Sync Fork from Upstream Repo

.pre-commit-config.yaml

@@ -86,11 +86,10 @@ repos:
         types: [python]
         exclude: ^pandas/_typing\.py$
     -   id: inconsistent-namespace-usage
-        name: 'Check for inconsistent use of pandas namespace in tests'
+        name: 'Check for inconsistent use of pandas namespace'
         entry: python scripts/check_for_inconsistent_pandas_namespace.py
         language: python
         types: [python]
-        files: ^pandas/tests/
     -   id: incorrect-code-directives
         name: Check for incorrect code block or IPython directives
         language: pygrep
@@ -213,3 +212,10 @@ repos:
             |\#\ type:\s?ignore(?!\[)
         language: pygrep
         types: [python]
+    -   id: use-pd_array-in-core
+        name: Import pandas.array as pd_array in core
+        language: python
+        entry: python scripts/use_pd_array_in_core.py
+        files: ^pandas/core/
+        exclude: ^pandas/core/api\.py$
+        types: [python]

asv_bench/benchmarks/arithmetic.py

@@ -140,9 +140,7 @@ def setup(self, op, shape):
         # construct dataframe with 2 blocks
         arr1 = np.random.randn(n_rows, n_cols // 2).astype("f8")
         arr2 = np.random.randn(n_rows, n_cols // 2).astype("f4")
-        df = pd.concat(
-            [pd.DataFrame(arr1), pd.DataFrame(arr2)], axis=1, ignore_index=True
-        )
+        df = pd.concat([DataFrame(arr1), DataFrame(arr2)], axis=1, ignore_index=True)
         # should already be the case, but just to be sure
         df._consolidate_inplace()
 
@@ -151,7 +149,7 @@ def setup(self, op, shape):
         arr2 = np.random.randn(n_rows, n_cols // 2).astype("i8")
         arr3 = np.random.randn(n_rows, n_cols // 4).astype("f8")
         df2 = pd.concat(
-            [pd.DataFrame(arr1), pd.DataFrame(arr2), pd.DataFrame(arr3)],
+            [DataFrame(arr1), DataFrame(arr2), DataFrame(arr3)],
             axis=1,
             ignore_index=True,
         )
@@ -459,9 +457,9 @@ class OffsetArrayArithmetic:
 
     def setup(self, offset):
         N = 10000
-        rng = pd.date_range(start="1/1/2000", periods=N, freq="T")
+        rng = date_range(start="1/1/2000", periods=N, freq="T")
         self.rng = rng
-        self.ser = pd.Series(rng)
+        self.ser = Series(rng)
 
     def time_add_series_offset(self, offset):
         with warnings.catch_warnings(record=True):
@@ -478,7 +476,7 @@ class ApplyIndex:
 
     def setup(self, offset):
         N = 10000
-        rng = pd.date_range(start="1/1/2000", periods=N, freq="T")
+        rng = date_range(start="1/1/2000", periods=N, freq="T")
         self.rng = rng
 
     def time_apply_index(self, offset):
@@ -490,17 +488,17 @@ class BinaryOpsMultiIndex:
     param_names = ["func"]
 
     def setup(self, func):
-        date_range = pd.date_range("20200101 00:00", "20200102 0:00", freq="S")
+        array = date_range("20200101 00:00", "20200102 0:00", freq="S")
         level_0_names = [str(i) for i in range(30)]
 
-        index = pd.MultiIndex.from_product([level_0_names, date_range])
+        index = pd.MultiIndex.from_product([level_0_names, array])
         column_names = ["col_1", "col_2"]
 
-        self.df = pd.DataFrame(
+        self.df = DataFrame(
             np.random.rand(len(index), 2), index=index, columns=column_names
         )
 
