探索酒类消费数据

步骤1 导入必要的库

import pandas as pd

步骤2 从以下地址导入数据

path1='pandas_exercise\exercise_data\drinks.csv'

步骤3 将数据框命名为drinks

drinks=pd.read_csv(path1)
print(drinks.head())

步骤4 哪个大陆(continent)平均消耗的啤酒(beer)更多？

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

步骤5 打印出每个大陆(continent)的红酒消耗(wine_servings)的描述性统计值

print(drinks.groupby('continent').wine_servings.describe())

步骤6 打印出每个大陆每种酒类别的消耗平均值

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

步骤7 打印出每个大陆每种酒类别的消耗中位数

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

步骤8 打印出每个大陆对spirit饮品消耗的平均值，最大值和最小值

print(drinks.groupby('continent').spirit_servings.agg(['mean','max','min']))

输出

# 步骤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]
# 步骤4
continent
AF     61.471698
AS     37.045455
EU    193.777778
OC     89.687500
SA    175.083333
Name: beer_servings, dtype: float64
# 步骤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
# 步骤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]
# 步骤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]
# 步骤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