Merge branch 'main' into 37715-remove-mypy-ignore

Author: natmokval

asv_bench/benchmarks/arithmetic.py

@@ -266,10 +266,14 @@ def setup(self, tz):
         self.ts = self.s[halfway]
 
         self.s2 = Series(date_range("20010101", periods=N, freq="s", tz=tz))
+        self.ts_different_reso = Timestamp("2001-01-02", tz=tz)
 
     def time_series_timestamp_compare(self, tz):
         self.s <= self.ts
 
+    def time_series_timestamp_different_reso_compare(self, tz):
+        self.s <= self.ts_different_reso
+
     def time_timestamp_series_compare(self, tz):
         self.ts >= self.s
 

pandas/_libs/parsers.pyx

@@ -10,7 +10,6 @@ import sys
 import time
 import warnings
 
-from pandas.errors import ParserError
 from pandas.util._exceptions import find_stack_level
 
 from pandas import StringDtype
@@ -106,15 +105,10 @@ from pandas.errors import (
     ParserWarning,
 )
 
-from pandas.core.dtypes.common import (
-    is_bool_dtype,
-    is_datetime64_dtype,
-    is_extension_array_dtype,
-    is_float_dtype,
-    is_integer_dtype,
-    is_object_dtype,
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    ExtensionDtype,
 )
-from pandas.core.dtypes.dtypes import CategoricalDtype
 from pandas.core.dtypes.inference import is_dict_like
 
 cdef:
@@ -1077,7 +1071,7 @@ cdef class TextReader:
 
             # don't try to upcast EAs
             if (
-                na_count > 0 and not is_extension_array_dtype(col_dtype)
+                na_count > 0 and not isinstance(col_dtype, ExtensionDtype)
                 or self.dtype_backend != "numpy"
             ):
                 use_dtype_backend = self.dtype_backend != "numpy" and col_dtype is None
@@ -1142,14 +1136,14 @@ cdef class TextReader:
             # (see _try_bool_flex()). Usually this would be taken care of using
             # _maybe_upcast(), but if col_dtype is a floating type we should just
             # take care of that cast here.
-            if col_res.dtype == np.bool_ and is_float_dtype(col_dtype):
+            if col_res.dtype == np.bool_ and col_dtype.kind == "f":
                 mask = col_res.view(np.uint8) == na_values[np.uint8]
                 col_res = col_res.astype(col_dtype)
                 np.putmask(col_res, mask, np.nan)
                 return col_res, na_count
 
             # NaNs are already cast to True here, so can not use astype
-            if col_res.dtype == np.bool_ and is_integer_dtype(col_dtype):
+            if col_res.dtype == np.bool_ and col_dtype.kind in "iu":
                 if na_count > 0:
                     raise ValueError(
                         f"cannot safely convert passed user dtype of "
@@ -1193,14 +1187,14 @@ cdef class TextReader:
                 cats, codes, dtype, true_values=true_values)
             return cat, na_count
 
-        elif is_extension_array_dtype(dtype):
+        elif isinstance(dtype, ExtensionDtype):
             result, na_count = self._string_convert(i, start, end, na_filter,
                                                     na_hashset)
 
             array_type = dtype.construct_array_type()
             try:
                 # use _from_sequence_of_strings if the class defines it
-                if is_bool_dtype(dtype):
+                if dtype.kind == "b":
                     true_values = [x.decode() for x in self.true_values]
                     false_values = [x.decode() for x in self.false_values]
                     result = array_type._from_sequence_of_strings(
@@ -1216,7 +1210,7 @@ cdef class TextReader:
 
             return result, na_count
 
-        elif is_integer_dtype(dtype):
+        elif dtype.kind in "iu":
             try:
                 result, na_count = _try_int64(self.parser, i, start,
                                               end, na_filter, na_hashset)
@@ -1233,14 +1227,14 @@ cdef class TextReader:
 
             return result, na_count
 
-        elif is_float_dtype(dtype):
+        elif dtype.kind == "f":
             result, na_count = _try_double(self.parser, i, start, end,
                                            na_filter, na_hashset, na_flist)
 
             if result is not None and dtype != "float64":
                 result = result.astype(dtype)
             return result, na_count
-        elif is_bool_dtype(dtype):
+        elif dtype.kind == "b":
             result, na_count = _try_bool_flex(self.parser, i, start, end,
                                               na_filter, na_hashset,
                                               self.true_set, self.false_set)
@@ -1267,10 +1261,10 @@ cdef class TextReader:
             # unicode variable width
             return self._string_convert(i, start, end, na_filter,
                                         na_hashset)
-        elif is_object_dtype(dtype):
+        elif dtype == object:
             return self._string_convert(i, start, end, na_filter,
                                         na_hashset)
-        elif is_datetime64_dtype(dtype):
+        elif dtype.kind == "M":
             raise TypeError(f"the dtype {dtype} is not supported "
                             f"for parsing, pass this column "
                             f"using parse_dates instead")
@@ -1438,7 +1432,7 @@ def _maybe_upcast(
     -------
     The casted array.
     """
-    if is_extension_array_dtype(arr.dtype):
+    if isinstance(arr.dtype, ExtensionDtype):
         # TODO: the docstring says arr is an ndarray, in which case this cannot
         #  be reached. Is that incorrect?
         return arr

