Visualizing Chipotle’s Data

This time we are going to pull data directly from the internet. Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.

Step 1. Import the necessary libraries

In [13]:

import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter

# set this so the graphs open internally
%matplotlib inline

Step 2. Import the dataset from this address.

Step 3. Assign it to a variable called chipo.

In [12]:

chipo = pd.read_csv('https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv',sep='\t')
chipo

	order_id	quantity	item_name	choice_description	item_price
0	1	1	Chips and Fresh Tomato Salsa	NaN	$2.39
1	1	1	Izze	[Clementine]	$3.39
2	1	1	Nantucket Nectar	[Apple]	$3.39
3	1	1	Chips and Tomatillo-Green Chili Salsa	NaN	$2.39
4	2	2	Chicken Bowl	[Tomatillo-Red Chili Salsa (Hot), [Black Beans...	$16.98
...	...	...	...	...	...
4617	1833	1	Steak Burrito	[Fresh Tomato Salsa, [Rice, Black Beans, Sour ...	$11.75
4618	1833	1	Steak Burrito	[Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...	$11.75
4619	1834	1	Chicken Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...	$11.25
4620	1834	1	Chicken Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Lettu...	$8.75
4621	1834	1	Chicken Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...	$8.75

4622 rows × 5 columns

Step 4. See the first 10 entries

In [11]:

chipo.head(10)

	order_id	quantity	item_name	choice_description	item_price
0	1	1	Chips and Fresh Tomato Salsa	NaN	2.39
1	1	1	Izze	[Clementine]	3.39
2	1	1	Nantucket Nectar	[Apple]	3.39
3	1	1	Chips and Tomatillo-Green Chili Salsa	NaN	2.39
4	2	2	Chicken Bowl	[Tomatillo-Red Chili Salsa (Hot), [Black Beans...	16.98
5	3	1	Chicken Bowl	[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...	10.98
6	3	1	Side of Chips	NaN	1.69
7	4	1	Steak Burrito	[Tomatillo Red Chili Salsa, [Fajita Vegetables...	11.75
8	4	1	Steak Soft Tacos	[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...	9.25
9	5	1	Steak Burrito	[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...	9.25

Step 5. Create a histogram of the top 5 items bought

In [6]:

# get the Series of the names
x = chipo.item_name

# use the Counter class from collections to create a dictionary with keys(text) and frequency
letter_counts = Counter(x)

# convert the dictionary to a DataFrame
df = pd.DataFrame.from_dict(letter_counts, orient='index')

# sort the values from the top to the least value and slice the first 5 items
df = df[0].sort_values(ascending = True)[45:50]

# create the plot
df.plot(kind='bar')

# Set the title and labels
plt.xlabel('Items')
plt.ylabel('Number of Times Ordered')
plt.title('Most ordered Chipotle\'s Items')

# show the plot
plt.show()

Step 6. Create a scatterplot with the number of items orderered per order price

Hint: Price should be in the X-axis and Items ordered in the Y-axis

In [7]:

# create a list of prices
chipo.item_price = [float(value[1:-1]) for value in chipo.item_price] # strip the dollar sign and trailing space

# then groupby the orders and sum
orders = chipo.groupby('order_id').sum()

# creates the scatterplot
# plt.scatter(orders.quantity, orders.item_price, s = 50, c = 'green')
plt.scatter(x = orders.item_price, y = orders.quantity, s = 50, c = 'green')

# Set the title and labels
plt.xlabel('Order Price')
plt.ylabel('Items ordered')
plt.title('Number of items ordered per order price')
plt.ylim(0)

Step 7. BONUS: Create a question and a graph to answer your own question.

In [14]:

# 用柱形图显示商品销售额前10的商品
def parse_price(price):
    return float(price[1:])

item_price_new = chipo['item_price'].apply(parse_price)
chipo['item_price_new']=item_price_new

a = chipo.groupby('item_name')['item_price_new'].sum()
b = a.sort_values(ascending=False)

b.head(10).plot(kind='bar' , figsize=(10,5))