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

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