Merge branch 'main' into fix/as_index_false_returns_multiindex

ci/code_checks.sh

@@ -80,10 +80,7 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.CategoricalIndex.codes SA01" \
         -i "pandas.CategoricalIndex.ordered SA01" \
         -i "pandas.DataFrame.__dataframe__ SA01" \
-        -i "pandas.DataFrame.__iter__ SA01" \
         -i "pandas.DataFrame.at_time PR01" \
-        -i "pandas.DataFrame.columns SA01" \
-        -i "pandas.DataFrame.droplevel SA01" \
         -i "pandas.DataFrame.hist RT03" \
         -i "pandas.DataFrame.infer_objects RT03" \
         -i "pandas.DataFrame.kurt RT03,SA01" \
@@ -93,7 +90,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.DataFrame.median RT03,SA01" \
         -i "pandas.DataFrame.min RT03" \
         -i "pandas.DataFrame.plot PR02,SA01" \
-        -i "pandas.DataFrame.pop SA01" \
         -i "pandas.DataFrame.prod RT03" \
         -i "pandas.DataFrame.product RT03" \
         -i "pandas.DataFrame.reorder_levels SA01" \
@@ -103,71 +99,51 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.DataFrame.std PR01,RT03,SA01" \
         -i "pandas.DataFrame.sum RT03" \
         -i "pandas.DataFrame.swaplevel SA01" \
-        -i "pandas.DataFrame.to_feather SA01" \
         -i "pandas.DataFrame.to_markdown SA01" \
         -i "pandas.DataFrame.to_parquet RT03" \
         -i "pandas.DataFrame.var PR01,RT03,SA01" \
-        -i "pandas.DatetimeIndex.ceil SA01" \
-        -i "pandas.DatetimeIndex.date SA01" \
         -i "pandas.DatetimeIndex.day_of_year SA01" \
         -i "pandas.DatetimeIndex.dayofyear SA01" \
-        -i "pandas.DatetimeIndex.floor SA01" \
         -i "pandas.DatetimeIndex.freqstr SA01" \
         -i "pandas.DatetimeIndex.indexer_at_time PR01,RT03" \
         -i "pandas.DatetimeIndex.indexer_between_time RT03" \
         -i "pandas.DatetimeIndex.inferred_freq SA01" \
         -i "pandas.DatetimeIndex.is_leap_year SA01" \
-        -i "pandas.DatetimeIndex.quarter SA01" \
-        -i "pandas.DatetimeIndex.round SA01" \
-        -i "pandas.DatetimeIndex.snap PR01,RT03,SA01" \
+        -i "pandas.DatetimeIndex.snap PR01,RT03" \
         -i "pandas.DatetimeIndex.std PR01,RT03" \
-        -i "pandas.DatetimeIndex.time SA01" \
-        -i "pandas.DatetimeIndex.timetz SA01" \
         -i "pandas.DatetimeIndex.to_period RT03" \
         -i "pandas.DatetimeIndex.to_pydatetime RT03,SA01" \
-        -i "pandas.DatetimeIndex.tz SA01" \
         -i "pandas.DatetimeIndex.tz_convert RT03" \
         -i "pandas.DatetimeTZDtype SA01" \
         -i "pandas.DatetimeTZDtype.tz SA01" \
         -i "pandas.DatetimeTZDtype.unit SA01" \
         -i "pandas.Grouper PR02" \
-        -i "pandas.HDFStore.info RT03,SA01" \
-        -i "pandas.HDFStore.keys SA01" \
-        -i "pandas.HDFStore.put PR01,SA01" \
-        -i "pandas.HDFStore.select SA01" \
         -i "pandas.HDFStore.walk SA01" \
         -i "pandas.Index PR07" \
         -i "pandas.Index.T SA01" \
         -i "pandas.Index.append PR07,RT03,SA01" \
-        -i "pandas.Index.astype SA01" \
         -i "pandas.Index.copy PR07,SA01" \
         -i "pandas.Index.difference PR07,RT03,SA01" \
         -i "pandas.Index.drop PR07,SA01" \
         -i "pandas.Index.drop_duplicates RT03" \
         -i "pandas.Index.droplevel RT03,SA01" \
         -i "pandas.Index.dropna RT03,SA01" \
-        -i "pandas.Index.dtype SA01" \
         -i "pandas.Index.duplicated RT03" \
         -i "pandas.Index.empty GL08" \
-        -i "pandas.Index.equals SA01" \
         -i "pandas.Index.fillna RT03" \
         -i "pandas.Index.get_indexer PR07,SA01" \
         -i "pandas.Index.get_indexer_for PR01,SA01" \
         -i "pandas.Index.get_indexer_non_unique PR07,SA01" \
         -i "pandas.Index.get_loc PR07,RT03,SA01" \
