurante
Big Daddy's
Moonstruck on Second
Cafe de La Esquina
Olive Garden
67 Burger
Tres Carnes
Schnipper's Quality Kitchen
Nha Trang One
Market Table
Galli Restaurant
Hampton Chutney Co.
Byblos Restaurant
Grand Sichuan International
Le Grainne Cafe
Il Bambino
Kori Restaurant and Bar
DespaÌ±a
Lamarca Pasta
Lucky Strike
Paul & Jimmy's
Hunan Manor
Coppola's East
Emporio
Wa Jeal
Le Zie 2000 Trattoria
Rye House
Hiroko's Place
Frank Restaurant
Sarabeth's West
'wichcraft
Cuisine type

Identify unique number of cuisine types

df['cuisine_type'].nunique()
14

Print list of cuisine types as a vertical list

for cuisine in df['cuisine_type'].unique():
print(cuisine)
Korean
Japanese
Mexican
American
Indian
Italian
Mediterranean
Chinese
Middle Eastern
Thai
Southern
French
Spanish
Vietnamese

Choose plot dimension

plt.figure(figsize=(15,7))

Set title and name x,y axis

plt.title(('Number of orders distributed by cuisine type'), fontsize=15, color='darkorchid')
plt.xlabel('Cuisine Type', fontsize=13, color='blueviolet')
plt.ylabel('Frequency', fontsize=13, color='mediumorchid')

Add tick marks to rotate your labels

plt.xticks(rotation = 45)
plt.yticks(rotation = 270)
sns.countplot(data = df, x = 'cuisine_type', palette='viridis',edgecolor='lightgoldenrodyellow')
:12: FutureWarning:

Passing palette without assigning hue is deprecated and will be removed in v0.14.0. Assign the x variable to hue and set legend=False for the same effect.

sns.countplot(data = df, x = 'cuisine_type', palette='viridis',edgecolor='lightgoldenrodyellow')

Count the total orders according to the most popular cuisines

american_count = df[df['cuisine_type'] == 'American']['cuisine_type'].count()
japanese_count = df[df['cuisine_type'] == 'Japanese']['cuisine_type'].count()
italian_count = df[df['cuisine_type'] == 'Italian']['cuisine_type'].count()
chinese_count = df[df['cuisine_type'] == 'Chinese']['cuisine_type'].count()
other_count = df[df['cuisine_type'].isin(['Korean','Japanese','Mexican', 'Indian', 'Mediterranean', 'Middle Eastern', 'Thai', 'Southern', 'French', 'Spanish','Vietnamese'])]['cuisine_type'].count()

print("Number of American cuisine orders:", american_count)
print("Number of Japanese cuisine orders:", japanese_count)
print("Number of Italian cuisine orders:", italian_count)
print("Number of Chinese cuisine orders:", chinese_count)
print("Number of orders from Korean, Japanese, Mexican, Indian, Mediterranean, Middle Eastern, Thai, Southern, French, Spanish,Vietnamese is:", other_count)
Number of American cuisine orders: 584
Number of Japanese cuisine orders: 470
Number of Italian cuisine orders: 298
Number of Chinese cuisine orders: 215
Number of orders from Korean, Japanese, Mexican, Indian, Mediterranean, Middle Eastern, Thai, Southern, French, Spanish,Vietnamese is: 801

Create a variable cuisine_popular that = the top 4 most popular cuisines and add other where other = all cuisines not in the top 4

top_cuisines = ['American', 'Japanese', 'Italian', 'Chinese']

Create a new column 'cuisine_popular'

df['cuisine_popular'] = df['cuisine_type'].apply(lambda x: x if x in top_cuisines else 'Other')
plt.figure(figsize=(15, 7))
sns.countplot(data=df, x='cuisine_popular', palette={'Other': 'lemonchiffon', 'Japanese': 'yellow', 'American': 'khaki', 'Italian': 'gold', 'Chinese': 'palegoldenrod'})
plt.title('Distribution of Cuisine Types (Popular vs Other)', fontsize=15, color='darkorchid')
plt.xlabel('Cuisine Type', fontsize=13, color='blueviolet')
plt.ylabel('Number of Orders', fontsize=13, color='mediumorchid')
:2: FutureWarning:

Passing palette without assigning hue is deprecated and will be removed in v0.14.0. Assign the x variable to hue and set legend=False for the same effect.

Text(0, 0.5, 'Number of Orders')

Calculate the probability of a customer ordering 'Other' cuisine

prob_other = df[df['cuisine_popular'] == 'Other']['cuisine_popular'].count() / df.shape[0]
print("Probability of ordering Other cuisine: {:.2f}".format(prob_other))

Calculate the probability of a customer ordering 'Italian' cuisine

prob_italian = df[df['cuisine_type'] == 'Italian']['cuisine_type'].count() / df.shape[0]
print("Probability of ordering Italian cuisine: {:.2f}".format(prob_italian))

Calculate the probability of a customer ordering 'Japanese' cuisine

prob_japanese = df[df['cuisine_type'] == 'Japanese']['cuisine_type'].count() / df.shape[0]
print("Probability of ordering Japanese cuisine: {:.2f}".format(prob_japanese))

Calculate the probability of a customer ordering 'American' cuisine

prob_american = df[df['cuisine_type'] == 'American']['cuisine_type'].count() / df.shape[0]
print("Probability of ordering American cuisine: {:.2f}".format(prob_american))

Calculate the probability of a customer ordering 'Chinese' cuisine

prob_chinese = df[df['cuisine_type'] == 'Chinese']['cuisine_type'].count() / df.shape[0]
print("Probability of ordering Chinese cuisine: {:.2f}".format(prob_chinese))
Probability of ordering Other cuisine: 0.17
Probability of ordering Italian cuisine: 0.16
Probability of ordering Japanese cuisine: 0.25
Probability of ordering American cuisine: 0.31
Probability of ordering Chinese cuisine: 0.11

Identify unique customers that order american and non american cuisine

american_customers = df[df['cuisine_type'] == 'American']['customer_id'].unique()
non_american_customers = df[df['cuisine_type'] != 'American']['customer_id'].unique()

unique_american_customers = set(american_customers)
unique_non_american_customers = set(non_american_customers)

print("Number of unique customers who ordered American cuisine:", len(unique_american_customers))
print("Number of unique customers who ordered non-American cuisine:", len(unique_non_american_customers))
Number of unique customers who ordered American cuisine: 417
Number of unique customers who ordered non-American cuisine: 856

Identify unique customers that ordered American and also ordered non-American cuisine

common_customers = unique_american_customers.intersection(unique_non_american_customers)
print("Number of unique customers who ordered both American and non-American cuisine:", len(common_customers))
Number of unique customers who ordered both American and non-American cuisine: 73

Identify unique customers that ordered American cuisine and also ordered non-American cuisine more than once

american_customer_counts = df[df['cuisine_type'] == 'American']['customer_id'].value_counts()
non_american_customer_counts = df[df['cuisine_type'] != 'American']['customer_id'].value_counts()

Customers who ordered American and non-American more than once

result_customers = set(american_customer_counts[american_customer_counts > 1].index).intersection(non_american_customer_counts[non_american_customer_counts > 1].index)

print("Number of unique customers who ordered both American and non-American cuisine more than once:", len(result_customers))
Number of unique customers who ordered both American and non-American cuisine more than once: 14

Identify unique customers that ordered American and Chinese cuisine

american_customers = df[df['cuisine_type'] == 'American']['customer_id'].unique()
chinese_customers = df[df['cuisine_type'] == 'Chinese']['customer_id'].unique()

unique_american_customers = set(american_customers)
unique_chinese_customers = set(chinese_customers)

print("Number of unique customers who ordered American cuisine:", len(unique_american_customers))
print("Number of unique customers who ordered Chinese cuisine:", len(unique_chinese_customers))

Identify unique customers that ordered American and also ordered Chinese cuisine

common_customers = unique_american_customers.intersection(unique_chinese_customers)
print("Number of unique customers who ordered both American and Chinese cuisine:", len(common_customers))
Number of unique customers who ordered American cuisine: 417
Number of unique customers who ordered Chinese cuisine: 162
Number of unique customers who ordered both American and Chinese cuisine: 13
Cost of the order

Use histogram to plot cost of the order

plt.figure(figsize=(15, 7))
plt.title('Distribution of Cost of the Order', fontsize=15, color='darkorchid')
plt.xlabel('Cost of the Order', fontsize=13, color='blueviolet')
plt.ylabel('Frequency', fontsize=13, color='mediumorchid')
sns.histplot(data=df,x='cost_of_the_order', color='lightgoldenrodyellow',edgecolor='goldenrod')

Use histogram to plot cost of the order

plt.figure(figsize=(15, 7))
plt.xlabel('Distribution of Cost of the Order', fontsize=13, color='darkorchid')
plt.ylabel('Frequency', fontsize=13, color='mediumorchid')
sns.boxplot(data=df,x='cost_of_the_order',color='yellow')

Use violin plot to plot cost of the order

plt.figure(figsize=(15, 7))
plt.xlabel('Distribution of Cost of the Order', fontsize=13, color='darkorchid')
plt.ylabel('Frequency', fontsize=13, color='mediumorchid')
sns.violinplot(data=df,x='cost_of_the_order',color='gold',edgecolor='lightgoldenrodyellow')

Day of the week

Unique values for day of the week

df['day_of_the_week'].unique()
array(['Weekend', 'Weekday'], dtype=object)
plt.figure(figsize=(15, 7))
plt.title('Distribution by Weekday and Weekend Day', fontsize=15, color='darkorchid')
plt.xlabel('Weekend Days are Sa,Su Weekday Days are M, T, W, Th, F', fontsize=13, color='blueviolet')
plt.ylabel('Frequency', fontsize=13, color='mediumorchid')
sns.countplot(data=df, x='day_of_the_week', color='goldenrod', edgecolor='lightgoldenrodyellow')

Rating

Unique values for rating

array_rating = df['rating'].unique()
print(array_rating)
['Not given' '5' '3' '4']

Calculate the percentage of orders that are not rated

not_rated_percentage = (df['rating'].value_counts()['Not given'] / df.shape[0]) * 100
print("Percentage of orders not rated: {:.2f}%".format(not_rated_percentage))
Percentage of orders not rated: 38.78%

Calculate the percentage of orders that are ordered 5

rated_5_percentage = (df['rating'].value_counts()['5'] / df.shape[0]) * 100
print("Percentage of orders rated 5: {:.2f}%".format(rated_5_percentage))
Percentage of orders rated 5: 30.98%
plt.figure(figsize=(15, 7))
plt.title('Available Ratings', fontsize=15, color='darkorchid')
plt.xlabel('Rating Given by the Customer', fontsize=13, color='blueviolet')
plt.ylabel('Frequency', fontsize=13, color='mediumorchid')
sns.countplot(data=df, x='rating', color='khaki', edgecolor='lightgoldenrodyellow', palette='Purples')
:5: FutureWarning:

Passing palette without assigning hue is deprecated and will be removed in v0.14.0. Assign the x variable to hue and set legend=False for the same effect.

sns.countplot(data=df, x='rating', color='khaki', edgecolor='lightgoldenrodyellow', palette='Purples')

Food Preparation time

Histogram of food preparation time

plt.figure(figsize=(15, 7))
plt.title('Distribution of Food Preparation Time', fontsize=15, color='darkorchid')
plt.xlabel('Time', fontsize=13, color='blueviolet')
plt.ylabel('Frequency', fontsize=13, color='mediumorchid')
sns.histplot(df['food_preparation_time'], edgecolor='lightgoldenrodyellow', color='Orange')

Boxplot of food preparation time

plt.figure(figsize=(15, 7))
plt.title('Distribution of Food Preparation Time', fontsize=15, color='darkorchid')
plt.xlabel('Time', fontsize=13, color='blueviolet')
plt.ylabel('Frequency', fontsize=13, color='mediumorchid')
sns.boxplot(data=df, x='food_preparation_time', color='yellow')

Delivery time

Histogram of delivery time

plt.figure(figsize=(15, 7))
plt.title('Distribution of Delivery Time', fontsize=15, color='darkorchid')
plt.xlabel('Time', fontsize=13, color='blueviolet')
plt.ylabel('Frequency', fontsize=13, color='mediumorchid')
plt.ylim(ymax=320)
sns.histplot(data=df,x='delivery_time', edgecolor='gold', color='lightgoldenrodyellow')

Boxplot of delivery time

plt.figure(figsize=(10,5))
plt.title('Distribution of Delivery Time', fontsize=15, color='darkorchid')
plt.xlabel('Time', fontsize=13, color='blueviolet')
plt.ylabel('Frequency', fontsize=13, color='mediumorchid')
sns.boxplot(data=df, x='delivery_time', color='darkgoldenrod')

Question 7: Which are the top 5 restaurants in terms of the number of orders received?

