Merge remote-tracking branch 'upstream/main' into cow_warn_mode_inter…

…polate_new

.github/workflows/comment-commands.yml

@@ -77,7 +77,7 @@ jobs:
           echo 'EOF' >> $GITHUB_ENV
           echo "REGEX=$REGEX" >> $GITHUB_ENV
 
-      - uses: actions/github-script@v6
+      - uses: actions/github-script@v7
         env:
           BENCH_OUTPUT: ${{env.BENCH_OUTPUT}}
           REGEX: ${{env.REGEX}}

.github/workflows/deprecation-tracking-bot.yml

@@ -21,7 +21,7 @@ jobs:
     env:
       DEPRECATION_TRACKER_ISSUE: 50578
     steps:
-    - uses: actions/github-script@v6
+    - uses: actions/github-script@v7
       id: update-deprecation-issue
       with:
         script: |

asv_bench/benchmarks/indexing.py

@@ -306,6 +306,10 @@ def time_loc_null_slice_plus_slice(self, unique_levels):
         target = (self.tgt_null_slice, self.tgt_slice)
         self.df.loc[target, :]
 
+    def time_loc_multiindex(self, unique_levels):
+        target = self.df.index[::10]
+        self.df.loc[target]
+
     def time_xs_level_0(self, unique_levels):
         target = self.tgt_scalar
         self.df.xs(target, level=0)

asv_bench/benchmarks/io/csv.py

@@ -621,4 +621,15 @@ def time_read_csv_index_col(self):
         )
 
 
+class ReadCSVCParserLowMemory:
+    # GH 16798
+    def setup(self):
+        self.csv = StringIO(
+            "strings\n" + "\n".join(["x" * (1 << 20) for _ in range(2100)])
+        )
+
+    def peakmem_over_2gb_input(self):
+        read_csv(self.csv, engine="c", low_memory=False)
+
+
 from ..pandas_vb_common import setup  # noqa: F401 isort:skip

doc/source/user_guide/copy_on_write.rst

@@ -6,6 +6,12 @@
 Copy-on-Write (CoW)
 *******************
 
+.. note::
+
+    Copy-on-Write will become the default in pandas 3.0. We recommend
+    :ref:`turning it on now <copy_on_write_enabling>`
+    to benefit from all improvements.
+
 Copy-on-Write was first introduced in version 1.5.0. Starting from version 2.0 most of the
 optimizations that become possible through CoW are implemented and supported. All possible
 optimizations are supported starting from pandas 2.1.
@@ -123,6 +129,8 @@ CoW triggers a copy when ``df`` is changed to avoid mutating ``view`` as well:
     df
     view
 
+.. _copy_on_write_chained_assignment:
+
 Chained Assignment
 ------------------
 
@@ -238,6 +246,8 @@ and :meth:`DataFrame.rename`.
 These methods return views when Copy-on-Write is enabled, which provides a significant
 performance improvement compared to the regular execution.
 
+.. _copy_on_write_enabling:
+
 How to enable CoW
 -----------------
 

doc/source/user_guide/indexing.rst

@@ -1727,6 +1727,22 @@ You can assign a custom index to the ``index`` attribute:
 Returning a view versus a copy
 ------------------------------
 
+.. warning::
+
+    :ref:`Copy-on-Write <copy_on_write>`
+    will become the new default in pandas 3.0. This means than chained indexing will
+    never work. As a consequence, the ``SettingWithCopyWarning`` won't be necessary
+    anymore.
+    See :ref:`this section <copy_on_write_chained_assignment>`
+    for more context.
+    We recommend turning Copy-on-Write on to leverage the improvements with
+
+    ```
+    pd.options.mode.copy_on_write = True
+    ```
+
+    even before pandas 3.0 is available.
+
 When setting values in a pandas object, care must be taken to avoid what is called
 ``chained indexing``. Here is an example.
 
