API/ENH: master issue for pd.rolling_apply · Issue #8659 · pandas-dev/pandas (original) (raw)

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Description

Catchall for rolling_* issues:

• ENH: window functions need to exclude nuiscance columns #12537 exclude nuiscance columns
• ENH: support timedeltas with window functions #12536 fully support timedeltas
• Non-Reducing Return Values in Rolling .apply #4130 (return type of rolling_apply)
• PERF/ENH: np.apply_along_axis -> reduce in moments.py/nanops.py #3185 (stop using np.apply_along_axis)
• ENH/API: rolling_apply to pass frames to the rolled function (rather than ndarrays) #5071, Functions applied on .expanding() receive ndarrays rather than pandas objects #12950 (pass frames, operate 2-D)
• BUG: pandas rolling_quantile does not use interpolation #9413 rolling_quantile
• Implement high performance rolling_rank #9481 rolling_idxmax
• Rolling qcut #10759 rolling_qcut
• ENH: Support returning the same dtype as the caller for window ops (including extension dtypes) #11446, rolling.apply or rolling.agg doesn't work on string columns #20773 casting in .apply
• Implementing rolling min/max functions that can retain the original type #12595 casting in all window functions (min/max are good)
• ENH: Add the moment function as DataFrame and Series method WITH namespacing #10702 namespacing (though prob just create a Rolling object), API: provide Rolling/Expanding/EWM objects for deferred rolling type calculations #10702 #11603
• TST: test_window/Datetimelike adjustments #12535 testing of datetimelike with nans
• BUG: window function count should count anything non-null #12541 count should be integer dtype (and deal with infs)
• API: Table-wise rolling / expanding / EWM function application #15095 Table-wise

Hi all,

I intended to apply a function that gives on each day a ranking based on means calculated from previous n-day's data. The natural way is to use pd.rolling_apply. A toy example:

In [93]: df = pd.DataFrame(np.random.randint(10, size=20).reshape(4, 5))

In [94]: df
Out[94]: 
   0  1  2  3  4
0  2  0  0  2  0
1  9  5  5  6  1
2  2  3  6  8  8
3  5  1  2  9  0

In [95]: import bottleneck as bn

In [96]: bn.nanrankdata(df.mean())
Out[96]: array([ 4. ,  1.5,  3. ,  5. ,  1.5])

Up to now, it is cool. Then:

In [97]: pd.rolling_apply(df, 2, lambda x: bn.nanrankdata(bn.nanmean(x, axis=0)))
Out[97]: 
    0   1   2   3   4
0 NaN NaN NaN NaN NaN
1   1   1   1   1   1
2   1   1   1   1   1
3   1   1   1   1   1

This is clearly wrong. Is this a bug?