Get top 5 restaurants with highest number of orders

df['restaurant_name'].value_counts().head(5)
count
restaurant_name
Shake Shack 219
The Meatball Shop 132
Blue Ribbon Sushi 119
Blue Ribbon Fried Chicken 96
Parm 68
dtype: int64
Question 8: Which is the most popular cuisine on weekends?

Get most popular cuisine on weekends

df_weekend = df[df['day_of_the_week'] == 'Weekend']
print('The most popular cuisine on the weekends is', df_weekend['cuisine_type'].value_counts().idxmax())
The most popular cuisine on the weekends is American

Get unique values of cuisine on the weekends

df_weekend['cuisine_type'].unique()
array(['Korean', 'Japanese', 'American', 'Italian', 'Mexican',
'Mediterranean', 'Chinese', 'Indian', 'Thai', 'Southern', 'French',
'Spanish', 'Middle Eastern', 'Vietnamese'], dtype=object)

Get the top 6 cuisine types

df['cuisine_type'].value_counts().head(6)
count
cuisine_type

American 584
Japanese 470
Italian 298
Chinese 215
Mexican 77
Indian 73
dtype: int64

Get the top 6 cuisines

top_6_cuisines = df['cuisine_type'].value_counts().head(6).index.tolist()

Filter the DataFrame for the top 6 cuisines

df_top_cuisines = df[df['cuisine_type'].isin(top_6_cuisines)]

Get the top 4 restaurants

top_4_restaurants = df['restaurant_name'].value_counts().head(4).index.tolist()

Filter the DataFrame for the top 4 restaurants

df_top_restaurants = df[df['restaurant_name'].isin(top_4_restaurants)]

Create subplots

fig, axes = plt.subplots(1, 2, figsize=(16, 6))

Plot for top 6 cuisines

sns.countplot(data=df_top_cuisines, x='cuisine_type', ax=axes[0], palette='viridis')
axes[0].set_title('Top 6 Cuisines by Number of Orders')
axes[0].set_xlabel('Cuisine Type')
axes[0].set_ylabel('Number of Orders')
axes[0].tick_params(axis='x', rotation=45)

Plot for top 4 restaurants

sns.countplot(data=df_top_restaurants, x='restaurant_name', ax=axes[1], palette='magma')
axes[1].set_title('Top 4 Restaurants by Number of Orders')
axes[1].set_xlabel('Restaurant Name')
axes[1].set_ylabel('Number of Orders')
axes[1].tick_params(axis='x', rotation=45)

Adjust layout and display the plot

plt.tight_layout()
:17: FutureWarning:

Passing palette without assigning hue is deprecated and will be removed in v0.14.0. Assign the x variable to hue and set legend=False for the same effect.

sns.countplot(data=df_top_cuisines, x='cuisine_type', ax=axes[0], palette='viridis')
:24: FutureWarning:

Passing palette without assigning hue is deprecated and will be removed in v0.14.0. Assign the x variable to hue and set legend=False for the same effect.

sns.countplot(data=df_top_restaurants, x='restaurant_name', ax=axes[1], palette='magma')

Get most popular cuisine on weekdays

df_weekday = df[df['day_of_the_week'] == 'Weekday']
df_weekday['cuisine_type'].value_counts().idxmax()
'American'

Get top 3 cuisines on weekends

df_weekend = df[df['day_of_the_week'] == 'Weekend']
df_weekend['cuisine_type'].value_counts().head(3)
count
cuisine_type

American 415
Japanese 335
Italian 207
dtype: int64

Calculate median order price on weekends

df[df['day_of_the_week'] == 'Weekend']['cost_of_the_order'].median()
14.41

Calculate median order price on weekdays

df[df['day_of_the_week'] == 'Weekday']['cost_of_the_order'].median()
14.07

Calculate highest median order by cuisine

df.groupby('cuisine_type')['cost_of_the_order'].median().sort_values(ascending=False)
cost_of_the_order
cuisine_type

French 20.470
Thai 19.350
Spanish 16.520
Middle Eastern 16.300
Indian 16.150
Southern 16.110
Mexican 15.570
Italian 14.480
American 14.120
Chinese 14.120
Japanese 14.070
Mediterranean 13.995
Korean 12.180
Vietnamese 12.130
dtype: float64

Find lowest priced order and identify cuisine type

df.groupby('cuisine_type')['cost_of_the_order'].min().sort_values(ascending=True).head(1)
cost_of_the_order
cuisine_type

Japanese 4.47
dtype: float64

Find the lowest priced order

lowest_price_order = df[df['cost_of_the_order'] == df['cost_of_the_order'].min()]

Get the cuisine type of the lowest priced order

lowest_price_cuisine = lowest_price_order['cuisine_type'].values[0]

print("Lowest priced order details:")
print(lowest_price_order[['order_id', 'customer_id', 'restaurant_name', 'cuisine_type', 'cost_of_the_order']]) # Select relevant columns for display
print("\nCuisine type of the lowest priced order:", lowest_price_cuisine)
Lowest priced order details:
order_id customer_id restaurant_name cuisine_type cost_of_the_order
624 1477349 52327 Nobu Next Door Japanese 4.47

Cuisine type of the lowest priced order: Japanese
Question 9: What percentage of the orders cost more than 20 dollars?

Calculate the number of total orders where the cost is above 20 dollars

df_greater_than_20 = df[df['cost_of_the_order'] > 20]
num_df_greater_than_20 = df_greater_than_20.shape[0]

