ENH: make Series.ptp() handle missing values · Issue #11163 · pandas-dev/pandas (original) (raw)

Currently (in master), Series.ptp() is just implemented using np.ptp() and so the method will return nan for any Series that has one or more missing values:

>>> s = pd.Series([5, 0, np.nan, -3, 2])
>>> s.ptp()
nan

It is simple to write s.max() - s.min() instead, but the ptp() result is surprising as most pandas methods are designed to handle missing data gracefully. I think most users would expect the ptp() method to ignore NaN.

If there is any agreement as to whether ptp() should be changed, I would like to work on a pull request!

Extending the idea, it might be useful to have both DataFrame.ptp() and groupby.ptp() methods.

For this example DataFrame...

df = pd.DataFrame({'a': [1, 2, 2, 1, 1],
                   'b': [3, 11, 72, 46, 32],
                   'c': [1.2, 6.7, 13.9, np.nan, -7.7],
                   'd': ['v', 'w', 'x', 'y', 'z']})

...I would expect the following behaviour:

>>> df.ptp()
a      1
b     69
c   12.7
dtype: float64

>>> df.ptp(axis=1)
0     2.0
1     9.0
2    70.0
3    45.0
4    39.7
dtype: float64

>>> df.groupby('a').ptp()
    b    c
a         
1  43  8.9
2  61  7.2

Again, if there is any consensus from the community on whether these additional methods should be added, I'd be happy to work on the pull request.