Ex2 - Getting and Knowing your 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 [50]:

import pandas as pd
import numpy as np

Step 2. Import the dataset from this address.

Step 3. Assign it to a variable called chipo.

In [51]:

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

Step 4. See the first 10 entries

In [52]:

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. What is the number of observations in the dataset?

In [53]:

# Solution 1

chipo.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4622 entries, 0 to 4621
Data columns (total 5 columns):
 #   Column              Non-Null Count  Dtype 
---  ------              --------------  ----- 
 0   order_id            4622 non-null   int64 
 1   quantity            4622 non-null   int64 
 2   item_name           4622 non-null   object
 3   choice_description  3376 non-null   object
 4   item_price          4622 non-null   object
dtypes: int64(2), object(3)
memory usage: 180.7+ KB

In [54]:

# Solution 2

chipo.shape

(4622, 5)

Step 6. What is the number of columns in the dataset?

In [55]:

chipo.shape[1]

Step 7. Print the name of all the columns.

In [56]:

chipo.head(0)
##chipo.columns

	order_id	quantity	item_name	choice_description	item_price

Step 8. How is the dataset indexed?

In [57]:

chipo.index

RangeIndex(start=0, stop=4622, step=1)

Step 9. Which was the most-ordered item?

In [58]:

chipo.groupby(by="item_name").sum().sort_values('quantity',ascending=False).head(1)

	order_id	quantity	choice_description	item_price
item_name
Chicken Bowl	713926	761	[Tomatillo-Red Chili Salsa (Hot), [Black Beans...	$16.98 $10.98 $11.25 $8.75 $8.49 $11.25 $8.75 ...

Step 10. For the most-ordered item, how many items were ordered?

In [59]:

chipo.groupby(by="item_name").sum().sort_values('quantity',ascending=False).head(1)

	order_id	quantity	choice_description	item_price
item_name
Chicken Bowl	713926	761	[Tomatillo-Red Chili Salsa (Hot), [Black Beans...	$16.98 $10.98 $11.25 $8.75 $8.49 $11.25 $8.75 ...

Step 11. What was the most ordered item in the choice_description column?

In [60]:

chipo.groupby(by="choice_description").sum().sort_values('quantity',ascending=False).head(1)

	order_id	quantity	item_name	item_price
choice_description
[Diet Coke]	123455	159	Canned SodaCanned SodaCanned Soda6 Pack Soft D...	$2.18 $1.09 $1.09 $6.49 $2.18 $1.25 $1.09 $6.4...

Step 12. How many items were orderd in total?

In [61]:

chipo.item_name.count()

Step 13. Turn the item price into a float

Step 13.a. Check the item price type

In [62]:

chipo.item_price.dtype

dtype('O')

Step 13.b. Create a lambda function and change the type of item price

In [63]:

dollarizer = lambda x: float(x[1:-1])
chipo.item_price = chipo.item_price.apply(dollarizer)

Step 13.c. Check the item price type

In [64]:

chipo.item_price.dtype

dtype('float64')

Step 14. How much was the revenue for the period in the dataset?

In [65]:

revenue =  (chipo.item_price * chipo.quantity).sum()
print('Revenue is : $ '+ str(revenue))

Revenue is : $ 39237.02

Step 15. How many orders were made in the period?

In [66]:

chipo.order_id.value_counts().count()

Step 16. What is the average revenue amount per order?

In [69]:

# Solution 1

chipo['revenue'] = chipo['quantity'] * chipo['item_price']
order_grouped = chipo.groupby(by=['order_id']).sum()
order_grouped['revenue'].mean()

21.39423118865867

In [68]:

# Solution 2

chipo.groupby(by=['order_id']).sum()['revenue'].mean()

21.39423118865867

Step 17. How many different items are sold?

In [45]:

chipo.item_name.value_counts().count()