Merge remote-tracking branch 'upstream/main' into dateoffset-bug

Author: rsm-23

ci/code_checks.sh

@@ -105,16 +105,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         pandas.errors.UnsupportedFunctionCall \
         pandas.test \
         pandas.NaT \
-        pandas.Timestamp.strptime \
-        pandas.Timestamp.time \
-        pandas.Timestamp.timetuple \
-        pandas.Timestamp.timetz \
-        pandas.Timestamp.to_datetime64 \
-        pandas.Timestamp.toordinal \
-        pandas.Timestamp.tzname \
-        pandas.Timestamp.utcoffset \
-        pandas.Timestamp.utctimetuple \
-        pandas.Timestamp.weekday \
         pandas.arrays.TimedeltaArray \
         pandas.Period.asfreq \
         pandas.Period.now \

doc/source/whatsnew/v2.1.0.rst

@@ -19,7 +19,11 @@ Enhancements
 Copy-on-Write improvements
 ^^^^^^^^^^^^^^^^^^^^^^^^^^
 
+- Calling :meth:`Index.values` will now return a read-only NumPy array (:issue:`53704`)
 - Setting a :class:`Series` into a :class:`DataFrame` now creates a lazy instead of a deep copy (:issue:`53142`)
+- The :class:`DataFrame` constructor, when constructing a DataFrame from a dictionary
+  of Index objects and specifying ``copy=False``, will now use a lazy copy
+  of those Index objects for the columns of the DataFrame (:issue:`52947`)
 
 .. _whatsnew_210.enhancements.enhancement2:
 
@@ -109,6 +113,7 @@ Other enhancements
 - Added ``engine_kwargs`` parameter to :meth:`DataFrame.to_excel` (:issue:`53220`)
 - Added a new parameter ``by_row`` to :meth:`Series.apply`. When set to ``False`` the supplied callables will always operate on the whole Series (:issue:`53400`).
 - Groupby aggregations (such as :meth:`DataFrameGroupby.sum`) now can preserve the dtype of the input instead of casting to ``float64`` (:issue:`44952`)
+- Improved error message when :meth:`DataFrameGroupBy.agg` failed (:issue:`52930`)
 - Many read/to_* functions, such as :meth:`DataFrame.to_pickle` and :func:`read_csv`, support forwarding compression arguments to lzma.LZMAFile (:issue:`52979`)
 - Performance improvement in :func:`concat` with homogeneous ``np.float64`` or ``np.float32`` dtypes (:issue:`52685`)
 - Performance improvement in :meth:`DataFrame.filter` when ``items`` is given (:issue:`52941`)
@@ -288,12 +293,14 @@ Deprecations
 - Deprecated behavior of :func:`assert_series_equal` and :func:`assert_frame_equal` considering NA-like values (e.g. ``NaN`` vs ``None`` as equivalent) (:issue:`52081`)
 - Deprecated constructing :class:`SparseArray` from scalar data, pass a sequence instead (:issue:`53039`)
 - Deprecated falling back to filling when ``value`` is not specified in :meth:`DataFrame.replace` and :meth:`Series.replace` with non-dict-like ``to_replace`` (:issue:`33302`)
+- Deprecated literal json input to :func:`read_json`. Wrap literal json string input in ``io.StringIO`` instead. (:issue:`53409`)
 - Deprecated option "mode.use_inf_as_na", convert inf entries to ``NaN`` before instead (:issue:`51684`)
+- Deprecated parameter ``obj`` in :meth:`GroupBy.get_group` (:issue:`53545`)
 - Deprecated positional indexing on :class:`Series` with :meth:`Series.__getitem__` and :meth:`Series.__setitem__`, in a future version ``ser[item]`` will *always* interpret ``item`` as a label, not a position (:issue:`50617`)
+- Deprecated strings ``T``, ``t``, ``L`` and ``l`` denoting units in :func:`to_timedelta` (:issue:`52536`)
 - Deprecated the "method" and "limit" keywords on :meth:`Series.fillna`, :meth:`DataFrame.fillna`, :meth:`SeriesGroupBy.fillna`, :meth:`DataFrameGroupBy.fillna`, and :meth:`Resampler.fillna`, use ``obj.bfill()`` or ``obj.ffill()`` instead (:issue:`53394`)
 - Deprecated the ``method`` and ``limit`` keywords in :meth:`DataFrame.replace` and :meth:`Series.replace` (:issue:`33302`)
 - Deprecated values "pad", "ffill", "bfill", "backfill" for :meth:`Series.interpolate` and :meth:`DataFrame.interpolate`, use ``obj.ffill()`` or ``obj.bfill()`` instead (:issue:`53581`)
--
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_210.performance:
@@ -319,6 +326,7 @@ Performance improvements
 - Performance improvement accessing :attr:`arrays.IntegerArrays.dtype` & :attr:`arrays.FloatingArray.dtype` (:issue:`52998`)
 - Performance improvement in :class:`Series` reductions (:issue:`52341`)
 - Performance improvement in :func:`concat` when ``axis=1`` and objects have different indexes (:issue:`52541`)
+- Performance improvement in :func:`concat` when the concatenation axis is a :class:`MultiIndex` (:issue:`53574`)
 - Performance improvement in :meth:`.DataFrameGroupBy.groups` (:issue:`53088`)
 - Performance improvement in :meth:`DataFrame.isin` for extension dtypes (:issue:`53514`)
 - Performance improvement in :meth:`DataFrame.loc` when selecting rows and columns (:issue:`53014`)
@@ -342,6 +350,7 @@ Bug fixes
 
