Merge branch 'main' into hotfix/cmp0xff/62063-def-of-scalar

Author: cmp0xff

.github/workflows/wheels.yml

@@ -162,7 +162,7 @@ jobs:
         run: echo "sdist_name=$(cd ./dist && ls -d */)" >> "$GITHUB_ENV"
 
       - name: Build wheels
-        uses: pypa/cibuildwheel@v3.1.4
+        uses: pypa/cibuildwheel@v3.2.0
         with:
          package-dir: ./dist/${{ startsWith(matrix.buildplat[1], 'macosx') && env.sdist_name || needs.build_sdist.outputs.sdist_file }}
         env:

asv_bench/benchmarks/algorithms.py

@@ -199,8 +199,8 @@ class SortIntegerArray:
     params = [10**3, 10**5]
 
     def setup(self, N):
-        data = np.arange(N, dtype=float)
-        data[40] = np.nan
+        data = np.arange(N, dtype=float).astype(object)
+        data[40] = pd.NA
         self.array = pd.array(data, dtype="Int64")
 
     def time_argsort(self, N):

asv_bench/benchmarks/frame_methods.py

@@ -4,6 +4,7 @@
 import numpy as np
 
 from pandas import (
+    NA,
     DataFrame,
     Index,
     MultiIndex,
@@ -445,6 +446,8 @@ def setup(self, inplace, dtype):
             values[::2] = np.nan
             if dtype == "Int64":
                 values = values.round()
+                values = values.astype(object)
+                values[::2] = NA
             self.df = DataFrame(values, dtype=dtype)
         self.fill_values = self.df.iloc[self.df.first_valid_index()].to_dict()
 

asv_bench/benchmarks/groupby.py

@@ -689,6 +689,10 @@ def setup(self, dtype, method, with_nans):
             null_vals = vals.astype(float, copy=True)
             null_vals[::2, :] = np.nan
             null_vals[::3, :] = np.nan
+            if dtype in ["Int64", "Float64"]:
+                null_vals = null_vals.astype(object)
+                null_vals[::2, :] = NA
+                null_vals[::3, :] = NA
             df = DataFrame(null_vals, columns=list("abcde"), dtype=dtype)
             df["key"] = keys
             self.df = df

doc/source/reference/missing_value.rst

@@ -11,14 +11,12 @@ NA is the way to represent missing values for nullable dtypes (see below):
 
 .. autosummary::
    :toctree: api/
-   :template: autosummary/class_without_autosummary.rst
 
    NA
 
 NaT is the missing value for timedelta and datetime data (see below):
 
 .. autosummary::
    :toctree: api/
-   :template: autosummary/class_without_autosummary.rst
 
    NaT

doc/source/user_guide/scale.rst

@@ -164,35 +164,35 @@ files. Each file in the directory represents a different year of the entire data
 .. ipython:: python
    :okwarning:
 
-   import pathlib
+   import glob
+   import tempfile
 
    N = 12
    starts = [f"20{i:>02d}-01-01" for i in range(N)]
    ends = [f"20{i:>02d}-12-13" for i in range(N)]
 
-   pathlib.Path("data/timeseries").mkdir(exist_ok=True)
+   tmpdir = tempfile.TemporaryDirectory(ignore_cleanup_errors=True)
 
    for i, (start, end) in enumerate(zip(starts, ends)):
        ts = make_timeseries(start=start, end=end, freq="1min", seed=i)
-       ts.to_parquet(f"data/timeseries/ts-{i:0>2d}.parquet")
+       ts.to_parquet(f"{tmpdir.name}/ts-{i:0>2d}.parquet")
 
 
 ::
 
-   data
-   └── timeseries
-       ├── ts-00.parquet
-       ├── ts-01.parquet
-       ├── ts-02.parquet
-       ├── ts-03.parquet
-       ├── ts-04.parquet
-       ├── ts-05.parquet
-       ├── ts-06.parquet
-       ├── ts-07.parquet
-       ├── ts-08.parquet
-       ├── ts-09.parquet
-       ├── ts-10.parquet
-       └── ts-11.parquet
+   tmpdir
+   ├── ts-00.parquet
+   ├── ts-01.parquet
+   ├── ts-02.parquet
+   ├── ts-03.parquet
+   ├── ts-04.parquet
+   ├── ts-05.parquet
+   ├── ts-06.parquet
+   ├── ts-07.parquet
+   ├── ts-08.parquet
+   ├── ts-09.parquet
+   ├── ts-10.parquet
+   └── ts-11.parquet
 
 Now we'll implement an out-of-core :meth:`pandas.Series.value_counts`. The peak memory usage of this
 workflow is the single largest chunk, plus a small series storing the unique value
@@ -202,13 +202,18 @@ work for arbitrary-sized datasets.
 .. ipython:: python
 
