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How do I get the percentage change of values ​​in a dataframe while keeping NaN values?

I have the following DataFrame:

   Date                 A                
   2015-01-01          10               
   2015-01-02          14               
   2015-01-05          NaN               
   2015-01-06          NaN                
   2015-01-07          5
   2015-01-10          1  
   2015-01-11          NaN  
   2015-01-12          21  
   2015-01-14          13

And I would like to receive a dataframe with pct change in a dataframe only if the two values ​​are adjacent and not separated by NaN.

i.e. I would like to:

        Date                 A                             
       2015-01-02         0.2857                           
       2015-01-10         -0.8  
       2015-01-14         -0.38

But if I do this : df.pct_change()

, it will return the pct change between 14 and 5 for 201-01-07

+3


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2 answers


Use pct_change

and shift

:

df.pct_change()[df.shift(1).notnull()].dropna()


Output:

                   A
Date                
2015-01-02  0.400000
2015-01-10 -0.800000
2015-01-14 -0.380952
+3


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use cumsum

on isnull

to find groups ingroupby 



s = df.set_index('Date').A
c = n.cumsum()
s.groupby(c).pct_change().dropna().reset_index()

        Date         A
0 2015-01-02  0.400000
1 2015-01-10 -0.800000
2 2015-01-14 -0.380952
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