@@ -1765,6 +1781,22 @@ faster, and allows one to index *both* axes if so desired.
 Why does assignment fail when using chained indexing?
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+.. warning::
+
+    :ref:`Copy-on-Write <copy_on_write>`
+    will become the new default in pandas 3.0. This means than chained indexing will
+    never work. As a consequence, the ``SettingWithCopyWarning`` won't be necessary
+    anymore.
+    See :ref:`this section <copy_on_write_chained_assignment>`
+    for more context.
+    We recommend turning Copy-on-Write on to leverage the improvements with
+
+    ```
+    pd.options.mode.copy_on_write = True
+    ```
+
+    even before pandas 3.0 is available.
+
 The problem in the previous section is just a performance issue. What's up with
 the ``SettingWithCopy`` warning? We don't **usually** throw warnings around when
 you do something that might cost a few extra milliseconds!
@@ -1821,6 +1853,22 @@ Yikes!
 Evaluation order matters
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
+.. warning::
+
+    :ref:`Copy-on-Write <copy_on_write>`
+    will become the new default in pandas 3.0. This means than chained indexing will
+    never work. As a consequence, the ``SettingWithCopyWarning`` won't be necessary
+    anymore.
+    See :ref:`this section <copy_on_write_chained_assignment>`
+    for more context.
+    We recommend turning Copy-on-Write on to leverage the improvements with
+
+    ```
+    pd.options.mode.copy_on_write = True
+    ```
+
+    even before pandas 3.0 is available.
+
 When you use chained indexing, the order and type of the indexing operation
 partially determine whether the result is a slice into the original object, or
 a copy of the slice.

doc/source/user_guide/timeseries.rst

@@ -882,7 +882,7 @@ into ``freq`` keyword arguments. The available date offsets and associated frequ
     :class:`~pandas.tseries.offsets.BMonthBegin` or :class:`~pandas.tseries.offsets.BusinessMonthBegin`, ``'BMS'``, "business month begin"
     :class:`~pandas.tseries.offsets.CBMonthEnd` or :class:`~pandas.tseries.offsets.CustomBusinessMonthEnd`, ``'CBME'``, "custom business month end"
     :class:`~pandas.tseries.offsets.CBMonthBegin` or :class:`~pandas.tseries.offsets.CustomBusinessMonthBegin`, ``'CBMS'``, "custom business month begin"
-    :class:`~pandas.tseries.offsets.SemiMonthEnd`, ``'SM'``, "15th (or other day_of_month) and calendar month end"
+    :class:`~pandas.tseries.offsets.SemiMonthEnd`, ``'SME'``, "15th (or other day_of_month) and calendar month end"
     :class:`~pandas.tseries.offsets.SemiMonthBegin`, ``'SMS'``, "15th (or other day_of_month) and calendar month begin"
     :class:`~pandas.tseries.offsets.QuarterEnd`, ``'QE'``, "calendar quarter end"
     :class:`~pandas.tseries.offsets.QuarterBegin`, ``'QS'``, "calendar quarter begin"
@@ -1241,7 +1241,7 @@ frequencies. We will refer to these aliases as *offset aliases*.
     "D", "calendar day frequency"
     "W", "weekly frequency"
     "ME", "month end frequency"
-    "SM", "semi-month end frequency (15th and end of month)"
+    "SME", "semi-month end frequency (15th and end of month)"
     "BME", "business month end frequency"
     "CBME", "custom business month end frequency"
     "MS", "month start frequency"

doc/source/whatsnew/v0.19.0.rst

@@ -329,11 +329,13 @@ These provide date offsets anchored (by default) to the 15th and end of month, a
 
 **SemiMonthEnd**:
 
-.. ipython:: python
+.. code-block:: python
 
-   pd.Timestamp("2016-01-01") + SemiMonthEnd()
+   In [46]: pd.Timestamp("2016-01-01") + SemiMonthEnd()
+   Out[46]: Timestamp('2016-01-15 00:00:00')
 
-   pd.date_range("2015-01-01", freq="SM", periods=4)
+   In [47]: pd.date_range("2015-01-01", freq="SM", periods=4)
+   Out[47]: DatetimeIndex(['2015-01-15', '2015-01-31', '2015-02-15', '2015-02-28'], dtype='datetime64[ns]', freq='SM-15')
 
 **SemiMonthBegin**:
 
@@ -345,11 +347,13 @@ These provide date offsets anchored (by default) to the 15th and end of month, a
 
 Using the anchoring suffix, you can also specify the day of month to use instead of the 15th.
 
