Merge branch 'main' into fix-doc-hyperlink-60368

Author: mroeschke

.circleci/config.yml

@@ -34,7 +34,6 @@ jobs:
             fi
             python -m pip install --no-build-isolation -ve . -Csetup-args="--werror"
             PATH=$HOME/miniconda3/envs/pandas-dev/bin:$HOME/miniconda3/condabin:$PATH
-            sudo apt-get update && sudo apt-get install -y libegl1 libopengl0
             ci/run_tests.sh
   test-linux-musl:
     docker:

ci/code_checks.sh

@@ -81,7 +81,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Timestamp.resolution PR02" \
         -i "pandas.Timestamp.tzinfo GL08" \
         -i "pandas.arrays.ArrowExtensionArray PR07,SA01" \
-        -i "pandas.arrays.IntervalArray.length SA01" \
         -i "pandas.arrays.NumpyExtensionArray SA01" \
         -i "pandas.arrays.TimedeltaArray PR07,SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.plot PR02" \
@@ -94,11 +93,8 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.core.resample.Resampler.std SA01" \
         -i "pandas.core.resample.Resampler.transform PR01,RT03,SA01" \
         -i "pandas.core.resample.Resampler.var SA01" \
-        -i "pandas.errors.UndefinedVariableError PR01,SA01" \
         -i "pandas.errors.ValueLabelTypeMismatch SA01" \
-        -i "pandas.io.json.build_table_schema PR07,RT03,SA01" \
         -i "pandas.plotting.andrews_curves RT03,SA01" \
-        -i "pandas.plotting.scatter_matrix PR07,SA01" \
         -i "pandas.tseries.offsets.BDay PR02,SA01" \
         -i "pandas.tseries.offsets.BQuarterBegin.is_on_offset GL08" \
         -i "pandas.tseries.offsets.BQuarterBegin.n GL08" \

doc/source/whatsnew/v3.0.0.rst

@@ -56,6 +56,7 @@ Other enhancements
 - :meth:`DataFrame.plot.scatter` argument ``c`` now accepts a column of strings, where rows with the same string are colored identically (:issue:`16827` and :issue:`16485`)
 - :func:`read_parquet` accepts ``to_pandas_kwargs`` which are forwarded to :meth:`pyarrow.Table.to_pandas` which enables passing additional keywords to customize the conversion to pandas, such as ``maps_as_pydicts`` to read the Parquet map data type as python dictionaries (:issue:`56842`)
 - :meth:`DataFrameGroupBy.transform`, :meth:`SeriesGroupBy.transform`, :meth:`DataFrameGroupBy.agg`, :meth:`SeriesGroupBy.agg`, :meth:`RollingGroupby.apply`, :meth:`ExpandingGroupby.apply`, :meth:`Rolling.apply`, :meth:`Expanding.apply`, :meth:`DataFrame.apply` with ``engine="numba"`` now supports positional arguments passed as kwargs (:issue:`58995`)
+- :meth:`Rolling.agg`, :meth:`Expanding.agg` and :meth:`ExponentialMovingWindow.agg` now accept :class:`NamedAgg` aggregations through ``**kwargs`` (:issue:`28333`)
 - :meth:`Series.map` can now accept kwargs to pass on to func (:issue:`59814`)
 - :meth:`pandas.concat` will raise a ``ValueError`` when ``ignore_index=True`` and ``keys`` is not ``None`` (:issue:`59274`)
 - :meth:`str.get_dummies` now accepts a  ``dtype`` parameter to specify the dtype of the resulting DataFrame (:issue:`47872`)
@@ -801,6 +802,7 @@ Other
 - Bug in ``Series.list`` methods not preserving the original :class:`Index`. (:issue:`58425`)
 - Bug in ``Series.list`` methods not preserving the original name. (:issue:`60522`)
 - Bug in printing a :class:`DataFrame` with a :class:`DataFrame` stored in :attr:`DataFrame.attrs` raised a ``ValueError`` (:issue:`60455`)
+- Bug in printing a :class:`Series` with a :class:`DataFrame` stored in :attr:`Series.attrs` raised a ``ValueError`` (:issue:`60568`)
 
 .. ***DO NOT USE THIS SECTION***
 

pandas/core/arrays/interval.py

@@ -1306,6 +1306,20 @@ def length(self) -> Index:
         """
         Return an Index with entries denoting the length of each Interval.
 