-        self.arg_df = pd.DataFrame(
+        self.arg_df = DataFrame(
             np.random.randint(1, 10, (len(level_0_names), 2)),
             index=level_0_names,
             columns=column_names,

asv_bench/benchmarks/sparse.py

@@ -28,7 +28,7 @@ def setup(self):
             data = np.random.randn(N)[:-i]
             idx = rng[:-i]
             data[100:] = np.nan
-            self.series[i] = pd.Series(pd.SparseArray(data), index=idx)
+            self.series[i] = Series(SparseArray(data), index=idx)
 
     def time_series_to_frame(self):
         pd.DataFrame(self.series)
@@ -63,7 +63,7 @@ def setup(self):
         )
 
     def time_sparse_series_from_coo(self):
-        pd.Series.sparse.from_coo(self.matrix)
+        Series.sparse.from_coo(self.matrix)
 
 
 class ToCoo:

doc/source/whatsnew/v1.3.0.rst

@@ -161,6 +161,7 @@ Other enhancements
 - :meth:`.Styler.apply` now more consistently accepts ndarray function returns, i.e. in all cases for ``axis`` is ``0, 1 or None`` (:issue:`39359`)
 - :meth:`.Styler.apply` and :meth:`.Styler.applymap` now raise errors if wrong format CSS is passed on render (:issue:`39660`)
 - :meth:`.Styler.format` adds keyword argument ``escape`` for optional HTML escaping (:issue:`40437`)
+- :meth:`.Styler.clear` now clears :attr:`Styler.hidden_index` and :attr:`Styler.hidden_columns` as well (:issue:`40484`)
 - Builtin highlighting methods in :class:`Styler` have a more consistent signature and css customisability (:issue:`40242`)
 - :meth:`Series.loc.__getitem__` and :meth:`Series.loc.__setitem__` with :class:`MultiIndex` now raising helpful error message when indexer has too many dimensions (:issue:`35349`)
 - :meth:`pandas.read_stata` and :class:`StataReader` support reading data from compressed files.
@@ -571,6 +572,7 @@ Conversion
 - Bug in creating a :class:`DataFrame` from an empty ``np.recarray`` not retaining the original dtypes (:issue:`40121`)
 - Bug in :class:`DataFrame` failing to raise ``TypeError`` when constructing from a ``frozenset`` (:issue:`40163`)
 - Bug in :class:`Index` construction silently ignoring a passed ``dtype`` when the data cannot be cast to that dtype (:issue:`21311`)
+- Bug in :meth:`StringArray.astype` falling back to numpy and raising when converting to ``dtype='categorical'`` (:issue:`40450`)
 - Bug in :class:`DataFrame` construction with a dictionary containing an arraylike with ``ExtensionDtype`` and ``copy=True`` failing to make a copy (:issue:`38939`)
 -
 

pandas/_testing/__init__.py

@@ -207,12 +207,12 @@ def box_expected(expected, box_cls, transpose=True):
     """
     if box_cls is pd.array:
         expected = pd.array(expected)
-    elif box_cls is pd.Index:
-        expected = pd.Index(expected)
-    elif box_cls is pd.Series:
-        expected = pd.Series(expected)
-    elif box_cls is pd.DataFrame:
-        expected = pd.Series(expected).to_frame()
+    elif box_cls is Index:
+        expected = Index(expected)
+    elif box_cls is Series:
+        expected = Series(expected)
+    elif box_cls is DataFrame:
+        expected = Series(expected).to_frame()
         if transpose:
             # for vector operations, we need a DataFrame to be a single-row,
             #  not a single-column, in order to operate against non-DataFrame
@@ -400,7 +400,7 @@ def _make_timeseries(start="2000-01-01", end="2000-12-31", freq="1D", seed=None)
         "x": state.rand(n) * 2 - 1,
         "y": state.rand(n) * 2 - 1,
     }
-    df = pd.DataFrame(columns, index=index, columns=sorted(columns))
+    df = DataFrame(columns, index=index, columns=sorted(columns))
     if df.index[-1] == end:
         df = df.iloc[:-1]
     return df

pandas/conftest.py

@@ -328,7 +328,7 @@ def unique_nulls_fixture(request):
 # ----------------------------------------------------------------
 