Print the DataFrame in a readable format

print(df_greater_than_20.to order_id customer_id 0 1477147 337525 3 1477334 106968 5 1477224 147468 12 1476966 129969 17 1477373 139885 20 1478296 250494 22 1478287 150599 25 1476693 41877 26 1476995 371590 30 1478289 318874 31 1477699 302790 34 1478244 181782 37 1476871 118709 38 1477999 208020 41 1478143 337525 42 1477267 133637 44 1476908 365237 46 1476930 198802 50 1477656 300003 51 1477883 91817 52 1477205 112036 53 1478107 73895 54 1477760 130507 60 1476896 388918 61 1478327 68551 62 1476820 152786 64 1478077 82041 65 1478359 361616 69 1477475 65009 73 1477518 133202 76 1477921 97079 78 1476943 87472 84 1478423 129969 88 1477740 129798 92 1478211 154030 97 1476689 305522 100 1478169 80460 101 1477702 128600 103 1476564 83287 104 1478408 134152 105 1477824 345899 108 1477281 309513 110 1477364 125123 113 1477296 222879 115 1477942 49793 116 1477552 137565 128 1476826 53543 131 1476978 177250 133 1477110 144997 135 1476862 58533 137 1478402 232582 142 1477177 42385 143 1478174 104555 145 1477369 139085 146 1476969 198309 150 1478219 41692 152 1477337 123780 155 1476817 97079 156 1476659 52463 160 1477185 362068 162 1477239 94524 174 1476805 93133 175 1478093 268958 182 1476875 373353 185 1477774 354719 187 1478334 190 1477401 326426 194 1477519 140477 195 1477004 250494 200 1476657 91192 211 1476860 336162 218 1478173 91005 221 1476696 231061 226 1478290 263199 238 1477333 111356 243 1478246 260680 244 1476931 124838 249 1477810 94483 256 1478210 320493 260 1476819 104548 265 1476579 85633 268 1476685 96877 270 1476892 177078 274 1477057 390490 282 1476846 82041 289 1477525 52360 290 1477920 83504 292 1477640 105992 295 1478324 250494 297 1477573 58898 298 1476728 250494 299 1477815 144997 308 1477032 60052 314 1478229 64153 322 1477834 22405 328 1478075 232359 335 1476702 152786 337 1477598 81110 341 1477326 52031 343 1478396 74925 349 1477375 235620 353 1476999 63417 355 1477040 301618 356 1476599 47386 358 1477099 391746 369 1476732 212469 370 1477987 104563 376 1477026 355592 377 1476897 197265 378 1476560 149508 380 1477958 309981 383 1478252 94167 386 1476623 277898 388 1478104 75722 389 1478065 212326 397 1477919 75484 405 1477690 370096 408 1478108 87188 411 1477517 77793 415 1476664 164016 417 1477482 114919 419 1477094 384571 430 1477663 93133 431 1477195 64830 433 1477058 65779 439 1478355 68103 442 1476806 443 1478000 167136 447 1477558 268365 448 1477458 114247 451 1477319 145389 452 1477731 122609 456 1478046 53289 457 1478398 137617 461 1478247 104555 469 1477372 55227 474 1477472 67845 475 1476738 233614 484 1478209 63141 486 1477809 148499 488 1477516 52256 497 1477220 41249 502 1476740 60873 504 1478052 77544 506 1477856 143984 507 1477592 87357 509 1477661 122852 515 1477109 208020 516 1476902 165110 518 1476703 405334 519 1477785 77306 521 1476643 88185 522 1477371 369452 524 1476934 126134 529 1478112 232542 531 1477400 68775 532 1478274 374826 535 1476904 60845 536 1477336 94480 537 1476929 61181 538 1477812 93505 546 1476572 356195 548 1478236 552 1478349 229946 553 1477850 216438 563 1476898 174437 565 1478335 84502 573 1477814 62359 579 1477178 47440 591 1476964 81405 593 1476717 151191 594 1477851 66373 599 1478073 121686 605 1478409 151333 606 1477471 103147 616 1477098 304509 617 1477399 378482 619 1478288 58231 622 1477777 73175 626 1476774 114400 628 1476775 66636 641 1477309 64151 649 1477211 304365 652 1476678 91817 653 1478363 138024 657 1476977 41246 666 1476613 667 1476739 290266 668 1476617 53513 676 1477852 74412 688 1477922 304587 690 1477219 389155 692 1477998 90113 696 1477890 284835 698 1478430 133701 703 1478202 345429 705 1476933 375704 706 1476656 124596 712 1477597 49010 713 1478001 64153 715 1477846 52574 716 1477297 164131 721 1477266 85422 724 1478400 725 1478403 294478 727 1476928 58205 731 1477701 328551 733 1477438 94132 736 1477754 133222 746 1477189 54630 755 1478441 228541 756 1478399 264399 757 1477635 124393 762 1478141 150865 764 1477959 175290 767 1477811 373152 769 1477002 133202 771 1478150 97806 774 1477073 87752 777 1477892 196320 778 1477481 47440 781 1477557 140501 784 1477612 56722 795 1478214 103049 798 1477841 18902 801 1477173 68300 803 1477496 132137 808 1478318 263426 809 1476734 148327 811 1478180 270525 820 1478208 371590 821 1477215 27609 825 1478275 334758 827 1477039 338923 833 1478042 201542 838 1478435 350180 843 1477706 68258 844 1476971 250494 845 1477470 350659 847 1478084 110461 848 1478163 372722 850 1477559 127149 854 1476863 40010 855 1477041 52832 857 1476616 348096 858 1476968 99621 859 1478045 97420 860 1477660 304365 862 1477619 75251 863 1477033 59105 874 1477357 5444 875 1476576 297130 881 1477116 126730 885 1478286 102580 887 1478443 96943 888 1478195 115519 891 1477140 366954 897 1477374 177078 898 1477882 80737 900 1477115 87752 901 1476658 127149 905 1478401 112272 909 1477183 127934 912 1477190 397588 914 1477136 120490 915 1476972 111356 919 1476622 399332 922 1477696 54809 925 1477960 375098 933 1476967 129798 936 1477416 123977 940 1477668 160957 943 1477662 6987 947 1477847 106478 949 1478429 355090 950 1476885 133349 962 1478371 258275 963 1476730 966 1478242 372276 970 1476864 381020 989 1477545 144352 990 1478215 92726 991 1478301 184325 995 1477005 87006 996 1476600 141912 998 1477299 60039 1000 1477221 125510 1001 1476691 68775 1003 1477913 92096 1007 1478404 97991 1009 1476903 53874 1017 1477332 43018 1019 1478410 84087 1022 1476761 64153 1026 1477072 113507 1029 1477142 87151 1035 1477502 367076 1036 1477734 81658 1037 1477151 97991 1038 1478333 93437 1041 1477779 39275 1048 1477368 132137 1052 1477579 349854 1056 1477108 47280 1060 1478392 77511 1065 1477035 141496 1066 1477923 75115 1068 1477590 145952 1073 1477191 93797 1078 1478078 54608 1079 1477443 105903 1090 1477727 333311 1092 1477948 141550 1096 1478140 62730 1105 1478245 52832 1107 1477591 97806 1113 1477928 70709 1117 1477698 60835 1118 1478080 312853 1120 1476729 396481 1132 1477961 167738 1135 1478041 96456 1148 1478044 354719 1149 1477789 185355 1150 1477736 361701 1165 1477634 387227 1171 1476861 94700 1172 1476899 335155 1173 1478248 113926 1177 1478181 97079 1186 1477474 357761 1195 1476733 264707 1197 1476825 143866 1203 1477618 184325 1213 1476695 68300 1214 1476573 370405 1215 1478323 354948 1216 1477735 302769 1218 1478006 42781 1220 1478186 302923 1221 1477776 115213 1227 1477896 42052 1230 1477853 115213 1231 1477001 318451 1236 1477393 79849 1239 1477261 76938 1240 1478127 78879 1244 1476784 105385 1248 1478240 44367 1258 1476900 240014 1260 1477697 362685 1263 1476655 91817 1265 1478175 349676 1267 1476697 46219 1269 1477152 92364 1271 1478028 44594 1276 1478362 134152 1278 1477503 99621 1280 1478076 96800 1285 1477469 96943 1290 1477884 348096 1296 1477924 288808 1300 1477153 120353 1304 1477669 260844 1308 1477143 165695 1311 1478149 303548 1312 1477761 123977 1313 1478311 326505 1320 1478100 62667 1323 1477553 52037 1325 1476692 175327 1332 1476785 78939 1333 1477182 383826 1335 1477304 88406 1342 1477166 402215 1344 1477003 120833 1347 1478328 58898 1355 1477370 89889 1361 1476731 94480 1363 1476777 69749 1364 1478027 41907 1366 1477184 108659 1368 1478021 97991 1370 1478329 116992 1371 1478385 250494 1372 1477300 232359 1383 1477347 66636 1387 1477180 1396 1478370 57638 1398 1477444 88075 1400 1477539 152675 1403 1476937 142045 1408 1476778 276192 1410 1478139 107909 1412 1476694 91105 1416 1477813 260844 1419 1477805 290313 1420 1477305 45846 1421 1477223 303548 1424 1477861 125320 1426 1478253 67345 1429 1478117 56642 1432 1477218 333216 1434 1476776 125123 1436 1477878 62730 1439 1478206 108659 1440 1478326 82901 1442 1477845 127056 1454 1477225 140530 1455 1477146 49793 1457 1477848 276192 1458 1478138 50323 1461 1477549 297130 1462 1476932 301032 1465 1477258 83287 1466 1477849 117033 1472 1477331 384390 1474 1477245 375585 1475 1477006 119465 1477 1477341 127122 1478 1477967 101241 1483 1477179 52832 1485 1477074 317904 1486 1478051 139639 1487 1477071 78939 1492 1476707 144133 1497 1477589 214028 1499 1478043 264707 1500 1476665 41246 1501 1477298 361728 1506 1477733 376499 1512 1478176 240982 1513 1476818 195927 1514 1477435 154030 1516 1477465 361220 1522 1477683 279715 1526 1478325 399373 1541 1478322 57943 1546 1477632 76942 1552 1476865 297232 1556 1477775 142461 1557 1478007 113238 1564 1478079 54608 1565 1478144 155124 1566 1476615 338599 1570 1476924 153048 1574 1477436 88853 1579 1477695 302131 1581 1478375 214028 1582 1476965 276192 1586 1477949 385023 1596 1477111 127149 1597 1477818 144352 1598 1477249 351561 1600 1477379 145962 1603 1477707 133330 1605 1476614 1610 1476754 229946 1611 1477260 1612 1478297 180618 1615 1477820 41746 1616 1478291 385134 1625 1477918 62912 1627 1476847 114085 1628 1477081 222734 1635 1478142 114085 1638 1476960 45577 1639 1478134 82041 1640 1477588 286386 1642 1477217 139639 1646 1477665 231061 1651 1477886 241310 1653 1477434 77857 1657 1476571 334758 1658 1477034 259341 1659 1477673 132906 1662 1478113 74048 1663 1477986 115213 1668 1478442 378035 1670 1477476 276192 1671 1477520 1679 1476938 164776 1682 1478361 371117 1688 1478074 41638 1690 1477181 302790 1692 1476870 94524 1696 1476574 376993 1708 1477402 377481 1709 1476760 180776 1710 1477030 240982 1714 1477448 142574 1715 1477784 146555 1717 1477031 201471 1727 1477551 277898 1731 1477891 142461 1734 1477082 107909 1738 1478397 121706 1740 1478241 60052 1741 1477216 259341 1744 1477732 399729 1748 1476989 64401 1752 1478360 375043 1759 1478243 159626 1761 1476953 252342 1762 1477700 60039 1763 1478106 129206 1765 1478213 122092 1766 1476779 378820 1767 1478207 62161 1768 1477075 52832 1769 1477145 207281 1776 1477694 192317 1780 1477144 62667 1782 1478182 135970 1785 1477857 363202 1786 1476840 143866 1787 1477641 128224 1788 1476690 124875 1789 1476593 175703 1794 1476976 300552 1795 1476575 141912 1796 1478105 143721 1799 1477259 145952 1800 1477141 66331 1802 1478254 239985 1803 1478365 60039 1804 1477380 108659 1806 1478444 355090 1810 1477526 344079 1811 1477664 47440 1812 1477917 71249 1813 1477381 316223 1815 1477885 1816 1476724 130956 1821 1478364 273690 1822 1477480 75722 1825 1477633 378753 1830 1478369 243341 1831 1476970 275689 1845 1477966 318451 1847 1477855 5139 1851 1477871 124875 1858 1477335 84457 1860 1477330 198802 1863 1477076 68834 1864 1476939 111894 1866 1478268 306119 1868 1477778 100889 1872 1477000 328731 1873 1478148 261371 1884 1477437 304993 1885 1477550 97324 1892 1477473 97838 1893 1476701 1895 1477819 35309 _string())
restaurant_name cuisine_type cost_of_the_order day_of_the_week rating food_preparation_time delivery_time cuisine_popular Revenue total_time
Hangawi Korean 30.75 Weekend Not given 25 20 Other 7.6875 45
Blue Ribbon Fried Chicken American 29.20 Weekend 3 25 15 American 7.3000 40
Tamarind TriBeCa Indian 25.22 Weekday 3 20 24 Other 6.3050 44
Blue Ribbon Fried Chicken American 24.30 Weekend 5 23 17 American 6.0750 40
Blue Ribbon Sushi Izakaya Japanese 33.03 Weekend Not given 21 22 Japanese 8.2575 43
Five Guys Burgers and Fries American 24.20 Weekend 4 21 24 American 6.0500 45
Shake Shack American 29.10 Weekday 5 21 30 American 7.2750 51
Cafe Mogador Middle Eastern 29.10 Weekday 5 20 29 Other 7.2750 49
The Meatball Shop Italian 21.88 Weekday 5 24 27 Italian 5.4700 51
The Kati Roll Company Indian 31.33 Weekend 5 28 25 Other 7.8325 53
Klong Thai 29.05 Weekend 5 35 24 Other 7.2625 59
Parm Italian 29.10 Weekend 5 30 26 Italian 7.2750 56
The Meatball Shop Italian 24.30 Weekday 4 31 29 Italian 6.0750 60
Haru Gramercy Park Japanese 29.83 Weekday 5 20 32 Japanese 7.4575 52
TAO Japanese 33.22 Weekend Not given 23 19 Japanese 8.3050 42
RedFarm Broadway Chinese 24.25 Weekend 5 22 15 Chinese 6.0625 37
Five Guys Burgers and Fries American 22.75 Weekend 5 30 20 American 5.6875 50
DuMont Burger American 29.05 Weekend 5 22 30 American 7.2625 52
Bistango Italian 21.88 Weekend Not given 21 19 Italian 5.4700 40
Blue Ribbon Fried Chicken American 29.39 Weekend Not given 27 28 American 7.3475 55
TAO Japanese 20.23 Weekend 5 22 20 Japanese 5.0575 42
