Merge remote-tracking branch 'upstream/main' into tst/cln/unneeded_pa_checking

Author: mroeschke

ci/code_checks.sh

@@ -574,9 +574,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         pandas.api.types.is_datetime64_ns_dtype \
         pandas.api.types.is_datetime64tz_dtype \
         pandas.api.types.is_integer_dtype \
-        pandas.api.types.is_interval_dtype \
-        pandas.api.types.is_period_dtype \
-        pandas.api.types.is_signed_integer_dtype \
         pandas.api.types.is_sparse \
         pandas.api.types.is_string_dtype \
         pandas.api.types.is_unsigned_integer_dtype \

doc/source/development/copy_on_write.rst

@@ -14,15 +14,15 @@ behaves as a copy.
 Reference tracking
 ------------------
 
-To be able to determine, if we have to make a copy when writing into a DataFrame,
-we have to be aware, if the values are shared with another DataFrame. pandas
+To be able to determine if we have to make a copy when writing into a DataFrame,
+we have to be aware if the values are shared with another DataFrame. pandas
 keeps track of all ``Blocks`` that share values with another block internally to
 be able to tell when a copy needs to be triggered. The reference tracking
 mechanism is implemented on the Block level.
 
 We use a custom reference tracker object, ``BlockValuesRefs``, that keeps
 track of every block, whose values share memory with each other. The reference
-is held through a weak-reference. Every two blocks that share some memory should
+is held through a weak-reference. Every pair of blocks that share some memory should
 point to the same ``BlockValuesRefs`` object. If one block goes out of
 scope, the reference to this block dies. As a consequence, the reference tracker
 object always knows how many blocks are alive and share memory.

