Merge branch 'main' into periodindex-to_datetime_inconsistent_with_its_docstring

Author: aram-cedarwood

.github/workflows/wheels.yml

@@ -152,7 +152,7 @@ jobs:
         run: echo "sdist_name=$(cd ./dist && ls -d */)" >> "$GITHUB_ENV"
 
       - name: Build wheels
-        uses: pypa/cibuildwheel@v2.21.3
+        uses: pypa/cibuildwheel@v2.22.0
         with:
          package-dir: ./dist/${{ startsWith(matrix.buildplat[1], 'macosx') && env.sdist_name || needs.build_sdist.outputs.sdist_file }}
         env:

ci/code_checks.sh

@@ -73,7 +73,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Period.freq GL08" \
         -i "pandas.Period.ordinal GL08" \
         -i "pandas.RangeIndex.from_range PR01,SA01" \
-        -i "pandas.Series.dt.freq GL08" \
         -i "pandas.Series.dt.unit GL08" \
         -i "pandas.Series.pad PR01,SA01" \
         -i "pandas.Timedelta.max PR02" \
@@ -92,15 +91,11 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.core.groupby.DataFrameGroupBy.boxplot PR07,RT03,SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.get_group RT03,SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.indices SA01" \
-        -i "pandas.core.groupby.DataFrameGroupBy.nth PR02" \
         -i "pandas.core.groupby.DataFrameGroupBy.nunique SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.plot PR02" \
         -i "pandas.core.groupby.DataFrameGroupBy.sem SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.get_group RT03,SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.indices SA01" \
-        -i "pandas.core.groupby.SeriesGroupBy.is_monotonic_decreasing SA01" \
-        -i "pandas.core.groupby.SeriesGroupBy.is_monotonic_increasing SA01" \
-        -i "pandas.core.groupby.SeriesGroupBy.nth PR02" \
         -i "pandas.core.groupby.SeriesGroupBy.plot PR02" \
         -i "pandas.core.groupby.SeriesGroupBy.sem SA01" \
         -i "pandas.core.resample.Resampler.get_group RT03,SA01" \
@@ -114,19 +109,11 @@ 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.AttributeConflictWarning SA01" \
-        -i "pandas.errors.ChainedAssignmentError SA01" \
-        -i "pandas.errors.DuplicateLabelError SA01" \
         -i "pandas.errors.IntCastingNaNError SA01" \
-        -i "pandas.errors.InvalidIndexError SA01" \
         -i "pandas.errors.NullFrequencyError SA01" \
-        -i "pandas.errors.NumExprClobberingError SA01" \
         -i "pandas.errors.NumbaUtilError SA01" \
-        -i "pandas.errors.OutOfBoundsTimedelta SA01" \
         -i "pandas.errors.PerformanceWarning SA01" \
-        -i "pandas.errors.PossibleDataLossError SA01" \
         -i "pandas.errors.UndefinedVariableError PR01,SA01" \
-        -i "pandas.errors.UnsortedIndexError SA01" \
         -i "pandas.errors.ValueLabelTypeMismatch SA01" \
         -i "pandas.infer_freq SA01" \
         -i "pandas.io.json.build_table_schema PR07,RT03,SA01" \

doc/source/user_guide/window.rst

@@ -567,9 +567,9 @@ One must have :math:`0 < \alpha \leq 1`, and while it is possible to pass
 
    \alpha =
     \begin{cases}
-        \frac{2}{s + 1},               & \text{for span}\ s \geq 1\\
-        \frac{1}{1 + c},               & \text{for center of mass}\ c \geq 0\\
-        1 - \exp^{\frac{\log 0.5}{h}}, & \text{for half-life}\ h > 0
+        \frac{2}{s + 1},            & \text{for span}\ s \geq 1\\
+        \frac{1}{1 + c},            & \text{for center of mass}\ c \geq 0\\
+        1 - e^{\frac{\log 0.5}{h}}, & \text{for half-life}\ h > 0
     \end{cases}
 
 One must specify precisely one of **span**, **center of mass**, **half-life**

doc/source/whatsnew/v3.0.0.rst

@@ -54,6 +54,7 @@ Other enhancements
 - :meth:`Series.cummin` and :meth:`Series.cummax` now supports :class:`CategoricalDtype` (:issue:`52335`)
 - :meth:`Series.plot` now correctly handle the ``ylabel`` parameter for pie charts, allowing for explicit control over the y-axis label (:issue:`58239`)
 - :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:`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`)
@@ -626,6 +627,7 @@ Datetimelike
 - Bug in :meth:`Series.dt.microsecond` producing incorrect results for pyarrow backed :class:`Series`. (:issue:`59154`)
 - Bug in :meth:`to_datetime` not respecting dayfirst if an uncommon date string was passed. (:issue:`58859`)
 - Bug in :meth:`to_datetime` reports incorrect index in case of any failure scenario. (:issue:`58298`)
+- Bug in :meth:`to_datetime` wrongly converts when ``arg`` is a ``np.datetime64`` object with unit of ``ps``. (:issue:`60341`)
 - Bug in setting scalar values with mismatched resolution into arrays with non-nanosecond ``datetime64``, ``timedelta64`` or :class:`DatetimeTZDtype` incorrectly truncating those scalars (:issue:`56410`)
 
 Timedelta
@@ -688,6 +690,7 @@ I/O
 - Bug in :meth:`DataFrame.from_records` where ``columns`` parameter with numpy structured array was not reordering and filtering out the columns (:issue:`59717`)
 - Bug in :meth:`DataFrame.to_dict` raises unnecessary ``UserWarning`` when columns are not unique and ``orient='tight'``. (:issue:`58281`)
 - Bug in :meth:`DataFrame.to_excel` when writing empty :class:`DataFrame` with :class:`MultiIndex` on both axes (:issue:`57696`)
+- Bug in :meth:`DataFrame.to_excel` where the :class:`MultiIndex` index with a period level was not a date (:issue:`60099`)
 - Bug in :meth:`DataFrame.to_stata` when writing :class:`DataFrame` and ``byteorder=`big```. (:issue:`58969`)
 - Bug in :meth:`DataFrame.to_stata` when writing more than 32,000 value labels. (:issue:`60107`)
 - Bug in :meth:`DataFrame.to_string` that raised ``StopIteration`` with nested DataFrames. (:issue:`16098`)
@@ -763,7 +766,7 @@ ExtensionArray
 
 Styler
 ^^^^^^
--
+- Bug in :meth:`Styler.to_latex` where styling column headers when combined with a hidden index or hidden index-levels is fixed.
 
 Other
 ^^^^^
@@ -787,6 +790,7 @@ Other
 - Bug in :meth:`Series.dt` methods in :class:`ArrowDtype` that were returning incorrect values. (:issue:`57355`)
 - Bug in :meth:`Series.rank` that doesn't preserve missing values for nullable integers when ``na_option='keep'``. (:issue:`56976`)
 - Bug in :meth:`Series.replace` and :meth:`DataFrame.replace` inconsistently replacing matching instances when ``regex=True`` and missing values are present. (:issue:`56599`)
+- Bug in :meth:`Series.to_string` when series contains complex floats with exponents (:issue:`60405`)
 - Bug in :meth:`read_csv` where chained fsspec TAR file and ``compression="infer"`` fails with ``tarfile.ReadError`` (:issue:`60028`)
 - Bug in Dataframe Interchange Protocol implementation was returning incorrect results for data buffers' associated dtype, for string and datetime columns (:issue:`54781`)
 - Bug in ``Series.list`` methods not preserving the original :class:`Index`. (:issue:`58425`)

pandas/_libs/src/vendored/numpy/datetime/np_datetime.c

@@ -660,11 +660,12 @@ void pandas_datetime_to_datetimestruct(npy_datetime dt, NPY_DATETIMEUNIT base,
     perday = 24LL * 60 * 60 * 1000 * 1000 * 1000 * 1000;
 
     set_datetimestruct_days(extract_unit(&dt, perday), out);
-    out->hour = (npy_int32)extract_unit(&dt, 1000LL * 1000 * 1000 * 60 * 60);
-    out->min = (npy_int32)extract_unit(&dt, 1000LL * 1000 * 1000 * 60);
-    out->sec = (npy_int32)extract_unit(&dt, 1000LL * 1000 * 1000);
-    out->us = (npy_int32)extract_unit(&dt, 1000LL);
-    out->ps = (npy_int32)(dt * 1000);
+    out->hour =
+        (npy_int32)extract_unit(&dt, 1000LL * 1000 * 1000 * 1000 * 60 * 60);
+    out->min = (npy_int32)extract_unit(&dt, 1000LL * 1000 * 1000 * 1000 * 60);
+    out->sec = (npy_int32)extract_unit(&dt, 1000LL * 1000 * 1000 * 1000);
+    out->us = (npy_int32)extract_unit(&dt, 1000LL * 1000);
+    out->ps = (npy_int32)(dt);
     break;
 
   case NPY_FR_fs:

pandas/_libs/tslibs/np_datetime.pyx

@@ -201,6 +201,10 @@ class OutOfBoundsTimedelta(ValueError):
 
     Representation should be within a timedelta64[ns].
 
+    See Also
+    --------
+    date_range : Return a fixed frequency DatetimeIndex.
+
     Examples
     --------
     >>> pd.date_range(start="1/1/1700", freq="B", periods=100000)

pandas/core/arrays/interval.py

@@ -1055,7 +1055,9 @@ def shift(self, periods: int = 1, fill_value: object = None) -> IntervalArray:
             from pandas import Index
 
             fill_value = Index(self._left, copy=False)._na_value
-            empty = IntervalArray.from_breaks([fill_value] * (empty_len + 1))
+            empty = IntervalArray.from_breaks(
+                [fill_value] * (empty_len + 1), closed=self.closed
+            )
         else:
             empty = self._from_sequence([fill_value] * empty_len, dtype=self.dtype)
 

pandas/core/frame.py

@@ -4742,7 +4742,8 @@ def eval(self, expr: str, *, inplace: bool = False, **kwargs) -> Any | None:
         3  4   4    7   8  0
         4  5   2    6   7  3
 
-        For columns with spaces in their name, you can use backtick quoting.
+        For columns with spaces or other disallowed characters in their name, you can
+        use backtick quoting.
 
         >>> df.eval("B * `C&C`")
         0    100

pandas/core/groupby/generic.py

@@ -1443,6 +1443,11 @@ def is_monotonic_increasing(self) -> Series:
         -------
         Series
 
+        See Also
+        --------
+        SeriesGroupBy.is_monotonic_decreasing : Return whether each group's values
+            are monotonically decreasing.
+
         Examples
         --------
         >>> s = pd.Series([2, 1, 3, 4], index=["Falcon", "Falcon", "Parrot", "Parrot"])
@@ -1462,6 +1467,11 @@ def is_monotonic_decreasing(self) -> Series:
         -------
         Series
 
+        See Also
+        --------
+        SeriesGroupBy.is_monotonic_increasing : Return whether each group's values
+            are monotonically increasing.
+
         Examples
         --------
         >>> s = pd.Series([2, 1, 3, 4], index=["Falcon", "Falcon", "Parrot", "Parrot"])

pandas/core/groupby/groupby.py

@@ -3983,19 +3983,6 @@ def nth(self) -> GroupByNthSelector:
         'all' or 'any'; this is equivalent to calling dropna(how=dropna)
         before the groupby.
 
-        Parameters
-        ----------
-        n : int, slice or list of ints and slices
-            A single nth value for the row or a list of nth values or slices.
-
-            .. versionchanged:: 1.4.0
-                Added slice and lists containing slices.
-                Added index notation.
-
-        dropna : {'any', 'all', None}, default None
-            Apply the specified dropna operation before counting which row is
-            the nth row. Only supported if n is an int.
-
         Returns
         -------
         Series or DataFrame