Calculate percent change on a Pandas DataFrame

Question

I have the following DataFrame:

                 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:

                 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?

Answer 1

You can just use pct_change() on the dataframe.

>>> 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.

Answer 2

Something like this?

>>> 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