Adjust some tests

Author: mroeschke

pandas/tests/dtypes/test_inference.py

@@ -34,7 +34,6 @@
     missing as libmissing,
     ops as libops,
 )
-from pandas.compat.numpy import np_version_gt2
 
 from pandas.core.dtypes import inference
 from pandas.core.dtypes.cast import find_result_type
@@ -1989,7 +1988,7 @@ def test_ensure_int32():
         # find a smaller floating dtype
         (300.0, np.uint16),  # for integer floats, we convert them to ints
         (300.1, np.float64),
-        (np.int16(300), np.int16 if np_version_gt2 else np.uint16),
+        (np.int16(300), np.uint16),
     ],
 )
 def test_find_result_type_uint_int(right, result):

pandas/tests/indexing/test_coercion.py

@@ -15,7 +15,6 @@
     IS64,
     is_platform_windows,
 )
-from pandas.compat.numpy import np_version_gt2
 
 import pandas as pd
 import pandas._testing as tm
@@ -219,8 +218,6 @@ def test_insert_int_index(
         "insert, coerced_val, coerced_dtype",
         [
             (1, 1.0, None),
-            # When float_numpy_dtype=float32, this is not the case
-            # see the correction below
             (1.1, 1.1, np.float64),
             (False, False, object),  # GH#36319
             ("x", "x", object),
@@ -232,11 +229,6 @@ def test_insert_float_index(
         dtype = float_numpy_dtype
         obj = pd.Index([1.0, 2.0, 3.0, 4.0], dtype=dtype)
         coerced_dtype = coerced_dtype if coerced_dtype is not None else dtype
-
-        if np_version_gt2 and dtype == "float32" and coerced_val == 1.1:
-            # Hack, in the 2nd test case, since 1.1 can be losslessly cast to float32
-            # the expected dtype will be float32 if the original dtype was float32
-            coerced_dtype = np.float32
         exp = pd.Index([1.0, coerced_val, 2.0, 3.0, 4.0], dtype=coerced_dtype)
         self._assert_insert_conversion(obj, insert, exp, coerced_dtype)
 

pandas/tests/indexing/test_loc.py

@@ -16,7 +16,6 @@
 from pandas._config import using_pyarrow_string_dtype
 
 from pandas._libs import index as libindex
-from pandas.compat.numpy import np_version_gt2
 from pandas.errors import IndexingError
 
 import pandas as pd
@@ -2976,16 +2975,7 @@ def test_loc_setitem_uint8_upcast(value):
     df = DataFrame([1, 2, 3, 4], columns=["col1"], dtype="uint8")
     with tm.assert_produces_warning(FutureWarning, match="item of incompatible dtype"):
         df.loc[2, "col1"] = value  # value that can't be held in uint8
-
-    if np_version_gt2 and isinstance(value, np.int16):
-        # Note, result type of uint8 + int16 is int16
-        # in numpy < 2, though, numpy would inspect the
-        # value and see that it could fit in an uint16, resulting in a uint16
-        dtype = "int16"
-    else:
-        dtype = "uint16"
-
-    expected = DataFrame([1, 2, 300, 4], columns=["col1"], dtype=dtype)
+    expected = DataFrame([1, 2, 300, 4], columns=["col1"], dtype="uint16")
     tm.assert_frame_equal(df, expected)
 
 

pandas/tests/series/test_constructors.py

@@ -769,12 +769,8 @@ def test_constructor_cast(self):
 
     def test_constructor_signed_int_overflow_raises(self):
         # GH#41734 disallow silent overflow, enforced in 2.0
-        if np_version_gt2:
-            msg = "The elements provided in the data cannot all be casted to the dtype"
-            err = OverflowError
-        else:
-            msg = "Values are too large to be losslessly converted"
-            err = ValueError
+        msg = "Values are too large to be losslessly converted"
+        err = ValueError
         with pytest.raises(err, match=msg):
             Series([1, 200, 923442], dtype="int8")
 
@@ -802,13 +798,7 @@ def test_constructor_numpy_uints(self, values):
 
     def test_constructor_unsigned_dtype_overflow(self, any_unsigned_int_numpy_dtype):
         # see gh-15832
-        if np_version_gt2:
-            msg = (
-                f"The elements provided in the data cannot "
-                f"all be casted to the dtype {any_unsigned_int_numpy_dtype}"
-            )
-        else:
-            msg = "Trying to coerce negative values to unsigned integers"
+        msg = "Trying to coerce negative values to unsigned integers"
         with pytest.raises(OverflowError, match=msg):
             Series([-1], dtype=any_unsigned_int_numpy_dtype)
 
@@ -1938,15 +1928,9 @@ def test_constructor_int64_dtype(self, any_int_dtype):
 
     def test_constructor_raise_on_lossy_conversion_of_strings(self):
         # GH#44923
-        if not np_version_gt2:
-            raises = pytest.raises(
-                ValueError, match="string values cannot be losslessly cast to int8"
-            )
-        else:
-            raises = pytest.raises(
-                OverflowError, match="The elements provided in the data"
-            )
-        with raises:
+        with pytest.raises(
+            ValueError, match="string values cannot be losslessly cast to int8"
+        ):
             Series(["128"], dtype="int8")
 
     def test_constructor_dtype_timedelta_alternative_construct(self):