-.. ipython:: python
+.. code-block:: python
 
-   pd.date_range("2015-01-01", freq="SMS-16", periods=4)
+   In [50]: pd.date_range("2015-01-01", freq="SMS-16", periods=4)
+   Out[50]: DatetimeIndex(['2015-01-01', '2015-01-16', '2015-02-01', '2015-02-16'], dtype='datetime64[ns]', freq='SMS-16')
 
-   pd.date_range("2015-01-01", freq="SM-14", periods=4)
+   In [51]: pd.date_range("2015-01-01", freq="SM-14", periods=4)
+   Out[51]: DatetimeIndex(['2015-01-14', '2015-01-31', '2015-02-14', '2015-02-28'], dtype='datetime64[ns]', freq='SM-14')
 
 .. _whatsnew_0190.enhancements.index:
 

doc/source/whatsnew/v2.1.4.rst

@@ -22,6 +22,8 @@ Fixed regressions
 Bug fixes
 ~~~~~~~~~
 - Bug in :class:`Series` constructor raising DeprecationWarning when ``index`` is a list of :class:`Series` (:issue:`55228`)
+- Fixed bug in :meth:`DataFrame.__setitem__` casting :class:`Index` with object-dtype to PyArrow backed strings when ``infer_string`` option is set (:issue:`55638`)
+- Fixed bug in :meth:`Index.insert` casting object-dtype to PyArrow backed strings when ``infer_string`` option is set (:issue:`55638`)
 -
 
 .. ---------------------------------------------------------------------------

doc/source/whatsnew/v2.2.0.rst

@@ -233,13 +233,17 @@ Other API changes
 Deprecations
 ~~~~~~~~~~~~
 
-Deprecate aliases ``M``, ``Q``, and ``Y`` in favour of ``ME``, ``QE``, and ``YE`` for offsets
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Deprecate aliases ``M``, ``SM``, ``BM``, ``CBM``, ``Q``, ``BQ``, and ``Y`` in favour of ``ME``, ``SME``, ``BME``, ``CBME``, ``QE``, ``BQE``, and ``YE`` for offsets
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Deprecated the following frequency aliases (:issue:`9586`):
 
 - ``M`` (month end) has been renamed ``ME`` for offsets
+- ``SM`` (semi month end) has been renamed ``SME`` for offsets
+- ``BM`` (business month end) has been renamed ``BME`` for offsets
+- ``CBM`` (custom business month end) has been renamed ``CBME`` for offsets
 - ``Q`` (quarter end) has been renamed ``QE`` for offsets
+- ``BQ`` (business quarter end) has been renamed ``BQE`` for offsets
 - ``Y`` (year end) has been renamed ``YE`` for offsets
 
