fixed resample.Resampler docstring errors in min, max, mean, prod, std, var as indicated by vaildate_docstrings.py

Author: knowltod

pandas/core/resample.py

@@ -1,7 +1,6 @@
 from __future__ import annotations
 
 import copy
-import warnings
 from textwrap import dedent
 from typing import (
     TYPE_CHECKING,
@@ -11,10 +10,10 @@
     no_type_check,
     overload,
 )
+import warnings
 
 import numpy as np
 
-import pandas.core.algorithms as algos
 from pandas._libs import lib
 from pandas._libs.tslibs import (
     BaseOffset,
@@ -26,12 +25,17 @@
     to_offset,
 )
 from pandas._typing import NDFrameT
-from pandas.core.apply import ResamplerWindowApply
-from pandas.core.arrays import ArrowExtensionArray
-from pandas.core.base import (
-    PandasObject,
-    SelectionMixin,
+from pandas.errors import AbstractMethodError
+from pandas.util._decorators import (
+    Appender,
+    Substitution,
+    doc,
 )
+from pandas.util._exceptions import (
+    find_stack_level,
+    rewrite_warning,
+)
+
 from pandas.core.dtypes.dtypes import (
     ArrowDtype,
     PeriodDtype,
@@ -40,6 +44,14 @@
     ABCDataFrame,
     ABCSeries,
 )
+
+import pandas.core.algorithms as algos
+from pandas.core.apply import ResamplerWindowApply
+from pandas.core.arrays import ArrowExtensionArray
+from pandas.core.base import (
+    PandasObject,
+    SelectionMixin,
+)
 from pandas.core.generic import (
     NDFrame,
     _shared_docs,
@@ -68,7 +80,7 @@
     timedelta_range,
 )
 from pandas.core.reshape.concat import concat
-from pandas.errors import AbstractMethodError
+
 from pandas.tseries.frequencies import (
     is_subperiod,
     is_superperiod,
@@ -77,26 +89,13 @@
     Day,
     Tick,
 )
-from pandas.util._decorators import (
-    Appender,
-    Substitution,
-    doc,
-)
-from pandas.util._exceptions import (
-    find_stack_level,
-    rewrite_warning,
-)
 
 if TYPE_CHECKING:
     from collections.abc import (
         Callable,
         Hashable,
     )
 
-    from pandas import (
-        DataFrame,
-        Series,
-    )
     from pandas._typing import (
         Any,
         AnyArrayLike,
@@ -115,6 +114,11 @@
         npt,
     )
 
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
 _shared_docs_kwargs: dict[str, str] = {}
 
 
@@ -356,8 +360,7 @@ def pipe(
         axis="",
     )
     def aggregate(self, func=None, *args, **kwargs):
-        result = ResamplerWindowApply(
-            self, func, args=args, kwargs=kwargs).agg()
+        result = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg()
         if result is None:
             how = func
             result = self._groupby_and_aggregate(how, *args, **kwargs)
@@ -444,14 +447,13 @@ def _groupby_and_aggregate(self, how, *args, **kwargs):
         # Excludes `on` column when provided
         obj = self._obj_with_exclusions
 
-        grouped = get_groupby(obj, by=None, grouper=grouper,
-                              group_keys=self.group_keys)
+        grouped = get_groupby(obj, by=None, grouper=grouper, group_keys=self.group_keys)
 
         try:
             if callable(how):
                 # TODO: test_resample_apply_with_additional_args fails if we go
                 #  through the non-lambda path, not clear that it should.
-                def func(x): return how(x, *args, **kwargs)
+                func = lambda x: how(x, *args, **kwargs)
                 result = grouped.aggregate(func)
             else:
                 result = grouped.aggregate(how, *args, **kwargs)
@@ -1647,16 +1649,14 @@ def _apply(self, f, *args, **kwargs):
         """
 
         def func(x):
-            x = self._resampler_cls(
-                x, timegrouper=self._timegrouper, gpr_index=self.ax)
+            x = self._resampler_cls(x, timegrouper=self._timegrouper, gpr_index=self.ax)
 
             if isinstance(f, str):
                 return getattr(x, f)(**kwargs)
 
             return x.apply(f, *args, **kwargs)
 
-        result = _apply(self._groupby, func,
-                        include_groups=self.include_groups)
+        result = _apply(self._groupby, func, include_groups=self.include_groups)
         return self._wrap_result(result)
 
     _upsample = _apply
@@ -2074,17 +2074,14 @@ def __init__(
         if closed not in {None, "left", "right"}:
             raise ValueError(f"Unsupported value {closed} for `closed`")
         if convention not in {None, "start", "end", "e", "s"}:
-            raise ValueError(
-                f"Unsupported value {convention} for `convention`")
+            raise ValueError(f"Unsupported value {convention} for `convention`")
 
         if (
-            (key is None and obj is not None and isinstance(
-                obj.index, PeriodIndex))  # type: ignore[attr-defined]
+            (key is None and obj is not None and isinstance(obj.index, PeriodIndex))  # type: ignore[attr-defined]
             or (
                 key is not None
                 and obj is not None
-                # type: ignore[index]
-                and getattr(obj[key], "dtype", None) == "period"
+                and getattr(obj[key], "dtype", None) == "period"  # type: ignore[index]
             )
         ):
             freq = to_offset(freq, is_period=True)
@@ -2304,8 +2301,7 @@ def _adjust_bin_edges(
                 edges_dti = binner.tz_localize(None)
                 edges_dti = (
                     edges_dti
-                    + Timedelta(days=1,
-                                unit=edges_dti.unit).as_unit(edges_dti.unit)
+                    + Timedelta(days=1, unit=edges_dti.unit).as_unit(edges_dti.unit)
                     - Timedelta(1, unit=edges_dti.unit).as_unit(edges_dti.unit)
                 )
                 bin_edges = edges_dti.tz_localize(binner.tz).asi8
@@ -2335,8 +2331,7 @@ def _get_time_delta_bins(self, ax: TimedeltaIndex):
             )
 
         if not len(ax):
-            binner = labels = TimedeltaIndex(
-                data=[], freq=self.freq, name=ax.name)
+            binner = labels = TimedeltaIndex(data=[], freq=self.freq, name=ax.name)
             return binner, [], labels
 
         start, end = ax.min(), ax.max()
@@ -2375,8 +2370,7 @@ def _get_time_period_bins(self, ax: DatetimeIndex):
             )
             return binner, [], labels
 
