#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2018/5/24 15:03
# @Author : zhang chao
# @File : s.py
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(10, 4),
index = pd.date_range('1/1/2019', periods=10),
columns = ['A', 'B', 'C', 'D'])
print (df)
print("=======================================")
r = df.rolling(window=3,min_periods=1)
print (r)
print("=======================================")
print("r.aggregate(np.sum)")
print (r.aggregate(np.sum))
print("=======================================")
print("r['A'].aggregate(np.sum)")
print (r['A'].aggregate(np.sum))
print("=======================================")
print("r[['A','B']].aggregate(np.sum)")
print (r[['A','B']].aggregate(np.sum))
print("=======================================")
print("r['A'].aggregate([np.sum,np.mean])")
print (r['A'].aggregate([np.sum,np.mean]))
print("=======================================")
print("r.aggregate({'A' : np.sum,'B' : np.mean})")
print (r.aggregate({'A' : np.sum,'B' : np.mean}))
print("=======================================")
print("r[['A','B']].aggregate([np.sum,np.mean]")
print (r[['A','B']].aggregate([np.sum,np.mean]))
D:\Download\python3\python3.exe D:/Download/pycharmworkspace/s.py
A B C D
2019-01-01 0.744560 0.208652 0.542045 -0.995837
2019-01-02 0.029809 -1.419936 -0.461988 2.177032
2019-01-03 0.613583 1.515249 0.256546 -0.973564
2019-01-04 0.124320 1.152804 0.152107 1.629035
2019-01-05 -0.287906 1.003523 -0.793393 0.231969
2019-01-06 -0.045296 -0.921622 0.894335 0.773035
2019-01-07 -0.695347 0.512206 0.208833 0.953205
2019-01-08 -1.197178 0.142301 -0.854875 -1.044017
2019-01-09 -2.352468 0.047127 -0.351634 -0.373885
2019-01-10 0.678406 0.500947 0.304250 -0.606522
=======================================
Rolling [window=3,min_periods=1,center=False,axis=0]
=======================================
r.aggregate(np.sum)
A B C D
2019-01-01 0.744560 0.208652 0.542045 -0.995837
2019-01-02 0.774369 -1.211283 0.080057 1.181195
2019-01-03 1.387952 0.303966 0.336603 0.207631
2019-01-04 0.767712 1.248117 -0.053335 2.832504
2019-01-05 0.449996 3.671576 -0.384740 0.887441
2019-01-06 -0.208882 1.234705 0.253049 2.634040
2019-01-07 -1.028549 0.594107 0.309775 1.958209
2019-01-08 -1.937820 -0.267115 0.248293 0.682223
2019-01-09 -4.244992 0.701633 -0.997676 -0.464698
2019-01-10 -2.871239 0.690374 -0.902259 -2.024425
=======================================
r['A'].aggregate(np.sum)
2019-01-01 0.744560
2019-01-02 0.774369
2019-01-03 1.387952
2019-01-04 0.767712
2019-01-05 0.449996
2019-01-06 -0.208882
2019-01-07 -1.028549
2019-01-08 -1.937820
2019-01-09 -4.244992
2019-01-10 -2.871239
Freq: D, Name: A, dtype: float64
=======================================
r[['A','B']].aggregate(np.sum)
A B
2019-01-01 0.744560 0.208652
2019-01-02 0.774369 -1.211283
2019-01-03 1.387952 0.303966
2019-01-04 0.767712 1.248117
2019-01-05 0.449996 3.671576
2019-01-06 -0.208882 1.234705
2019-01-07 -1.028549 0.594107
2019-01-08 -1.937820 -0.267115
2019-01-09 -4.244992 0.701633
2019-01-10 -2.871239 0.690374
=======================================
r['A'].aggregate([np.sum,np.mean])
sum mean
2019-01-01 0.744560 0.744560
2019-01-02 0.774369 0.387185
2019-01-03 1.387952 0.462651
2019-01-04 0.767712 0.255904
2019-01-05 0.449996 0.149999
2019-01-06 -0.208882 -0.069627
2019-01-07 -1.028549 -0.342850
2019-01-08 -1.937820 -0.645940
2019-01-09 -4.244992 -1.414997
2019-01-10 -2.871239 -0.957080
=======================================
r.aggregate({'A' : np.sum,'B' : np.mean})
B A
2019-01-01 0.208652 0.744560
2019-01-02 -0.605642 0.774369
2019-01-03 0.101322 1.387952
2019-01-04 0.416039 0.767712
2019-01-05 1.223859 0.449996
2019-01-06 0.411568 -0.208882
2019-01-07 0.198036 -1.028549
2019-01-08 -0.089038 -1.937820
2019-01-09 0.233878 -4.244992
2019-01-10 0.230125 -2.871239
=======================================
r[['A','B']].aggregate([np.sum,np.mean]
A B
sum mean sum mean
2019-01-01 0.744560 0.744560 0.208652 0.208652
2019-01-02 0.774369 0.387185 -1.211283 -0.605642
2019-01-03 1.387952 0.462651 0.303966 0.101322
2019-01-04 0.767712 0.255904 1.248117 0.416039
2019-01-05 0.449996 0.149999 3.671576 1.223859
2019-01-06 -0.208882 -0.069627 1.234705 0.411568
2019-01-07 -1.028549 -0.342850 0.594107 0.198036
2019-01-08 -1.937820 -0.645940 -0.267115 -0.089038
2019-01-09 -4.244992 -1.414997 0.701633 0.233878
2019-01-10 -2.871239 -0.957080 0.690374 0.230125
Process finished with exit code 0