通过?pandas.DataFrame.shift命令查看帮助文档
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Signature: pandas.DataFrame.shift(self, periods=1, freq=None, axis=0)
Docstring:
Shift index by desired number of periods with an optional time freq
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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
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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
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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
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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
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通过?pandas.DataFrame.diff命令查看帮助文档，发现和shift函数形式一样
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Signature: pd.DataFrame.diff(self, periods=1, axis=0)
Docstring:
1st discrete difference of object
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下面看看diff函数和shift函数之间的关系
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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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以上这篇浅谈pandas中shift和diff函数关系就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/kancy110/article/details/77138929