pandas/_testing/asserters.py

@@ -13,10 +13,8 @@
 
 from pandas.core.dtypes.common import (
     is_bool,
-    is_categorical_dtype,
     is_extension_array_dtype,
     is_integer_dtype,
-    is_interval_dtype,
     is_number,
     is_numeric_dtype,
     needs_i8_conversion,
@@ -33,6 +31,7 @@
     DataFrame,
     DatetimeIndex,
     Index,
+    IntervalDtype,
     IntervalIndex,
     MultiIndex,
     PeriodIndex,
@@ -238,7 +237,9 @@ def _check_types(left, right, obj: str = "Index") -> None:
         assert_attr_equal("inferred_type", left, right, obj=obj)
 
         # Skip exact dtype checking when `check_categorical` is False
-        if is_categorical_dtype(left.dtype) and is_categorical_dtype(right.dtype):
+        if isinstance(left.dtype, CategoricalDtype) and isinstance(
+            right.dtype, CategoricalDtype
+        ):
             if check_categorical:
                 assert_attr_equal("dtype", left, right, obj=obj)
                 assert_index_equal(left.categories, right.categories, exact=exact)
@@ -335,7 +336,9 @@ def _get_ilevel_values(index, level):
         assert_interval_array_equal(left._values, right._values)
 
     if check_categorical:
-        if is_categorical_dtype(left.dtype) or is_categorical_dtype(right.dtype):
+        if isinstance(left.dtype, CategoricalDtype) or isinstance(
+            right.dtype, CategoricalDtype
+        ):
             assert_categorical_equal(left._values, right._values, obj=f"{obj} category")
 
 
@@ -946,7 +949,9 @@ def assert_series_equal(
                 f"is not equal to {right._values}."
             )
             raise AssertionError(msg)
-    elif is_interval_dtype(left.dtype) and is_interval_dtype(right.dtype):
+    elif isinstance(left.dtype, IntervalDtype) and isinstance(
+        right.dtype, IntervalDtype
+    ):
         assert_interval_array_equal(left.array, right.array)
     elif isinstance(left.dtype, CategoricalDtype) or isinstance(
         right.dtype, CategoricalDtype

pandas/core/algorithms.py

@@ -42,7 +42,6 @@
     ensure_platform_int,
     is_array_like,
     is_bool_dtype,
-    is_categorical_dtype,
     is_complex_dtype,
     is_dict_like,
     is_extension_array_dtype,
@@ -59,6 +58,7 @@
 from pandas.core.dtypes.concat import concat_compat
 from pandas.core.dtypes.dtypes import (
     BaseMaskedDtype,
+    CategoricalDtype,
     ExtensionDtype,
     PandasDtype,
 )
@@ -141,7 +141,7 @@ def _ensure_data(values: ArrayLike) -> np.ndarray:
             return _ensure_data(values._data)
         return np.asarray(values)
 
-    elif is_categorical_dtype(values.dtype):
+    elif isinstance(values.dtype, CategoricalDtype):
         # NB: cases that go through here should NOT be using _reconstruct_data
         #  on the back-end.
         values = cast("Categorical", values)
@@ -417,7 +417,7 @@ def unique_with_mask(values, mask: npt.NDArray[np.bool_] | None = None):
     """See algorithms.unique for docs. Takes a mask for masked arrays."""
     values = _ensure_arraylike(values)
 
-    if is_extension_array_dtype(values.dtype):
+    if isinstance(values.dtype, ExtensionDtype):
         # Dispatch to extension dtype's unique.
         return values.unique()
 
@@ -1534,7 +1534,7 @@ def safe_sort(
     ordered: AnyArrayLike
 
     if (
-        not is_extension_array_dtype(values)
+        not isinstance(values.dtype, ExtensionDtype)
         and lib.infer_dtype(values, skipna=False) == "mixed-integer"
     ):
         ordered = _sort_mixed(values)

pandas/core/arrays/arrow/array.py

@@ -1634,6 +1634,10 @@ def _replace_with_mask(
                 indices = pa.array(indices, type=pa.int64())
                 replacements = replacements.take(indices)
             return cls._if_else(mask, replacements, values)
+        if isinstance(values, pa.ChunkedArray) and pa.types.is_boolean(values.type):
+            # GH#52059 replace_with_mask segfaults for chunked array
+            # https://github.com/apache/arrow/issues/34634
+            values = values.combine_chunks()
         try:
             return pc.replace_with_mask(values, mask, replacements)
         except pa.ArrowNotImplementedError: