TST: Use pytest or assert_produces_warnings instead of catch_warnings

doc/source/development/contributing_codebase.rst

@@ -576,16 +576,6 @@ ignore the error.
     def test_thing(self):
         pass
 
-If you need finer-grained control, you can use Python's
-`warnings module <https://docs.python.org/3/library/warnings.html>`__
-to control whether a warning is ignored or raised at different places within
-a single test.
-
-.. code-block:: python
-
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore", FutureWarning)
-
 Testing an exception
 ^^^^^^^^^^^^^^^^^^^^
 

pandas/tests/apply/test_frame_apply.py

@@ -293,6 +293,7 @@ def test_apply_mixed_dtype_corner_indexing():
     tm.assert_series_equal(result, expected)
 
 
+@pytest.mark.filterwarnings("ignore::RuntimeWarning")
 @pytest.mark.parametrize("ax", ["index", "columns"])
 @pytest.mark.parametrize(
     "func", [lambda x: x, lambda x: x.mean()], ids=["identity", "mean"]
@@ -303,9 +304,7 @@ def test_apply_empty_infer_type(ax, func, raw, axis):
     df = DataFrame(**{ax: ["a", "b", "c"]})
 
     with np.errstate(all="ignore"):
-        with warnings.catch_warnings(record=True):
-            warnings.simplefilter("ignore", RuntimeWarning)
-            test_res = func(np.array([], dtype="f8"))
+        test_res = func(np.array([], dtype="f8"))
         is_reduction = not isinstance(test_res, np.ndarray)
 
         result = df.apply(func, axis=axis, raw=raw)

pandas/tests/apply/test_invalid_arg.py

@@ -8,7 +8,6 @@
 
 from itertools import chain
 import re
-import warnings
 
 import numpy as np
 import pytest
@@ -298,6 +297,7 @@ def test_transform_and_agg_err_agg(axis, float_frame):
             float_frame.agg(["max", "sqrt"], axis=axis)
 
 
+@pytest.mark.filterwarnings("ignore::FutureWarning")  # GH53325
 @pytest.mark.parametrize(
     "func, msg",
     [
@@ -312,10 +312,7 @@ def test_transform_and_agg_err_series(string_series, func, msg):
     # we are trying to transform with an aggregator
     with pytest.raises(ValueError, match=msg):
         with np.errstate(all="ignore"):
-            # GH53325
-            with warnings.catch_warnings():
-                warnings.simplefilter("ignore", FutureWarning)
-                string_series.agg(func)
+            string_series.agg(func)
 
 
 @pytest.mark.parametrize("func", [["max", "min"], ["max", "sqrt"]])

pandas/tests/arithmetic/test_datetime64.py

@@ -11,7 +11,6 @@
     starmap,
 )
 import operator
-import warnings
 
 import numpy as np
 import pytest
@@ -1166,6 +1165,7 @@ def test_dt64arr_add_sub_parr(
         )
         assert_invalid_addsub_type(dtarr, parr, msg)
 
+    @pytest.mark.filterwarnings("ignore::pandas.errors.PerformanceWarning")
     def test_dt64arr_addsub_time_objects_raises(self, box_with_array, tz_naive_fixture):
         # https://github.com/pandas-dev/pandas/issues/10329
 
@@ -1183,14 +1183,10 @@ def test_dt64arr_addsub_time_objects_raises(self, box_with_array, tz_naive_fixtu
                 "cannot subtract DatetimeArray from ndarray",
             ]
         )
-
-        with warnings.catch_warnings(record=True):
-            # pandas.errors.PerformanceWarning: Non-vectorized DateOffset being
-            # applied to Series or DatetimeIndex
-            # we aren't testing that here, so ignore.
-            warnings.simplefilter("ignore", PerformanceWarning)
-
-            assert_invalid_addsub_type(obj1, obj2, msg=msg)
+        # pandas.errors.PerformanceWarning: Non-vectorized DateOffset being
+        # applied to Series or DatetimeIndex
+        # we aren't testing that here, so ignore.
+        assert_invalid_addsub_type(obj1, obj2, msg=msg)
 
     # -------------------------------------------------------------
     # Other invalid operations
@@ -1370,6 +1366,7 @@ def test_dt64arr_add_sub_relativedelta_offsets(self, box_with_array):
 
     # TODO: redundant with test_dt64arr_add_sub_DateOffset?  that includes
     #  tz-aware cases which this does not
+    @pytest.mark.filterwarnings("ignore::pandas.errors.PerformanceWarning")
     @pytest.mark.parametrize(
         "cls_and_kwargs",
         [
@@ -1458,28 +1455,26 @@ def test_dt64arr_add_sub_DateOffsets(
 
         offset_cls = getattr(pd.offsets, cls_name)
 
