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