[Pandas] Pandas03 - Alcohol_Consumption 풀이

Ex - GroupBy

Introduction:¶

GroupBy can be summarizes as Split-Apply-Combine.

Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.

Check out this Diagram

Step 1. Import the necessary libraries

In [ ]:

import pandas as pd 

Step 2. Import the dataset from this address.

Step 3. Assign it to a variable called drinks.

In [5]:

url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/drinks.csv'
drinks = pd.read_csv(url,',')
drinks.head()

Out[5]:

	country	beer_servings	spirit_servings	wine_servings	total_litres_of_pure_alcohol	continent
0	Afghanistan	0	0	0	0.0	AS
1	Albania	89	132	54	4.9	EU
2	Algeria	25	0	14	0.7	AF
3	Andorra	245	138	312	12.4	EU
4	Angola	217	57	45	5.9	AF

Step 4. Which continent drinks more beer on average?

In [18]:

drinks.groupby('continent')['beer_servings'].mean()

Out[18]:

continent
AF     61.471698
AS     37.045455
EU    193.777778
OC     89.687500
SA    175.083333
Name: beer_servings, dtype: float64

Step 5. For each continent print the statistics for wine consumption.

In [19]:

drinks.groupby('continent')['wine_servings'].describe()

Out[19]:

	count	mean	std	min	25%	50%	75%	max
continent
AF	53.0	16.264151	38.846419	0.0	1.0	2.0	13.00	233.0
AS	44.0	9.068182	21.667034	0.0	0.0	1.0	8.00	123.0
EU	45.0	142.222222	97.421738	0.0	59.0	128.0	195.00	370.0
OC	16.0	35.625000	64.555790	0.0	1.0	8.5	23.25	212.0
SA	12.0	62.416667	88.620189	1.0	3.0	12.0	98.50	221.0

Step 6. Print the mean alcohol consumption per continent for every column

In [27]:

drinks.groupby('continent').mean()

Out[27]:

	beer_servings	spirit_servings	wine_servings	total_litres_of_pure_alcohol
continent
AF	61.471698	16.339623	16.264151	3.007547
AS	37.045455	60.840909	9.068182	2.170455
EU	193.777778	132.555556	142.222222	8.617778
OC	89.687500	58.437500	35.625000	3.381250
SA	175.083333	114.750000	62.416667	6.308333

Step 7. Print the median alcoohol consumption per continent for every column

In [28]:

drinks.groupby('continent').median()

Out[28]:

	beer_servings	spirit_servings	wine_servings	total_litres_of_pure_alcohol
continent
AF	32.0	3.0	2.0	2.30
AS	17.5	16.0	1.0	1.20
EU	219.0	122.0	128.0	10.00
OC	52.5	37.0	8.5	1.75
SA	162.5	108.5	12.0	6.85

Step 8. Print the mean, min and max values for spirit consumption.

This time output a DataFrame

In [33]:

drinks.groupby('continent').spirit_servings.agg(['mean','min','max'])
# agg로 합치기

Out[33]:

	mean	min	max
continent
AF	16.339623	0	152
AS	60.840909	0	326
EU	132.555556	0	373
OC	58.437500	0	254
SA	114.750000	25	302