-        labels = binner = period_range(
-            start=ax[0], end=ax[-1], freq=freq, name=ax.name)
+        labels = binner = period_range(start=ax[0], end=ax[-1], freq=freq, name=ax.name)
 
         end_stamps = (labels + freq).asfreq(freq, "s").to_timestamp()
         if ax.tz:
@@ -2405,12 +2399,10 @@ def _get_period_bins(self, ax: PeriodIndex):
         if not len(memb):
             # index contains no valid (non-NaT) values
             bins = np.array([], dtype=np.int64)
-            binner = labels = PeriodIndex(
-                data=[], freq=self.freq, name=ax.name)
+            binner = labels = PeriodIndex(data=[], freq=self.freq, name=ax.name)
             if len(ax) > 0:
                 # index is all NaT
-                binner, bins, labels = _insert_nat_bin(
-                    binner, bins, labels, len(ax))
+                binner, bins, labels = _insert_nat_bin(binner, bins, labels, len(ax))
             return binner, bins, labels
 
         freq_mult = self.freq.n
@@ -2434,8 +2426,7 @@ def _get_period_bins(self, ax: PeriodIndex):
             )
 
             # Get offset for bin edge (not label edge) adjustment
-            start_offset = Period(start, self.freq) - \
-                Period(p_start, self.freq)
+            start_offset = Period(start, self.freq) - Period(p_start, self.freq)
             # error: Item "Period" of "Union[Period, Any]" has no attribute "n"
             bin_shift = start_offset.n % freq_mult  # type: ignore[union-attr]
             start = p_start
@@ -2459,8 +2450,7 @@ def _get_period_bins(self, ax: PeriodIndex):
         bins = memb.searchsorted(prng, side="left")
 
         if nat_count > 0:
-            binner, bins, labels = _insert_nat_bin(
-                binner, bins, labels, nat_count)
+            binner, bins, labels = _insert_nat_bin(binner, bins, labels, nat_count)
 
         return binner, bins, labels
 
@@ -2497,8 +2487,7 @@ def _take_new_index(
         new_values = algos.take_nd(obj._values, indexer)
         return obj._constructor(new_values, index=new_index, name=obj.name)
     elif isinstance(obj, ABCDataFrame):
-        new_mgr = obj._mgr.reindex_indexer(
-            new_axis=new_index, indexer=indexer, axis=1)
+        new_mgr = obj._mgr.reindex_indexer(new_axis=new_index, indexer=indexer, axis=1)
         return obj._constructor_from_mgr(new_mgr, axes=new_mgr.axes)
     else:
         raise ValueError("'obj' should be either a Series or a DataFrame")
@@ -2548,8 +2537,7 @@ def _get_timestamp_range_edges(
     if isinstance(freq, Tick):
         index_tz = first.tz
         if isinstance(origin, Timestamp) and (origin.tz is None) != (index_tz is None):
-            raise ValueError(
-                "The origin must have the same timezone as the index.")
+            raise ValueError("The origin must have the same timezone as the index.")
         if origin == "epoch":
             # set the epoch based on the timezone to have similar bins results when
             # resampling on the same kind of indexes on different timezones
@@ -2778,8 +2766,7 @@ def asfreq(
         if isinstance(obj.index, DatetimeIndex):
             # TODO: should we disallow non-DatetimeIndex?
             unit = obj.index.unit
-        dti = date_range(obj.index.min(), obj.index.max(),
-                         freq=freq, unit=unit)
+        dti = date_range(obj.index.min(), obj.index.max(), freq=freq, unit=unit)
         dti.name = obj.index.name
         new_obj = obj.reindex(dti, method=method, fill_value=fill_value)
         if normalize:
@@ -2809,11 +2796,9 @@ def _asfreq_compat(index: FreqIndexT, freq) -> FreqIndexT:
     if isinstance(index, PeriodIndex):
         new_index = index.asfreq(freq=freq)
     elif isinstance(index, DatetimeIndex):
-        new_index = DatetimeIndex(
-            [], dtype=index.dtype, freq=freq, name=index.name)
+        new_index = DatetimeIndex([], dtype=index.dtype, freq=freq, name=index.name)
     elif isinstance(index, TimedeltaIndex):
-        new_index = TimedeltaIndex(
-            [], dtype=index.dtype, freq=freq, name=index.name)
+        new_index = TimedeltaIndex([], dtype=index.dtype, freq=freq, name=index.name)
     else:  # pragma: no cover
         raise TypeError(type(index))
     return new_index
@@ -2830,6 +2815,5 @@ def _apply(
         target_category=DeprecationWarning,
         new_message=new_message,
     ):
-        result = grouped.apply(
-            how, *args, include_groups=include_groups, **kwargs)
-    return result
+        result = grouped.apply(how, *args, include_groups=include_groups, **kwargs)
+    return result