一 agg,聚合，可以使用内置的函数
>>> import pandas as pd >>> import numpy as np >>> pp = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'],index=pd.date_range('1/1/2000', periods=10)) >>> pp A B C 2000-01-01 0.754524 -0.855136 0.135573 2000-01-02 0.224428 -2.025685 0.590259 2000-01-03 -0.894270 1.956547 -0.515041 2000-01-04 0.794662 0.005409 -1.846422 2000-01-05 0.808849 1.283276 -0.681725 2000-01-06 0.538258 -0.249534 0.217653 2000-01-07 0.582666 -0.656912 -0.780406 2000-01-08 -0.981985 1.125303 0.230330 2000-01-09 1.303636 0.806432 0.556127 2000-01-10 -1.207910 2.382836 0.959141 >>> pp.iloc[3:7]=np.nan #直接给赋值 >>> pp A B C 2000-01-01 0.754524 -0.855136 0.135573 2000-01-02 0.224428 -2.025685 0.590259 2000-01-03 -0.894270 1.956547 -0.515041 2000-01-04 NaN NaN NaN 2000-01-05 NaN NaN NaN 2000-01-06 NaN NaN NaN 2000-01-07 NaN NaN NaN 2000-01-08 -0.981985 1.125303 0.230330 2000-01-09 1.303636 0.806432 0.556127 2000-01-10 -1.207910 2.382836 0.959141 >>> pp.agg(np.sum) #使用方法一 A -0.801575 B 3.390298 C 1.956388 dtype: float64 >>> pp.agg('sum') #使用方法二 A -0.801575 B 3.390298 C 1.956388 dtype: float64 >>> pp.A.agg('sum') #给当个列使用 -0.8015753184519548
>>> tsdf.agg({'A':['mean','sum'],'B':'sum'}) #分别对列进行多个或单个函数计算
A B
mean -0.133596 NaN
sum -0.801575 3.390298