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BUG: groupby().rolling(freq) with monotonic dates within groups #46065

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merged 1 commit into from Feb 19, 2022
Merged

BUG: groupby().rolling(freq) with monotonic dates within groups #46065

merged 1 commit into from Feb 19, 2022

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@mroeschke
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@mroeschke mroeschke commented Feb 18, 2022

@mroeschke mroeschke added this to the 1.4.2 milestone Feb 19, 2022
rhshadrach
rhshadrach reviewed Feb 19, 2022
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@rhshadrach rhshadrach left a comment

lgtm, does the iteration in _validate_datetimelike_monotonic have a big performance impact?

@mroeschke
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@mroeschke mroeschke commented Feb 19, 2022

There will be a slight performance impact during the validation (that can be a 1 time hit) that scales with the number of groups. Here's validating 3 vs 100 groups

In [1]:
   ...:         dates = date_range(start="2016-01-01 09:30:00", periods=20, freq="s")
   ...:         df = DataFrame(
   ...:             {
   ...:                 "A": [1] * 20 + [2] * 12 + [3] * 8,
   ...:                 "B": np.concatenate((dates, dates)),
   ...:                 "C": np.arange(40),
   ...:             }
   ...:         )

In [2]: %timeit df.groupby("A").rolling("4s", on="B")
764 µs ± 38.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

In [6]:
   ...:         dates = date_range(start="2016-01-01 09:30:00", periods=50, freq="s")
   ...:         df = DataFrame(
   ...:             {
   ...:                 "A": np.arange(100),
   ...:                 "B": np.concatenate((dates, dates)),
   ...:                 "C": np.arange(100),
   ...:             }
   ...:         )

In [7]: %timeit df.groupby("A").rolling("4s", on="B")
2.26 ms ± 30.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

@mroeschke mroeschke merged commit ad0f1bf into pandas-dev:main Feb 19, 2022
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@meeseeksmachine meeseeksmachine mentioned this pull request Feb 19, 2022
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meeseeksmachine added a commit to meeseeksmachine/pandas that referenced this issue Feb 19, 2022
@mroeschke @meeseeksmachine
Backport PR pandas-dev#46065: BUG: groupby().rolling(freq) with monot…
7e68fe6 
…onic dates within groups
@mroeschke mroeschke deleted the bug/gb_roll_mon branch Feb 19, 2022
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@rhshadrach rhshadrach commented Feb 19, 2022

@mroeschke - It looks like the call to groupby(...).rolling(...) went from O(1) to O(n), is that right? I would describe that as being something significant. Perhaps it can't be avoided, but I'd like to have another look. In any case, I don't think we should be merging PRs (especially our own) when there are outstanding questions.

jreback
jreback reviewed Feb 19, 2022
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pandas/core/window/rolling.py
if self._on.hasnans:
raise ValueError(f"{on} must not have any NaT values.")
for group_indices in self._grouper.indices.values():
group_on = self._on.take(group_indices)
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yeah this is materializing things
prob not a great idea

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@mroeschke mroeschke Feb 19, 2022 •
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self._on is already materialized in __init__. Is that what is meant by materialized?

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@jreback jreback commented Feb 19, 2022

@mroeschke there was no hurry on this one

mroeschke added a commit to mroeschke/pandas that referenced this issue Feb 19, 2022
@mroeschke
Revert "BUG: groupby().rolling(freq) with monotonic dates within grou…
feae8d1 
…ps (pandas-dev#46065)"

This reverts commit ad0f1bf.
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@mroeschke mroeschke commented Feb 19, 2022 •
edited

Sorry I was too eager on the LGTM. #46078 reverts this PR.

This validation is O(number of groups). Happy to have a suggestion regarding a more performant solution.

@simonjayhawkins simonjayhawkins removed this from the 1.4.2 milestone Feb 20, 2022
simonjayhawkins pushed a commit that referenced this issue Feb 20, 2022
@mroeschke
Revert "BUG: groupby().rolling(freq) with monotonic dates within grou…
afc06e7 
…ps (#46065)" (#46078)

This reverts commit ad0f1bf.
@simonjayhawkins simonjayhawkins mentioned this pull request Feb 20, 2022
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BUG: groupby().rolling("freq") validation raises even if dates are sorted within each group
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