    %%time
-   files = pathlib.Path("data/timeseries/").glob("ts*.parquet")
+   files = glob.iglob(f"{tmpdir.name}/ts*.parquet")
    counts = pd.Series(dtype=int)
    for path in files:
        df = pd.read_parquet(path)
        counts = counts.add(df["name"].value_counts(), fill_value=0)
    counts.astype(int)
 
+.. ipython:: python
+   :suppress:
+
+   tmpdir.cleanup()
+
 Some readers, like :meth:`pandas.read_csv`, offer parameters to control the
 ``chunksize`` when reading a single file.
 

doc/source/user_guide/text.rst

@@ -75,7 +75,7 @@ or convert from existing pandas data:
 
 .. ipython:: python
 
-   s1 = pd.Series([1, 2, np.nan], dtype="Int64")
+   s1 = pd.Series([1, 2, pd.NA], dtype="Int64")
    s1
    s2 = s1.astype("string")
    s2

doc/source/whatsnew/v0.21.0.rst

@@ -635,22 +635,17 @@ Previous behavior:
 
 New behavior:
 
-.. code-block:: ipython
+.. ipython:: python
 
-   In [1]: pi = pd.period_range('2017-01', periods=12, freq='M')
+   pi = pd.period_range('2017-01', periods=12, freq='M')
 
-   In [2]: s = pd.Series(np.arange(12), index=pi)
+   s = pd.Series(np.arange(12), index=pi)
 
-   In [3]: resampled = s.resample('2Q').mean()
+   resampled = s.resample('2Q').mean()
 
-   In [4]: resampled
-   Out[4]:
-   2017Q1    2.5
-   2017Q3    8.5
-   Freq: 2Q-DEC, dtype: float64
+   resampled
 
-   In [5]: resampled.index
-   Out[5]: PeriodIndex(['2017Q1', '2017Q3'], dtype='period[2Q-DEC]')
+   resampled.index
 
 Upsampling and calling ``.ohlc()`` previously returned a ``Series``, basically identical to calling ``.asfreq()``. OHLC upsampling now returns a DataFrame with columns ``open``, ``high``, ``low`` and ``close`` (:issue:`13083`). This is consistent with downsampling and ``DatetimeIndex`` behavior.
 

doc/source/whatsnew/v0.24.0.rst

@@ -50,7 +50,7 @@ marker of ``np.nan`` will infer to integer dtype. The display of the ``Series``
 
 .. ipython:: python
 
-   s = pd.Series([1, 2, np.nan], dtype='Int64')
+   s = pd.Series([1, 2, pd.NA], dtype='Int64')
    s
 
 
@@ -166,7 +166,7 @@ See the :ref:`dtypes docs <basics.dtypes>` for more on extension arrays.
 
 .. ipython:: python
 
-   pd.array([1, 2, np.nan], dtype='Int64')
+   pd.array([1, 2, pd.NA], dtype='Int64')
    pd.array(['a', 'b', 'c'], dtype='category')
 
 Passing data for which there isn't dedicated extension type (e.g. float, integer, etc.)

doc/source/whatsnew/v2.2.0.rst

@@ -664,7 +664,7 @@ Other Deprecations
 - Deprecated :meth:`DatetimeArray.__init__` and :meth:`TimedeltaArray.__init__`, use :func:`array` instead (:issue:`55623`)
 - Deprecated :meth:`Index.format`, use ``index.astype(str)`` or ``index.map(formatter)`` instead (:issue:`55413`)
 - Deprecated :meth:`Series.ravel`, the underlying array is already 1D, so ravel is not necessary (:issue:`52511`)
-- Deprecated :meth:`Series.resample` and :meth:`DataFrame.resample` with a :class:`PeriodIndex` (and the 'convention' keyword), convert to :class:`DatetimeIndex` (with ``.to_timestamp()``) before resampling instead (:issue:`53481`)
+- Deprecated :meth:`Series.resample` and :meth:`DataFrame.resample` with a :class:`PeriodIndex` (and the 'convention' keyword), convert to :class:`DatetimeIndex` (with ``.to_timestamp()``) before resampling instead (:issue:`53481`). Note: this deprecation was later undone in pandas 2.3.3 (:issue:`57033`)
 - Deprecated :meth:`Series.view`, use :meth:`Series.astype` instead to change the dtype (:issue:`20251`)
 - Deprecated :meth:`offsets.Tick.is_anchored`, use ``False`` instead (:issue:`55388`)
 - Deprecated ``core.internals`` members ``Block``, ``ExtensionBlock``, and ``DatetimeTZBlock``, use public APIs instead (:issue:`55139`)