doc/source/development/policies.rst

@@ -9,8 +9,6 @@ Policies
 Version policy
 ~~~~~~~~~~~~~~
 
-.. versionchanged:: 1.0.0
-
 pandas uses a loose variant of semantic versioning (`SemVer`_) to govern
 deprecations, API compatibility, and version numbering.
 

doc/source/user_guide/boolean.rst

@@ -17,9 +17,6 @@ Nullable Boolean data type
    BooleanArray is currently experimental. Its API or implementation may
    change without warning.
 
-.. versionadded:: 1.0.0
-
-
 .. _boolean.indexing:
 
 Indexing with NA values

doc/source/user_guide/indexing.rst

@@ -311,13 +311,6 @@ Selection by label
 
      dfl.loc['20130102':'20130104']
 
-.. warning::
-
-   .. versionchanged:: 1.0.0
-
-   pandas will raise a ``KeyError`` if indexing with a list with missing labels. See :ref:`list-like Using loc with
-   missing keys in a list is Deprecated <indexing.deprecate_loc_reindex_listlike>`.
-
 pandas provides a suite of methods in order to have **purely label based indexing**. This is a strict inclusion based protocol.
 Every label asked for must be in the index, or a ``KeyError`` will be raised.
 When slicing, both the start bound **AND** the stop bound are *included*, if present in the index.
@@ -622,12 +615,6 @@ For getting *multiple* indexers, using ``.get_indexer``:
 Indexing with list with missing labels is deprecated
 ----------------------------------------------------
 
-.. warning::
-
-   .. versionchanged:: 1.0.0
-
-   Using ``.loc`` or ``[]`` with a list with one or more missing labels will no longer reindex, in favor of ``.reindex``.
-
 In prior versions, using ``.loc[list-of-labels]`` would work as long as *at least 1* of the keys was found (otherwise it
 would raise a ``KeyError``). This behavior was changed and will now raise a ``KeyError`` if at least one label is missing.
 The recommended alternative is to use ``.reindex()``.

doc/source/user_guide/integer_na.rst

@@ -11,12 +11,7 @@ Nullable integer data type
 .. note::
 
    IntegerArray is currently experimental. Its API or implementation may
-   change without warning.
-
-.. versionchanged:: 1.0.0
-
-   Now uses :attr:`pandas.NA` as the missing value rather
-   than :attr:`numpy.nan`.
+   change without warning. Uses :attr:`pandas.NA` as the missing value.
 
 In :ref:`missing_data`, we saw that pandas primarily uses ``NaN`` to represent
 missing data. Because ``NaN`` is a float, this forces an array of integers with

doc/source/user_guide/io.rst

@@ -3634,11 +3634,6 @@ It is often the case that users will insert columns to do temporary computations
 in Excel and you may not want to read in those columns. ``read_excel`` takes
 a ``usecols`` keyword to allow you to specify a subset of columns to parse.
 
-.. versionchanged:: 1.0.0
-
-Passing in an integer for ``usecols`` will no longer work. Please pass in a list
-of ints from 0 to ``usecols`` inclusive instead.
-
 You can specify a comma-delimited set of Excel columns and ranges as a string:
 
 .. code-block:: python
@@ -3881,8 +3876,6 @@ Similarly, the :func:`~pandas.to_excel` method can write OpenDocument spreadshee
 Binary Excel (.xlsb) files
 --------------------------
 
-.. versionadded:: 1.0.0
-
 The :func:`~pandas.read_excel` method can also read binary Excel files
 using the ``pyxlsb`` module. The semantics and features for reading
 binary Excel files mostly match what can be done for `Excel files`_ using
@@ -5419,8 +5412,6 @@ The above example creates a partitioned dataset that may look like:
 ORC
 ---
 
-.. versionadded:: 1.0.0
-
 Similar to the :ref:`parquet <io.parquet>` format, the `ORC Format <https://orc.apache.org/>`__ is a binary columnar serialization
 for data frames. It is designed to make reading data frames efficient. pandas provides both the reader and the writer for the
 ORC format, :func:`~pandas.read_orc` and :func:`~pandas.DataFrame.to_orc`. This requires the `pyarrow <https://arrow.apache.org/docs/python/>`__ library.

doc/source/user_guide/missing_data.rst

@@ -753,8 +753,6 @@ Experimental ``NA`` scalar to denote missing values
 
    Experimental: the behaviour of ``pd.NA`` can still change without warning.
 
-.. versionadded:: 1.0.0
-
 Starting from pandas 1.0, an experimental ``pd.NA`` value (singleton) is
 available to represent scalar missing values. At this moment, it is used in
 the nullable :doc:`integer <integer_na>`, boolean and

doc/source/user_guide/text.rst

@@ -11,8 +11,6 @@ Working with text data
 Text data types
 ---------------
 
-.. versionadded:: 1.0.0
-
 There are two ways to store text data in pandas:
 
 1. ``object`` -dtype NumPy array.

doc/source/user_guide/timeseries.rst

@@ -335,8 +335,6 @@ which can be specified. These are computed from the starting point specified by
    that was discussed :ref:`above<timeseries.converting.format>`). The
    available units are listed on the documentation for :func:`pandas.to_datetime`.
 
-.. versionchanged:: 1.0.0
-
 Constructing a :class:`Timestamp` or :class:`DatetimeIndex` with an epoch timestamp
 with the ``tz`` argument specified will raise a ValueError. If you have
 epochs in wall time in another timezone, you can read the epochs

doc/source/user_guide/visualization.rst

@@ -1318,8 +1318,6 @@ with "(right)" in the legend. To turn off the automatic marking, use the
 Custom formatters for timeseries plots
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. versionchanged:: 1.0.0
-
 pandas provides custom formatters for timeseries plots. These change the
 formatting of the axis labels for dates and times. By default,
 the custom formatters are applied only to plots created by pandas with

doc/source/whatsnew/v2.0.0.rst

@@ -814,6 +814,7 @@ Other API changes
 - :func:`to_datetime` with ``unit`` of either "Y" or "M" will now raise if a sequence contains a non-round ``float`` value, matching the ``Timestamp`` behavior (:issue:`50301`)
 - The methods :meth:`Series.round`, :meth:`DataFrame.__invert__`, :meth:`Series.__invert__`, :meth:`DataFrame.swapaxes`, :meth:`DataFrame.first`, :meth:`DataFrame.last`, :meth:`Series.first`, :meth:`Series.last` and :meth:`DataFrame.align` will now always return new objects (:issue:`51032`)
 - :class:`DataFrame` and :class:`DataFrameGroupBy` aggregations (e.g. "sum") with object-dtype columns no longer infer non-object dtypes for their results, explicitly call ``result.infer_objects(copy=False)`` on the result to obtain the old behavior (:issue:`51205`, :issue:`49603`)
+- Division by zero with :class:`ArrowDtype` dtypes returns ``-inf``, ``nan``, or ``inf`` depending on the numerator, instead of raising (:issue:`51541`)
 - Added :func:`pandas.api.types.is_any_real_numeric_dtype` to check for real numeric dtypes (:issue:`51152`)
 
 .. note::

doc/source/whatsnew/v2.1.0.rst

@@ -92,7 +92,7 @@ Other API changes
 
 Deprecations
 ~~~~~~~~~~~~
--
+- Deprecated accepting slices in :meth:`DataFrame.take`, call ``obj[slicer]`` or pass a sequence of integers instead (:issue:`51539`)
 -
 