 Categorical
 ^^^^^^^^^^^
+- Bug in :meth:`Series.astype` with ``dtype="category"`` for nullable arrays with read-only null value masks (:issue:`53658`)
 - Bug in :meth:`Series.map` , where the value of the ``na_action`` parameter was not used if the series held a :class:`Categorical` (:issue:`22527`).
 -
 
@@ -350,6 +359,7 @@ Datetimelike
 - :meth:`DatetimeIndex.map` with ``na_action="ignore"`` now works as expected. (:issue:`51644`)
 - Bug in :class:`DateOffset` which had inconsistent behavior when multiplying a :class:`DateOffset` object by a constant (:issue:`47953`)
 - Bug in :func:`date_range` when ``freq`` was a :class:`DateOffset` with ``nanoseconds`` (:issue:`46877`)
+- Bug in :meth:`Timestamp.date`, :meth:`Timestamp.isocalendar` were returning incorrect results for inputs outside those supported by the Python standard library's datetime module (:issue:`53668`)
 - Bug in :meth:`Timestamp.round` with values close to the implementation bounds returning incorrect results instead of raising ``OutOfBoundsDatetime`` (:issue:`51494`)
 - Bug in :meth:`arrays.DatetimeArray.map` and :meth:`DatetimeIndex.map`, where the supplied callable operated array-wise instead of element-wise (:issue:`51977`)
 - Bug in constructing a :class:`Series` or :class:`DataFrame` from a datetime or timedelta scalar always inferring nanosecond resolution instead of inferring from the input (:issue:`52212`)
@@ -360,6 +370,7 @@ Timedelta
 ^^^^^^^^^
 - :meth:`TimedeltaIndex.map` with ``na_action="ignore"`` now works as expected (:issue:`51644`)
 - Bug in :class:`TimedeltaIndex` division or multiplication leading to ``.freq`` of "0 Days" instead of ``None`` (:issue:`51575`)
+- Bug in :class:`Timedelta` with Numpy timedelta64 objects not properly raising ``ValueError`` (:issue:`52806`)
 - Bug in :meth:`Timedelta.round` with values close to the implementation bounds returning incorrect results instead of raising ``OutOfBoundsTimedelta`` (:issue:`51494`)
 - Bug in :meth:`arrays.TimedeltaArray.map` and :meth:`TimedeltaIndex.map`, where the supplied callable operated array-wise instead of element-wise (:issue:`51977`)
 -
@@ -401,7 +412,7 @@ Strings
 
 Interval
 ^^^^^^^^
--
+- :meth:`pd.IntervalIndex.get_indexer` and :meth:`pd.IntervalIndex.get_indexer_nonunique` raising if ``target`` is read-only array (:issue:`53703`)
 -
 
 Indexing
@@ -453,6 +464,7 @@ Plotting
 Groupby/resample/rolling
 ^^^^^^^^^^^^^^^^^^^^^^^^
 - Bug in :meth:`DataFrame.resample` and :meth:`Series.resample` in incorrectly allowing non-fixed ``freq`` when resampling on a :class:`TimedeltaIndex` (:issue:`51896`)
+- Bug in :meth:`DataFrame.resample` and :meth:`Series.resample` losing time zone when resampling empty data (:issue:`53664`)
 - Bug in :meth:`DataFrameGroupBy.idxmin`, :meth:`SeriesGroupBy.idxmin`, :meth:`DataFrameGroupBy.idxmax`, :meth:`SeriesGroupBy.idxmax` return wrong dtype when used on empty DataFrameGroupBy or SeriesGroupBy (:issue:`51423`)
 - Bug in weighted rolling aggregations when specifying ``min_periods=0`` (:issue:`51449`)
 - Bug in :meth:`DataFrame.groupby` and :meth:`Series.groupby`, where, when the index of the
@@ -472,6 +484,7 @@ Groupby/resample/rolling
 
 Reshaping
 ^^^^^^^^^
+- Bug in :func:`concat` coercing to ``object`` dtype when one column has ``pa.null()`` dtype (:issue:`53702`)
 - Bug in :func:`crosstab` when ``dropna=False`` would not keep ``np.nan`` in the result (:issue:`10772`)
 - Bug in :func:`merge_asof` raising ``KeyError`` for extension dtypes (:issue:`52904`)
 - Bug in :func:`merge_asof` raising ``ValueError`` for data backed by read-only ndarrays (:issue:`53513`)
@@ -511,6 +524,7 @@ Metadata
 
 Other
 ^^^^^
+- Bug in :class:`DataFrame` and :class:`Series` raising for data of complex dtype when ``NaN`` values are present (:issue:`53627`)
 - Bug in :class:`FloatingArray.__contains__` with ``NaN`` item incorrectly returning ``False`` when ``NaN`` values are present (:issue:`52840`)
 - Bug in :func:`api.interchange.from_dataframe` when converting an empty DataFrame object (:issue:`53155`)
 - Bug in :func:`assert_almost_equal` now throwing assertion error for two unequal sets (:issue:`51727`)

pandas/_libs/tslibs/nattype.pyx

@@ -256,7 +256,16 @@ cdef class _NaT(datetime):
 
     def to_datetime64(self) -> np.datetime64:
         """
-        Return a numpy.datetime64 object with 'ns' precision.
+        Return a numpy.datetime64 object with same precision.
+
+        Examples
+        --------
+        >>> ts = pd.Timestamp(year=2023, month=1, day=1,
+        ...                   hour=10, second=15)
+        >>> ts
+        Timestamp('2023-01-01 10:00:15')
+        >>> ts.to_datetime64()
+        numpy.datetime64('2023-01-01T10:00:15.000000')
         """
         return np.datetime64("NaT", "ns")
 
@@ -429,6 +438,14 @@ class NaTType(_NaT):
         Return the day of the week represented by the date.
 
         Monday == 0 ... Sunday == 6.
+
+        Examples
+        --------
+        >>> ts = pd.Timestamp('2023-01-01')
+        >>> ts
+        Timestamp('2023-01-01  00:00:00')
+        >>> ts.weekday()
+        6
         """,
     )
     isoweekday = _make_nan_func(
@@ -514,13 +531,6 @@ class NaTType(_NaT):
         """,
     )
     # _nat_methods
-    utctimetuple = _make_error_func("utctimetuple", datetime)
-    timetz = _make_error_func("timetz", datetime)
-    timetuple = _make_error_func("timetuple", datetime)
-    time = _make_error_func("time", datetime)
-    toordinal = _make_error_func("toordinal", datetime)
-    tzname = _make_error_func("tzname", datetime)
-    utcoffset = _make_error_func("utcoffset", datetime)
 
