數(shù)據(jù)源：鏈接: https://pan.baidu.com/s/1EFqJFXf70t2Rubkh6D19aw 提取碼: syqg
數(shù)據(jù)源示例：
探索酒類消費(fèi)數(shù)據(jù)
步驟1 導(dǎo)入必要的庫
import pandas as pd
步驟2 從以下地址導(dǎo)入數(shù)據(jù)
path1='pandas_exercise\exercise_data\drinks.csv'
步驟3 將數(shù)據(jù)框命名為drinks
drinks=pd.read_csv(path1)
print(drinks.head())
步驟4 哪個大陸(continent)平均消耗的啤酒(beer)更多疼约？
print(drinks.groupby('continent').beer_servings.mean())
步驟5 打印出每個大陸(continent)的紅酒消耗(wine_servings)的描述性統(tǒng)計(jì)值
print(drinks.groupby('continent').wine_servings.describe())
步驟6 打印出每個大陸每種酒類別的消耗平均值
print(drinks.groupby('continent').mean())
步驟7 打印出每個大陸每種酒類別的消耗中位數(shù)
print(drinks.groupby('continent').median())
步驟8 打印出每個大陸對spirit飲品消耗的平均值，最大值和最小值
print(drinks.groupby('continent').spirit_servings.agg(['mean','max','min']))
輸出
# 步驟3
country beer_servings ... total_litres_of_pure_alcohol continent
0 Afghanistan 0 ... 0.0 AS
1 Albania 89 ... 4.9 EU
2 Algeria 25 ... 0.7 AF
3 Andorra 245 ... 12.4 EU
4 Angola 217 ... 5.9 AF
[5 rows x 6 columns]
# 步驟4
continent
AF 61.471698
AS 37.045455
EU 193.777778
OC 89.687500
SA 175.083333
Name: beer_servings, dtype: float64
# 步驟5
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
# 步驟6
beer_servings ... total_litres_of_pure_alcohol
continent ...
AF 61.471698 ... 3.007547
AS 37.045455 ... 2.170455
EU 193.777778 ... 8.617778
OC 89.687500 ... 3.381250
SA 175.083333 ... 6.308333
[5 rows x 4 columns]
# 步驟7
beer_servings ... total_litres_of_pure_alcohol
continent ...
AF 32.0 ... 2.30
AS 17.5 ... 1.20
EU 219.0 ... 10.00
OC 52.5 ... 1.75
SA 162.5 ... 6.85
[5 rows x 4 columns]
# 步驟8
mean max min
continent
AF 16.339623 152 0
AS 60.840909 326 0
EU 132.555556 373 0
OC 58.437500 254 0
SA 114.750000 302 25