RedFarm Broadway Chinese 29.10 Weekend Not given 22 23 Chinese 7.2750 45
Jack's Wife Freda Mediterranean 22.75 Weekend 3 35 29 Other 5.6875 64
Blue Ribbon Fried Chicken American 24.25 Weekend Not given 28 21 American 6.0625 49
Blue Ribbon Sushi Bar & Grill Japanese 33.03 Weekend Not given 33 20 Japanese 8.2575 53
Cafe Habana Mexican 31.38 Weekend 4 23 27 Other 7.8450 50
Shake Shack American 33.03 Weekday Not given 22 32 American 8.2575 54
Blue Ribbon Brooklyn American 24.20 Weekday 3 22 32 American 6.0500 54
Blue Ribbon Fried Chicken American 32.93 Weekend 5 24 23 American 8.2325 47
Sushi Samba Japanese 29.05 Weekend 5 31 16 Japanese 7.2625 47
Benihana Japanese 29.29 Weekend 5 20 18 Japanese 7.3225 38
RedFarm Hudson Chinese 22.75 Weekend Not given 31 30 Chinese 5.6875 61
The Meatball Shop Italian 21.20 Weekend Not given 34 16 Italian 5.3000 50
TAO Japanese 25.27 Weekday 4 24 24 Japanese 6.3175 48
RedFarm Hudson Chinese 33.03 Weekend 4 26 22 Chinese 8.2575 48
Shake Shack American 24.20 Weekday 4 29 29 American 6.0500 58
Rubirosa Italian 21.83 Weekend 3 30 27 Italian 5.4575 57
Blue Ribbon Fried Chicken American 31.29 Weekday Not given 22 33 American 7.8225 55
Five Guys Burgers and Fries American 24.30 Weekend 5 30 20 American 6.0750 50
The Meatball Shop Italian 22.26 Weekend Not given 28 25 Italian 5.5650 53
TAO Japanese 20.47 Weekend 4 26 30 Japanese 5.1175 56
Otto Enoteca Pizzeria Italian 22.36 Weekend 5 26 27 Italian 5.5900 53
Shake Shack American 21.83 Weekend Not given 23 22 American 5.4575 45
Parm Italian 29.15 Weekday 5 23 27 Italian 7.2875 50
Yama Japanese Restaurant Japanese 22.36 Weekend 5 30 16 Japanese 5.5900 46
Shake Shack American 31.29 Weekday Not given 26 32 American 7.8225 58
CafÌ© China Chinese 24.30 Weekend Not given 20 23 Chinese 6.0750 43
RedFarm Hudson Chinese 25.27 Weekend Not given 34 28 Chinese 6.3175 62
RedFarm Hudson Chinese 29.05 Weekend 4 22 28 Chinese 7.2625 50
The Meatball Shop Italian 29.20 Weekend Not given 31 24 Italian 7.3000 55
CafÌ© China Chinese 32.93 Weekday 3 26 28 Chinese 8.2325 54
Song Thai Restaurant & Bar Thai 24.20 Weekend 5 21 26 Other 6.0500 47
Sushi of Gari Japanese 29.34 Weekday Not given 32 25 Japanese 7.3350 57
Shake Shack American 24.25 Weekday Not given 32 24 American 6.0625 56
Blue Ribbon Sushi Japanese 29.20 Weekend Not given 22 25 Japanese 7.3000 47
Shake Shack American 22.80 Weekend Not given 30 19 American 5.7000 49
Five Guys Burgers and Fries American 33.03 Weekend Not given 29 25 American 8.2575 54
Benihana Japanese 29.05 Weekend Not given 25 26 Japanese 7.2625 51
RedFarm Broadway Chinese 31.33 Weekend 3 33 23 Chinese 7.8325 56
The Kati Roll Company Indian 33.03 Weekend Not given 35 21 Other 8.2575 56
The Meatball Shop Italian 22.36 Weekend 4 28 18 Italian 5.5900 46
Parm Italian 22.80 Weekday 5 33 33 Italian 5.7000 66
Barbounia Mediterranean 20.18 Weekend 5 29 19 Other 5.0450 48
indikitch Indian 22.26 Weekend Not given 35 16 Other 5.5650 51
The Meatball Shop Italian 29.15 Weekend 5 34 20 Italian 7.2875 54
352247 Chipotle Mexican Grill $1.99 Delivery Mexican 25.22 Weekend 4 27 22 Other 6.3050 49
Sushi of Gari Japanese 29.15 Weekend Not given 34 24 Japanese 7.2875 58
Momoya Japanese 31.43 Weekend Not given 24 17 Japanese 7.8575 41
Five Guys Burgers and Fries American 33.03 Weekend 5 29 15 American 8.2575 44
Hill Country Fried Chicken Southern 29.35 Weekend 4 26 24 Other 7.3375 50
Nobu Next Door Japanese 29.15 Weekday 5 28 25 Japanese 7.2875 53
Han Dynasty Chinese 24.25 Weekend 5 33 26 Chinese 6.0625 59
Han Dynasty Chinese 33.03 Weekday Not given 33 31 Chinese 8.2575 64
Sushi of Gari 46 Japanese 31.43 Weekend 4 35 23 Japanese 7.8575 58
Bhatti Indian Grill Indian 29.25 Weekend 5 21 15 Other 7.3125 36
Delicatessen American 32.98 Weekend 5 34 16 American 8.2450 50
Hill Country Fried Chicken Southern 29.15 Weekend 5 33 25 Other 7.2875 58
Bistango Italian 24.20 Weekend Not given 28 21 Italian 6.0500 49
Vanessa's Dumplings Chinese 29.20 Weekday Not given 27 30 Chinese 7.3000 57
Tarallucci e Vino Restaurant Italian 29.10 Weekday Not given 28 33 Italian 7.2750 61
The Meatball Shop Italian 24.20 Weekend 5 24 24 Italian 6.0500 48
The Meatball Shop Italian 22.26 Weekday 4 30 26 Italian 5.5650 56
Lantern Thai Kitchen Thai 21.93 Weekday Not given 22 25 Other 5.4825 47
Blue Ribbon Brooklyn American 22.85 Weekend 3 23 19 American 5.7125 42
Parm Italian 22.80 Weekend Not given 35 20 Italian 5.7000 55
TAO Japanese 24.30 Weekend Not given 24 22 Japanese 6.0750 46
Balthazar Boulangerie French 29.15 Weekday 4 31 28 Other 7.2875 59
RedFarm Broadway Chinese 24.30 Weekend 5 27 29 Chinese 6.0750 56
Blue Ribbon Fried Chicken American 29.10 Weekend 4 21 22 American 7.2750 43
RedFarm Broadway Chinese 21.39 Weekend 5 23 24 Chinese 5.3475 47
Bubby's American 24.25 Weekend Not given 34 28 American 6.0625 62
RedFarm Hudson Chinese 32.93 Weekend Not given 33 30 Chinese 8.2325 63
Rubirosa Italian 29.10 Weekend 5 30 28 Italian 7.2750 58
Blue Ribbon Fried Chicken American 21.34 Weekend Not given 35 23 American 5.3350 58
The Meatball Shop Italian 20.18 Weekend 4 32 26 Italian 5.0450 58
The Meatball Shop American 29.25 Weekend 3 21 19 American 7.3125 40
Cafe Habana Mexican 25.22 Weekday Not given 32 28 Other 6.3050 60
Hill Country Fried Chicken Southern 24.20 Weekend 4 25 15 Other 6.0500 40
Han Dynasty Chinese 21.93 Weekend Not given 24 20 Chinese 5.4825 44
Cafe Mogador Middle Eastern 24.20 Weekend 4 32 22 Other 6.0500 54
Shake Shack American 32.93 Weekend Not given 32 25 American 8.2325 57
Pepe Rosso To Go Italian 24.20 Weekend 3 27 19 Italian 6.0500 46
Vanessa's Dumplings Chinese 25.22 Weekday Not given 32 24 Chinese 6.3050 56
Bubby's American 22.75 Weekend 5 32 16 American 5.6875 48
Blue Ribbon Sushi Japanese 24.30 Weekend 4 35 18 Japanese 6.0750 53
Blue Ribbon Fried Chicken American 33.03 Weekend 5 34 24 American 8.2575 58
Blue Ribbon Sushi Japanese 24.25 Weekend 4 24 23 Japanese 6.0625 47
CafÌ© China Chinese 21.83 Weekend 4 32 15 Chinese 5.4575 47
Hill Country Fried Chicken Southern 29.15 Weekend Not given 20 17 Other 7.2875 37
Shake Shack American 22.26 Weekend 4 32 17 American 5.5650 49
Blue Ribbon Fried Chicken American 29.10 Weekend Not given 31 27 American 7.2750 58
Dos Caminos Mexican 22.36 Weekend Not given 30 26 Other 5.5900 56
Blue Ribbon Fried Chicken American 24.30 Weekend 5 22 26 American 6.0750 48
Osteria Morini Italian 24.25 Weekday Not given 22 24 Italian 6.0625 46
RedFarm Broadway Chinese 20.18 Weekday Not given 30 26 Chinese 5.0450 56
Five Guys Burgers and Fries American 29.15 Weekend Not given 20 16 American 7.2875 36
Melt Shop American 21.93 Weekend 5 30 18 American 5.4825 48
Momoya Japanese 32.93 Weekday 3 28 27 Japanese 8.2325 55
The Meatball Shop Italian 29.15 Weekend 5 23 30 Italian 7.2875 53
Blue Ribbon Fried Chicken American 24.30 Weekday 5 20 27 American 6.0750 47
Yama Japanese Restaurant Japanese 25.27 Weekend Not given 30 16 Japanese 6.3175 46
Blue Ribbon Fried Chicken American 22.31 Weekday 5 28 27 American 5.5775 55
Nobu Next Door Japanese 29.29 Weekday 4 30 24 Japanese 7.3225 54
Cafeteria American 20.47 Weekday 3 32 24 American 5.1175 56
The Kati Roll Company Indian 24.35 Weekday Not given 33 29 Other 6.0875 62
The Meatball Shop Italian 21.83 Weekend 4 30 20 Italian 5.4575 50
203370 Chipotle Mexican Grill $1.99 Delivery Mexican 24.30 Weekday Not given 21 33 Other 6.0750 54
ilili Restaurant Middle Eastern 29.05 Weekend Not given 24 29 Other 7.2625 53
Cafe Mogador Middle Eastern 25.17 Weekend 4 23 30 Other 6.2925 53
Shake Shack American 20.23 Weekend Not given 32 18 American 5.0575 50
Westville Hudson American 20.23 Weekday Not given 31 29 American 5.0575 60
Blue Ribbon Sushi Izakaya Japanese 24.25 Weekend 5 33 21 Japanese 6.0625 54
The Loop Japanese 31.38 Weekend 4 26 24 Japanese 7.8450 50
Blue Ribbon Fried Chicken American 24.30 Weekend 5 34 21 American 6.0750 55
Sushi of Gari Japanese 32.98 Weekend 4 34 21 Japanese 8.2450 55
Sushi of Gari Tribeca Japanese 29.20 Weekend 5 32 22 Japanese 7.3000 54
RedFarm Hudson Chinese 29.34 Weekend 5 27 29 Chinese 7.3350 56
Yama 49 Japanese 22.36 Weekend Not given 33 25 Japanese 5.5900 58
Shake Shack American 29.10 Weekend Not given 23 28 American 7.2750 51
Posto Italian 24.25 Weekend Not given 23 25 Italian 6.0625 48
Han Dynasty Chinese 29.15 Weekend Not given 31 19 Chinese 7.2875 50
Kambi Ramen House Japanese 32.93 Weekend 5 35 19 Japanese 8.2325 54
Jack's Wife Freda Mediterranean 25.17 Weekday 4 31 25 Other 6.2925 56
Shake Shack American 24.30 Weekend 4 35 16 American 6.0750 51
The Smile American 25.17 Weekend 3 31 15 American 6.2925 46
Shake Shack American 31.43 Weekend 5 30 19 American 7.8575 49
Blue Ribbon Fried Chicken American 24.20 Weekend Not given 28 29 American 6.0500 57
Sushi of Gari 46 Japanese 29.05 Weekend Not given 21 23 Japanese 7.2625 44
Room Service Thai 22.31 Weekend Not given 22 15 Other 5.5775 37
TAO Japanese 25.22 Weekend Not given 26 16 Japanese 6.3050 42
Xi'an Famous Foods Chinese 24.30 Weekend 4 32 29 Chinese 6.0750 61
Momoya Japanese 22.85 Weekend 4 32 17 Japanese 5.7125 49
UVA Wine Bar & Restaurant Italian 29.15 Weekend Not given 29 30 Italian 7.2875 59
Jack's Wife Freda Mediterranean 32.93 Weekend 3 21 16 Other 8.2325 37
The Meatball Shop Italian 22.36 Weekend 4 35 21 Italian 5.5900 56
Sushi of Gari Tribeca Japanese 29.15 Weekend 4 22 17 Japanese 7.2875 39
Waverly Diner American 22.80 Weekend 4 25 21 American 5.7000 46
Blue Ribbon Sushi Japanese 25.27 Weekend 3 20 20 Japanese 6.3175 40
The Meatball Shop Italian 32.93 Weekend 5 34 29 Italian 8.2325 63
S'MAC American 24.20 Weekend Not given 28 17 American 6.0500 45
Rubirosa Italian 24.20 Weekend 5 31 15 Italian 6.0500 46
Blue Ribbon Fried Chicken American 29.15 Weekday 5 20 29 American 7.2875 49
121905 Chipotle Mexican Grill $1.99 Delivery Mexican 22.26 Weekend 4 21 21 Other 5.5650 42
Balthazar Boulangerie French 21.39 Weekend Not given 35 29 Other 5.3475 64
Blue Ribbon Sushi Japanese 29.25 Weekend Not given 33 17 Japanese 7.3125 50
Han Dynasty Chinese 29.20 Weekend Not given 26 30 Chinese 7.3000 56
Nobu Next Door Japanese 25.17 Weekend 4 31 29 Japanese 6.2925 60
Pylos Mediterranean 35.41 Weekday 4 21 29 Other 8.8525 50
Bareburger American 24.20 Weekend 4 23 23 American 6.0500 46
RedFarm Broadway Chinese 24.25 Weekend 5 29 22 Chinese 6.0625 51
Pongsri Thai Thai 20.18 Weekday Not given 23 25 Other 5.0450 48
Blue Ribbon Sushi Izakaya Japanese 32.93 Weekend 4 21 19 Japanese 8.2325 40
TAO Japanese 24.25 Weekend 4 22 21 Japanese 6.0625 43
Melt Shop American 25.27 Weekend 5 27 30 American 6.3175 57
Hot Kitchen Chinese 29.54 Weekend 5 34 28 Chinese 7.3850 62
RedFarm Broadway Chinese 22.80 Weekday Not given 33 33 Chinese 5.7000 66
Shake Shack American 29.49 Weekend Not given 34 24 American 7.3725 58
Rohm Thai Thai 29.10 Weekend Not given 31 23 Other 7.2750 54
Hill Country Fried Chicken Southern 31.43 Weekday 5 25 28 Other 7.8575 53
Shake Shack American 29.39 Weekday Not given 23 33 American 7.3475 56
Shake Shack American 29.10 Weekday 5 29 31 American 7.2750 60
Rubirosa Italian 22.80 Weekday 5 32 27 Italian 5.7000 59
Sarabeth's Restaurant American 21.93 Weekend Not given 25 27 American 5.4825 52
Shake Shack American 21.39 Weekend 4 28 18 American 5.3475 46
Cho Dang Gol Korean 29.25 Weekend 4 23 21 Other 7.3125 44
Shake Shack American 25.17 Weekend 5 27 26 American 6.2925 53
65561 Chipotle Mexican Grill $1.99 Delivery Mexican 29.10 Weekend 5 24 28 Other 7.2750 52
Socarrat Paella Bar Spanish 25.22 Weekend 5 23 20 Other 6.3050 43
The Meatball Shop Italian 31.33 Weekend 3 30 15 Italian 7.8325 45
da Umberto Italian 32.98 Weekend Not given 30 28 Italian 8.2450 58
Westville Hudson American 33.03 Weekend 5 25 18 American 8.2575 43
The Meatball Shop Italian 32.98 Weekday 3 23 25 Italian 8.2450 48
Blue Ribbon Sushi Japanese 29.10 Weekend 5 20 16 Japanese 7.2750 36
Shake Shack American 22.36 Weekday 3 20 32 American 5.5900 52
The Loop Japanese 24.30 Weekend 5 21 29 Japanese 6.0750 50
Burger Joint American 21.88 Weekend 5 26 24 American 5.4700 50
Shake Shack American 32.98 Weekend 3 22 21 American 8.2450 43
