Series pct_change fill_method behavior

[Loupiol]

In[2]: import pandas as pd
  ...: import numpy as np
  ...: pd.__version__
Out[2]: u'0.23.4'

In[3]: ts = pd.Series([np.nan, 1., 2., 3., np.nan, 4., np.nan])
In[4]: ts.pct_change(fill_method = None)
Out[4]: 
0    NaN
1    NaN
2    1.0
3    0.5
4    NaN
5    NaN
6    NaN
dtype: float64

In[5]: ts.pct_change(fill_method = 'pad')
Out[5]: 
0         NaN
1         NaN
2    1.000000
3    0.500000
4    0.000000
5    0.333333
6    0.000000
dtype: float64

In[6]: ts.pct_change(fill_method = 'pad').mask(ts.isnull())
Out[6]: 
0         NaN
1         NaN
2    1.000000
3    0.500000
4         NaN
5    0.333333
6         NaN
dtype: float64

Hello,

After recently updating my version, I noticed a change in behavior of pct_change with missing data. This is related to #19873 .

First example without fill_method is as expected. The second example is the result now and the third is what it used to be. I think the user should be able to choose if she prefers the second or third behavior. I agree that the second example is correct, as it forward fills as expected, but if the time series is a stock price for example, returns on missing days (holidays) were not 0, which can bias some statistics.

I would suggest adding a new parameter, like skipna. I could not find any solution with existing parameters, if I missed something please let me know.

Thanks

[WillAyd]

Thanks for the issue and clear example. I think skipna could make sense as an argument here. PRs are always welcome!

[albertvillanova]

@WillAyd and what should be the expected result for a DataFrame with non-aligned NaNs?

df = pd.DataFrame({'a': [np.nan, 1., 2., 3., np.nan, 4., np.nan], 
                   'b': [np.nan, np.nan,  1.,  2.,  3., np.nan,  4.]})

[WillAyd]

@albertvillanova not sure I understand the distinction you are trying to make; this should work against each series in a DataFrame individually