 .. ---------------------------------------------------------------------------

pandas/_libs/missing.pyx

@@ -372,8 +372,6 @@ class NAType(C_NAType):
 
        Experimental: the behaviour of NA can still change without warning.
 
-    .. versionadded:: 1.0.0
-
     The NA singleton is a missing value indicator defined by pandas. It is
     used in certain new extension dtypes (currently the "string" dtype).
     """

pandas/_testing/asserters.py

@@ -279,8 +279,7 @@ def _get_ilevel_values(index, level):
         right = safe_sort_index(right)
 
     # MultiIndex special comparison for little-friendly error messages
-    if left.nlevels > 1:
-        left = cast(MultiIndex, left)
+    if isinstance(left, MultiIndex):
         right = cast(MultiIndex, right)
 
         for level in range(left.nlevels):
@@ -303,7 +302,7 @@ def _get_ilevel_values(index, level):
             _check_types(left.levels[level], right.levels[level], obj=obj)
 
     # skip exact index checking when `check_categorical` is False
-    if check_exact and check_categorical:
+    elif check_exact and check_categorical:
         if not left.equals(right):
             mismatch = left._values != right._values
 

pandas/conftest.py

@@ -637,9 +637,7 @@ def index_flat(request):
         key
         for key, value in indices_dict.items()
         if not (
-            key.startswith("int")
-            or key.startswith("uint")
-            or key.startswith("float")
+            key.startswith(("int", "uint", "float"))
             or key in ["range", "empty", "repeats", "bool-dtype"]
         )
         and not isinstance(value, MultiIndex)

pandas/core/arrays/arrow/array.py

@@ -106,7 +106,7 @@ def floordiv_compat(
     ) -> pa.ChunkedArray:
         # Ensure int // int -> int mirroring Python/Numpy behavior
         # as pc.floor(pc.divide_checked(int, int)) -> float
-        result = pc.floor(pc.divide_checked(left, right))
+        result = pc.floor(pc.divide(left, right))
         if pa.types.is_integer(left.type) and pa.types.is_integer(right.type):
             result = result.cast(left.type)
         return result
@@ -118,8 +118,8 @@ def floordiv_compat(
         "rsub": lambda x, y: pc.subtract_checked(y, x),
         "mul": pc.multiply_checked,
         "rmul": lambda x, y: pc.multiply_checked(y, x),
-        "truediv": lambda x, y: pc.divide_checked(cast_for_truediv(x, y), y),
-        "rtruediv": lambda x, y: pc.divide_checked(y, cast_for_truediv(x, y)),
+        "truediv": lambda x, y: pc.divide(cast_for_truediv(x, y), y),
+        "rtruediv": lambda x, y: pc.divide(y, cast_for_truediv(x, y)),
         "floordiv": lambda x, y: floordiv_compat(x, y),
         "rfloordiv": lambda x, y: floordiv_compat(y, x),
         "mod": NotImplemented,

pandas/core/arrays/base.py

@@ -462,8 +462,6 @@ def to_numpy(
         """
         Convert to a NumPy ndarray.
 
-        .. versionadded:: 1.0.0
-
         This is similar to :meth:`numpy.asarray`, but may provide additional control
         over how the conversion is done.
 

pandas/core/arrays/boolean.py

@@ -43,8 +43,6 @@ class BooleanDtype(BaseMaskedDtype):
     """
     Extension dtype for boolean data.
 