-        with warnings.catch_warnings(record=True):
-            # pandas.errors.PerformanceWarning: Non-vectorized DateOffset being
-            # applied to Series or DatetimeIndex
-            # we aren't testing that here, so ignore.
-            warnings.simplefilter("ignore", PerformanceWarning)
+        # pandas.errors.PerformanceWarning: Non-vectorized DateOffset being
+        # applied to Series or DatetimeIndex
+        # we aren't testing that here, so ignore.
 
-            offset = offset_cls(n, normalize=normalize, **kwargs)
+        offset = offset_cls(n, normalize=normalize, **kwargs)
 
-            expected = DatetimeIndex([x + offset for x in vec_items])
-            expected = tm.box_expected(expected, box_with_array)
-            tm.assert_equal(expected, vec + offset)
+        expected = DatetimeIndex([x + offset for x in vec_items])
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(expected, vec + offset)
 
-            expected = DatetimeIndex([x - offset for x in vec_items])
-            expected = tm.box_expected(expected, box_with_array)
-            tm.assert_equal(expected, vec - offset)
+        expected = DatetimeIndex([x - offset for x in vec_items])
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(expected, vec - offset)
 
-            expected = DatetimeIndex([offset + x for x in vec_items])
-            expected = tm.box_expected(expected, box_with_array)
-            tm.assert_equal(expected, offset + vec)
-            msg = "(bad|unsupported) operand type for unary"
-            with pytest.raises(TypeError, match=msg):
-                offset - vec
+        expected = DatetimeIndex([offset + x for x in vec_items])
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(expected, offset + vec)
+        msg = "(bad|unsupported) operand type for unary"
+        with pytest.raises(TypeError, match=msg):
+            offset - vec
 
     def test_dt64arr_add_sub_DateOffset(self, box_with_array):
         # GH#10699

pandas/tests/arrays/categorical/test_operators.py

@@ -1,5 +1,3 @@
-import warnings
-
 import numpy as np
 import pytest
 
@@ -196,6 +194,7 @@ def test_comparison_with_tuple(self):
         result = cat != (0, 1)
         tm.assert_numpy_array_equal(result, ~expected)
 
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
     def test_comparison_of_ordered_categorical_with_nan_to_scalar(
         self, compare_operators_no_eq_ne
     ):
@@ -206,12 +205,11 @@ def test_comparison_of_ordered_categorical_with_nan_to_scalar(
 
         cat = Categorical([1, 2, 3, None], categories=[1, 2, 3], ordered=True)
         scalar = 2
-        with warnings.catch_warnings():
-            warnings.simplefilter("ignore", RuntimeWarning)
-            expected = getattr(np.array(cat), compare_operators_no_eq_ne)(scalar)
+        expected = getattr(np.array(cat), compare_operators_no_eq_ne)(scalar)
         actual = getattr(cat, compare_operators_no_eq_ne)(scalar)
         tm.assert_numpy_array_equal(actual, expected)
 
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
     def test_comparison_of_ordered_categorical_with_nan_to_listlike(
         self, compare_operators_no_eq_ne
     ):
@@ -221,9 +219,7 @@ def test_comparison_of_ordered_categorical_with_nan_to_listlike(
 
         cat = Categorical([1, 2, 3, None], categories=[1, 2, 3], ordered=True)
         other = Categorical([2, 2, 2, 2], categories=[1, 2, 3], ordered=True)
-        with warnings.catch_warnings():
-            warnings.simplefilter("ignore", RuntimeWarning)
-            expected = getattr(np.array(cat), compare_operators_no_eq_ne)(2)
+        expected = getattr(np.array(cat), compare_operators_no_eq_ne)(2)
         actual = getattr(cat, compare_operators_no_eq_ne)(other)
         tm.assert_numpy_array_equal(actual, expected)
 

pandas/tests/arrays/test_datetimelike.py

@@ -49,7 +49,9 @@ def period_index(freqstr):
     # TODO: non-monotone indexes; NaTs, different start dates
     with warnings.catch_warnings():
         # suppress deprecation of Period[B]
-        warnings.simplefilter("ignore")
+        warnings.filterwarnings(
+            "ignore", message="Period with BDay freq", category=FutureWarning
+        )
         pi = pd.period_range(start=Timestamp("2000-01-01"), periods=100, freq=freqstr)
     return pi