淺談pandas中shift和diff函數(shù)關系
通過?pandas.DataFrame.shift命令查看幫助文檔
Signature: pandas.DataFrame.shift(self, periods=1, freq=None, axis=0) Docstring: Shift index by desired number of periods with an optional time freq
該函數(shù)主要的功能就是使數(shù)據(jù)框中的數(shù)據(jù)移動,若freq=None時,根據(jù)axis的設置,行索引數(shù)據(jù)保持不變,列索引數(shù)據(jù)可以在行上上下移動或在列上左右移動;若行索引為時間序列,則可以設置freq參數(shù),根據(jù)periods和freq參數(shù)值組合,使行索引每次發(fā)生periods*freq偏移量滾動,列索引數(shù)據(jù)不會移動
① 對于DataFrame的行索引是日期型,行索引發(fā)生移動,列索引數(shù)據(jù)不變
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
結(jié)論:對于時間索引而言,shift使時間索引發(fā)生移動,其他數(shù)據(jù)保存原樣,且axis設置沒有任何影響
② 對于DataFrame行索引為非時間序列,行索引數(shù)據(jù)保持不變,列索引數(shù)據(jù)發(fā)生移動
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
通過?pandas.DataFrame.diff命令查看幫助文檔,發(fā)現(xiàn)和shift函數(shù)形式一樣
Signature: pd.DataFrame.diff(self, periods=1, axis=0) Docstring: 1st discrete difference of object
下面看看diff函數(shù)和shift函數(shù)之間的關系
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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