-    .. versionadded:: 1.0.0
-
     .. warning::
 
        BooleanDtype is considered experimental. The implementation and
@@ -246,8 +244,6 @@ class BooleanArray(BaseMaskedArray):
     :func:`pandas.array` specifying ``dtype="boolean"`` (see examples
     below).
 
-    .. versionadded:: 1.0.0
-
     .. warning::
 
        BooleanArray is considered experimental. The implementation and

pandas/core/arrays/categorical.py

@@ -1987,18 +1987,14 @@ def min(self, *, skipna: bool = True, **kwargs):
 
         Only ordered `Categoricals` have a minimum!
 
-        .. versionchanged:: 1.0.0
-
-           Returns an NA value on empty arrays
-
         Raises
         ------
         TypeError
             If the `Categorical` is not `ordered`.
 
         Returns
         -------
-        min : the minimum of this `Categorical`
+        min : the minimum of this `Categorical`, NA value if empty
         """
         nv.validate_minmax_axis(kwargs.get("axis", 0))
         nv.validate_min((), kwargs)
@@ -2023,18 +2019,14 @@ def max(self, *, skipna: bool = True, **kwargs):
 
         Only ordered `Categoricals` have a maximum!
 
-        .. versionchanged:: 1.0.0
-
-           Returns an NA value on empty arrays
-
         Raises
         ------
         TypeError
             If the `Categorical` is not `ordered`.
 
         Returns
         -------
-        max : the maximum of this `Categorical`
+        max : the maximum of this `Categorical`, NA if array is empty
         """
         nv.validate_minmax_axis(kwargs.get("axis", 0))
         nv.validate_max((), kwargs)

pandas/core/arrays/integer.py

@@ -16,7 +16,7 @@ class IntegerDtype(NumericDtype):
     An ExtensionDtype to hold a single size & kind of integer dtype.
 
     These specific implementations are subclasses of the non-public
-    IntegerDtype. For example we have Int8Dtype to represent signed int 8s.
+    IntegerDtype. For example, we have Int8Dtype to represent signed int 8s.
 
     The attributes name & type are set when these subclasses are created.
     """
@@ -63,10 +63,7 @@ class IntegerArray(NumericArray):
     """
     Array of integer (optional missing) values.
 
-    .. versionchanged:: 1.0.0
-
-       Now uses :attr:`pandas.NA` as the missing value rather
-       than :attr:`numpy.nan`.
+    Uses :attr:`pandas.NA` as the missing value.
 
     .. warning::
 
@@ -141,10 +138,7 @@ class IntegerArray(NumericArray):
 _dtype_docstring = """
 An ExtensionDtype for {dtype} integer data.
 
-.. versionchanged:: 1.0.0
-
-   Now uses :attr:`pandas.NA` as its missing value,
-   rather than :attr:`numpy.nan`.
+Uses :attr:`pandas.NA` as its missing value, rather than :attr:`numpy.nan`.
 
 Attributes
 ----------

pandas/core/arrays/string_.py

@@ -70,8 +70,6 @@ class StringDtype(StorageExtensionDtype):
     """
     Extension dtype for string data.
 
-    .. versionadded:: 1.0.0
-
     .. warning::
 
        StringDtype is considered experimental. The implementation and
@@ -233,8 +231,6 @@ class StringArray(BaseStringArray, PandasArray):
     """
     Extension array for string data.
 
-    .. versionadded:: 1.0.0
-
     .. warning::
 
        StringArray is considered experimental. The implementation and

pandas/core/base.py

@@ -496,15 +496,10 @@ def to_numpy(
         na_value : Any, optional
             The value to use for missing values. The default value depends
             on `dtype` and the type of the array.
-
-            .. versionadded:: 1.0.0
-
         **kwargs
             Additional keywords passed through to the ``to_numpy`` method
             of the underlying array (for extension arrays).
 
-            .. versionadded:: 1.0.0
-
         Returns
         -------
         numpy.ndarray

pandas/core/construction.py

@@ -128,12 +128,6 @@ def array(
 
         For all other cases, NumPy's usual inference rules will be used.
 
-        .. versionchanged:: 1.0.0
-
-           Pandas infers nullable-integer dtype for integer data,
-           string dtype for string data, and nullable-boolean dtype
-           for boolean data.
-
         .. versionchanged:: 1.2.0
 
             Pandas now also infers nullable-floating dtype for float-like