STYLE: dont use pd api types in tests (#39293)

Author: nofarm3

.pre-commit-config.yaml

@@ -168,6 +168,12 @@ repos:
             pandas/tests/io/excel/test_writers\.py
             |pandas/tests/io/pytables/common\.py
             |pandas/tests/io/pytables/test_store\.py$
+    -   id: no-pandas-api-types
+        name: Check code for instances of pd.api.types
+        entry: (pd|pandas)\.api\.types\.
+        language: pygrep
+        types: [python]
+        files: ^pandas/tests/
 -   repo: https://github.com/asottile/yesqa
     rev: v1.2.2
     hooks:

asv_bench/benchmarks/reshape.py

@@ -5,6 +5,7 @@
 
 import pandas as pd
 from pandas import DataFrame, MultiIndex, date_range, melt, wide_to_long
+from pandas.api.types import CategoricalDtype
 
 
 class Melt:
@@ -196,7 +197,7 @@ def setup(self):
         categories = list(string.ascii_letters[:12])
         s = pd.Series(
             np.random.choice(categories, size=1000000),
-            dtype=pd.api.types.CategoricalDtype(categories),
+            dtype=CategoricalDtype(categories),
         )
         self.s = s
 

pandas/core/dtypes/inference.py

@@ -50,19 +50,20 @@ def is_number(obj) -> bool:
 
     Examples
     --------
-    >>> pd.api.types.is_number(1)
+    >>> from pandas.api.types import is_number
+    >>> is_number(1)
     True
-    >>> pd.api.types.is_number(7.15)
+    >>> is_number(7.15)
     True
 
     Booleans are valid because they are int subclass.
 
-    >>> pd.api.types.is_number(False)
+    >>> is_number(False)
     True
 
-    >>> pd.api.types.is_number("foo")
+    >>> is_number("foo")
     False
-    >>> pd.api.types.is_number("5")
+    >>> is_number("5")
     False
     """
     return isinstance(obj, (Number, np.number))

pandas/core/generic.py

@@ -5813,7 +5813,8 @@ def astype(
 
         Convert to ordered categorical type with custom ordering:
 
-        >>> cat_dtype = pd.api.types.CategoricalDtype(
+        >>> from pandas.api.types import CategoricalDtype
+        >>> cat_dtype = CategoricalDtype(
         ...     categories=[2, 1], ordered=True)
         >>> ser.astype(cat_dtype)
         0    1

pandas/testing.py

@@ -2,6 +2,7 @@
 Public testing utility functions.
 """
 
+
 from pandas._testing import (
     assert_extension_array_equal,
     assert_frame_equal,

pandas/tests/extension/arrow/arrays.py

@@ -23,6 +23,7 @@
     register_extension_dtype,
     take,
 )
+from pandas.api.types import is_scalar
 from pandas.core.arraylike import OpsMixin
 
 
@@ -91,7 +92,7 @@ def __repr__(self):
         return f"{type(self).__name__}({repr(self._data)})"
 
     def __getitem__(self, item):
-        if pd.api.types.is_scalar(item):
+        if is_scalar(item):
             return self._data.to_pandas()[item]
         else:
             vals = self._data.to_pandas()[item]

pandas/tests/extension/arrow/test_bool.py

@@ -3,6 +3,7 @@
 
 import pandas as pd
 import pandas._testing as tm
+from pandas.api.types import is_bool_dtype
 from pandas.tests.extension import base
 
 pytest.importorskip("pyarrow", minversion="0.13.0")
@@ -89,7 +90,7 @@ class TestReduceBoolean(base.BaseBooleanReduceTests):
 
 
 def test_is_bool_dtype(data):
-    assert pd.api.types.is_bool_dtype(data)
+    assert is_bool_dtype(data)
     assert pd.core.common.is_bool_indexer(data)
     s = pd.Series(range(len(data)))
     result = s[data]

pandas/tests/extension/base/dtype.py

@@ -4,6 +4,7 @@
 import pytest
 
 import pandas as pd
+from pandas.api.types import is_object_dtype, is_string_dtype
 
 from .base import BaseExtensionTests
 
@@ -41,10 +42,10 @@ def test_is_dtype_other_input(self, dtype):
         assert dtype.is_dtype([1, 2, 3]) is False
 
     def test_is_not_string_type(self, dtype):
-        return not pd.api.types.is_string_dtype(dtype)
+        return not is_string_dtype(dtype)
 
     def test_is_not_object_type(self, dtype):
-        return not pd.api.types.is_object_dtype(dtype)
+        return not is_object_dtype(dtype)
 
     def test_eq_with_str(self, dtype):
         assert dtype == dtype.name

pandas/tests/extension/decimal/array.py

@@ -9,10 +9,11 @@
 import numpy as np
 
 from pandas.core.dtypes.base import ExtensionDtype
-from pandas.core.dtypes.common import is_dtype_equal, is_list_like, pandas_dtype
+from pandas.core.dtypes.common import is_dtype_equal, pandas_dtype
 
 import pandas as pd
 from pandas.api.extensions import no_default, register_extension_dtype
+from pandas.api.types import is_list_like, is_scalar
 from pandas.core.arraylike import OpsMixin
 from pandas.core.arrays import ExtensionArray, ExtensionScalarOpsMixin
 from pandas.core.indexers import check_array_indexer
@@ -144,8 +145,8 @@ def astype(self, dtype, copy=True):
         return super().astype(dtype, copy=copy)
 
     def __setitem__(self, key, value):
-        if pd.api.types.is_list_like(value):
-            if pd.api.types.is_scalar(key):
+        if is_list_like(value):
+            if is_scalar(key):
                 raise ValueError("setting an array element with a sequence.")
             value = [decimal.Decimal(v) for v in value]
         else:

