Merge remote-tracking branch 'upstream/main' into bugfix--pprint-embedded-quotes

Author: thedataninja1786

.github/workflows/comment-commands.yml

@@ -11,7 +11,7 @@ permissions:
 jobs:
   issue_assign:
     runs-on: ubuntu-22.04
-    if: (!github.event.issue.pull_request) && github.event.comment.body == 'take'
+    if: (!github.event.issue.pull_request) && trim(github.event.comment.body) == 'take'
     concurrency:
       group: ${{ github.actor }}-issue-assign
     steps:

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" \
@@ -114,8 +109,6 @@ 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" \

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
@@ -762,7 +764,7 @@ 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
 ^^^^^

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/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

pandas/core/indexes/accessors.py

@@ -373,6 +373,28 @@ def to_pydatetime(self) -> Series:
 
     @property
     def freq(self):
+        """
+        Tries to return a string representing a frequency generated by infer_freq.
+
+        Returns None if it can't autodetect the frequency.
+
+        See Also
+        --------
+        Series.dt.to_period : Cast to PeriodArray/PeriodIndex at a particular
+            frequency.
+
+        Examples
+        --------
+        >>> ser = pd.Series(["2024-01-01", "2024-01-02", "2024-01-03", "2024-01-04"])
+        >>> ser = pd.to_datetime(ser)
+        >>> ser.dt.freq
+        'D'
+
+        >>> ser = pd.Series(["2022-01-01", "2024-01-01", "2026-01-01", "2028-01-01"])
+        >>> ser = pd.to_datetime(ser)
+        >>> ser.dt.freq
+        '2YS-JAN'
+        """
         return self._get_values().inferred_freq
 
     def isocalendar(self) -> DataFrame:

pandas/errors/__init__.py

@@ -487,6 +487,11 @@ class ChainedAssignmentError(Warning):
     For more information on Copy-on-Write,
     see :ref:`the user guide<copy_on_write>`.
 
+    See Also
+    --------
+    options.mode.copy_on_write : Global setting for enabling or disabling
+        Copy-on-Write behavior.
+
     Examples
     --------
     >>> pd.options.mode.copy_on_write = True
@@ -672,6 +677,12 @@ class AttributeConflictWarning(Warning):
     name than the existing index on an HDFStore or attempting to append an index with a
     different frequency than the existing index on an HDFStore.
 
+    See Also
+    --------
+    HDFStore : Dict-like IO interface for storing pandas objects in PyTables.
+    DataFrame.to_hdf : Write the contained data to an HDF5 file using HDFStore.
+    read_hdf : Read from an HDF5 file into a DataFrame.
+
     Examples
     --------
     >>> idx1 = pd.Index(["a", "b"], name="name1")

pandas/io/_util.py

@@ -60,9 +60,12 @@ def arrow_table_to_pandas(
     table: pyarrow.Table,
     dtype_backend: DtypeBackend | Literal["numpy"] | lib.NoDefault = lib.no_default,
     null_to_int64: bool = False,
+    to_pandas_kwargs: dict | None = None,
 ) -> pd.DataFrame:
     pa = import_optional_dependency("pyarrow")
 
+    to_pandas_kwargs = {} if to_pandas_kwargs is None else to_pandas_kwargs
+
     types_mapper: type[pd.ArrowDtype] | None | Callable
     if dtype_backend == "numpy_nullable":
         mapping = _arrow_dtype_mapping()
@@ -80,5 +83,5 @@ def arrow_table_to_pandas(
     else:
         raise NotImplementedError
 
-    df = table.to_pandas(types_mapper=types_mapper)
+    df = table.to_pandas(types_mapper=types_mapper, **to_pandas_kwargs)
     return df