Sync Fork from Upstream Repo

.pre-commit-config.yaml

@@ -51,7 +51,7 @@ repos:
     hooks:
     -   id: isort
 -   repo: https://github.com/asottile/pyupgrade
-    rev: v2.11.0
+    rev: v2.12.0
     hooks:
     -   id: pyupgrade
         args: [--py37-plus]

doc/source/whatsnew/v1.2.4.rst

@@ -1,7 +1,7 @@
 .. _whatsnew_124:
 
-What's new in 1.2.4 (April ??, 2021)
----------------------------------------
+What's new in 1.2.4 (April 12, 2021)
+------------------------------------
 
 These are the changes in pandas 1.2.4. See :ref:`release` for a full changelog
 including other versions of pandas.
@@ -20,27 +20,8 @@ Fixed regressions
 - Fixed regression in (in)equality comparison of ``pd.NaT`` with a non-datetimelike numpy array returning a scalar instead of an array (:issue:`40722`)
 - Fixed regression in :meth:`DataFrame.where` not returning a copy in the case of an all True condition (:issue:`39595`)
 - Fixed regression in :meth:`DataFrame.replace` raising ``IndexError`` when ``regex`` was a multi-key dictionary (:issue:`39338`)
--
-
-.. ---------------------------------------------------------------------------
-
-.. _whatsnew_124.bug_fixes:
-
-Bug fixes
-~~~~~~~~~
-
--
--
-
-.. ---------------------------------------------------------------------------
-
-.. _whatsnew_124.other:
-
-Other
-~~~~~
-
--
--
+- Fixed regression in repr of floats in an ``object`` column not respecting ``float_format`` when printed in the console or outputted through :meth:`DataFrame.to_string`, :meth:`DataFrame.to_html`, and :meth:`DataFrame.to_latex` (:issue:`40024`)
+- Fixed regression in NumPy ufuncs such as ``np.add`` not passing through all arguments for :class:`DataFrame` (:issue:`40662`)
 
 .. ---------------------------------------------------------------------------
 

doc/source/whatsnew/v1.3.0.rst

@@ -584,6 +584,7 @@ Performance improvements
 - Performance improvement in :class:`Styler` where render times are more than 50% reduced (:issue:`39972` :issue:`39952`)
 - Performance improvement in :meth:`core.window.ewm.ExponentialMovingWindow.mean` with ``times`` (:issue:`39784`)
 - Performance improvement in :meth:`.GroupBy.apply` when requiring the python fallback implementation (:issue:`40176`)
+- Performance improvement for concatenation of data with type :class:`CategoricalDtype` (:issue:`40193`)
 
 .. ---------------------------------------------------------------------------
 
@@ -786,6 +787,7 @@ Reshaping
 ^^^^^^^^^
 - Bug in :func:`merge` raising error when performing an inner join with partial index and ``right_index`` when no overlap between indices (:issue:`33814`)
 - Bug in :meth:`DataFrame.unstack` with missing levels led to incorrect index names (:issue:`37510`)
+- Bug in :func:`merge_asof` propagating the right Index with ``left_index=True`` and ``right_on`` specification instead of left Index (:issue:`33463`)
 - Bug in :func:`join` over :class:`MultiIndex` returned wrong result, when one of both indexes had only one level (:issue:`36909`)
 - :meth:`merge_asof` raises ``ValueError`` instead of cryptic ``TypeError`` in case of non-numerical merge columns (:issue:`29130`)
 - Bug in :meth:`DataFrame.join` not assigning values correctly when having :class:`MultiIndex` where at least one dimension is from dtype ``Categorical`` with non-alphabetically sorted categories (:issue:`38502`)
@@ -813,6 +815,7 @@ ExtensionArray
 
 - Bug in :meth:`DataFrame.where` when ``other`` is a :class:`Series` with :class:`ExtensionArray` dtype (:issue:`38729`)
 - Fixed bug where :meth:`Series.idxmax`, :meth:`Series.idxmin` and ``argmax/min`` fail when the underlying data is :class:`ExtensionArray` (:issue:`32749`, :issue:`33719`, :issue:`36566`)
+- Fixed a bug where some properties of subclasses of :class:`PandasExtensionDtype` where improperly cached (:issue:`40329`)
 -
 
 Other

pandas/core/arraylike.py

@@ -357,15 +357,17 @@ def reconstruct(result):
         # * len(inputs) > 1 is doable when we know that we have
         #   aligned blocks / dtypes.
         inputs = tuple(np.asarray(x) for x in inputs)
-        result = getattr(ufunc, method)(*inputs)
+        result = getattr(ufunc, method)(*inputs, **kwargs)
     elif self.ndim == 1:
         # ufunc(series, ...)
         inputs = tuple(extract_array(x, extract_numpy=True) for x in inputs)
         result = getattr(ufunc, method)(*inputs, **kwargs)
     else:
         # ufunc(dataframe)
-        if method == "__call__":
+        if method == "__call__" and not kwargs:
             # for np.<ufunc>(..) calls
+            # kwargs cannot necessarily be handled block-by-block, so only
+            # take this path if there are no kwargs
             mgr = inputs[0]._mgr
             result = mgr.apply(getattr(ufunc, method))
         else:

pandas/core/dtypes/concat.py

@@ -30,11 +30,13 @@
 )
 