RedFarm Hudson Chinese 29.10 Weekday Not given 27 26 Chinese 7.2750 53
Shake Shack American 24.25 Weekend 5 34 24 American 6.0625 58
Blue Ribbon Fried Chicken American 31.38 Weekend 3 35 20 American 7.8450 55
Blue Ribbon Sushi Japanese 24.25 Weekday 5 30 26 Japanese 6.0625 56
Tony's Di Napoli Italian 29.10 Weekend Not given 26 19 Italian 7.2750 45
Saravanaa Bhavan Indian 24.25 Weekday 5 29 26 Other 6.0625 55
353331 Chipotle Mexican Grill $1.99 Delivery Mexican 29.05 Weekend Not given 28 27 Other 7.2625 55
RedFarm Broadway Chinese 32.98 Weekend 4 25 15 Chinese 8.2450 40
The Meatball Shop Italian 24.30 Weekend Not given 33 21 Italian 6.0750 54
Kanoyama Japanese 32.98 Weekend 5 35 16 Japanese 8.2450 51
Parm Italian 31.33 Weekend Not given 24 16 Italian 7.8325 40
P.J. Clarke's American 21.34 Weekday 5 25 28 American 5.3350 53
V-Nam Cafe Vietnamese 22.26 Weekend 5 33 26 Other 5.5650 59
RedFarm Hudson Chinese 29.10 Weekend Not given 27 28 Chinese 7.2750 55
Han Dynasty Chinese 29.15 Weekday Not given 20 24 Chinese 7.2875 44
Amma Indian 31.43 Weekday 5 20 27 Other 7.8575 47
Vanessa's Dumpling House Chinese 29.10 Weekday Not given 22 25 Chinese 7.2750 47
Otto Enoteca Pizzeria Italian 29.05 Weekend 5 24 18 Italian 7.2625 42
Blue Ribbon Sushi Japanese 24.30 Weekend 5 32 28 Japanese 6.0750 60
ilili Restaurant Middle Eastern 29.25 Weekend 3 27 21 Other 7.3125 48
Shake Shack American 25.22 Weekend 4 35 27 American 6.3050 62
Shake Shack American 29.05 Weekend 5 21 22 American 7.2625 43
Parm Italian 25.17 Weekday 4 33 32 Italian 6.2925 65
Bareburger American 25.22 Weekday 3 31 27 American 6.3050 58
The Meatball Shop Italian 22.36 Weekend 5 32 21 Italian 5.5900 53
Shake Shack American 22.31 Weekday Not given 33 32 American 5.5775 65
TAO Japanese 25.22 Weekend 5 28 17 Japanese 6.3050 45
Rubirosa Italian 21.88 Weekend 4 28 27 Italian 5.4700 55
Blue Ribbon Fried Chicken American 21.83 Weekend Not given 20 27 American 5.4575 47
Parm Italian 22.36 Weekend Not given 27 27 Italian 5.5900 54
TAO Japanese 22.12 Weekday 5 32 30 Japanese 5.5300 62
Shake Shack American 33.03 Weekday Not given 31 28 American 8.2575 59
The Smile American 22.26 Weekend 5 25 26 American 5.5650 51
The Meatball Shop Italian 29.15 Weekend 5 29 27 Italian 7.2875 56
Blue Ribbon Sushi Bar & Grill Japanese 24.25 Weekend 5 25 26 Japanese 6.0625 51
Five Guys Burgers and Fries American 24.30 Weekend 5 28 18 American 6.0750 46
Hill Country Fried Chicken Southern 22.31 Weekday 3 21 25 Other 5.5775 46
TAO Japanese 29.05 Weekend 4 35 24 Japanese 7.2625 59
RedFarm Hudson Chinese 20.32 Weekday 5 30 29 Chinese 5.0800 59
Jack's Wife Freda Mediterranean 22.31 Weekend Not given 23 28 Other 5.5775 51
Parm Italian 28.57 Weekday 5 22 32 Italian 7.1425 54
Shake Shack American 24.25 Weekend 4 27 30 American 6.0625 57
Blue Ribbon Sushi Japanese 22.07 Weekend 4 33 15 Japanese 5.5175 48
Shake Shack American 21.29 Weekend Not given 35 21 American 5.3225 56
P.J. Clarke's American 25.27 Weekday 5 34 29 American 6.3175 63
The Meatball Shop American 31.43 Weekend Not given 31 22 American 7.8575 53
Junoon Indian 25.17 Weekday Not given 23 24 Other 6.2925 47
Parm Italian 31.43 Weekday 5 28 25 Italian 7.8575 53
Jack's Wife Freda Mediterranean 29.05 Weekend 5 35 28 Other 7.2625 63
Yama 49 Japanese 31.33 Weekend 5 21 27 Japanese 7.8325 48
Sarabeth's Restaurant American 24.25 Weekend 5 23 24 American 6.0625 47
Blue Ribbon Fried Chicken American 24.25 Weekend Not given 27 21 American 6.0625 48
Sushi of Gari 46 Japanese 29.05 Weekend 3 35 26 Japanese 7.2625 61
Go! Go! Curry! Japanese 20.18 Weekend 3 22 29 Japanese 5.0450 51
Blue Ribbon Sushi Japanese 31.43 Weekend 5 24 17 Japanese 7.8575 41
The Kati Roll Company Indian 24.20 Weekend 5 26 16 Other 6.0500 42
Blue Ribbon Sushi Izakaya Japanese 29.05 Weekday 4 28 25 Japanese 7.2625 53
Cafe Mogador Middle Eastern 29.88 Weekend 4 20 17 Other 7.4700 37
Blue Ribbon Fried Chicken American 20.23 Weekday 4 20 26 American 5.0575 46
Blue Ribbon Sushi Japanese 24.25 Weekend 5 35 16 Japanese 6.0625 51
Lantern Thai Kitchen Thai 32.93 Weekend Not given 34 27 Other 8.2325 61
RedFarm Broadway Chinese 24.20 Weekday Not given 22 33 Chinese 6.0500 55
Shake Shack American 24.25 Weekday 5 20 27 American 6.0625 47
P.J. Clarke's American 31.43 Weekend 4 31 23 American 7.8575 54
The Kati Roll Company Indian 29.25 Weekend Not given 30 21 Other 7.3125 51
Blue Ribbon Fried Chicken American 33.03 Weekend 5 35 25 American 8.2575 60
Kanoyama Japanese 25.27 Weekday Not given 29 33 Japanese 6.3175 62
Parm Italian 21.88 Weekend 4 23 30 Italian 5.4700 53
Bhatti Indian Grill Indian 32.98 Weekday Not given 32 30 Other 8.2450 62
Philippe Chow Chinese 24.20 Weekday 5 28 30 Chinese 6.0500 58
RedFarm Broadway Chinese 24.30 Weekday 5 24 31 Chinese 6.0750 55
12 Chairs Middle Eastern 29.10 Weekend Not given 22 26 Other 7.2750 48
TAO Japanese 29.49 Weekday Not given 22 32 Japanese 7.3725 54
Vezzo Thin Crust Pizza Italian 22.26 Weekday Not given 22 32 Italian 5.5650 54
Han Dynasty Chinese 22.31 Weekend 5 29 15 Chinese 5.5775 44
Hill Country Fried Chicken Southern 29.78 Weekday Not given 35 33 Other 7.4450 68
Blue Ribbon Sushi Japanese 24.25 Weekday 4 22 29 Japanese 6.0625 51
Shake Shack American 22.85 Weekend 5 24 23 American 5.7125 47
Vanessa's Dumplings Chinese 20.23 Weekend 5 33 17 Chinese 5.0575 50
Cafe Habana Mexican 25.17 Weekend Not given 27 26 Other 6.2925 53
344577 Chipotle Mexican Grill $1.99 Delivery Mexican 29.05 Weekend 4 22 21 Other 7.2625 43
Pinto Nouveau Thai Bistro Thai 24.20 Weekend 5 29 15 Other 6.0500 44
RedFarm Broadway Chinese 31.86 Weekend 4 25 19 Chinese 7.9650 44
Shake Shack American 21.93 Weekday Not given 21 33 American 5.4825 54
Sarabeth's East American 25.27 Weekend 4 23 19 American 6.3175 42
Asuka Sushi Japanese 22.75 Weekday 5 25 32 Japanese 5.6875 57
Nobu Next Door Japanese 25.17 Weekend Not given 21 16 Japanese 6.2925 37
P.J. Clarke's American 24.25 Weekend Not given 24 15 American 6.0625 39
ilili Restaurant Middle Eastern 31.33 Weekday 4 29 27 Other 7.8325 56
Blue Ribbon Sushi Izakaya Japanese 33.03 Weekend 3 34 23 Japanese 8.2575 57
Sushi of Gari Tribeca Japanese 24.20 Weekend Not given 22 21 Japanese 6.0500 43
Aurora Italian 21.93 Weekend Not given 31 18 Italian 5.4825 49
Blue Ribbon Sushi Bar & Grill Japanese 33.03 Weekend 5 28 15 Japanese 8.2575 43
Sarabeth's American 25.17 Weekend 3 28 23 American 6.2925 51
RedFarm Hudson Chinese 24.20 Weekend 5 27 19 Chinese 6.0500 46
The Meatball Shop Italian 25.22 Weekend 5 32 25 Italian 6.3050 57
Blue Ribbon Fried Chicken American 24.25 Weekday 5 33 30 American 6.0625 63
Parm Italian 29.39 Weekday Not given 26 30 Italian 7.3475 56
Rubirosa Italian 24.20 Weekend 4 23 26 Italian 6.0500 49
Sushi of Gari 46 Japanese 22.80 Weekend Not given 30 25 Japanese 5.7000 55
The Meatball Shop Italian 29.25 Weekday 5 35 29 Italian 7.3125 64
Blue Ribbon Sushi Bar & Grill Japanese 22.36 Weekend 4 25 18 Japanese 5.5900 43
Sushi of Gari 46 Japanese 22.26 Weekend 5 27 26 Japanese 5.5650 53
TAO Japanese 31.43 Weekend Not given 34 20 Japanese 7.8575 54
Parm Italian 24.25 Weekend 4 34 21 Italian 6.0625 55
Shake Shack American 24.35 Weekday 5 34 26 American 6.0875 60
Blue Ribbon Sushi Japanese 29.68 Weekend 3 26 30 Japanese 7.4200 56
Parm Italian 21.93 Weekend 4 22 25 Italian 5.4825 47
Dickson's Farmstand Meats American 33.03 Weekend 5 35 20 American 8.2575 55
RedFarm Hudson Chinese 33.03 Weekend 3 32 19 Chinese 8.2575 51
Blue Ribbon Sushi Japanese 29.15 Weekday Not given 26 24 Japanese 7.2875 50
ilili Restaurant Middle Eastern 25.22 Weekday 5 25 33 Other 6.3050 58
Tamarind TriBeCa Indian 22.26 Weekend 5 34 15 Other 5.5650 49
Shake Shack American 24.30 Weekday 4 35 29 American 6.0750 64
Blue Ribbon Sushi Izakaya Japanese 21.93 Weekend Not given 35 26 Japanese 5.4825 61
Serafina Fabulous Pizza Italian 22.80 Weekday 4 28 31 Italian 5.7000 59
Westville Hudson American 29.10 Weekend Not given 20 24 American 7.2750 44
Junoon Indian 29.49 Weekend 5 23 17 Other 7.3725 40
Shake Shack American 31.43 Weekend Not given 30 19 American 7.8575 49
RedFarm Hudson Chinese 25.17 Weekend Not given 21 17 Chinese 6.2925 38
Parm Italian 29.35 Weekend 5 29 17 Italian 7.3375 46
Five Guys Burgers and Fries American 25.27 Weekend 5 27 29 American 6.3175 56
Han Dynasty Chinese 29.15 Weekend Not given 27 15 Chinese 7.2875 42
Jack's Wife Freda Mediterranean 31.43 Weekend 5 34 20 Other 7.8575 54
67 Burger American 29.05 Weekday 5 20 28 American 7.2625 48
Jack's Wife Freda Mediterranean 31.38 Weekend Not given 28 18 Other 7.8450 46
Sushi Samba Japanese 22.85 Weekday 5 21 32 Japanese 5.7125 53
Westville Hudson American 32.93 Weekend 5 30 20 American 8.2325 50
Blue Ribbon Sushi Bar & Grill Japanese 29.25 Weekday Not given 25 29 Japanese 7.3125 54
Tamarind TriBeCa Indian 29.15 Weekend 3 35 22 Other 7.2875 57
Shake Shack American 29.05 Weekend 4 33 16 American 7.2625 49
Han Dynasty Chinese 33.03 Weekday 5 35 32 Chinese 8.2575 67
Benihana Japanese 25.22 Weekday 4 27 27 Japanese 6.3050 54
Go! Go! Curry! Japanese 33.03 Weekend Not given 22 15 Japanese 8.2575 37
TAO Japanese 32.93 Weekend Not given 34 16 Japanese 8.2325 50
Otto Enoteca Pizzeria Italian 24.20 Weekend Not given 34 15 Italian 6.0500 49
Asuka Sushi Japanese 22.80 Weekend Not given 30 28 Japanese 5.7000 58
Blue Ribbon Fried Chicken American 33.18 Weekend Not given 26 20 American 8.2950 46
Haveli Indian Restaurant Indian 29.10 Weekday 3 23 24 Other 7.2750 47
Westville Hudson American 24.25 Weekend Not given 25 21 American 6.0625 46
Blue Ribbon Fried Chicken American 29.05 Weekend 5 29 20 American 7.2625 49
Blue Ribbon Sushi Japanese 24.30 Weekday Not given 21 26 Japanese 6.0750 47
Shake Shack American 20.52 Weekend 3 31 15 American 5.1300 46
Tamarind TriBeCa Indian 29.39 Weekend 5 30 25 Other 7.3475 55
Pepe Rosso To Go Italian 22.80 Weekend 5 35 24 Italian 5.7000 59
Junoon Indian 28.62 Weekday 5 21 32 Other 7.1550 53
Blue Ribbon Sushi Japanese 29.25 Weekend Not given 22 27 Japanese 7.3125 49
Parm Italian 22.36 Weekend 4 21 30 Italian 5.5900 51
Sushi of Gari Japanese 31.43 Weekend Not given 21 27 Japanese 7.8575 48
Blue Ribbon Fried Chicken American 20.18 Weekend 3 22 20 American 5.0450 42
Han Dynasty Chinese 24.20 Weekend 5 23 15 Chinese 6.0500 38
Rubirosa Italian 24.20 Weekend 4 34 16 Italian 6.0500 50
Sarabeth's American 32.93 Weekday 4 33 33 American 8.2325 66
Momoya Japanese 29.10 Weekday 4 21 29 Japanese 7.2750 50
Blue Ribbon Fried Chicken American 29.15 Weekend 4 31 17 American 7.2875 48
Sarabeth's American 29.30 Weekend 5 23 17 American 7.3250 40
The Meatball Shop American 32.93 Weekend 3 35 16 American 8.2325 51
Sushi of Gari 46 Japanese 25.17 Weekend Not given 29 27 Japanese 6.2925 56
Shake Shack American 24.25 Weekend 5 31 28 American 6.0625 59
The Meatball Shop Italian 29.10 Weekday 5 22 33 Italian 7.2750 55
Westville Hudson American 24.30 Weekend Not given 32 15 American 6.0750 47
Sarabeth's East American 29.30 Weekend Not given 27 20 American 7.3250 47
Cafe Mogador Middle Eastern 24.25 Weekend Not given 26 22 Other 6.0625 48
Parm Italian 29.44 Weekend 4 33 24 Italian 7.3600 57
The Meatball Shop Italian 32.98 Weekday 5 26 27 Italian 8.2450 53
Parm Italian 25.27 Weekday Not given 27 27 Italian 6.3175 54
Byblos Restaurant Middle Eastern 25.27 Weekend 3 33 16 Other 6.3175 49
The Smile American 29.10 Weekend Not given 22 16 American 7.2750 38
Shake Shack American 25.22 Weekend 3 27 25 American 6.3050 52
Blue Ribbon Sushi Japanese 24.35 Weekend 5 33 22 Japanese 6.0875 55
Blue Ribbon Fried Chicken American 21.29 Weekend 5 28 29 American 5.3225 57
Balthazar Boulangerie French 21.93 Weekend Not given 31 20 Other 5.4825 51
L'Express French 22.02 Weekday Not given 21 27 Other 5.5050 48
Nobu Next Door Japanese 29.10 Weekday 3 35 27 Japanese 7.2750 62
Blue Ribbon Sushi Japanese 24.25 Weekday Not given 29 25 Japanese 6.0625 54
Song Thai Restaurant & Bar Thai 29.20 Weekend 5 21 28 Other 7.3000 49
The Meatball Shop Italian 24.20 Weekday 4 28 30 Italian 6.0500 58
RedFarm Hudson Chinese 21.29 Weekend 4 32 25 Chinese 5.3225 57
Bareburger American 32.93 Weekend Not given 29 24 American 8.2325 53
