下面小编就为大家分享一篇浅谈pandas中shift和diff函数关系，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧
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-<span style="color:transparent">来源gaodai#ma#com搞*!代#%^码网</span>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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