Pandas练习1

来自 https://github.com/guipsamora/pandas_exercises

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

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.

url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv'
chipo = pd.read_csv(url,sep='	')

Step 4. See the first 10 entries

# Solution 1

chipo[: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

# Solution 2

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?

type(chipo)

pandas.core.frame.DataFrame

# Solution 1

len(chipo.index)

4622

# Solution 2

chipo.shape[0]

4622

# Solution 3

chipo.info()

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

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

# Solution 1

len(chipo.columns)

5

# Solution 2

chipo.shape[1]

5

Step 7. Print the name of all the columns.

list(chipo.columns)

['order_id', 'quantity', 'item_name', 'choice_description', 'item_price']

Step 8. How is the dataset indexed?

chipo.index

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

Step 9. Which was the most-ordered item?

c = chipo.groupby('item_name')
c = c.sum()
c = c.sort_values(['quantity'],ascending=False)
c['quantity'].head(1)

item_name
Chicken Bowl    761
Name: quantity, dtype: int64

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

c = chipo.groupby('item_name')
c = c.sum()
c = c.sort_values(['quantity'],ascending=False)
c['quantity'].head(1)

item_name
Chicken Bowl    761
Name: quantity, dtype: int64

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

c = chipo.groupby('choice_description')
c = c.sum()
c = c.sort_values(['quantity'],ascending=False)
c.head(1)

	order_id	quantity
choice_description
[Diet Coke]	123455	159

Step 12. How many items were orderd in total?

chipo['quantity'].sum()

4972

Step 13. Turn the item price into a float

Step 13.a. Check the item price type

chipo['item_price'].dtypes

dtype('O')

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

chipo['item_price'] = chipo['item_price'].apply(lambda x:x.replace('$','')).astype(np.float64);
# dollarizer = lambda x:float(x[1:-1])
# chipo.item_price = chipo.item_price.apply(dollarizer)

Step 13.c. Check the item price type

chipo['item_price'].dtypes

dtype('float64')

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

(chipo['quantity']*chipo['item_price']).sum()

39237.02

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

# Solution 1

g = chipo.groupby(['order_id'])
g.ngroups

1834

# Solution 2

orders = chipo.order_id.value_counts().count()
orders

1834

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

# Solution 1

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

21.394231188658654

# Solution 2

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

21.394231188658654

Step 17. How many different items are sold?

chipo.item_name.value_counts().count()

50