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
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
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
- 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