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DOC: Fix PR01 errors in multiple files (#57438) (#57504)
* DOC: Fix PR01 errors in multiple files (#57438) Fixed PR01 errors (#57438) and added parameter descriptions for: - `pandas.Grouper` - `pandas.core.groupby.DataFrameGroupBy.cummax` - `pandas.core.groupby.DataFrameGroupBy.cummin` - `pandas.core.groupby.DataFrameGroupBy.cumprod` - `pandas.core.groupby.DataFrameGroupBy.cumsum` - `pandas.core.groupby.DataFrameGroupBy.filter` - `pandas.core.groupby.DataFrameGroupBy.pct_change` - `pandas.core.groupby.DataFrameGroupBy.rolling` - `pandas.core.groupby.SeriesGroupBy.cummax` - `pandas.core.groupby.SeriesGroupBy.cummin` - `pandas.core.groupby.SeriesGroupBy.cumprod` - `pandas.core.groupby.SeriesGroupBy.cumsum` - `pandas.core.groupby.SeriesGroupBy.cumprod` Fixed functions also removed from code_checks.sh * Updated formatting * Corrected E501 formatting * Remove trailing whitespaces * Fix PR02 error from docstring inheritance for resampler nunique method Resolved a PR02 documentation error arising from the inherited `nunique` method docstring within `groupby.SeriesGroupBy.nunique`. The issue was due to the `resample.Resampler.nunique` method lacking a `dropna` parameter, which is present in the inherited docstring. - Introduced `_nunique_extra_params` to dynamically insert parameter documentation only where applicable (i.e. where `groupby.SeriesGroupBy.nunique`is). * Resolve request * Undo mistake * Remove unnecessary import to fix ruff check
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‎ci/code_checks.sh‎

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@@ -1477,7 +1477,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
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pandas.DatetimeIndex.std\
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pandas.ExcelFile\
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pandas.ExcelFile.parse\
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pandas.Grouper\
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pandas.HDFStore.append\
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pandas.HDFStore.put\
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pandas.Index.get_indexer_for\
@@ -1538,21 +1537,7 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
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pandas.api.types.is_float\
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pandas.api.types.is_hashable\
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pandas.api.types.is_integer\
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pandas.core.groupby.DataFrameGroupBy.cummax\
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pandas.core.groupby.DataFrameGroupBy.cummin\
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pandas.core.groupby.DataFrameGroupBy.cumprod\
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pandas.core.groupby.DataFrameGroupBy.cumsum\
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pandas.core.groupby.DataFrameGroupBy.filter\
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pandas.core.groupby.DataFrameGroupBy.pct_change\
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pandas.core.groupby.DataFrameGroupBy.rolling\
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pandas.core.groupby.SeriesGroupBy.cummax\
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pandas.core.groupby.SeriesGroupBy.cummin\
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pandas.core.groupby.SeriesGroupBy.cumprod\
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pandas.core.groupby.SeriesGroupBy.cumsum\
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pandas.core.groupby.SeriesGroupBy.filter\
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pandas.core.groupby.SeriesGroupBy.nunique\
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pandas.core.groupby.SeriesGroupBy.pct_change\
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pandas.core.groupby.SeriesGroupBy.rolling\
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pandas.core.resample.Resampler.max\
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pandas.core.resample.Resampler.min\
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pandas.core.resample.Resampler.quantile\

‎pandas/core/groupby/generic.py‎

Lines changed: 13 additions & 21 deletions
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@@ -723,15 +723,22 @@ def nunique(self, dropna: bool = True) -> Series | DataFrame:
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"""
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Return number of unique elements in the group.
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Parameters
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----------
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dropna : bool, default True
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Don't include NaN in the counts.
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Returns
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-------
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Series
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Number of unique values within each group.
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Examples
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See Also
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--------
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For SeriesGroupby:
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core.resample.Resampler.nunique : Method nunique for Resampler.
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Examples
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--------
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>>> lst = ["a", "a", "b", "b"]
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>>> ser = pd.Series([1, 2, 3, 3], index=lst)
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>>> ser
@@ -744,25 +751,6 @@ def nunique(self, dropna: bool = True) -> Series | DataFrame:
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a 2
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b 1
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dtype: int64
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For Resampler:
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>>> ser = pd.Series(
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... [1, 2, 3, 3],
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... index=pd.DatetimeIndex(
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... ["2023-01-01", "2023-01-15", "2023-02-01", "2023-02-15"]
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... ),
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... )
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>>> ser
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2023-01-01 1
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2023-01-15 2
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2023-02-01 3
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2023-02-15 3
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dtype: int64
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>>> ser.resample("MS").nunique()
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2023-01-01 2
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2023-02-01 1
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Freq: MS, dtype: int64
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"""
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ids, ngroups = self._grouper.group_info
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val = self.obj._values
@@ -1942,6 +1930,10 @@ def filter(self, func, dropna: bool = True, *args, **kwargs) -> DataFrame:
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dropna : bool
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Drop groups that do not pass the filter. True by default; if False,
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groups that evaluate False are filled with NaNs.
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*args
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Additional positional arguments to pass to `func`.
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**kwargs
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Additional keyword arguments to pass to `func`.
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Returns
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-------