 
-@pytest.fixture(params=[pd.DataFrame, pd.Series])
+@pytest.fixture(params=[DataFrame, Series])
 def frame_or_series(request):
     """
     Fixture to parametrize over DataFrame and Series.
@@ -338,7 +338,7 @@ def frame_or_series(request):
 
 # error: List item 0 has incompatible type "Type[Index]"; expected "Type[IndexOpsMixin]"
 @pytest.fixture(
-    params=[pd.Index, pd.Series], ids=["index", "series"]  # type: ignore[list-item]
+    params=[Index, Series], ids=["index", "series"]  # type: ignore[list-item]
 )
 def index_or_series(request):
     """
@@ -356,9 +356,7 @@ def index_or_series(request):
 index_or_series2 = index_or_series
 
 
-@pytest.fixture(
-    params=[pd.Index, pd.Series, pd.array], ids=["index", "series", "array"]
-)
+@pytest.fixture(params=[Index, Series, pd.array], ids=["index", "series", "array"])
 def index_or_series_or_array(request):
     """
     Fixture to parametrize over Index, Series, and ExtensionArray
@@ -559,7 +557,7 @@ def index_with_missing(request):
 # ----------------------------------------------------------------
 @pytest.fixture
 def empty_series():
-    return pd.Series([], index=[], dtype=np.float64)
+    return Series([], index=[], dtype=np.float64)
 
 
 @pytest.fixture
@@ -596,7 +594,7 @@ def _create_series(index):
     """ Helper for the _series dict """
     size = len(index)
     data = np.random.randn(size)
-    return pd.Series(data, index=index, name="a")
+    return Series(data, index=index, name="a")
 
 
 _series = {
@@ -1437,16 +1435,16 @@ def any_numpy_dtype(request):
     ("boolean", [True, np.nan, False]),
     ("boolean", [True, pd.NA, False]),
     ("datetime64", [np.datetime64("2013-01-01"), np.nan, np.datetime64("2018-01-01")]),
-    ("datetime", [pd.Timestamp("20130101"), np.nan, pd.Timestamp("20180101")]),
+    ("datetime", [Timestamp("20130101"), np.nan, Timestamp("20180101")]),
     ("date", [date(2013, 1, 1), np.nan, date(2018, 1, 1)]),
     # The following two dtypes are commented out due to GH 23554
     # ('complex', [1 + 1j, np.nan, 2 + 2j]),
     # ('timedelta64', [np.timedelta64(1, 'D'),
     #                  np.nan, np.timedelta64(2, 'D')]),
     ("timedelta", [timedelta(1), np.nan, timedelta(2)]),
     ("time", [time(1), np.nan, time(2)]),
-    ("period", [pd.Period(2013), pd.NaT, pd.Period(2018)]),
-    ("interval", [pd.Interval(0, 1), np.nan, pd.Interval(0, 2)]),
+    ("period", [Period(2013), pd.NaT, Period(2018)]),
+    ("interval", [Interval(0, 1), np.nan, Interval(0, 2)]),
 ]
 ids, _ = zip(*_any_skipna_inferred_dtype)  # use inferred type as fixture-id
 

pandas/core/arrays/string_.py

@@ -327,6 +327,9 @@ def astype(self, dtype, copy=True):
             arr[mask] = "0"
             values = arr.astype(dtype.numpy_dtype)
             return FloatingArray(values, mask, copy=False)
+        elif isinstance(dtype, ExtensionDtype):
+            cls = dtype.construct_array_type()
+            return cls._from_sequence(self, dtype=dtype, copy=copy)
         elif np.issubdtype(dtype, np.floating):
             arr = self._ndarray.copy()
             mask = self.isna()

pandas/core/computation/expressions.py

@@ -104,11 +104,20 @@ def _evaluate_numexpr(op, op_str, a, b):
         a_value = a
         b_value = b
 
