optimize checks

Author: jbrockmendel

pandas/_config/__init__.py

@@ -33,3 +33,8 @@
 def using_string_dtype() -> bool:
     _mode_options = _global_config["future"]
     return _mode_options["infer_string"]
+
+
+def using_pdep16_nan_behavior() -> bool:
+    _mode_options = _global_config["mode"]
+    return _mode_options["pdep16_nan_behavior"]

pandas/conftest.py

@@ -2127,5 +2127,5 @@ def monkeysession():
 @pytest.fixture(params=[True, False])
 def pdep16_nan_behavior(request):
     opt = request.param
-    with pd.option_context("mode.pdep16_nan_behavior", opt):
+    with pd.option_context("mode.PDEP16_nan_behavior", opt):
         yield opt

pandas/core/arrays/masked.py

@@ -11,7 +11,7 @@
 
 import numpy as np
 
-from pandas._config import get_option
+from pandas._config import using_pdep16_nan_behavior
 
 from pandas._libs import (
     lib,
@@ -310,7 +310,7 @@ def __setitem__(self, key, value) -> None:
     def __contains__(self, key) -> bool:
         if isna(key) and key is not self.dtype.na_value:
             # GH#52840
-            if lib.is_float(key) and get_option("mode.PDEP16_nan_behavior"):
+            if lib.is_float(key) and using_pdep16_nan_behavior():
                 key = self.dtype.na_value
             elif self._data.dtype.kind == "f" and lib.is_float(key):
                 return bool((np.isnan(self._data) & ~self._mask).any())
@@ -659,7 +659,7 @@ def reconstruct(x: np.ndarray):
                     # reached in e.g. np.sqrt on BooleanArray
                     # we don't support float16
                     x = x.astype(np.float32)
-                if get_option("mode.PDEP16_nan_behavior"):
+                if using_pdep16_nan_behavior():
                     m[np.isnan(x)] = True
                 return FloatingArray(x, m)
             else:
@@ -866,7 +866,7 @@ def _maybe_mask_result(
         if result.dtype.kind == "f":
             from pandas.core.arrays import FloatingArray
 
-            if get_option("mode.PDEP16_nan_behavior"):
+            if using_pdep16_nan_behavior():
                 mask[np.isnan(result)] = True
 
             return FloatingArray(result, mask, copy=False)

pandas/core/arrays/numeric.py

@@ -8,7 +8,7 @@
 
 import numpy as np
 
-from pandas._config import get_option
+from pandas._config import using_pdep16_nan_behavior
 
 from pandas._libs import (
     lib,
@@ -103,7 +103,7 @@ def __from_arrow__(
                 array = array.combine_chunks()
 
         data, mask = pyarrow_array_to_numpy_and_mask(array, dtype=self.numpy_dtype)
-        if data.dtype.kind == "f" and get_option("mode.PDEP16_nan_behavior"):
+        if data.dtype.kind == "f" and using_pdep16_nan_behavior():
             mask[np.isnan(data)] = False
         return array_class(data.copy(), ~mask, copy=False)
 
@@ -274,7 +274,7 @@ def __init__(
             # If we don't raise here, then accessing self.dtype would raise
             raise TypeError("FloatingArray does not support np.float16 dtype.")
 
-        # NB: if get_option("mode.PDEP16_nan_behavior") is True
+        # NB: if using_pdep16_nan_behavior() is True
         #  then caller is responsible for ensuring
         #  assert mask[np.isnan(values)].all()
 

pandas/core/indexes/base.py

@@ -21,6 +21,7 @@
 
 from pandas._config import (
     get_option,
+    using_pdep16_nan_behavior,
     using_string_dtype,
 )
 
@@ -162,6 +163,7 @@
     ExtensionArray,
     TimedeltaArray,
 )
+from pandas.core.arrays.floating import FloatingDtype
 from pandas.core.arrays.string_ import (
     StringArray,
     StringDtype,
@@ -6587,9 +6589,8 @@ def _maybe_cast_indexer(self, key):
         if (
             is_float(key)
             and np.isnan(key)
-            and isinstance(self.dtype, ExtensionDtype)
-            and self.dtype.kind == "f"
-            and get_option("mode.pdep16_nan_behavior")
+            and isinstance(self.dtype, FloatingDtype)
+            and using_pdep16_nan_behavior()
         ):
             # TODO: better place to do this?
             key = self.dtype.na_value