 For example:
@@ -266,7 +270,9 @@ Other Deprecations
 - Deprecated :func:`read_gbq` and :meth:`DataFrame.to_gbq`. Use ``pandas_gbq.read_gbq`` and ``pandas_gbq.to_gbq`` instead https://pandas-gbq.readthedocs.io/en/latest/api.html (:issue:`55525`)
 - Deprecated :meth:`.DataFrameGroupBy.fillna` and :meth:`.SeriesGroupBy.fillna`; use :meth:`.DataFrameGroupBy.ffill`, :meth:`.DataFrameGroupBy.bfill` for forward and backward filling or :meth:`.DataFrame.fillna` to fill with a single value (or the Series equivalents) (:issue:`55718`)
 - Deprecated :meth:`Index.format`, use ``index.astype(str)`` or ``index.map(formatter)`` instead (:issue:`55413`)
+- Deprecated ``core.internals`` members ``Block``, ``ExtensionBlock``, and ``DatetimeTZBlock``, use public APIs instead (:issue:`55139`)
 - Deprecated ``year``, ``month``, ``quarter``, ``day``, ``hour``, ``minute``, and ``second`` keywords in the :class:`PeriodIndex` constructor, use :meth:`PeriodIndex.from_fields` instead (:issue:`55960`)
+- Deprecated allowing non-integer ``periods`` argument in :func:`date_range`, :func:`timedelta_range`, :func:`period_range`, and :func:`interval_range` (:issue:`56036`)
 - Deprecated allowing non-keyword arguments in :meth:`DataFrame.to_clipboard`. (:issue:`54229`)
 - Deprecated allowing non-keyword arguments in :meth:`DataFrame.to_csv` except ``path_or_buf``. (:issue:`54229`)
 - Deprecated allowing non-keyword arguments in :meth:`DataFrame.to_dict`. (:issue:`54229`)
@@ -285,13 +291,14 @@ Other Deprecations
 - Deprecated automatic downcasting of object-dtype results in :meth:`Series.replace` and :meth:`DataFrame.replace`, explicitly call ``result = result.infer_objects(copy=False)`` instead. To opt in to the future version, use ``pd.set_option("future.no_silent_downcasting", True)`` (:issue:`54710`)
 - Deprecated downcasting behavior in :meth:`Series.where`, :meth:`DataFrame.where`, :meth:`Series.mask`, :meth:`DataFrame.mask`, :meth:`Series.clip`, :meth:`DataFrame.clip`; in a future version these will not infer object-dtype columns to non-object dtype, or all-round floats to integer dtype. Call ``result.infer_objects(copy=False)`` on the result for object inference, or explicitly cast floats to ints. To opt in to the future version, use ``pd.set_option("future.no_silent_downcasting", True)`` (:issue:`53656`)
 - Deprecated including the groups in computations when using :meth:`DataFrameGroupBy.apply` and :meth:`DataFrameGroupBy.resample`; pass ``include_groups=False`` to exclude the groups (:issue:`7155`)
+- Deprecated indexing an :class:`Index`  with a boolean indexer of length zero (:issue:`55820`)
 - Deprecated not passing a tuple to :class:`DataFrameGroupBy.get_group` or :class:`SeriesGroupBy.get_group` when grouping by a length-1 list-like (:issue:`25971`)
 - Deprecated string ``AS`` denoting frequency in :class:`YearBegin` and strings ``AS-DEC``, ``AS-JAN``, etc. denoting annual frequencies with various fiscal year starts (:issue:`54275`)
 - Deprecated string ``A`` denoting frequency in :class:`YearEnd` and strings ``A-DEC``, ``A-JAN``, etc. denoting annual frequencies with various fiscal year ends (:issue:`54275`)
 - Deprecated string ``BAS`` denoting frequency in :class:`BYearBegin` and strings ``BAS-DEC``, ``BAS-JAN``, etc. denoting annual frequencies with various fiscal year starts (:issue:`54275`)
 - Deprecated string ``BA`` denoting frequency in :class:`BYearEnd` and strings ``BA-DEC``, ``BA-JAN``, etc. denoting annual frequencies with various fiscal year ends (:issue:`54275`)