pandas/tests/extension/json/array.py

@@ -27,6 +27,7 @@
 
 import pandas as pd
 from pandas.api.extensions import ExtensionArray, ExtensionDtype
+from pandas.api.types import is_bool_dtype
 
 
 class JSONDtype(ExtensionDtype):
@@ -82,7 +83,7 @@ def __getitem__(self, item):
             return type(self)(self.data[item])
         else:
             item = pd.api.indexers.check_array_indexer(self, item)
-            if pd.api.types.is_bool_dtype(item.dtype):
+            if is_bool_dtype(item.dtype):
                 return self._from_sequence([x for x, m in zip(self, item) if m])
             # integer
             return type(self)([self.data[i] for i in item])

pandas/tests/extension/list/array.py

@@ -15,6 +15,7 @@
 from pandas.core.dtypes.base import ExtensionDtype
 
 import pandas as pd
+from pandas.api.types import is_object_dtype, is_string_dtype
 from pandas.core.arrays import ExtensionArray
 
 
@@ -106,9 +107,7 @@ def astype(self, dtype, copy=True):
             if copy:
                 return self.copy()
             return self
-        elif pd.api.types.is_string_dtype(dtype) and not pd.api.types.is_object_dtype(
-            dtype
-        ):
+        elif is_string_dtype(dtype) and not is_object_dtype(dtype):
             # numpy has problems with astype(str) for nested elements
             return np.array([str(x) for x in self.data], dtype=dtype)
         return np.array(self.data, dtype=dtype, copy=copy)

pandas/tests/extension/test_floating.py

@@ -20,6 +20,7 @@
 
 import pandas as pd
 import pandas._testing as tm
+from pandas.api.types import is_float_dtype
 from pandas.core.arrays.floating import Float32Dtype, Float64Dtype
 from pandas.tests.extension import base
 
@@ -101,7 +102,7 @@ def _check_op(self, s, op, other, op_name, exc=NotImplementedError):
             if (
                 hasattr(other, "dtype")
                 and not is_extension_array_dtype(other.dtype)
-                and pd.api.types.is_float_dtype(other.dtype)
+                and is_float_dtype(other.dtype)
             ):
                 # other is np.float64 and would therefore always result in
                 # upcasting, so keeping other as same numpy_dtype

pandas/tests/extension/test_integer.py

@@ -16,10 +16,9 @@
 import numpy as np
 import pytest
 
-from pandas.core.dtypes.common import is_extension_array_dtype
-
 import pandas as pd
 import pandas._testing as tm
+from pandas.api.types import is_extension_array_dtype, is_integer_dtype
 from pandas.core.arrays.integer import (
     Int8Dtype,
     Int16Dtype,
@@ -119,7 +118,7 @@ def _check_op(self, s, op, other, op_name, exc=NotImplementedError):
             if (
                 hasattr(other, "dtype")
                 and not is_extension_array_dtype(other.dtype)
-                and pd.api.types.is_integer_dtype(other.dtype)
+                and is_integer_dtype(other.dtype)
             ):
                 # other is np.int64 and would therefore always result in
                 # upcasting, so keeping other as same numpy_dtype

pandas/tests/frame/test_ufunc.py

@@ -5,6 +5,7 @@
 
 import pandas as pd
 import pandas._testing as tm
+from pandas.api.types import is_extension_array_dtype
 
 dtypes = [
     "int64",
@@ -28,7 +29,7 @@ def test_unary_unary(dtype):
 @pytest.mark.parametrize("dtype", dtypes)
 def test_unary_binary(request, dtype):
     # unary input, binary output
-    if pd.api.types.is_extension_array_dtype(dtype) or isinstance(dtype, dict):
+    if is_extension_array_dtype(dtype) or isinstance(dtype, dict):
         request.node.add_marker(
             pytest.mark.xfail(
                 reason="Extension / mixed with multiple outputs not implemented."
@@ -63,9 +64,9 @@ def test_binary_input_dispatch_binop(dtype):
 @pytest.mark.parametrize("dtype_b", dtypes)
 def test_binary_input_aligns_columns(request, dtype_a, dtype_b):
     if (
-        pd.api.types.is_extension_array_dtype(dtype_a)
+        is_extension_array_dtype(dtype_a)
         or isinstance(dtype_a, dict)
-        or pd.api.types.is_extension_array_dtype(dtype_b)
+        or is_extension_array_dtype(dtype_b)
         or isinstance(dtype_b, dict)
     ):
         request.node.add_marker(
@@ -98,7 +99,7 @@ def test_binary_input_aligns_columns(request, dtype_a, dtype_b):
 
 @pytest.mark.parametrize("dtype", dtypes)
 def test_binary_input_aligns_index(request, dtype):
-    if pd.api.types.is_extension_array_dtype(dtype) or isinstance(dtype, dict):
+    if is_extension_array_dtype(dtype) or isinstance(dtype, dict):
         request.node.add_marker(
             pytest.mark.xfail(
                 reason="Extension / mixed with multiple inputs not implemented."

pandas/tests/indexing/multiindex/test_loc.py

@@ -255,7 +255,7 @@ def test_loc_getitem_int_slice(self):
     def test_loc_getitem_nested_indexer(self, indexer_type_1, indexer_type_2):
         # GH #19686
         # .loc should work with nested indexers which can be
-        # any list-like objects (see `pandas.api.types.is_list_like`) or slices
+        # any list-like objects (see `is_list_like` (`pandas.api.types`)) or slices
 
         def convert_nested_indexer(indexer_type, keys):
             if indexer_type == np.ndarray: