Question

^{Burrito-ful San Diego Tam, Margaret and Winifred are trying to use Data Science to find the best burritos in San Diego! Their friends Irene and Maya provided them with two comprehensive datasets on many burrito establishments in the San Diego area taken from (and cleaned from): https://www.kaggle.com/srcole/burritos-in-san- diego/data The following cell reads in a table called ratings which contains names of burrito restaurants, their Yelp rating, Google rating, as well as their Overall rating. It also reads in a table called burritos_types which contains names of burrito restaurants, their menu items, and the cost of the respective menu item at the restaurant In [11]: #Just run this cell ratings = Table.read_table("ratings.csv") ratings.show(5) burritos_types = Table.read_table("burritos_types.csv") burritos_types.show(5) Name Yelp Google Overall Albertacos 3.5 3.9 3.45 Burrito Factory 4.5 4.8 3.5 Burros and Fries 3.5 4.1 3.575 Caliente Mexican Food 3.5 4.4 3.25 California Burrito Company 3.5 4.4 3.2 ... (77 rows omitted) Name Albertacos Albertacos Alberto's 623 N Escondido Blvd, Escondido, CA 92025 Burrito Box Burrito Factory Menu_Item Cost California 5.7 Carne asada 5.25 Carne Asada 4.59 Steak with guacamole 11.5 Steak everything 7.35 ... (244 rows omitted) Question 1. It would be easier if we could combine the information in both tables. Assign burritos to the result of joining the two tables together. Note: it doesn't matter which table you put in as the argument to the table method, either order will work for the autograder tests. Hint: If you need refreshers on table methods, look at the python reference, In [19]: burritos = ratings.join('Name', burritos_types, 'Name') burritos.show(5) Name Yelp Google Overall Albertacos 3.5 3.9 3.45 Albertacos 3.5 3.9 3.45 Burrito Factory 4.5 4.3 3.5 Burros and Fries 3.5 4.1 3.575 Burros and Fries 3.5 4.1 3.575 Menu_Item Cost California 5.7 Carne asada 5.25 Steak everything 7.35 California 7.19 Carne asada 6.89}

Hi, can you solve Question 7 with code? Thanks.

Answer 1

#Code for Question 7

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
bin=np.arange(0,15,1) #Use of bin to start from 0 end upto 15 increment by 1 (start,end,step_size)
df = pd.read_csv("burritos.csv", usecols = ['Cost'])#Read csv burritos file and reading Cost column values in df
plt.hist(df, bins=15) #plotting the histogram
plt.title("Distribution of cost of burritos from San Diego")


import numpy as np import pandas as pd import matplotlib.pyplot as plt bin=np.arange(0,15,1) #Use of bin to start from end upto 15 increment by 1 (start, end, step_size) df = pd. read_csv"burritos.csv", usecols = ['Cost']) Read csv burritos file and reading Cost column values in df plt.hist(df, bins=15) #plotting the histogram plt.title("Distribution of cost of burritos from San Diego")

In [51]: import numpy as np ...: import pandas as pd ...: import matplotlib.pyplot as plt ...: bin=np.arange(0,15,1) #Use of bin to start from o end upto 15 increment by 1 (start, end, step_size) ...: df = pd. read_csv ("burritos.csv", usecols = ['Cost']) #Read csv burritos file and reading Cost column values in df ...: plt.hist(df, bins=15) #plotting the histogram ...: plt.title("Distribution of cost of burritos from San Diego") Out[51]: Text(0.5,1, 'Distribution of cost of burritos from San Diego') Distribution of cost of burritos from San Diego WATOTHORARBER In [52]: