浅谈pandas中shift和diff函数关系

2019-11-25 14:58:46

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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  D2017-01-01  0  1  2  32017-01-02  4  5  6  72017-01-03  8  9 10 112017-01-04 12 13 14 152017-01-05 16 17 18 192017-01-06 20 21 22 23In [3]: df.shift(2,axis=0,freq='2D')Out[3]:       A  B  C  D2017-01-05  0  1  2  32017-01-06  4  5  6  72017-01-07  8  9 10 112017-01-08 12 13 14 152017-01-09 16 17 18 192017-01-10 20 21 22 23In [4]: df.shift(2,axis=1,freq='2D')Out[4]:       A  B  C  D2017-01-05  0  1  2  32017-01-06  4  5  6  72017-01-07  8  9 10 112017-01-08 12 13 14 152017-01-09 16 17 18 192017-01-10 20 21 22 23In [5]: df.shift(2,freq='2D')Out[5]:       A  B  C  D2017-01-05  0  1  2  32017-01-06  4  5  6  72017-01-07  8  9 10 112017-01-08 12 13 14 152017-01-09 16 17 18 192017-01-10 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  Dr1  0  1  2  3r2  4  5  6  7r3  8  9 10 11r4 12 13 14 15r5 16 17 18 19r6 20 21 22 23In [7]: df.shift(periods=2,axis=0)Out[7]:    A   B   C   Dr1  NaN  NaN  NaN  NaNr2  NaN  NaN  NaN  NaNr3  0.0  1.0  2.0  3.0r4  4.0  5.0  6.0  7.0r5  8.0  9.0 10.0 11.0r6 12.0 13.0 14.0 15.0In [8]: df.shift(periods=-2,axis=0)Out[8]:    A   B   C   Dr1  8.0  9.0 10.0 11.0r2 12.0 13.0 14.0 15.0r3 16.0 17.0 18.0 19.0r4 20.0 21.0 22.0 23.0r5  NaN  NaN  NaN  NaNr6  NaN  NaN  NaN  NaNIn [9]: df.shift(periods=2,axis=1)Out[9]:   A  B   C   Dr1 NaN NaN  0.0  1.0r2 NaN NaN  4.0  5.0r3 NaN NaN  8.0  9.0r4 NaN NaN 12.0 13.0r5 NaN NaN 16.0 17.0r6 NaN NaN 20.0 21.0In [10]: df.shift(periods=-2,axis=1)Out[10]:    A   B  C  Dr1  2.0  3.0 NaN NaNr2  6.0  7.0 NaN NaNr3 10.0 11.0 NaN NaNr4 14.0 15.0 NaN NaNr5 18.0 19.0 NaN NaNr6 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  Dr1 NaN NaN NaN NaNr2 NaN NaN NaN NaNr3 8.0 8.0 8.0 8.0r4 8.0 8.0 8.0 8.0r5 8.0 8.0 8.0 8.0r6 8.0 8.0 8.0 8.0In [14]: df -df.diff(periods=2,axis=0)Out[14]:    A   B   C   Dr1  NaN  NaN  NaN  NaNr2  NaN  NaN  NaN  NaNr3  0.0  1.0  2.0  3.0r4  4.0  5.0  6.0  7.0r5  8.0  9.0 10.0 11.0r6 12.0 13.0 14.0 15.0In [15]: df.shift(periods=2,axis=0)Out[15]:    A   B   C   Dr1  NaN  NaN  NaN  NaNr2  NaN  NaN  NaN  NaNr3  0.0  1.0  2.0  3.0r4  4.0  5.0  6.0  7.0r5  8.0  9.0 10.0 11.0r6 12.0 13.0 14.0 15.0

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