如何解决如何将两个groupby合并为一个
我有两个GroubBy：
第一个
ser2 = ser.groupby(pd.cut(ser,10)).sum()
(-2620.137,476638.7] 12393813
(476638.7,951152.4] 9479666
(951152.4,1425666.1] 14381033
(1425666.1,1900179.8] 5113056
(1900179.8,2374693.5] 4114429
(2374693.5,2849207.2] 4929537
(2849207.2,3323720.9] 0
(3323720.9,3798234.6] 0
(3798234.6,4272748.3] 3978230
(4272748.3,4747262.0] 4747262
第二个：
ser1= pd.cut(ser,10)
print(ser1.value_counts())
(-2620.137,476638.7] 110
(476638.7,951152.4] 15
(951152.4,1425666.1] 12
(1425666.1,1900179.8] 3
(2374693.5,2849207.2] 2
(1900179.8,2374693.5] 2
(4272748.3,4747262.0] 1
(3798234.6,4272748.3] 1
(3323720.9,3798234.6] 0
(2849207.2,3323720.9] 0
问题：是否有办法将这些操作组合为一个代码，以将两个计算都存储在同一数据透视表中
解决方法
使用GroupBy.agg，而不是value_counts使用GroupBy.size：
np.random.seed(2020)
ser = pd.Series(np.random.randint(40,size=100))
df = ser.groupby(pd.cut(ser,10)).agg(['sum','size'])
print (df)
sum size
(-0.039,3.9] 27 14
(3.9,7.8] 49 9
(7.8,11.7] 142 15
(11.7,15.6] 151 11
(15.6,19.5] 159 9
(19.5,23.4] 187 9
(23.4,27.3] 253 10
(27.3,31.2] 176 6
(31.2,35.1] 231 7
(35.1,39.0] 375 10
如果需要自定义列名称：
np.random.seed(2020)
ser = pd.Series(np.random.randint(40,10)).agg([('col1','sum'),('col2','size')])
print (df)
col1 col2
(-0.039,3.9] 27 14
(3.9,7.8] 49 9
(7.8,11.7] 142 15
(11.7,15.6] 151 11
(15.6,19.5] 159 9
(19.5,23.4] 187 9
(23.4,27.3] 253 10
(27.3,31.2] 176 6
(31.2,35.1] 231 7
(35.1,39.0] 375 10
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