+        The length of an interval is calculated as the difference between
+        its `right` and `left` bounds. This property is particularly useful
+        when working with intervals where the size of the interval is an important
+        attribute, such as in time-series analysis or spatial data analysis.
+
+        See Also
+        --------
+        arrays.IntervalArray.left : Return the left endpoints of each Interval in
+            the IntervalArray as an Index.
+        arrays.IntervalArray.right : Return the right endpoints of each Interval in
+            the IntervalArray as an Index.
+        arrays.IntervalArray.mid : Return the midpoint of each Interval in the
+            IntervalArray as an Index.
+
         Examples
         --------
 

pandas/core/computation/expressions.py

@@ -108,7 +108,7 @@ def _evaluate_numexpr(op, op_str, left_op, right_op):
         try:
             result = ne.evaluate(
                 f"left_value {op_str} right_value",
-                local_dict={"left_value": left_value, "right_value": right_op},
+                local_dict={"left_value": left_value, "right_value": right_value},
                 casting="safe",
             )
         except TypeError:
@@ -257,7 +257,10 @@ def where(cond, left_op, right_op, use_numexpr: bool = True):
         Whether to try to use numexpr.
     """
     assert _where is not None
-    return _where(cond, left_op, right_op) if use_numexpr else _where_standard(cond, left_op, right_op)
+    if use_numexpr:
+        return _where(cond, left_op, right_op)
+    else:
+        return _where_standard(cond, left_op, right_op)
 
 
 def set_test_mode(v: bool = True) -> None:

pandas/core/computation/pytables.py

@@ -274,7 +274,9 @@ def stringify(value):
             # string quoting
             return TermValue(conv_val, stringify(conv_val), "string")
         else:
-            raise TypeError(f"Cannot compare {conv_val} of type {type(conv_val)} to {kind} column")
+            raise TypeError(
+                f"Cannot compare {conv_val} of type {type(conv_val)} to {kind} column"
+            )
 
     def convert_values(self) -> None:
         pass

pandas/core/window/ewm.py

@@ -490,7 +490,7 @@ def online(
         klass="Series/Dataframe",
         axis="",
     )
-    def aggregate(self, func, *args, **kwargs):
+    def aggregate(self, func=None, *args, **kwargs):
         return super().aggregate(func, *args, **kwargs)
 
     agg = aggregate
@@ -981,7 +981,7 @@ def reset(self) -> None:
         """
         self._mean.reset()
 
-    def aggregate(self, func, *args, **kwargs):
+    def aggregate(self, func=None, *args, **kwargs):
         raise NotImplementedError("aggregate is not implemented.")
 
     def std(self, bias: bool = False, *args, **kwargs):

pandas/core/window/expanding.py

@@ -167,7 +167,7 @@ def _get_window_indexer(self) -> BaseIndexer:
         klass="Series/Dataframe",
         axis="",
     )
-    def aggregate(self, func, *args, **kwargs):
+    def aggregate(self, func=None, *args, **kwargs):
         return super().aggregate(func, *args, **kwargs)
 
     agg = aggregate

pandas/core/window/rolling.py

@@ -44,7 +44,10 @@
 
 from pandas.core._numba import executor
 from pandas.core.algorithms import factorize
-from pandas.core.apply import ResamplerWindowApply
+from pandas.core.apply import (
+    ResamplerWindowApply,
+    reconstruct_func,
+)
 from pandas.core.arrays import ExtensionArray
 from pandas.core.base import SelectionMixin
 import pandas.core.common as com
@@ -646,8 +649,12 @@ def _numba_apply(
             out = obj._constructor(result, index=index, columns=columns)
             return self._resolve_output(out, obj)
 
-    def aggregate(self, func, *args, **kwargs):
+    def aggregate(self, func=None, *args, **kwargs):
+        relabeling, func, columns, order = reconstruct_func(func, **kwargs)
         result = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg()
+        if isinstance(result, ABCDataFrame) and relabeling:
+            result = result.iloc[:, order]
+            result.columns = columns  # type: ignore[union-attr]
         if result is None:
             return self.apply(func, raw=False, args=args, kwargs=kwargs)
         return result
@@ -1239,7 +1246,7 @@ def calc(x):
         klass="Series/DataFrame",
         axis="",
     )
-    def aggregate(self, func, *args, **kwargs):
+    def aggregate(self, func=None, *args, **kwargs):
         result = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg()
         if result is None:
             # these must apply directly
@@ -1951,7 +1958,7 @@ def _raise_monotonic_error(self, msg: str):
         klass="Series/Dataframe",
         axis="",
     )
-    def aggregate(self, func, *args, **kwargs):
+    def aggregate(self, func=None, *args, **kwargs):
         return super().aggregate(func, *args, **kwargs)
 
     agg = aggregate

pandas/errors/__init__.py

@@ -588,6 +588,20 @@ class UndefinedVariableError(NameError):
 
     It will also specify whether the undefined variable is local or not.
 
+    Parameters
+    ----------
+    name : str
+        The name of the undefined variable.
+    is_local : bool or None, optional
+        Indicates whether the undefined variable is considered a local variable.
+        If ``True``, the error message specifies it as a local variable.
+        If ``False`` or ``None``, the variable is treated as a non-local name.
+
+    See Also
+    --------
+    DataFrame.query : Query the columns of a DataFrame with a boolean expression.
+    DataFrame.eval : Evaluate a string describing operations on DataFrame columns.
+
     Examples
     --------
     >>> df = pd.DataFrame({"A": [1, 1, 1]})