Rubirosa Italian 33.03 Weekend 5 22 28 Italian 8.2575 50
Sushi of Gari 46 Japanese 29.10 Weekday 5 32 30 Japanese 7.2750 62
TAO Japanese 29.10 Weekend Not given 28 23 Japanese 7.2750 51
Blue Ribbon Sushi Japanese 31.38 Weekend Not given 26 27 Japanese 7.8450 53
Parm Italian 22.75 Weekday Not given 30 27 Italian 5.6875 57
Rubirosa Italian 32.98 Weekend Not given 33 30 Italian 8.2450 63
Blue Ribbon Sushi Bar & Grill Japanese 22.26 Weekday Not given 20 26 Japanese 5.5650 46
Tres Carnes Mexican 33.32 Weekday 4 21 33 Other 8.3300 54
Blue Ribbon Fried Chicken American 21.34 Weekend 5 28 21 American 5.3350 49
The Meatball Shop American 31.43 Weekend 4 20 21 American 7.8575 41
Shake Shack American 20.52 Weekend Not given 31 26 American 5.1300 57
154339 Chipotle Mexican Grill $1.99 Delivery Mexican 29.15 Weekday Not given 21 24 Other 7.2875 45
Grand Sichuan International Chinese 25.27 Weekday 5 35 30 Chinese 6.3175 65
Shake Shack American 24.30 Weekday 4 33 30 American 6.0750 63
Tres Carnes Mexican 20.13 Weekend Not given 35 18 Other 5.0325 53
Shake Shack American 22.36 Weekday Not given 33 25 American 5.5900 58
Sushi of Gari Tribeca Japanese 31.33 Weekday Not given 34 26 Japanese 7.8325 60
Shake Shack American 24.20 Weekday Not given 22 31 American 6.0500 53
Cafe Mogador Middle Eastern 29.05 Weekend 4 27 30 Other 7.2625 57
Byblos Restaurant Middle Eastern 24.20 Weekend Not given 32 20 Other 6.0500 52
RedFarm Broadway Chinese 21.93 Weekend Not given 22 22 Chinese 5.4825 44
The Meatball Shop American 24.20 Weekend 5 21 30 American 6.0500 51
J. G. Melon American 22.26 Weekend 5 22 25 American 5.5650 47
Shake Shack American 20.57 Weekend 5 24 19 American 5.1425 43
Parm Italian 25.27 Weekend 5 28 16 Italian 6.3175 44
Bareburger American 22.85 Weekday Not given 21 30 American 5.7125 51
Shake Shack American 29.49 Weekend 5 20 24 American 7.3725 44
Le Grainne Cafe French 29.25 Weekend 5 27 24 Other 7.3125 51
Westville Hudson American 21.88 Weekend 5 20 15 American 5.4700 35
Osteria Morini Italian 24.20 Weekday 3 28 28 Italian 6.0500 56
Il Bambino Italian 29.25 Weekend 4 34 29 Italian 7.3125 63
Blue Ribbon Sushi Japanese 24.30 Weekday 5 21 33 Japanese 6.0750 54
Sushi of Gari 46 Japanese 25.22 Weekend Not given 32 28 Japanese 6.3050 60
Yama Japanese Restaurant Japanese 32.93 Weekend 5 29 17 Japanese 8.2325 46
12 Chairs Middle Eastern 29.05 Weekday 5 28 32 Other 7.2625 60
Rubirosa Italian 24.30 Weekend Not given 30 26 Italian 6.0750 56
L'Express French 29.20 Weekend 5 21 29 Other 7.3000 50
Mission Cantina Mexican 29.15 Weekend Not given 31 26 Other 7.2875 57
Blue Ribbon Sushi Izakaya Japanese 29.10 Weekend Not given 32 26 Japanese 7.2750 58
P.J. Clarke's American 29.20 Weekend Not given 30 22 American 7.3000 52
El Parador Cafe Mexican 24.20 Weekend Not given 28 18 Other 6.0500 46
TAO Japanese 24.25 Weekend Not given 31 27 Japanese 6.0625 58
Cafe Mogador Middle Eastern 25.27 Weekday 5 30 25 Other 6.3175 55
RedFarm Broadway Chinese 22.36 Weekend Not given 24 27 Chinese 5.5900 51
Empanada Mama (closed) Mexican 24.30 Weekday Not given 25 24 Other 6.0750 49
L'Express French 24.20 Weekday Not given 28 24 Other 6.0500 52
Rubirosa Italian 31.67 Weekend Not given 25 15 Italian 7.9175 40
Sushi of Gari 46 Japanese 24.30 Weekend 3 29 23 Japanese 6.0750 52
Nobu Next Door Japanese 29.05 Weekend 5 33 29 Japanese 7.2625 62
Shake Shack American 22.75 Weekday 4 21 27 American 5.6875 48
Boqueria Spanish 29.05 Weekend 4 24 24 Other 7.2625 48
The Meatball Shop Italian 29.20 Weekend 5 29 23 Italian 7.3000 52
Shake Shack American 24.20 Weekend Not given 25 24 American 6.0500 49
The Meatball Shop Italian 29.05 Weekend 4 32 19 Italian 7.2625 51
Lucky Strike American 29.25 Weekend 4 21 21 American 7.3125 42
Rubirosa Italian 29.10 Weekend 5 28 16 Italian 7.2750 44
Boqueria Spanish 29.10 Weekend Not given 28 19 Other 7.2750 47
Hatsuhana Japanese 29.49 Weekend 4 22 23 Japanese 7.3725 45
Carmine's Italian 21.93 Weekend 5 20 28 Italian 5.4825 48
RedFarm Hudson Chinese 21.34 Weekend Not given 23 17 Chinese 5.3350 40
Shake Shack American 29.10 Weekday 4 29 24 American 7.2750 53
RedFarm Broadway Chinese 24.25 Weekday 5 25 25 Chinese 6.0625 50
Sarabeth's Restaurant American 29.15 Weekday Not given 20 26 American 7.2875 46
Empanada Mama (closed) Mexican 31.38 Weekend 5 24 18 Other 7.8450 42
Shake Shack American 29.25 Weekend 4 25 19 American 7.3125 44
Boqueria Spanish 24.25 Weekend Not given 28 18 Other 6.0625 46
Tamarind TriBeCa Indian 25.22 Weekend 5 30 26 Other 6.3050 56
The Meatball Shop Italian 33.03 Weekend 5 30 16 Italian 8.2575 46
RedFarm Hudson Chinese 29.44 Weekday 4 29 32 Chinese 7.3600 61
Shake Shack American 21.83 Weekend Not given 28 23 American 5.4575 51
RedFarm Hudson Chinese 29.29 Weekend 4 27 21 Chinese 7.3225 48
Rubirosa Italian 24.20 Weekend Not given 33 28 Italian 6.0500 61
Boqueria Spanish 22.85 Weekend 5 28 30 Other 5.7125 58
Delicatessen American 24.20 Weekend 4 21 19 American 6.0500 40
Rubirosa Italian 24.35 Weekend 3 30 28 Italian 6.0875 58
P.J. Clarke's American 31.33 Weekend 4 26 19 American 7.8325 45
Shake Shack American 22.36 Weekend 4 27 27 American 5.5900 54
The Kati Roll Company Indian 20.13 Weekday Not given 24 31 Other 5.0325 55
Joe's Shanghai _Àü£¾÷´ Chinese 22.31 Weekday 3 27 31 Chinese 5.5775 58
Blue Ribbon Fried Chicken American 25.27 Weekend 5 29 28 American 6.3175 57
138462 Chipotle Mexican Grill $1.99 Delivery Mexican 29.10 Weekday 5 21 29 Other 7.2750 50
Balthazar Boulangerie French 21.25 Weekend Not given 25 20 Other 5.3125 45
229946 Chipotle Mexican Grill $1.99 Delivery Mexican 29.10 Weekend 4 27 25 Other 7.2750 52
Bareburger American 24.20 Weekend 5 27 24 American 6.0500 51
Schnipper's Quality Kitchen American 25.22 Weekend 4 33 18 American 6.3050 51
RedFarm Broadway Chinese 31.87 Weekend 5 27 18 Chinese 7.9675 45
Yama Japanese Restaurant Japanese 24.25 Weekday Not given 23 30 Japanese 6.0625 53
The Meatball Shop Italian 24.30 Weekend 4 29 15 Italian 6.0750 44
Shake Shack American 24.25 Weekday Not given 23 30 American 6.0625 53
Hatsuhana Japanese 29.15 Weekend 3 33 28 Japanese 7.2875 61
Parm Italian 21.83 Weekday Not given 23 32 Italian 5.4575 55
Sushi of Gari Tribeca Japanese 21.83 Weekday Not given 34 30 Japanese 5.4575 64
5 Napkin Burger American 29.05 Weekend 3 26 21 American 7.2625 47
Sushi of Gari 46 Japanese 29.15 Weekday Not given 32 32 Japanese 7.2875 64
Han Dynasty Chinese 34.19 Weekday Not given 21 31 Chinese 8.5475 52
Blue Ribbon Sushi Japanese 32.93 Weekday 3 34 31 Japanese 8.2325 65
Shake Shack American 29.15 Weekend 5 20 21 American 7.2875 41
Melt Shop American 29.15 Weekend 4 26 17 American 7.2875 43
J. G. Melon American 29.05 Weekend Not given 26 28 American 7.2625 54
Sushi of Gari 46 Japanese 22.46 Weekday Not given 21 29 Japanese 5.6150 50
ilili Restaurant Middle Eastern 25.22 Weekday 4 32 25 Other 6.3050 57
Junoon Indian 22.75 Weekend 5 29 28 Other 5.6875 57
Five Guys Burgers and Fries American 29.73 Weekend 5 21 25 American 7.4325 46
Delicatessen American 32.93 Weekend 4 30 18 American 8.2325 48
178158 Chipotle Mexican Grill $1.99 Delivery Mexican 31.33 Weekend 4 35 21 Other 7.8325 56
TAO Japanese 25.27 Weekday Not given 33 30 Japanese 6.3175 63
Shake Shack American 29.10 Weekend 5 26 24 American 7.2750 50
Blue Ribbon Fried Chicken American 29.05 Weekend 5 30 29 American 7.2625 59
Carmine's Italian 29.05 Weekday Not given 33 32 Italian 7.2625 65
The Meatball Shop Italian 24.30 Weekend 5 28 25 Italian 6.0750 53
Emporio Italian 31.43 Weekend Not given 25 29 Italian 7.8575 54
Otto Enoteca Pizzeria Italian 24.25 Weekday Not given 31 30 Italian 6.0625 61
Empanada Mama (closed) Mexican 22.75 Weekend Not given 29 18 Other 5.6875 47
Rubirosa Italian 24.30 Weekday 3 29 31 Italian 6.0750 60
Sushi of Gari Japanese 22.75 Weekend 5 30 22 Japanese 5.6875 52
Momoya Japanese 24.30 Weekend 5 28 20 Japanese 6.0750 48
Friend of a Farmer American 24.20 Weekend Not given 33 27 American 6.0500 60
Blue Ribbon Fried Chicken American 29.44 Weekend 4 33 27 American 7.3600 60
Shake Shack American 25.22 Weekend Not given 28 26 American 6.3050 54
ilili Restaurant Middle Eastern 24.20 Weekday 4 34 24 Other 6.0500 58
Blue Ribbon Fried Chicken American 24.30 Weekend 5 22 15 American 6.0750 37
RedFarm Broadway Chinese 24.20 Weekend 5 31 19 Chinese 6.0500 50
Momoya Japanese 29.35 Weekday Not given 31 30 Japanese 7.3375 61
Shake Shack American 24.25 Weekend 4 20 29 American 6.0625 49
The Meatball Shop Italian 21.34 Weekday 5 22 24 Italian 5.3350 46
Tamarind TriBeCa Indian 24.20 Weekend Not given 24 29 Other 6.0500 53
Blue Ribbon Sushi Japanese 29.10 Weekend 4 21 15 Japanese 7.2750 36
Westville Hudson American 20.13 Weekend 4 29 19 American 5.0325 48
Blue Ribbon Sushi Japanese 33.37 Weekday 3 30 27 Japanese 8.3425 57
Shake Shack American 29.15 Weekday Not given 34 28 American 7.2875 62
Han Dynasty Chinese 22.31 Weekend 3 23 29 Chinese 5.5775 52
Sushi Samba Japanese 31.33 Weekend 5 35 20 Japanese 7.8325 55
Tamarind TriBeCa Indian 24.20 Weekend 4 28 26 Other 6.0500 54
ilili Restaurant Middle Eastern 31.43 Weekday 3 26 29 Other 7.8575 55
Shake Shack American 29.30 Weekday 5 33 32 American 7.3250 65
Shake Shack American 24.30 Weekend 4 25 15 American 6.0750 40
Balthazar Boulangerie French 29.10 Weekend 5 31 27 Other 7.2750 58
Momoya Japanese 25.27 Weekday 5 31 30 Japanese 6.3175 61
S'MAC American 25.27 Weekend 5 20 29 American 6.3175 49
Jack's Wife Freda Mediterranean 20.71 Weekend Not given 35 16 Other 5.1775 51
Blue Ribbon Sushi Japanese 24.20 Weekend 4 29 18 Japanese 6.0500 47
Vanessa's Dumplings Chinese 24.20 Weekend 4 20 18 Chinese 6.0500 38
The Meatball Shop Italian 20.61 Weekend Not given 34 30 Italian 5.1525 64
Shake Shack American 22.26 Weekday 5 33 32 American 5.5650 65
P.J. Clarke's American 31.38 Weekend Not given 31 23 American 7.8450 54
Hummus Place Middle Eastern 29.05 Weekend 5 26 17 Other 7.2625 43
Blue Ribbon Sushi Japanese 29.15 Weekend 4 31 19 Japanese 7.2875 50
Shake Shack American 24.30 Weekend 5 21 24 American 6.0750 45
5 Napkin Burger American 25.22 Weekend 5 27 21 American 6.3050 48
ilili Restaurant Middle Eastern 32.93 Weekend 4 25 17 Other 8.2325 42
Rubirosa Italian 25.27 Weekday 4 23 31 Italian 6.3175 54
Shake Shack American 31.43 Weekend 3 22 24 American 7.8575 46
Olea Mediterranean 24.20 Weekend Not given 20 16 Other 6.0500 36
Blue Ribbon Fried Chicken American 29.30 Weekend 4 28 16 American 7.3250 44
Momoya Japanese 24.30 Weekend 4 35 30 Japanese 6.0750 65
The Smile American 25.22 Weekday 4 25 28 American 6.3050 53
178158 Chipotle Mexican Grill $1.99 Delivery Mexican 29.10 Weekday 4 22 32 Other 7.2750 54
Blue Ribbon Sushi Japanese 21.83 Weekday 3 21 26 Japanese 5.4575 47
Grand Sichuan International Chinese 32.93 Weekend Not given 32 28 Chinese 8.2325 60
Yama 49 Japanese 22.31 Weekend 5 29 17 Japanese 5.5775 46
Shake Shack American 29.10 Weekend 5 29 17 American 7.2750 46
Sushi Samba Japanese 22.36 Weekend 5 31 30 Japanese 5.5900 61
Nobu Next Door Japanese 33.37 Weekend 4 32 21 Japanese 8.3425 53
Blue Ribbon Sushi Japanese 24.30 Weekend Not given 29 15 Japanese 6.0750 44
Sushi of Gari 46 Japanese 22.26 Weekend 5 28 22 Japanese 5.5650 50
Shake Shack American 21.39 Weekend 3 33 22 American 5.3475 55
Nobu Next Door Japanese 29.15 Weekend Not given 27 15 Japanese 7.2875 42
DuMont Burger American 24.25 Weekend 3 28 20 American 6.0625 48
Sushi of Gari 46 Japanese 31.43 Weekend 5 29 30 Japanese 7.8575 59
The Meatball Shop Italian 25.27 Weekend Not given 25 29 Italian 6.3175 54
Xi'an Famous Foods Chinese 20.86 Weekend 4 35 26 Chinese 5.2150 61
RedFarm Broadway Chinese 31.43 Weekend 4 30 23 Chinese 7.8575 53
Blue Ribbon Fried Chicken American 29.59 Weekend 5 23 25 American 7.3975 48
Shake Shack American 22.31 Weekend Not given 35 28 American 5.5775 63
Shake Shack American 31.43 Weekend 3 31 24 American 7.8575 55
Shake Shack American 29.05 Weekday 4 27 29 American 7.2625 56
Han Dynasty Chinese 29.15 Weekend Not given 29 21 Chinese 7.2875 50
292602 Chipotle Mexican Grill $1.99 Delivery Mexican 22.31 Weekend 5 31 17 Other 5.5775 48
Blue Ribbon Sushi Japanese 25.22 Weekday Not given 31 24 Japanese 6.3050 55

