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DOC: Add example for pandas.DataFrame.rolling() with on #50139

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Merged
merged 5 commits into from
Dec 14, 2022
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DOC: Add example for pandas.DataFrame.rolling() with on #50139

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b5052c3
Add example for pandas.DataFrame.rolling()
Dec 9, 2022
2df759b
Reduce two examples to one
Dec 11, 2022
752c41f
Merge branch 'pandas-dev:main' into add_exam_rolling_on
luke396 Dec 12, 2022
0cd3714
a better exmple
Dec 13, 2022
e65f18f
a better layout
Dec 14, 2022
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23 changes: 23 additions & 0 deletions pandas/core/window/rolling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1110,6 +1110,29 @@ class Window(BaseWindow):
2 2.958621
3 NaN
4 NaN

**on**

Rolling sum with a window length of 2 on specific columon.
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2 days?


>>> df = pd.DataFrame({'A' : [1, 3, 5, np.nan, 7],
... 'B' : [2, 4, np.nan, 6, 8]})

>>> df
A B
0 1.0 2.0
1 3.0 4.0
2 5.0 NaN
3 NaN 6.0
4 7.0 8.0

>>> df.rolling(2, on='A').sum()
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wouldn't this give the same result even without on='A'? might be more illustrative to have an example in which on= makes a difference

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Without on='A', the result will be like this:

>>> df.rolling(2).sum()
     A     B
0  NaN   NaN
1  4.0   6.0
2  8.0   NaN
3  NaN   NaN
4  NaN  14.0

Do I need to add this result as comparison?

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@MarcoGorelli MarcoGorelli Dec 13, 2022 •
edited
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I was thinking more something like

In [62]: df = pd.DataFrame({
    ...:     'A': to_datetime(['2020-01-01', '2020-01-01', '2020-01-02']),
    ...:     'B': [1,2,3],
    ...:     },
    ...:     index=date_range('2020', periods=3))

in which if you do rolling('D'), then the values of 'B' differ if you do on='A' (instead of the default, which uses the index)

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Oh, I see what that means. How about the following:

>>> df = pd.DataFrame({
...     'A': [pd.to_datetime('2020-01-01'),
...           pd.to_datetime('2020-01-01'),
...           pd.to_datetime('2020-01-02'),],
...     'B': [1, 2, 3], },
...     index=pd.date_range('2020', periods=3))

>>> df
                    A  B
2020-01-01 2020-01-01  1
2020-01-02 2020-01-01  2
2020-01-03 2020-01-02  3

>>> df['B'].rolling('2D').sum()  # to avoid warning when sum on 'A'
2020-01-01    1.0
2020-01-02    3.0
2020-01-03    5.0
Freq: D, Name: B, dtype: float64

>>> df.rolling('2D', on='A').sum()   # value of 'B' is differ from above
                    A    B
2020-01-01 2020-01-01  1.0
2020-01-02 2020-01-01  3.0
2020-01-03 2020-01-02  6.0

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@MarcoGorelli MarcoGorelli Dec 13, 2022 •
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# value of 'B' differs from above

other than that, looks fine

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Thanks for the correction, my English skills are a little rusty.

A B
0 1.0 NaN
1 3.0 6.0
2 5.0 NaN
3 NaN NaN
4 7.0 14.0
"""

_attributes = [
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