计算 Pandas DataFrame 的百分比变化
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Calculate percent change on a Pandas DataFrame
提问by abutremutante
I have the following DataFrame:
我有以下数据帧：
Value 1lag
Date
2005-04-01 258.682029 214.382786
2005-05-01 173.253998 258.682029
2005-06-01 244.432029 173.253998
2005-07-01 213.706019 244.432029
2005-08-01 213.670665 213.706019
Those are absolute values of two time series. However, I don't want those absolute values, I want the variations of them, so they get to look like this:
这些是两个时间序列的绝对值。但是，我不想要这些绝对值，我想要它们的变化，所以它们看起来像这样：
Value 1lag
Date
2005-04-01 NaN NaN
2005-05-01 0.3302 -0.2066
2005-06-01 -0.4108 0.3302
2005-07-01 0.1257 -0.4108
2005-08-01 0.0002 0.1257
Is there an easy command to do that? If not, what would be your suggestion to achieve that result?
有没有一个简单的命令来做到这一点？如果没有，您对实现该结果的建议是什么？
回答by Alexander
You can just use pct_change()on the dataframe.
您可以只pct_change()在数据框上使用。
>>> df.pct_change()
Value 1lag
Date
2005-04-01 NaN NaN
2005-05-01 -0.330243 0.206636
2005-06-01 0.410831 -0.330243
2005-07-01 -0.125704 0.410831
2005-08-01 -0.000165 -0.125704
Comparing the results above with yours, you would need to use -df.pct_change()if you want reverse the change as you have done.
将上面的结果与您的结果进行比较，-df.pct_change()如果您想像所做的那样逆转更改，则需要使用。
回答by Roman Pekar
Something like this?
像这样的东西？
>>> 1 - df / df.shift(1)
Value 1lag
Date
2005-04-01 NaN NaN
2005-05-01 0.330243 -0.206636
2005-06-01 -0.410831 0.330243
2005-07-01 0.125704 -0.410831
2005-08-01 0.000165 0.125704
回答by Harold Henson
You can replicate Alexander's response with the code below. I am afraid Roman's response has the wrong sign
您可以使用以下代码复制 Alexander 的回复。恐怕罗曼的回应有错误的迹象
>>> (df-df.shift(1))/df.shift(1)
Value 1lag
2011-01-31 NaN NaN
2011-02-28 -0.330243 0.206636
2011-03-31 0.410831 -0.330243
2011-04-30 -0.125704 0.410831
2011-05-31 -0.000165 -0.125704