Calculate the percentage orders where the cost is above 20 dollars

percentage_df_greater_than_20 = (num_df_greater_than_20 / df.shape[0]) * 100
print(percentage_df_greater_than_20)
29.24130663856691
Question 10: What is the mean order delivery time?

Get the mean delivery time

mean_delivery_time = df['delivery_time'].mean()
print('The mean delivery time for this dataset is', round(mean_delivery_time, 2), 'minutes')
The mean delivery time for this dataset is 24.16 minutes
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

Get top 3 most frequent customers

df['customer_id'].value_counts().head(3)
count
customer_id
52832 13
47440 10
83287 9
dtype: int64
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)
Cuisine vs Cost of the order

Relationship between cost of the order and cuisine type

plt.figure(figsize=(15,7))
plt.title('Relationship between cost of the order and cuisine type', fontsize=15, color='gold')
plt.xlabel('Cuisine Type', fontsize=13, color='darkkhaki')
plt.ylabel('Cost of the Order', fontsize=13, color='mediumorchid')
sns.boxplot(x='cuisine_type', y='cost_of_the_order', data=df, palette='Oranges')
plt.xticks(rotation = 45)
:6: FutureWarning:

Passing palette without assigning hue is deprecated and will be removed in v0.14.0. Assign the x variable to hue and set legend=False for the same effect.

sns.boxplot(x='cuisine_type', y='cost_of_the_order', data=df, palette='Oranges')
([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13],
[Text(0, 0, 'Korean'),
Text(1, 0, 'Japanese'),
Text(2, 0, 'Mexican'),
Text(3, 0, 'American'),
Text(4, 0, 'Indian'),
Text(5, 0, 'Italian'),
Text(6, 0, 'Mediterranean'),
Text(7, 0, 'Chinese'),
Text(8, 0, 'Middle Eastern'),
Text(9, 0, 'Thai'),
Text(10, 0, 'Southern'),
Text(11, 0, 'French'),
Text(12, 0, 'Spanish'),
Text(13, 0, 'Vietnamese')])

Filter the DataFrame for Southern cuisine

southern_restaurants = df[df['cuisine_type'] == 'Southern']['restaurant_name'].unique()

Print the list of Southern restaurants

print("Restaurants serving Southern cuisine:")
for restaurant in southern_restaurants:
print(restaurant)
Restaurants serving Southern cuisine:
Hill Country Fried Chicken
Amy Ruth's

Calculate number of american restaurants

Filter the DataFrame for American cuisine

american_restaurants = df[df['cuisine_type'] == 'American']['restaurant_name'].unique()

Print the number of American restaurants

print("Number of restaurants serving American cuisine:", len(american_restaurants))
Number of restaurants serving American cuisine: 41

Filter the DataFrame for American cuisine

american_restaurants = df[df['cuisine_type'] == 'American']['restaurant_name'].unique()

Print the list of American restaurants

print("Restaurants serving American cuisine:")
for restaurant in american_restaurants:
print(restaurant)
Restaurants serving American cuisine:
Blue Ribbon Fried Chicken
Dirty Bird to Go
Lucky's Famous Burgers
Shake Shack
Five Guys Burgers and Fries
5 Napkin Burger
Cafeteria
DuMont Burger
Sarabeth's East
Westville Hudson
Blue Ribbon Brooklyn
Bareburger
The Odeon
The Meatball Shop
Five Leaves
Delicatessen
S'MAC
Waverly Diner
P.J. Clarke's
The Smile
Bubby's
Dirty Bird To Go (archived)
Burger Joint
Sarabeth's Restaurant
Melt Shop
J. G. Melon
Friend of a Farmer
Dickson's Farmstand Meats
Dig Inn Seasonal Market
Alidoro
brgr
Sarabeth's
Big Daddy's
Moonstruck on Second
67 Burger
Schnipper's Quality Kitchen
Market Table
Lucky Strike
Rye House
Sarabeth's West
'wichcraft
Cuisine vs Food Preparation time

Correlate food preparation time and cuisine type

plt.figure(figsize=(15,7))
plt.title('Relationship between food preparation time and cuisine type', fontsize=15, color='gold')
plt.xlabel('Cuisine Type', fontsize=13, color='crimson')
plt.ylabel('Food Preparation Time', fontsize=13, color='purple')
plt.xticks(rotation=45)
sns.boxplot(x='cuisine_type', y='food_preparation_time', data=df, palette='inferno')
:7: FutureWarning:

Passing palette without assigning hue is deprecated and will be removed in v0.14.0. Assign the x variable to hue and set legend=False for the same effect.

sns.boxplot(x='cuisine_type', y='food_preparation_time', data=df, palette='inferno')

Correlate food preparation time and cuisine type

plt.figure(figsize=(15,7))
plt.title('Relationship between food preparation time and cuisine type', fontsize=15, color='darkorchid')
plt.xlabel('Cuisine Type', fontsize=13, color='crimson')
plt.ylabel('Food Preparation Time', fontsize=13, color='purple')
plt.xticks(rotation=45)
sns.violinplot(x='cuisine_type', y='food_preparation_time', edgecolor='gold', data=df, palette='inferno')
:7: FutureWarning:

Passing palette without assigning hue is deprecated and will be removed in v0.14.0. Assign the x variable to hue and set legend=False for the same effect.

sns.violinplot(x='cuisine_type', y='food_preparation_time', edgecolor='gold', data=df, palette='inferno')

Correlate food preparation time and rating violin

Relationship between food preparation time and rating

plt.figure(figsize=(15,7))
plt.title('Relationship between food preparation time and rating', fontsize=15, color='darkviolet')
plt.xlabel('Rating', fontsize=13, color='crimson')
plt.ylabel('Food Preparation Time', fontsize=13, color='purple')
plt.xticks(rotation=45)
sns.violinplot(x='rating', y='food_preparation_time', data=df, edgecolor='gold', palette='plasma')
:9: FutureWarning:

Passing palette without assigning hue is deprecated and will be removed in v0.14.0. Assign the x variable to hue and set legend=False for the same effect.

sns.violinplot(x='rating', y='food_preparation_time', data=df, edgecolor='gold', palette='plasma')

