CLN: remove no-longer-needed warning filters

pandas/core/generic.py

@@ -9660,13 +9660,7 @@ def _where(
 
         # make sure we are boolean
         fill_value = bool(inplace)
-        with warnings.catch_warnings():
-            warnings.filterwarnings(
-                "ignore",
-                "Downcasting object dtype arrays",
-                category=FutureWarning,
-            )
-            cond = cond.fillna(fill_value)
+        cond = cond.fillna(fill_value)
         cond = cond.infer_objects()
 
         msg = "Boolean array expected for the condition, not {dtype}"

pandas/io/formats/xml.py

@@ -11,7 +11,6 @@
     Any,
     final,
 )
-import warnings
 
 from pandas.errors import AbstractMethodError
 from pandas.util._decorators import (
@@ -208,13 +207,7 @@ def _process_dataframe(self) -> dict[int | str, dict[str, Any]]:
             df = df.reset_index()
 
         if self.na_rep is not None:
-            with warnings.catch_warnings():
-                warnings.filterwarnings(
-                    "ignore",
-                    "Downcasting object dtype arrays",
-                    category=FutureWarning,
-                )
-                df = df.fillna(self.na_rep)
+            df = df.fillna(self.na_rep)
 
         return df.to_dict(orient="index")
 

pandas/io/json/_json.py

@@ -16,7 +16,6 @@
     final,
     overload,
 )
-import warnings
 
 import numpy as np
 
@@ -1173,13 +1172,7 @@ def _try_convert_data(
                 if all(notna(data)):
                     return data, False
 
-                with warnings.catch_warnings():
-                    warnings.filterwarnings(
-                        "ignore",
-                        "Downcasting object dtype arrays",
-                        category=FutureWarning,
-                    )
-                    filled = data.fillna(np.nan)
+                filled = data.fillna(np.nan)
 
                 return filled, True
 

pandas/io/stata.py

@@ -2887,13 +2887,7 @@ def _prepare_data(self) -> np.rec.recarray:
         for i, col in enumerate(data):
             typ = typlist[i]
             if typ <= self._max_string_length:
-                with warnings.catch_warnings():
-                    warnings.filterwarnings(
-                        "ignore",
-                        "Downcasting object dtype arrays",
-                        category=FutureWarning,
-                    )
-                    dc = data[col].fillna("")
+                dc = data[col].fillna("")
                 data[col] = dc.apply(_pad_bytes, args=(typ,))
                 stype = f"S{typ}"
                 dtypes[col] = stype

pandas/plotting/_matplotlib/core.py

@@ -1725,13 +1725,7 @@ def _kind(self) -> Literal["area"]:
 
     def __init__(self, data, **kwargs) -> None:
         kwargs.setdefault("stacked", True)
-        with warnings.catch_warnings():
-            warnings.filterwarnings(
-                "ignore",
-                "Downcasting object dtype arrays",
-                category=FutureWarning,
-            )
-            data = data.fillna(value=0)
+        data = data.fillna(value=0)
         LinePlot.__init__(self, data, **kwargs)
 
         if not self.stacked:

pandas/tests/extension/test_masked.py

@@ -14,8 +14,6 @@
 
 """
 
-import warnings
-
 import numpy as np
 import pytest
 
@@ -215,13 +213,7 @@ def _cast_pointwise_result(self, op_name: str, obj, other, pointwise_result):
 
         if sdtype.kind in "iu":
             if op_name in ("__rtruediv__", "__truediv__", "__div__"):
-                with warnings.catch_warnings():
-                    warnings.filterwarnings(
-                        "ignore",
-                        "Downcasting object dtype arrays",
-                        category=FutureWarning,
-                    )
-                    filled = expected.fillna(np.nan)
+                filled = expected.fillna(np.nan)
                 expected = filled.astype("Float64")
             else:
                 # combine method result in 'biggest' (int64) dtype

pandas/tests/frame/indexing/test_where.py

@@ -96,7 +96,6 @@ def test_where_upcasting(self):
 
         tm.assert_series_equal(result, expected)
 
-    @pytest.mark.filterwarnings("ignore:Downcasting object dtype arrays:FutureWarning")
     def test_where_alignment(self, where_frame, float_string_frame):
         # aligning
         def _check_align(df, cond, other, check_dtypes=True):
@@ -171,7 +170,6 @@ def test_where_invalid(self):
         with pytest.raises(ValueError, match=msg):
             df.mask(0)
 
-    @pytest.mark.filterwarnings("ignore:Downcasting object dtype arrays:FutureWarning")
     def test_where_set(self, where_frame, float_string_frame, mixed_int_frame):
         # where inplace
 

pandas/tests/frame/test_reductions.py

@@ -1272,7 +1272,6 @@ def test_any_all_bool_with_na(
     ):
         getattr(bool_frame_with_na, all_boolean_reductions)(axis=axis, bool_only=False)
 
-    @pytest.mark.filterwarnings("ignore:Downcasting object dtype arrays:FutureWarning")
     def test_any_all_bool_frame(self, all_boolean_reductions, bool_frame_with_na):
         # GH#12863: numpy gives back non-boolean data for object type
         # so fill NaNs to compare with pandas behavior

pandas/tests/frame/test_stack_unstack.py

@@ -1223,7 +1223,6 @@ def test_stack_preserve_categorical_dtype_values(self, future_stack):
     @pytest.mark.filterwarnings(
         "ignore:The previous implementation of stack is deprecated"
     )
-    @pytest.mark.filterwarnings("ignore:Downcasting object dtype arrays:FutureWarning")
     @pytest.mark.parametrize(
         "index",
         [

pandas/tests/groupby/test_numeric_only.py

@@ -310,7 +310,6 @@ def test_numeric_only(kernel, has_arg, numeric_only, keys):
             method(*args, **kwargs)
 
 
-@pytest.mark.filterwarnings("ignore:Downcasting object dtype arrays:FutureWarning")
 @pytest.mark.parametrize("dtype", [bool, int, float, object])
 def test_deprecate_numeric_only_series(dtype, groupby_func, request):
     # GH#46560

pandas/tests/series/test_api.py

@@ -195,7 +195,6 @@ def test_series_datetimelike_attribute_access_invalid(self):
         with pytest.raises(AttributeError, match=msg):
             ser.weekday
 
-    @pytest.mark.filterwarnings("ignore:Downcasting object dtype arrays:FutureWarning")
     @pytest.mark.parametrize(
         "kernel, has_numeric_only",
         [

pandas/tests/series/test_logical_ops.py

@@ -15,7 +15,6 @@
 
 
 class TestSeriesLogicalOps:
-    @pytest.mark.filterwarnings("ignore:Downcasting object dtype arrays:FutureWarning")
     @pytest.mark.parametrize("bool_op", [operator.and_, operator.or_, operator.xor])
     def test_bool_operators_with_nas(self, bool_op):
         # boolean &, |, ^ should work with object arrays and propagate NAs