‎pandas/core/groupby/groupby.py‎

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@@ -4672,6 +4672,14 @@ def cumprod(self, *args, **kwargs) -> NDFrameT:
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"""
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Cumulative product for each group.
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Parameters
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----------
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*args : tuple
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Positional arguments to be passed to `func`.
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**kwargs : dict
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Additional/specific keyword arguments to be passed to the function,
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such as `numeric_only` and `skipna`.
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Returns
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-------
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Series or DataFrame
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"""
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Cumulative sum for each group.
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Parameters
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----------
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*args : tuple
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Positional arguments to be passed to `func`.
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**kwargs : dict
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Additional/specific keyword arguments to be passed to the function,
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such as `numeric_only` and `skipna`.
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Returns
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-------
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Series or DataFrame
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"""
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Cumulative min for each group.
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Parameters
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----------
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numeric_only : bool, default False
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Include only `float`, `int` or `boolean` data.
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**kwargs : dict, optional
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Additional keyword arguments to be passed to the function, such as `skipna`,
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to control whether NA/null values are ignored.
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Returns
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-------
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Series or DataFrame
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"""
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Cumulative max for each group.
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Parameters
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----------
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numeric_only : bool, default False
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Include only `float`, `int` or `boolean` data.
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**kwargs : dict, optional
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Additional keyword arguments to be passed to the function, such as `skipna`,
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to control whether NA/null values are ignored.
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Returns
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-------
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Series or DataFrame
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"""
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Calculate pct_change of each value to previous entry in group.
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Parameters
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----------
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periods : int, default 1
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Periods to shift for calculating percentage change. Comparing with
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a period of 1 means adjacent elements are compared, whereas a period
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of 2 compares every other element.
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fill_method : FillnaOptions or None, default None
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Specifies how to handle missing values after the initial shift
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operation necessary for percentage change calculation. Users are
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encouraged to handle missing values manually in future versions.
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Valid options are:
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- A FillnaOptions value ('ffill', 'bfill') for forward or backward filling.
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- None to avoid filling.
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Note: Usage is discouraged due to impending deprecation.
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limit : int or None, default None
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The maximum number of consecutive NA values to fill, based on the chosen
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`fill_method`. Address NaN values prior to using `pct_change` as this
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parameter is nearing deprecation.
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freq : str, pandas offset object, or None, default None
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The frequency increment for time series data (e.g., 'M' for month-end).
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If None, the frequency is inferred from the index. Relevant for time
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series data only.
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Returns
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-------
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Series or DataFrame

‎pandas/core/groupby/grouper.py‎

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@@ -68,6 +68,10 @@ class Grouper:
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Parameters
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*args
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Currently unused, reserved for future use.
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**kwargs
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Dictionary of the keyword arguments to pass to Grouper.
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key : str, defaults to None
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Groupby key, which selects the grouping column of the target.
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level : name/number, defaults to None

‎pandas/core/resample.py‎

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@@ -59,7 +59,6 @@
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NDFrame,
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_shared_docs,
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)
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from pandas.core.groupby.generic import SeriesGroupBy
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from pandas.core.groupby.groupby import (
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BaseGroupBy,
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GroupBy,
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return self._downsample("ohlc")
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@final
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@doc(SeriesGroupBy.nunique)
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def nunique(self):
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"""
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Return number of unique elements in the group.
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Returns
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-------
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Series
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Number of unique values within each group.
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See Also
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--------
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core.groupby.SeriesGroupBy.nunique : Method nunique for SeriesGroupBy.
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Examples
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--------
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>>> ser = pd.Series(
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... [1, 2, 3, 3],
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... index=pd.DatetimeIndex(
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... ["2023-01-01", "2023-01-15", "2023-02-01", "2023-02-15"]
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... ),
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... )
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>>> ser
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2023-01-01 1
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2023-01-15 2
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2023-02-01 3
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2023-02-15 3
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dtype: int64
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>>> ser.resample("MS").nunique()
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2023-01-01 2
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Freq: MS, dtype: int64
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"""
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return self._downsample("nunique")
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@final

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