Day of the Week vs Delivery time

Correlate rating and delivery time

plt.figure(figsize=(15, 7))
plt.title('Relationship between rating and delivery time', fontsize=15, color='gold')
plt.xlabel('Rating', fontsize=13, color='orange')
plt.ylabel('Delivery Time', fontsize=13, color='purple')
sns.pointplot(x='rating', y='delivery_time', data=df, palette='plasma')
:6: FutureWarning:

Passing palette without assigning hue is deprecated and will be removed in v0.14.0. Assign the x variable to hue and set legend=False for the same effect.

sns.pointplot(x='rating', y='delivery_time', data=df, palette='plasma')

Correlate day of the week and delivery time

plt.figure(figsize=(15,7))
plt.title('Relationship between day of the week and delivery time', fontsize=15, color='gold')
plt.xlabel('Day of the Week', fontsize=13, color='orange')
plt.ylabel('Delivery Time', fontsize=13, color='purple')
sns.boxplot(x='day_of_the_week', y='delivery_time', data=df, palette='plasma')
:6: FutureWarning:

Passing palette without assigning hue is deprecated and will be removed in v0.14.0. Assign the x variable to hue and set legend=False for the same effect.

sns.boxplot(x='day_of_the_week', y='delivery_time', data=df, palette='plasma')

Calculate mean delivery time on weekend

mean_delivery_time_weekend = df[df['day_of_the_week'] == 'Weekend']['delivery_time'].mean()
print("Mean delivery time on weekends: {:.2f} minutes".format(mean_delivery_time_weekend))
Mean delivery time on weekends: 22.47 minutes

Calculate mean delivery time on weekday

mean_delivery_time_weekday = df[df['day_of_the_week'] == 'Weekday']['delivery_time'].mean()
print("Mean delivery time on weekdays: {:.2f} minutes".format(mean_delivery_time_weekday))
Mean delivery time on weekdays: 28.34 minutes

Calculate the percentage difference mean delivery on weekday to weekend

percentage_difference = ((mean_delivery_time_weekday - mean_delivery_time_weekend) / mean_delivery_time_weekend) * 100
print("Mean delivery time on weekdays is {:.2f}% greater than on weekends.".format(percentage_difference))
Mean delivery time on weekdays is 26.12% greater than on weekends.
Run the below code and write your observations on the revenue generated by the restaurants
My observation is that Shake Shack is the most popular restaurant, followed by The Meatball Shop, Blue Ribbon Sushi and Blue Ribbon Fried Chicken.

df.groupby(['restaurant_name'])['cost_of_the_order'].sum().sort_values(ascending = False).head(14)
cost_of_the_order
restaurant_name
Shake Shack 3579.53
The Meatball Shop 2145.21
Blue Ribbon Sushi 1903.95
Blue Ribbon Fried Chicken 1662.29
Parm 1112.76
RedFarm Broadway 965.13
RedFarm Hudson 921.21
TAO 834.50
Han Dynasty 755.29
Blue Ribbon Sushi Bar & Grill 666.62
Rubirosa 660.45
Sushi of Gari 46 640.87
Nobu Next Door 623.67
Five Guys Burgers and Fries 506.47
dtype: float64
Rating vs Delivery time
Rating vs Food preparation time
Rating vs Cost of the order

Relationship between rating and cost of the order

plt.figure(figsize=(15, 7))
plt.title('Relationship between rating and cost of the order', fontsize=15, color='darkviolet')
plt.xlabel('Rating', fontsize=13, color='orange')
plt.ylabel('Cost of the Order', fontsize=13, color='purple')
sns.violinplot(x='rating', y='cost_of_the_order', data=df, edgecolor='goldenrod', palette='Purples')
:6: FutureWarning:

Passing palette without assigning hue is deprecated and will be removed in v0.14.0. Assign the x variable to hue and set legend=False for the same effect.

sns.violinplot(x='rating', y='cost_of_the_order', data=df, edgecolor='goldenrod', palette='Purples')

Correlation among variables

Plot the heatmap

col_list = ['cost_of_the_order', 'food_preparation_time', 'delivery_time']
plt.figure(figsize=(15, 7))
sns.heatmap(df[col_list].corr(), annot=True, vmin=-1, vmax=1, fmt='.2f', cmap='Spectral')

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

Filter the DataFrame for ratings 4 and above

df_rated = df[df['rating'].isin(['4', '5']) ]

Filter the rated restaurants

df_filtered = df[df['rating'] != 'Not given'].copy()

Convert rating column from object to integer

df_rated['rating'] = df_rated['rating'].astype('int')

Create a dataframe that contains the restaurant names with their rating counts

df_rating_count = df_rated.groupby(['restaurant_name'])['rating'].count().sort_values(ascending = False).reset_index()
df_rating_count.head()
:8: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
df_rated['rating'] = df_rated['rating'].astype('int')
restaurant_name rating
0 Shake Shack 110
1 The Meatball Shop 74
2 Blue Ribbon Sushi 57
3 Blue Ribbon Fried Chicken 53
4 RedFarm Broadway 33

Calculate the average rating for each restaurant

average_ratings = df_rated.groupby('restaurant_name')['rating'].mean().reset_index()
average_ratings.rename(columns={'rating': 'average_rating'}, inplace=True)

Merge the average ratings with the rating counts

df_rating_summary = pd.merge(df_rating_count, average_ratings, on='restaurant_name')

Filter restaurants with more than 50 ratings and average rating greater than 4

promo_restaurants = df_rating_summary[(df_rating_summary['rating'] > 50) & (df_rating_summary['average_rating'] > 4)]

Create a visually appealing plot

plt.figure(figsize=(15, 7))
plt.title('Restaurants Eligible for Promotion', fontsize=15, color='darkviolet')
plt.xlabel('Number of Ratings (Above 4 or 5)', fontsize=13, color='orange')
plt.ylabel('Restaurant Name', fontsize=13, color='purple')
plt.grid(axis='x')
sns.barplot(x='rating', y='restaurant_name', data=promo_restaurants, palette='YlOrRd')
:17: FutureWarning:

Passing palette without assigning hue is deprecated and will be removed in v0.14.0. Assign the y variable to hue and set legend=False for the same effect.

sns.barplot(x='rating', y='restaurant_name', data=promo_restaurants, palette='YlOrRd')

Get the restaurant names that have rating count more than 50

rest_names = df_rating_count[df_rating_count['rating'] > 50]['restaurant_name']

Filter to get the data of restaurants that have rating count more than 50

df_mean_4 = df_rated[df_rated['restaurant_name'].isin(rest_names)].copy()

Group the restaurant names with their ratings and find the mean rating of each restaurant

df_mean_4.groupby(['restaurant_name'])['rating'].mean().sort_values(ascending = False).reset_index().dropna()
restaurant_name rating
0 The Meatball Shop 4.716216
1 Blue Ribbon Fried Chicken 4.603774
2 Blue Ribbon Sushi 4.561404
3 Shake Shack 4.545455
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

Determine the revenue

def compute_rev(x):
if x > 20:
return x*0.25
elif x > 5:
return x*0.15
else:
return x*0
df['Revenue'] = df['cost_of_the_order'].apply(compute_rev)
df.head()
order_id customer_id restaurant_name cuisine_type cost_of_the_order day_of_the_week rating food_preparation_time delivery_time cuisine_popular Revenue
0 1477147 337525 Hangawi Korean 30.75 Weekend Not given 25 20 Other 7.6875
1 1477685 358141 Blue Ribbon Sushi Izakaya Japanese 12.08 Weekend Not given 25 23 Japanese 1.8120
2 1477070 66393 Cafe Habana Mexican 12.23 Weekday 5 23 28 Other 1.8345
3 1477334 106968 Blue Ribbon Fried Chicken American 29.20 Weekend 3 25 15 American 7.3000
4 1478249 76942 Dirty Bird to Go American 11.59 Weekday 4 25 24 American 1.7385

Calculate revenue from orders over 20 dollars

revenue_over_20 = df[df['cost_of_the_order'] > 20]['cost_of_the_order'].sum() * 0.25

Calculate revenue from orders between 5 and 20 dollars

revenue_5_to_20 = df[(df['cost_of_the_order'] > 5) & (df['cost_of_the_order'] <= 20)]['cost_of_the_order'].sum() * 0.15

Calculate total net revenue

net_revenue = revenue_over_20 + revenue_5_to_20

print("Net revenue generated by the company: $", round(net_revenue, 2))
Net revenue generated by the company: $ 6166.3

Calculate the total revenue

total_revenue = df['Revenue'].sum()
print("The net revenue generated by the company is $",round(total_revenue,2))
The net revenue generated by the company is $ 6166.3

Group the data by restaurant name and calculate the total revenue for each restaurant

revenue_by_restaurant = df.groupby('restaurant_name')['Revenue'].sum().sort_values(ascending=False)

Calculate the percentage of revenue for each restaurant

revenue_percentage = revenue_by_restaurant / revenue_by_restaurant.sum() * 100

Filter out restaurants with less than 3% revenue

revenue_above_3_percent = revenue_percentage[revenue_percentage >= 3]

Create a pie chart

plt.figure(figsize=(10, 10))
plt.pie(revenue_above_3_percent, labels=revenue_above_3_percent.index, autopct='%1.1f%%', startangle=90)
plt.title('Revenue Distribution by Restaurant (Above 3%)')
Text(0.5, 1.0, 'Revenue Distribution by Restaurant (Above 3%)')

Create a pie chart without percentages or labels

plt.figure(figsize=(10, 10))
plt.pie(revenue_by_restaurant)
plt.title('Revenue Distribution by Restaurant')
Text(0.5, 1.0, 'Revenue Distribution by Restaurant')

prompt: remove restaurant names # Group the data by restaurant name and calculate the total revenue for each restaurant

revenue_by_restaurant = df.groupby('restaurant_name')['Revenue'].sum().sort_values(ascending=False)

# Create a pie chart

plt.figure(figsize=(10, 10))

plt.pie(revenue_by_restaurant, labels=revenue_by_restaurant.index, autopct='%1.1f%%', startangle=90)

plt.title('Revenue Distribution by R

Group the data by restaurant name and calculate the total revenue for each restaurant

revenue_by_restaurant = df.groupby('restaurant_name')['Revenue'].sum().sort_values(ascending=False)

Create a pie chart

plt.figure(figsize=(10, 10))
plt.pie(revenue_by_restaurant, labels=None, autopct='%1.1f%%', startangle=90)
plt.title('Revenue Distribution by Restaurant')
plt.legend(revenue_by_restaurant.index, loc='center left', bbox_to_anchor=(1, 0.5))

/usr/local/lib/python3.10/dist-packages/IPython/core/events.py:89: UserWarning: Glyph 142 (\x8e) missing from current font.
func(*args, **kwargs)
/usr/local/lib/python3.10/dist-packages/IPython/core/events.py:89: UserWarning: Glyph 140 (\x8c) missing from current font.
func(*args, **kwargs)
/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 142 (\x8e) missing from current font.
fig.canvas.print_figure(bytes_io, **kw)
/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 140 (\x8c) missing from current font.
fig.canvas.print_figure(bytes_io, **kw)

prompt: beautiful 3d scatter plot of company revenue

!pip install plotly
import plotly.express as px

Assuming 'df' is your DataFrame and 'Revenue' is the column with revenue values

Create a 3D scatter plot

fig = px.scatter_3d(df, x='cost_of_the_order', y='delivery_time', z='Revenue',
color='cuisine_type', size='Revenue',
opacity=0.7, title='Company Revenue by Order Cost, Delivery Time, and Cuisine')

Customize the appearance

fig.update_layout(scene=dict(xaxis_title='Order Cost',
yaxis_title='Delivery Time',
zaxis_title='Revenue'))

Display the plot

fig.show()
Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (5.15.0)
Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly) (9.0.0)
Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from plotly) (24.1)
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)

Calculate total delivery time and add a new column to the dataframe df to store the total delivery time

df['total_time'] = df['food_preparation_time'] + df['delivery_time']

Calculate the number of orders that take more than 60 minutes

num_orders_over_60 = df[df['total_time'] > 60].shape[0]

Calculate the percentage of orders that take more than 60 minutes

percentage_over_60 = (num_orders_over_60 / df.shape[0]) * 100

print("Percentage of orders taking more than 60 minutes to deliver:", round(percentage_over_60, 2), '%')
Percentage of orders taking more than 60 minutes to deliver: 10.54 %
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?

Get the mean delivery time on weekdays and print it

print('The mean delivery time on weekdays is around',
round(df[df['day_of_the_week'] == 'Weekday']['delivery_time'].mean()),
'minutes')
The mean delivery time on weekdays is around 28 minutes

Get the mean delivery time on weekends and print it

print('The mean delivery time on weekends is around',
round(df[df['day_of_the_week'] == 'Weekend']['delivery_time'].mean()),
'minutes')
The mean delivery time on weekends is around 22 minutes
Conclusion and Recommendations
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)
Conclusions: Make your own
Recommendations: Have fun