         -i "pandas.Index.get_slice_bound PR07" \
-        -i "pandas.Index.hasnans SA01" \
         -i "pandas.Index.identical PR01,SA01" \
         -i "pandas.Index.inferred_type SA01" \
         -i "pandas.Index.insert PR07,RT03,SA01" \
         -i "pandas.Index.intersection PR07,RT03,SA01" \
         -i "pandas.Index.item SA01" \
         -i "pandas.Index.join PR07,RT03,SA01" \
-        -i "pandas.Index.map SA01" \
         -i "pandas.Index.memory_usage RT03" \
-        -i "pandas.Index.name SA01" \
         -i "pandas.Index.names GL08" \
-        -i "pandas.Index.nbytes SA01" \
         -i "pandas.Index.nunique RT03" \
         -i "pandas.Index.putmask PR01,RT03" \
         -i "pandas.Index.ravel PR01,RT03" \
@@ -287,34 +263,28 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Series.cat.reorder_categories PR01,PR02" \
         -i "pandas.Series.cat.set_categories PR01,PR02" \
         -i "pandas.Series.div PR07" \
-        -i "pandas.Series.droplevel SA01" \
         -i "pandas.Series.dt.as_unit PR01,PR02" \
-        -i "pandas.Series.dt.ceil PR01,PR02,SA01" \
+        -i "pandas.Series.dt.ceil PR01,PR02" \
         -i "pandas.Series.dt.components SA01" \
-        -i "pandas.Series.dt.date SA01" \
         -i "pandas.Series.dt.day_name PR01,PR02" \
         -i "pandas.Series.dt.day_of_year SA01" \
         -i "pandas.Series.dt.dayofyear SA01" \
         -i "pandas.Series.dt.days SA01" \
         -i "pandas.Series.dt.days_in_month SA01" \
         -i "pandas.Series.dt.daysinmonth SA01" \
-        -i "pandas.Series.dt.floor PR01,PR02,SA01" \
+        -i "pandas.Series.dt.floor PR01,PR02" \
         -i "pandas.Series.dt.freq GL08" \
         -i "pandas.Series.dt.is_leap_year SA01" \
         -i "pandas.Series.dt.microseconds SA01" \
         -i "pandas.Series.dt.month_name PR01,PR02" \
         -i "pandas.Series.dt.nanoseconds SA01" \
         -i "pandas.Series.dt.normalize PR01" \
-        -i "pandas.Series.dt.quarter SA01" \
         -i "pandas.Series.dt.qyear GL08" \
-        -i "pandas.Series.dt.round PR01,PR02,SA01" \
+        -i "pandas.Series.dt.round PR01,PR02" \
         -i "pandas.Series.dt.seconds SA01" \
         -i "pandas.Series.dt.strftime PR01,PR02" \
-        -i "pandas.Series.dt.time SA01" \
-        -i "pandas.Series.dt.timetz SA01" \
         -i "pandas.Series.dt.to_period PR01,PR02,RT03" \
         -i "pandas.Series.dt.total_seconds PR01" \
-        -i "pandas.Series.dt.tz SA01" \
         -i "pandas.Series.dt.tz_convert PR01,PR02,RT03" \
         -i "pandas.Series.dt.tz_localize PR01,PR02" \
         -i "pandas.Series.dt.unit GL08" \
@@ -344,7 +314,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Series.mod PR07" \
         -i "pandas.Series.mode SA01" \
         -i "pandas.Series.mul PR07" \
-        -i "pandas.Series.nbytes SA01" \
         -i "pandas.Series.ne PR07,SA01" \
         -i "pandas.Series.nunique RT03" \
         -i "pandas.Series.pad PR01,SA01" \
@@ -432,14 +401,11 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Timedelta.total_seconds SA01" \
         -i "pandas.Timedelta.view SA01" \
         -i "pandas.TimedeltaIndex.as_unit RT03,SA01" \
-        -i "pandas.TimedeltaIndex.ceil SA01" \
         -i "pandas.TimedeltaIndex.components SA01" \
         -i "pandas.TimedeltaIndex.days SA01" \
-        -i "pandas.TimedeltaIndex.floor SA01" \
         -i "pandas.TimedeltaIndex.inferred_freq SA01" \
         -i "pandas.TimedeltaIndex.microseconds SA01" \
         -i "pandas.TimedeltaIndex.nanoseconds SA01" \
-        -i "pandas.TimedeltaIndex.round SA01" \
         -i "pandas.TimedeltaIndex.seconds SA01" \
         -i "pandas.TimedeltaIndex.to_pytimedelta RT03,SA01" \
         -i "pandas.Timestamp PR07,SA01" \