 
-def _cast_to_common_type(arr: ArrayLike, dtype: DtypeObj) -> ArrayLike:
+def cast_to_common_type(arr: ArrayLike, dtype: DtypeObj) -> ArrayLike:
     """
     Helper function for `arr.astype(common_dtype)` but handling all special
     cases.
     """
+    if is_dtype_equal(arr.dtype, dtype):
+        return arr
     if (
         is_categorical_dtype(arr.dtype)
         and isinstance(dtype, np.dtype)
@@ -121,7 +123,7 @@ def is_nonempty(x) -> bool:
         # for axis=0
         if not single_dtype:
             target_dtype = find_common_type([x.dtype for x in to_concat])
-            to_concat = [_cast_to_common_type(arr, target_dtype) for arr in to_concat]
+            to_concat = [cast_to_common_type(arr, target_dtype) for arr in to_concat]
 
         if isinstance(to_concat[0], ExtensionArray):
             cls = type(to_concat[0])

pandas/core/dtypes/dtypes.py

@@ -15,6 +15,7 @@
 import pytz
 
 from pandas._libs.interval import Interval
+from pandas._libs.properties import cache_readonly
 from pandas._libs.tslibs import (
     BaseOffset,
     NaT,
@@ -81,7 +82,7 @@ class PandasExtensionDtype(ExtensionDtype):
     base: DtypeObj | None = None
     isbuiltin = 0
     isnative = 0
-    _cache: dict[str_type, PandasExtensionDtype] = {}
+    _cache_dtypes: dict[str_type, PandasExtensionDtype] = {}
 
     def __str__(self) -> str_type:
         """
@@ -105,7 +106,7 @@ def __getstate__(self) -> dict[str_type, Any]:
     @classmethod
     def reset_cache(cls) -> None:
         """ clear the cache """
-        cls._cache = {}
+        cls._cache_dtypes = {}
 
 
 class CategoricalDtypeType(type):
@@ -177,7 +178,7 @@ class CategoricalDtype(PandasExtensionDtype, ExtensionDtype):
     str = "|O08"
     base = np.dtype("O")
     _metadata = ("categories", "ordered")
-    _cache: dict[str_type, PandasExtensionDtype] = {}
+    _cache_dtypes: dict[str_type, PandasExtensionDtype] = {}
 
     def __init__(self, categories=None, ordered: Ordered = False):
         self._finalize(categories, ordered, fastpath=False)
@@ -355,7 +356,7 @@ def __hash__(self) -> int:
             else:
                 return -2
         # We *do* want to include the real self.ordered here
-        return int(self._hash_categories(self.categories, self.ordered))
+        return int(self._hash_categories)
 
     def __eq__(self, other: Any) -> bool:
         """
@@ -429,14 +430,17 @@ def __repr__(self) -> str_type:
             data = data.rstrip(", ")
         return f"CategoricalDtype(categories={data}, ordered={self.ordered})"
 
-    @staticmethod
-    def _hash_categories(categories, ordered: Ordered = True) -> int:
+    @cache_readonly
+    def _hash_categories(self) -> int:
         from pandas.core.util.hashing import (
             combine_hash_arrays,
             hash_array,
             hash_tuples,
         )
 
+        categories = self.categories
+        ordered = self.ordered
+
         if len(categories) and isinstance(categories[0], tuple):
             # assumes if any individual category is a tuple, then all our. ATM
             # I don't really want to support just some of the categories being
@@ -671,7 +675,7 @@ class DatetimeTZDtype(PandasExtensionDtype):
     na_value = NaT
     _metadata = ("unit", "tz")
     _match = re.compile(r"(datetime64|M8)\[(?P<unit>.+), (?P<tz>.+)\]")
-    _cache: dict[str_type, PandasExtensionDtype] = {}
+    _cache_dtypes: dict[str_type, PandasExtensionDtype] = {}
 
     def __init__(self, unit: str_type | DatetimeTZDtype = "ns", tz=None):
         if isinstance(unit, DatetimeTZDtype):
@@ -837,7 +841,7 @@ class PeriodDtype(dtypes.PeriodDtypeBase, PandasExtensionDtype):
     num = 102
     _metadata = ("freq",)
     _match = re.compile(r"(P|p)eriod\[(?P<freq>.+)\]")
-    _cache: dict[str_type, PandasExtensionDtype] = {}
+    _cache_dtypes: dict[str_type, PandasExtensionDtype] = {}
 
     def __new__(cls, freq=None):
         """
@@ -859,12 +863,12 @@ def __new__(cls, freq=None):
             freq = cls._parse_dtype_strict(freq)
 