 - Deprecated string ``BQ`` denoting frequency in :class:`BQuarterEnd` (:issue:`52064`)
-- Deprecated strings ``BM``, and ``CBM`` denoting frequencies in :class:`BusinessMonthEnd`, :class:`CustomBusinessMonthEnd` (:issue:`52064`)
+- Deprecated strings ``BM``, ``CBM``, and ``SM`` denoting frequencies in :class:`BusinessMonthEnd`, :class:`CustomBusinessMonthEnd, :class:`SemiMonthEnd` (:issue:`52064`)
 - Deprecated strings ``H``, ``BH``, and ``CBH`` denoting frequencies in :class:`Hour`, :class:`BusinessHour`, :class:`CustomBusinessHour` (:issue:`52536`)
 - Deprecated strings ``H``, ``S``, ``U``, and ``N`` denoting units in :func:`to_timedelta` (:issue:`52536`)
 - Deprecated strings ``H``, ``T``, ``S``, ``L``, ``U``, and ``N`` denoting units in :class:`Timedelta` (:issue:`52536`)
@@ -312,10 +319,12 @@ Performance improvements
 ~~~~~~~~~~~~~~~~~~~~~~~~
 - Performance improvement in :func:`.testing.assert_frame_equal` and :func:`.testing.assert_series_equal` (:issue:`55949`, :issue:`55971`)
 - Performance improvement in :func:`concat` with ``axis=1`` and objects with unaligned indexes (:issue:`55084`)
+- Performance improvement in :func:`get_dummies` (:issue:`56089`)
 - Performance improvement in :func:`merge_asof` when ``by`` is not ``None`` (:issue:`55580`, :issue:`55678`)
 - Performance improvement in :func:`read_stata` for files with many variables (:issue:`55515`)
 - Performance improvement in :func:`to_dict` on converting DataFrame to dictionary (:issue:`50990`)
 - Performance improvement in :meth:`DataFrame.groupby` when aggregating pyarrow timestamp and duration dtypes (:issue:`55031`)
+- Performance improvement in :meth:`DataFrame.loc` and :meth:`Series.loc` when indexing with a :class:`MultiIndex` (:issue:`56062`)
 - Performance improvement in :meth:`DataFrame.sort_index` and :meth:`Series.sort_index` when indexed by a :class:`MultiIndex` (:issue:`54835`)
 - Performance improvement in :meth:`Index.difference` (:issue:`55108`)
 - Performance improvement in :meth:`MultiIndex.get_indexer` when ``method`` is not ``None`` (:issue:`55839`)
@@ -359,6 +368,8 @@ Datetimelike
 - Bug in creating a :class:`Index`, :class:`Series`, or :class:`DataFrame` with a non-nanosecond :class:`DatetimeTZDtype` and inputs that would be out of bounds with nanosecond resolution incorrectly raising ``OutOfBoundsDatetime`` (:issue:`54620`)
 - Bug in creating a :class:`Index`, :class:`Series`, or :class:`DataFrame` with a non-nanosecond ``datetime64`` (or :class:`DatetimeTZDtype`) from mixed-numeric inputs treating those as nanoseconds instead of as multiples of the dtype's unit (which would happen with non-mixed numeric inputs) (:issue:`56004`)
 - Bug in creating a :class:`Index`, :class:`Series`, or :class:`DataFrame` with a non-nanosecond ``datetime64`` dtype and inputs that would be out of bounds for a ``datetime64[ns]`` incorrectly raising ``OutOfBoundsDatetime`` (:issue:`55756`)
+- Bug in parsing datetime strings with nanosecond resolution with non-ISO8601 formats incorrectly truncating sub-microsecond components (:issue:`56051`)
+- Bug in parsing datetime strings with sub-second resolution and trailing zeros incorrectly inferring second or millisecond resolution (:issue:`55737`)
 -
 
 Timedelta
@@ -370,7 +381,7 @@ Timezones
 ^^^^^^^^^
 - Bug in :class:`AbstractHolidayCalendar` where timezone data was not propagated when computing holiday observances (:issue:`54580`)
 - Bug in :class:`Timestamp` construction with an ambiguous value and a ``pytz`` timezone failing to raise ``pytz.AmbiguousTimeError`` (:issue:`55657`)
--
+- Bug in :meth:`Timestamp.tz_localize` with ``nonexistent="shift_forward`` around UTC+0 during DST (:issue:`51501`)
 
 Numeric
 ^^^^^^^
@@ -459,10 +470,11 @@ Reshaping
 - Bug in :func:`merge` returning columns in incorrect order when left and/or right is empty (:issue:`51929`)
 - Bug in :meth:`pandas.DataFrame.melt` where an exception was raised if ``var_name`` was not a string (:issue:`55948`)
 - Bug in :meth:`pandas.DataFrame.melt` where it would not preserve the datetime (:issue:`55254`)
+- Bug in :meth:`pandas.DataFrame.pivot_table` where the row margin is incorrect when the columns have numeric names (:issue:`26568`)
 
 Sparse
 ^^^^^^
--
+- Bug in :meth:`SparseArray.take` when using a different fill value than the array's fill value (:issue:`55181`)
 -
 
 ExtensionArray

pandas/_libs/tslibs/conversion.pyx

@@ -508,7 +508,7 @@ cdef _TSObject convert_str_to_tsobject(str ts, tzinfo tz,
         npy_datetimestruct dts
         int out_local = 0, out_tzoffset = 0, string_to_dts_failed
         datetime dt
-        int64_t ival
+        int64_t ival, nanos = 0
         NPY_DATETIMEUNIT out_bestunit, reso
         _TSObject obj
 
@@ -560,10 +560,14 @@ cdef _TSObject convert_str_to_tsobject(str ts, tzinfo tz,
                 return obj
 
         dt = parse_datetime_string(
-            ts, dayfirst=dayfirst, yearfirst=yearfirst, out_bestunit=&out_bestunit
+            ts,
+            dayfirst=dayfirst,
+            yearfirst=yearfirst,
+            out_bestunit=&out_bestunit,
+            nanos=&nanos,
         )
         reso = get_supported_reso(out_bestunit)
-        return convert_datetime_to_tsobject(dt, tz, nanos=0, reso=reso)
+        return convert_datetime_to_tsobject(dt, tz, nanos=nanos, reso=reso)
 
     return convert_datetime_to_tsobject(dt, tz)
 

pandas/_libs/tslibs/dtypes.pyx

@@ -175,6 +175,7 @@ OFFSET_TO_PERIOD_FREQSTR: dict = {
     "WEEKDAY": "D",
     "EOM": "M",
     "BME": "M",
+    "SME": "M",
     "BQS": "Q",
     "QS": "Q",
     "BQE": "Q",
@@ -275,6 +276,7 @@ cdef dict c_OFFSET_DEPR_FREQSTR = {
     "A-NOV": "YE-NOV",
     "BM": "BME",
     "CBM": "CBME",
+    "SM": "SME",
     "BQ": "BQE",
     "BQ-DEC": "BQE-DEC",
     "BQ-JAN": "BQE-JAN",
@@ -358,8 +360,6 @@ cdef dict c_DEPR_ABBREVS = {
     "BAS-SEP": "BYS-SEP",
     "BAS-OCT": "BYS-OCT",
     "BAS-NOV": "BYS-NOV",
-    "BM": "BME",
-    "CBM": "CBME",
     "H": "h",
     "BH": "bh",
     "CBH": "cbh",

pandas/_libs/tslibs/offsets.pyx

@@ -3129,7 +3129,7 @@ cdef class SemiMonthEnd(SemiMonthOffset):
     >>> pd.offsets.SemiMonthEnd().rollforward(ts)
     Timestamp('2022-01-15 00:00:00')
     """
-    _prefix = "SM"
+    _prefix = "SME"
     _min_day_of_month = 1
 
     def is_on_offset(self, dt: datetime) -> bool:
@@ -4549,7 +4549,7 @@ prefix_mapping = {
         MonthEnd,  # 'ME'
         MonthBegin,  # 'MS'
         Nano,  # 'ns'
-        SemiMonthEnd,  # 'SM'
+        SemiMonthEnd,  # 'SME'
         SemiMonthBegin,  # 'SMS'
         Week,  # 'W'
         Second,  # 's'

pandas/_libs/tslibs/parsing.pxd

@@ -1,4 +1,5 @@
 from cpython.datetime cimport datetime
+from numpy cimport int64_t
 
 from pandas._libs.tslibs.np_datetime cimport NPY_DATETIMEUNIT
 
@@ -10,5 +11,6 @@ cdef datetime parse_datetime_string(
     str date_string,
     bint dayfirst,
     bint yearfirst,
-    NPY_DATETIMEUNIT* out_bestunit
+    NPY_DATETIMEUNIT* out_bestunit,
+    int64_t* nanos,
 )