导入模块

import pandas as pd

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 1.groupby迭代操作

frame = pd.DataFrame({'color':['yellow','red','green','red','green'],
                      'object':['pen','pencil','pencil','ashtray','pen'],
                      'price1':[5.56,4.2,1.3,0.56,2.75],
                      'price2':[4.75,4.12,1.6,0.75,3.15]})
for name,group in frame.groupby('color'):
    print(name)
    print(group)
'''
green
   color  object  price1  price2
2  green  pencil    1.30    1.60
4  green     pen    2.75    3.15
red
  color   object  price1  price2
1   red   pencil    4.20    4.12
3   red  ashtray    0.56    0.75
yellow
    color object  price1  price2
0  yellow    pen    5.56    4.75
'''

2.分组函数

frame = pd.DataFrame({'color':['yellow','red','green','red','green'],
                      'object':['pen','pencil','pencil','ashtray','pen'],
                      'price1':[5.56,4.2,1.3,0.56,2.75],
                      'price2':[4.75,4.12,1.6,0.75,3.15]})
group = frame.groupby('color')
group['price1'].quantile(0.6)  #quantile()函数计算分位数
'''
color
green     2.170
red       2.744
yellow    5.560
'''

自定义聚合函数

定义好一个函数，将其作为参数传给agg()函数

def range(series):
    return series.max()-series.min()
group['price1'].agg(range)
'''
color
green     1.45
red       3.64
yellow    0.00
'''

对整个DataFrame对象用agg()函数

def range(series):
    return series.max()-series.min()
group.agg(range)

 同时使用多个聚合函数

group['price1'].agg([range,'mean','std'])

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参考:

法比奥·内利. Python数据分析实战：第2版.北京：人民邮电出版社, 2019.11.