Merge remote-tracking branch 'upstream/main' into fix-string-frame-methods

Author: jorisvandenbossche

.github/workflows/unit-tests.yml

@@ -385,10 +385,12 @@ jobs:
           nogil: true
 
       - name: Build Environment
+        # TODO: Once numpy 2.2.1 is out, don't install nightly version
+        # Tests segfault with numpy 2.2.0: https://github.com/numpy/numpy/pull/27955
         run: |
           python --version
-          python -m pip install --upgrade pip setuptools wheel numpy meson[ninja]==1.2.1 meson-python==0.13.1
-          python -m pip install --pre --extra-index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple cython
+          python -m pip install --upgrade pip setuptools wheel meson[ninja]==1.2.1 meson-python==0.13.1
+          python -m pip install --pre --extra-index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple cython numpy
           python -m pip install versioneer[toml]
           python -m pip install python-dateutil pytz tzdata hypothesis>=6.84.0 pytest>=7.3.2 pytest-xdist>=3.4.0 pytest-cov
           python -m pip install -ve . --no-build-isolation --no-index --no-deps -Csetup-args="--werror"

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/development/maintaining.rst

@@ -488,7 +488,7 @@ Post-Release
    for reference):
 
     - The pandas-dev and pydata mailing lists
-    - Twitter, Mastodon, Telegram and LinkedIn
+    - X, Mastodon, Telegram and LinkedIn
 
 7. Update this release instructions to fix anything incorrect and to update about any
    change since the last release.

doc/source/user_guide/cookbook.rst

@@ -459,7 +459,7 @@ Unlike agg, apply's callable is passed a sub-DataFrame which gives you access to
    df
 
    # List the size of the animals with the highest weight.
-   df.groupby("animal").apply(lambda subf: subf["size"][subf["weight"].idxmax()], include_groups=False)
+   df.groupby("animal").apply(lambda subf: subf["size"][subf["weight"].idxmax()])
 
 `Using get_group
 <https://stackoverflow.com/questions/14734533/how-to-access-pandas-groupby-dataframe-by-key>`__
@@ -482,7 +482,7 @@ Unlike agg, apply's callable is passed a sub-DataFrame which gives you access to
        return pd.Series(["L", avg_weight, True], index=["size", "weight", "adult"])
 
 
-   expected_df = gb.apply(GrowUp, include_groups=False)
+   expected_df = gb.apply(GrowUp)
    expected_df
 
 `Expanding apply

doc/source/user_guide/groupby.rst

@@ -1074,7 +1074,7 @@ missing values with the ``ffill()`` method.
    ).set_index("date")
    df_re
 
-   df_re.groupby("group").resample("1D", include_groups=False).ffill()
+   df_re.groupby("group").resample("1D").ffill()
 
 .. _groupby.filter:
 
@@ -1252,13 +1252,13 @@ the argument ``group_keys`` which defaults to ``True``. Compare
 
 .. ipython:: python
 
-    df.groupby("A", group_keys=True).apply(lambda x: x, include_groups=False)
+    df.groupby("A", group_keys=True).apply(lambda x: x)
 
 with
 
 .. ipython:: python
 
-    df.groupby("A", group_keys=False).apply(lambda x: x, include_groups=False)
+    df.groupby("A", group_keys=False).apply(lambda x: x)
 
 
 Numba accelerated routines
@@ -1742,7 +1742,7 @@ column index name will be used as the name of the inserted column:
        result = {"b_sum": x["b"].sum(), "c_mean": x["c"].mean()}
        return pd.Series(result, name="metrics")
 
-   result = df.groupby("a").apply(compute_metrics, include_groups=False)
+   result = df.groupby("a").apply(compute_metrics)
 
    result
 

doc/source/user_guide/visualization.rst

@@ -1210,11 +1210,6 @@ You may set the ``xlabel`` and ``ylabel`` arguments to give the plot custom labe
 for x and y axis. By default, pandas will pick up index name as xlabel, while leaving
 it empty for ylabel.
 
-.. ipython:: python
-   :suppress:
-
-   plt.figure();
-
 .. ipython:: python
 
    df.plot();

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`)
@@ -553,6 +554,7 @@ Other Removals
 - Removed the ``method`` keyword in ``ExtensionArray.fillna``, implement ``ExtensionArray._pad_or_backfill`` instead (:issue:`53621`)
 - Removed the attribute ``dtypes`` from :class:`.DataFrameGroupBy` (:issue:`51997`)
 - Enforced deprecation of ``argmin``, ``argmax``, ``idxmin``, and ``idxmax`` returning a result when ``skipna=False`` and an NA value is encountered or all values are NA values; these operations will now raise in such cases (:issue:`33941`, :issue:`51276`)
+- Removed specifying ``include_groups=True`` in :class:`.DataFrameGroupBy.apply` and :class:`.Resampler.apply` (:issue:`7155`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_300.performance:
@@ -801,6 +803,7 @@ 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/_libs/tslibs/nattype.pyx

@@ -704,7 +704,7 @@ class NaTType(_NaT):
         difference between the current timezone and UTC.
 
         Returns
-        --------
+        -------
         timedelta
             The difference between UTC and the local time as a `timedelta` object.
 

pandas/_libs/tslibs/timestamps.pyx

@@ -2217,7 +2217,7 @@ class Timestamp(_Timestamp):
         difference between the current timezone and UTC.
 
         Returns
-        --------
+        -------
         timedelta
             The difference between UTC and the local time as a `timedelta` object.
 

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
         --------