浅谈pandas中shift和diff函数关系


            通过?pandas.DataFrame.shift命令查看帮助文档





Signature: pandas.DataFrame.shift(self, periods=1, freq=None, axis=0) 

Docstring: 

Shift index by desired number of periods with an optional time freq 



该函数主要的功能就是使数据框中的数据移动，若freq=None时，根据axis的设置，行索引数据保持不变，列索引数据可以在行上上下移动或在列上左右移动；若行索引为时间序列，则可以设置freq参数，根据periods和freq参数值组合，使行索引每次发生periods*freq偏移量滚动，列索引数据不会移动

① 对于DataFrame的行索引是日期型，行索引发生移动，列索引数据不变





In [2]: import pandas as pd

  ...: import numpy as np

  ...: df = pd.DataFrame(np.arange(24).reshape(6,4),index=pd.date_range(start=

  ...: '20170101',periods=6),columns=['A','B','C','D'])

  ...: df

  ...:

Out[2]:

       A  B  C  D

2017-01-01  0  1  2  3

2017-01-02  4  5  6  7

2017-01-03  8  9 10 11

2017-01-04 12 13 14 15

2017-01-05 16 17 18 19

2017-01-06 20 21 22 23

In [3]: df.shift(2,axis=0,freq='2D')

Out[3]:

       A  B  C  D

2017-01-05  0  1  2  3

2017-01-06  4  5  6  7

2017-01-07  8  9 10 11

2017-01-08 12 13 14 15

2017-01-09 16 17 18 19

2017-01-10 20 21 22 23

In [4]: df.shift(2,axis=1,freq='2D')

Out[4]:

       A  B  C  D

2017-01-05  0  1  2  3

2017-01-06  4  5  6  7

2017-01-07  8  9 10 11

2017-01-08 12 13 14 15

2017-01-09 16 17 18 19

2017-01-10 20 21 22 23

In [5]: df.shift(2,freq='2D')

Out[5]:

       A  B  C  D

2017-01-05  0  1  2  3

2017-01-06  4  5  6  7

2017-01-07  8  9 10 11

2017-01-08 12 13 14 15

2017-01-09 16 17 18 19

2017-01-10 20 21 22 23



结论：对于时间索引而言，shift使时间索引发生移动，其他数据保存原样，且axis设置没有任何影响

② 对于DataFrame行索引为非时间序列，行索引数据保持不变，列索引数据发生移动





In [6]: import pandas as pd

  ...: import numpy as np

  ...: df = pd.DataFrame(np.arange(24).reshape(6,4),index=['r1','r2','r3','r4'

  ...: ,'r5','r6'],columns=['A','B','C','D'])

  ...: df

  ...:

Out[6]:

   A  B  C  D

r1  0  1  2  3

r2  4  5  6  7

r3  8  9 10 11

r4 12 13 14 15

r5 16 17 18 19

r6 20 21 22 23

In [7]: df.shift(periods=2,axis=0)

Out[7]:

    A   B   C   D

r1  NaN  NaN  NaN  NaN

r2  NaN  NaN  NaN  NaN

r3  0.0  1.0  2.0  3.0

r4  4.0  5.0  6.0  7.0

r5  8.0  9.0 10.0 11.0

r6 12.0 13.0 14.0 15.0

In [8]: df.shift(periods=-2,axis=0)

Out[8]:

    A   B   C   D

r1  8.0  9.0 10.0 11.0

r2 12.0 13.0 14.0 15.0

r3 16.0 17.0 18.0 19.0

r4 20.0 21.0 22.0 23.0

r5  NaN  NaN  NaN  NaN

r6  NaN  NaN  NaN  NaN

In [9]: df.shift(periods=2,axis=1)

Out[9]:

   A  B   C   D

r1 NaN NaN  0.0  1.0

r2 NaN NaN  4.0  5.0

r3 NaN NaN  8.0  9.0

r4 NaN NaN 12.0 13.0

r5 NaN NaN 16.0 17.0

r6 NaN NaN 20.0 21.0

In [10]: df.shift(periods=-2,axis=1)

Out[10]:

    A   B  C  D

r1  2.0  3.0 NaN NaN

r2  6.0  7.0 NaN NaN

r3 10.0 11.0 NaN NaN

r4 14.0 15.0 NaN NaN

r5 18.0 19.0 NaN NaN

r6 22.0 23.0 NaN NaN



通过?pandas.DataFrame.diff命令查看帮助文档，发现和shift函数形式一样





Signature: pd.DataFrame.diff(self, periods=1, axis=0) 

Docstring: 

1st discrete difference of object 



下面看看diff函数和shift函数之间的关系





In [13]: df.diff(periods=2,axis=0)

Out[13]:

   A  B  C  D

r1 NaN NaN NaN NaN

r2 NaN NaN NaN NaN

r3 8.0 8.0 8.0 8.0

r4 8.0 8.0 8.0 8.0

r5 8.0 8.0 8.0 8.0

r6 8.0 8.0 8.0 8.0

In [14]: df -df.diff(periods=2,axis=0)

Out[14]:

    A   B   C   D

r1  NaN  NaN  NaN  NaN

r2  NaN  NaN  NaN  NaN

r3  0.0  1.0  2.0  3.0

r4  4.0  5.0  6.0  7.0

r5  8.0  9.0 10.0 11.0

r6 12.0 13.0 14.0 15.0

In [15]: df.shift(periods=2,axis=0)

Out[15]:

    A   B   C   D

r1  NaN  NaN  NaN  NaN

r2  NaN  NaN  NaN  NaN

r3  0.0  1.0  2.0  3.0

r4  4.0  5.0  6.0  7.0

r5  8.0  9.0 10.0 11.0

r6 12.0 13.0 14.0 15.0



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