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:是否有方法将这些操作组合为一个代码,以在同一数据透视表中获得两个计算
使用GroupBy.agg
,改为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, size=100))
df = ser.groupby(pd.cut(ser, 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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我来说两句