     # "fromisocalendar" was introduced in 3.8
     fromisocalendar = _make_error_func("fromisocalendar", datetime)
@@ -570,7 +580,106 @@ class NaTType(_NaT):
         datetime.date(2023, 1, 1)
         """
         )
+    utctimetuple = _make_error_func(
+        "utctimetuple",
+        """
+        Return UTC time tuple, compatible with time.localtime().
+
+        Examples
+        --------
+        >>> ts = pd.Timestamp('2023-01-01 10:00:00', tz='Europe/Brussels')
+        >>> ts
+        Timestamp('2023-01-01 10:00:00+0100', tz='Europe/Brussels')
+        >>> ts.utctimetuple()
+        time.struct_time(tm_year=2023, tm_mon=1, tm_mday=1, tm_hour=9,
+        tm_min=0, tm_sec=0, tm_wday=6, tm_yday=1, tm_isdst=0)
+        """
+        )
+    utcoffset = _make_error_func(
+        "utcoffset",
+        """
+        Return utc offset.
+
+        Examples
+        --------
+        >>> ts = pd.Timestamp('2023-01-01 10:00:00', tz='Europe/Brussels')
+        >>> ts
+        Timestamp('2023-01-01 10:00:00+0100', tz='Europe/Brussels')
+        >>> ts.utcoffset()
+        datetime.timedelta(seconds=3600)
+        """
+        )
+    tzname = _make_error_func(
+        "tzname",
+        """
+        Return time zone name.
+
+        Examples
+        --------
+        >>> ts = pd.Timestamp('2023-01-01 10:00:00', tz='Europe/Brussels')
+        >>> ts
+        Timestamp('2023-01-01 10:00:00+0100', tz='Europe/Brussels')
+        >>> ts.tzname()
+        'CET'
+        """
+        )
+    time = _make_error_func(
+        "time",
+        """
+        Return time object with same time but with tzinfo=None.
+
+        Examples
+        --------
+        >>> ts = pd.Timestamp('2023-01-01 10:00:00')
+        >>> ts
+        Timestamp('2023-01-01 10:00:00')
+        >>> ts.time()
+        datetime.time(10, 0)
+        """,
+        )
+    timetuple = _make_error_func(
+        "timetuple",
+        """
+        Return time tuple, compatible with time.localtime().
+
+        Examples
+        --------
+        >>> ts = pd.Timestamp('2023-01-01 10:00:00')
+        >>> ts
+        Timestamp('2023-01-01 10:00:00')
+        >>> ts.timetuple()
+        time.struct_time(tm_year=2023, tm_mon=1, tm_mday=1,
+        tm_hour=10, tm_min=0, tm_sec=0, tm_wday=6, tm_yday=1, tm_isdst=-1)
+        """
+        )
+    timetz = _make_error_func(
+        "timetz",
+        """
+        Return time object with same time and tzinfo.
+
+        Examples
+        --------
+        >>> ts = pd.Timestamp('2023-01-01 10:00:00', tz='Europe/Brussels')
+        >>> ts
+        Timestamp('2023-01-01 10:00:00+0100', tz='Europe/Brussels')
+        >>> ts.timetz()
+        datetime.time(10, 0, tzinfo=<DstTzInfo 'Europe/Brussels' CET+1:00:00 STD>)
+        """
+        )
+    toordinal = _make_error_func(
+        "toordinal",
+        """
+        Return proleptic Gregorian ordinal. January 1 of year 1 is day 1.
 
+        Examples
+        --------
+        >>> ts = pd.Timestamp('2023-01-01 10:00:50')
+        >>> ts
+        Timestamp('2023-01-01 10:00:50')
+        >>> ts.toordinal()
+        738521
+        """
+        )
     ctime = _make_error_func(
         "ctime",
         """
@@ -612,6 +721,12 @@ class NaTType(_NaT):
         Timestamp.strptime(string, format)
 
         Function is not implemented. Use pd.to_datetime().
+
+        Examples
+        --------
+        >>> pd.Timestamp.strptime("2023-01-01", "%d/%m/%y")
+        Traceback (most recent call last):
+        NotImplementedError
         """,
     )
 

pandas/_libs/tslibs/period.pyx

@@ -1917,7 +1917,8 @@ cdef class _Period(PeriodMixin):
         Parameters
         ----------
         freq : str, BaseOffset
-            The desired frequency.
+            The desired frequency. If passing a `str`, it needs to be a
+            valid :ref:`period alias <timeseries.period_aliases>`.
         how : {'E', 'S', 'end', 'start'}, default 'end'
             Start or end of the timespan.