doc/source/development/community.rst

@@ -100,6 +100,8 @@ The pandas mailing list `[email protected] <mailto://pandas-dev@python
 conversations and to engage people in the wider community who might not
 be active on the issue tracker but we would like to include in discussions.
 
+Join the mailing list and view the archives `here <https://mail.python.org/mailman/listinfo/pandas-dev>`_.
+
 .. _community.slack:
 
 Community slack

doc/source/user_guide/style.ipynb

@@ -1908,7 +1908,7 @@
     "- Provide an API that is pleasing to use interactively and is \"good enough\" for many tasks\n",
     "- Provide the foundations for dedicated libraries to build on\n",
     "\n",
-    "If you build a great library on top of this, let us know and we'll [link](https://pandas.pydata.org/pandas-docs/stable/ecosystem.html) to it.\n",
+    "If you build a great library on top of this, let us know and we'll [link](https://pandas.pydata.org/community/ecosystem.html) to it.\n",
     "\n",
     "### Subclassing\n",
     "\n",

doc/source/whatsnew/v3.0.0.rst

@@ -199,6 +199,7 @@ Other Deprecations
 - Deprecated allowing non-keyword arguments in :meth:`DataFrame.all`, :meth:`DataFrame.min`, :meth:`DataFrame.max`, :meth:`DataFrame.sum`, :meth:`DataFrame.prod`, :meth:`DataFrame.mean`, :meth:`DataFrame.median`, :meth:`DataFrame.sem`, :meth:`DataFrame.var`, :meth:`DataFrame.std`, :meth:`DataFrame.skew`, :meth:`DataFrame.kurt`, :meth:`Series.all`,  :meth:`Series.min`, :meth:`Series.max`, :meth:`Series.sum`, :meth:`Series.prod`, :meth:`Series.mean`, :meth:`Series.median`, :meth:`Series.sem`, :meth:`Series.var`, :meth:`Series.std`, :meth:`Series.skew`, and :meth:`Series.kurt`. (:issue:`57087`)
 - Deprecated allowing non-keyword arguments in :meth:`Series.to_markdown` except ``buf``. (:issue:`57280`)
 - Deprecated allowing non-keyword arguments in :meth:`Series.to_string` except ``buf``. (:issue:`57280`)
+- Deprecated behavior of :meth:`Series.dt.to_pytimedelta`, in a future version this will return a :class:`Series` containing python ``datetime.timedelta`` objects instead of an ``ndarray`` of timedelta; this matches the behavior of other :meth:`Series.dt` properties. (:issue:`57463`)
 - Deprecated using ``epoch`` date format in :meth:`DataFrame.to_json` and :meth:`Series.to_json`, use ``iso`` instead. (:issue:`57063`)
 -
 
@@ -360,6 +361,7 @@ Datetimelike
 - Bug in :func:`date_range` where the last valid timestamp would sometimes not be produced (:issue:`56134`)
 - Bug in :func:`date_range` where using a negative frequency value would not include all points between the start and end values (:issue:`56382`)
 - Bug in :func:`tseries.api.guess_datetime_format` would fail to infer time format when "%Y" == "%H%M" (:issue:`57452`)
+- 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
 ^^^^^^^^^
@@ -458,6 +460,7 @@ Other
 - Bug in :class:`DataFrame` when passing a ``dict`` with a NA scalar and ``columns`` that would always return ``np.nan`` (:issue:`57205`)
 - Bug in :func:`unique` on :class:`Index` not always returning :class:`Index` (:issue:`57043`)
 - Bug in :meth:`DataFrame.eval` and :meth:`DataFrame.query` which caused an exception when using NumPy attributes via ``@`` notation, e.g., ``df.eval("@np.floor(a)")``. (:issue:`58041`)
+- Bug in :meth:`DataFrame.eval` and :meth:`DataFrame.query` which did not allow to use ``tan`` function. (:issue:`55091`)
 - Bug in :meth:`DataFrame.sort_index` when passing ``axis="columns"`` and ``ignore_index=True`` and ``ascending=False`` not returning a :class:`RangeIndex` columns (:issue:`57293`)
 - Bug in :meth:`DataFrame.transform` that was returning the wrong order unless the index was monotonically increasing. (:issue:`57069`)
 - Bug in :meth:`DataFrame.where` where using a non-bool type array in the function would return a ``ValueError`` instead of a ``TypeError`` (:issue:`56330`)

pandas/_libs/lib.pyx

@@ -477,7 +477,7 @@ def has_infs(const floating[:] arr) -> bool:
 
 @cython.boundscheck(False)
 @cython.wraparound(False)
-def has_only_ints_or_nan(floating[:] arr) -> bool:
+def has_only_ints_or_nan(const floating[:] arr) -> bool:
     cdef:
         floating val
         intp_t i
@@ -631,7 +631,7 @@ ctypedef fused int6432_t:
 
 @cython.wraparound(False)
 @cython.boundscheck(False)
-def is_range_indexer(ndarray[int6432_t, ndim=1] left, Py_ssize_t n) -> bool:
+def is_range_indexer(const int6432_t[:] left, Py_ssize_t n) -> bool:
     """
     Perform an element by element comparison on 1-d integer arrays, meant for indexer
     comparisons
@@ -652,7 +652,7 @@ def is_range_indexer(ndarray[int6432_t, ndim=1] left, Py_ssize_t n) -> bool:
 
 @cython.wraparound(False)
 @cython.boundscheck(False)
-def is_sequence_range(ndarray[int6432_t, ndim=1] sequence, int64_t step) -> bool:
+def is_sequence_range(const int6432_t[:] sequence, int64_t step) -> bool:
     """
     Check if sequence is equivalent to a range with the specified step.
     """

pandas/_libs/reshape.pyx

@@ -19,7 +19,7 @@ from pandas._libs.lib cimport c_is_list_like
 
 @cython.wraparound(False)
 @cython.boundscheck(False)
-def unstack(numeric_object_t[:, :] values, const uint8_t[:] mask,
+def unstack(const numeric_object_t[:, :] values, const uint8_t[:] mask,
             Py_ssize_t stride, Py_ssize_t length, Py_ssize_t width,
             numeric_object_t[:, :] new_values, uint8_t[:, :] new_mask) -> None:
     """
@@ -80,7 +80,7 @@ def unstack(numeric_object_t[:, :] values, const uint8_t[:] mask,
 
 @cython.wraparound(False)
 @cython.boundscheck(False)
-def explode(ndarray[object] values):
+def explode(object[:] values):
     """
     transform array list-likes to long form
     preserve non-list entries

pandas/conftest.py

@@ -157,6 +157,7 @@ def pytest_collection_modifyitems(items, config) -> None:
         ("SeriesGroupBy.fillna", "SeriesGroupBy.fillna is deprecated"),
         ("SeriesGroupBy.idxmin", "The behavior of Series.idxmin"),
         ("SeriesGroupBy.idxmax", "The behavior of Series.idxmax"),
+        ("to_pytimedelta", "The behavior of TimedeltaProperties.to_pytimedelta"),
         # Docstring divides by zero to show behavior difference
         ("missing.mask_zero_div_zero", "divide by zero encountered"),
         (

pandas/core/arrays/datetimelike.py

@@ -1825,6 +1825,11 @@ def strftime(self, date_format: str) -> npt.NDArray[np.object_]:
     ------
     ValueError if the `freq` cannot be converted.
 
