feat: add on parameter in dataframe.rolling() and dataframe.groupby.rolling()
#1556
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| @@ -1052,6 +1052,8 @@ def rolling( | |||
| self, | |||
| window, | |||
| min_periods: int | None = None, | |||
| on: str | None = None, | |||
| closed: Literal["right", "left", "both", "neither"] = "right", | |||
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No code examples are added in this rolling method yet. Could you please help to fill the gaps here?
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Thanks! Maybe add more examples with supported parameters.
| @@ -1083,6 +1085,16 @@ def rolling( | |||
| For a window that is specified by an integer, ``min_periods`` will default | |||
| to the size of the window. | |||
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| on (str, optional): | |||
| For a DataFrame, a column label or Index level on which to calculate the rolling window, | |||
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Can you try the "Index level" cases here?
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Good catch. I removed index level here because I do not plan to support that at this time
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| @@ -1052,6 +1052,8 @@ def rolling( | |||
| self, | |||
| window, | |||
| min_periods: int | None = None, | |||
| on: str | None = None, | |||
| closed: Literal["right", "left", "both", "neither"] = "right", | |||
There was a problem hiding this comment.
Thanks! Maybe add more examples with supported parameters.
| @@ -1035,6 +1035,16 @@ def rolling(self, *args, **kwargs): | |||
| For a window that is specified by an integer, ``min_periods`` will default | |||
| to the size of the window. | |||
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| on (str, optional): | |||
| For a DataFrame, a column label or Index level on which to calculate the rolling window, | |||
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Should "Index level" be removed here too?
bigframes/core/window_spec.py
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| """ | ||
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| grouping_keys: Tuple[ex.DerefOp, ...] = tuple() | ||
| ordering: Tuple[orderings.OrderingExpression, ...] = tuple() | ||
| bounds: Union[RowsWindowBounds, RangeWindowBounds, None] = None | ||
| min_periods: int = 0 | ||
| on: str | None = None |
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hmm, not sure I understand this? I don't see why it should be part of the window spec? we already have "input column".
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Parameter removed. PTAL
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| ): | ||
| self._block = block | ||
| self._window_spec = window_spec | ||
| self._value_column_ids = value_column_ids | ||
| self._drop_null_groups = drop_null_groups | ||
| self._is_series = is_series | ||
| # The column ID that won't be aggregated on. | ||
| # This is equivalent to pandas `on` parameter in rolling() | ||
| self._skip_agg_column_id = skip_agg_column_id |
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I think this will eventually be a set in order to handle multiple columns for on?
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That would be unlikely. Both pandas and SQL allow only one column to be used for on (or its equivalent)
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Hmm, maybe not for on, but for grouping columns. We diverge a bit right now I think for some grouping cases. Anyways, can always change later if need be.
I didn't add this parameter for Series because it does seem to make much sense: Series doesn't have columns.