pandas聚合aggregate

#!/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

 

转载于:https://www.cnblogs.com/ggzhangxiaochao/p/9094251.html