+    See Also
+    --------
+    DatetimeIndex.floor : Perform floor operation on the data to the specified `freq`.
+    DatetimeIndex.snap : Snap time stamps to nearest occurring frequency.
+
     Notes
     -----
     If the timestamps have a timezone, {op}ing will take place relative to the

pandas/core/arrays/datetimes.py

@@ -539,7 +539,7 @@ def _unbox_scalar(self, value) -> np.datetime64:
         if value is NaT:
             return np.datetime64(value._value, self.unit)
         else:
-            return value.as_unit(self.unit).asm8
+            return value.as_unit(self.unit, round_ok=False).asm8
 
     def _scalar_from_string(self, value) -> Timestamp | NaTType:
         return Timestamp(value, tz=self.tz)
@@ -593,6 +593,13 @@ def tz(self) -> tzinfo | None:
         datetime.tzinfo, pytz.tzinfo.BaseTZInfo, dateutil.tz.tz.tzfile, or None
             Returns None when the array is tz-naive.
 
+        See Also
+        --------
+        DatetimeIndex.tz_localize : Localize tz-naive DatetimeIndex to a
+            given time zone, or remove timezone from a tz-aware DatetimeIndex.
+        DatetimeIndex.tz_convert : Convert tz-aware DatetimeIndex from
+            one time zone to another.
+
         Examples
         --------
         For Series:
@@ -1391,6 +1398,14 @@ def time(self) -> npt.NDArray[np.object_]:
 
         The time part of the Timestamps.
 
+        See Also
+        --------
+        DatetimeIndex.timetz : Returns numpy array of :class:`datetime.time`
+            objects with timezones. The time part of the Timestamps.
+        DatetimeIndex.date : Returns numpy array of python :class:`datetime.date`
+            objects. Namely, the date part of Timestamps without time and timezone
+            information.
+
         Examples
         --------
         For Series:
@@ -1428,6 +1443,12 @@ def timetz(self) -> npt.NDArray[np.object_]:
 
         The time part of the Timestamps.
 
+        See Also
+        --------
+        DatetimeIndex.time : Returns numpy array of :class:`datetime.time` objects.
+            The time part of the Timestamps.
+        DatetimeIndex.tz : Return the timezone.
+
         Examples
         --------
         For Series:
@@ -1462,6 +1483,14 @@ def date(self) -> npt.NDArray[np.object_]:
         Namely, the date part of Timestamps without time and
         timezone information.
 
+        See Also
+        --------
+        DatetimeIndex.time : Returns numpy array of :class:`datetime.time` objects.
+            The time part of the Timestamps.
+        DatetimeIndex.year : The year of the datetime.
+        DatetimeIndex.month : The month as January=1, December=12.
+        DatetimeIndex.day : The day of the datetime.
+
         Examples
         --------
         For Series:
@@ -1836,6 +1865,12 @@ def isocalendar(self) -> DataFrame:
         """
         The quarter of the date.
 
+        See Also
+        --------
+        DatetimeIndex.snap : Snap time stamps to nearest occurring frequency.
+        DatetimeIndex.time : Returns numpy array of datetime.time objects.
+            The time part of the Timestamps.
+
         Examples
         --------
         For Series:

pandas/core/arrays/timedeltas.py

@@ -322,7 +322,7 @@ def _unbox_scalar(self, value) -> np.timedelta64:
         if value is NaT:
             return np.timedelta64(value._value, self.unit)
         else:
-            return value.as_unit(self.unit).asm8
+            return value.as_unit(self.unit, round_ok=False).asm8
 
     def _scalar_from_string(self, value) -> Timedelta | NaTType:
         return Timedelta(value)

pandas/core/base.py

@@ -419,6 +419,11 @@ def nbytes(self) -> int:
         """
         Return the number of bytes in the underlying data.
 
+        See Also
+        --------
+        Series.ndim : Number of dimensions of the underlying data.
+        Series.size : Return the number of elements in the underlying data.
+
         Examples
         --------
         For Series:

pandas/core/computation/ops.py

@@ -45,6 +45,7 @@
 _unary_math_ops = (
     "sin",
     "cos",
+    "tan",
     "exp",
     "log",
     "expm1",