         try:
-            return cls._cache[freq.freqstr]
+            return cls._cache_dtypes[freq.freqstr]
         except KeyError:
             dtype_code = freq._period_dtype_code
             u = dtypes.PeriodDtypeBase.__new__(cls, dtype_code)
             u._freq = freq
-            cls._cache[freq.freqstr] = u
+            cls._cache_dtypes[freq.freqstr] = u
             return u
 
     def __reduce__(self):
@@ -1042,7 +1046,7 @@ class IntervalDtype(PandasExtensionDtype):
     _match = re.compile(
         r"(I|i)nterval\[(?P<subtype>[^,]+)(, (?P<closed>(right|left|both|neither)))?\]"
     )
-    _cache: dict[str_type, PandasExtensionDtype] = {}
+    _cache_dtypes: dict[str_type, PandasExtensionDtype] = {}
 
     def __new__(cls, subtype=None, closed: str_type | None = None):
         from pandas.core.dtypes.common import (
@@ -1099,12 +1103,12 @@ def __new__(cls, subtype=None, closed: str_type | None = None):
 
         key = str(subtype) + str(closed)
         try:
-            return cls._cache[key]
+            return cls._cache_dtypes[key]
         except KeyError:
             u = object.__new__(cls)
             u._subtype = subtype
             u._closed = closed
-            cls._cache[key] = u
+            cls._cache_dtypes[key] = u
             return u
 
     @property

pandas/core/generic.py

@@ -7352,16 +7352,124 @@ def _clip_with_one_bound(self, threshold, method, axis, inplace):
                 threshold = align_method_FRAME(self, threshold, axis, flex=None)[1]
         return self.where(subset, threshold, axis=axis, inplace=inplace)
 
+    @overload
+    def clip(
+        self: FrameOrSeries,
+        lower=...,
+        upper=...,
+        axis: Axis | None = ...,
+        inplace: Literal[False] = ...,
+        *args,
+        **kwargs,
+    ) -> FrameOrSeries:
+        ...
+
+    @overload
+    def clip(
+        self: FrameOrSeries,
+        lower,
+        *,
+        axis: Axis | None,
+        inplace: Literal[True],
+        **kwargs,
+    ) -> None:
+        ...
+
+    @overload
+    def clip(
+        self: FrameOrSeries,
+        lower,
+        *,
+        inplace: Literal[True],
+        **kwargs,
+    ) -> None:
+        ...
+
+    @overload
+    def clip(
+        self: FrameOrSeries,
+        *,
+        upper,
+        axis: Axis | None,
+        inplace: Literal[True],
+        **kwargs,
+    ) -> None:
+        ...
+
+    @overload
+    def clip(
+        self: FrameOrSeries,
+        *,
+        upper,
+        inplace: Literal[True],
+        **kwargs,
+    ) -> None:
+        ...
+
+    @overload
+    def clip(
+        self: FrameOrSeries,
+        *,
+        axis: Axis | None,
+        inplace: Literal[True],
+        **kwargs,
+    ) -> None:
+        ...
+
+    @overload
+    def clip(
+        self: FrameOrSeries,
+        lower,
+        upper,
+        axis: Axis | None,
+        inplace: Literal[True],
+        *args,
+        **kwargs,
+    ) -> None:
+        ...
+
+    @overload
+    def clip(
+        self: FrameOrSeries,
+        lower,
+        upper,
+        *,
+        inplace: Literal[True],
+        **kwargs,
+    ) -> None:
+        ...
+
+    @overload
+    def clip(
+        self: FrameOrSeries,
+        *,
+        inplace: Literal[True],
+        **kwargs,
+    ) -> None:
+        ...
+
+    @overload
+    def clip(
+        self: FrameOrSeries,
+        lower=...,
+        upper=...,
+        axis: Axis | None = ...,
+        inplace: bool_t = ...,
+        *args,
+        **kwargs,
+    ) -> FrameOrSeries | None:
+        ...
+
     @final
     def clip(
         self: FrameOrSeries,
         lower=None,
         upper=None,
-        axis=None,
+        axis: Axis | None = None,
         inplace: bool_t = False,
         *args,
         **kwargs,
-    ) -> FrameOrSeries:
+    ) -> FrameOrSeries | None:
         """
         Trim values at input threshold(s).
 
@@ -10843,7 +10951,7 @@ def median(
         @doc(
             _num_doc,
             desc="Return the maximum of the values over the requested axis.\n\n"
-            "If you want the *index* of the maximum, use ``idxmax``. This is"
+            "If you want the *index* of the maximum, use ``idxmax``. This is "
             "the equivalent of the ``numpy.ndarray`` method ``argmax``.",
             name1=name1,
             name2=name2,
@@ -10860,7 +10968,7 @@ def max(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
         @doc(
             _num_doc,
             desc="Return the minimum of the values over the requested axis.\n\n"
-            "If you want the *index* of the minimum, use ``idxmin``. This is"
+            "If you want the *index* of the minimum, use ``idxmin``. This is "
             "the equivalent of the ``numpy.ndarray`` method ``argmin``.",
             name1=name1,
             name2=name2,