-        result = ne.evaluate(
-            f"a_value {op_str} b_value",
-            local_dict={"a_value": a_value, "b_value": b_value},
-            casting="safe",
-        )
+        try:
+            result = ne.evaluate(
+                f"a_value {op_str} b_value",
+                local_dict={"a_value": a_value, "b_value": b_value},
+                casting="safe",
+            )
+        except TypeError:
+            # numexpr raises eg for array ** array with integers
+            # (https://github.com/pydata/numexpr/issues/379)
+            pass
+
+        if is_reversed:
+            # reverse order to original for fallback
+            a, b = b, a
 
     if _TEST_MODE:
         _store_test_result(result is not None)

pandas/core/dtypes/cast.py

@@ -498,55 +498,6 @@ def maybe_cast_to_extension_array(
     return result
 
 
-def maybe_upcast_putmask(result: np.ndarray, mask: np.ndarray) -> np.ndarray:
-    """
-    A safe version of putmask that potentially upcasts the result.
-
-    The result is replaced with the first N elements of other,
-    where N is the number of True values in mask.
-    If the length of other is shorter than N, other will be repeated.
-
-    Parameters
-    ----------
-    result : ndarray
-        The destination array. This will be mutated in-place if no upcasting is
-        necessary.
-    mask : np.ndarray[bool]
-
-    Returns
-    -------
-    result : ndarray
-
-    Examples
-    --------
-    >>> arr = np.arange(1, 6)
-    >>> mask = np.array([False, True, False, True, True])
-    >>> result = maybe_upcast_putmask(arr, mask)
-    >>> result
-    array([ 1., nan,  3., nan, nan])
-    """
-    if not isinstance(result, np.ndarray):
-        raise ValueError("The result input must be a ndarray.")
-
-    # NB: we never get here with result.dtype.kind in ["m", "M"]
-
-    if mask.any():
-
-        # we want to decide whether place will work
-        # if we have nans in the False portion of our mask then we need to
-        # upcast (possibly), otherwise we DON't want to upcast (e.g. if we
-        # have values, say integers, in the success portion then it's ok to not
-        # upcast)
-        new_dtype = ensure_dtype_can_hold_na(result.dtype)
-
-        if new_dtype != result.dtype:
-            result = result.astype(new_dtype, copy=True)
-
-        np.place(result, mask, np.nan)
-
-    return result
-
-
 @overload
 def ensure_dtype_can_hold_na(dtype: np.dtype) -> np.dtype:
     ...

pandas/core/internals/__init__.py

@@ -9,12 +9,10 @@
 )
 from pandas.core.internals.blocks import (  # io.pytables, io.packers
     Block,
-    DatetimeBlock,
     DatetimeTZBlock,
     ExtensionBlock,
     NumericBlock,
     ObjectBlock,
-    TimeDeltaBlock,
 )
 from pandas.core.internals.concat import concatenate_managers
 from pandas.core.internals.managers import (
@@ -28,11 +26,9 @@
     "Block",
     "CategoricalBlock",
     "NumericBlock",
-    "DatetimeBlock",
     "DatetimeTZBlock",
     "ExtensionBlock",
     "ObjectBlock",
-    "TimeDeltaBlock",
     "make_block",
     "DataManager",
     "ArrayManager",

pandas/core/internals/array_manager.py

@@ -471,7 +471,7 @@ def apply_with_block(self: T, f, align_keys=None, swap_axis=True, **kwargs) -> T
             # error: Item "ExtensionArray" of "Union[Any, ExtensionArray]" has no
             # attribute "tz"
             if hasattr(arr, "tz") and arr.tz is None:  # type: ignore[union-attr]
-                # DatetimeArray needs to be converted to ndarray for DatetimeBlock
+                # DatetimeArray needs to be converted to ndarray for DatetimeLikeBlock
 
                 # error: Item "ExtensionArray" of "Union[Any, ExtensionArray]" has no
                 # attribute "_data"