REF: Remove dynamic docstrings from option methods (#57710)

* REF: Remove dynamic docstrings from option methods

* Fix arguments

* Reuse

* Fix drop duplicate section, numpydoc valudation

* Fix formatting

Author: mroeschke

pandas/_config/config.py

@@ -5,7 +5,7 @@
 import numpy as np
 import pyarrow
 
-from pandas._config.config import _get_option
+from pandas._config.config import get_option
 
 from pandas.errors import PerformanceWarning
 from pandas.util._exceptions import find_stack_level
@@ -16,7 +16,7 @@ def fallback_performancewarning(version: str | None = None) -> None:
     Raise a PerformanceWarning for falling back to ExtensionArray's
     non-pyarrow method
     """
-    if _get_option("performance_warnings"):
+    if get_option("performance_warnings"):
         msg = "Falling back on a non-pyarrow code path which may decrease performance."
         if version is not None:
             msg += f" Upgrade to pyarrow >={version} to possibly suppress this warning."

pandas/core/arrays/arrow/_arrow_utils.py

@@ -20,7 +20,7 @@
 
 import numpy as np
 
-from pandas._config.config import _get_option
+from pandas._config.config import get_option
 
 from pandas._libs import (
     algos,
@@ -1336,7 +1336,7 @@ def _addsub_object_array(self, other: npt.NDArray[np.object_], op) -> np.ndarray
             # If both 1D then broadcasting is unambiguous
             return op(self, other[0])
 
-        if _get_option("performance_warnings"):
+        if get_option("performance_warnings"):
             warnings.warn(
                 "Adding/subtracting object-dtype array to "
                 f"{type(self).__name__} not vectorized.",

pandas/core/arrays/datetimelike.py

@@ -15,7 +15,7 @@
 
 import numpy as np
 
-from pandas._config.config import _get_option
+from pandas._config.config import get_option
 
 from pandas._libs import (
     lib,
@@ -820,7 +820,7 @@ def _add_offset(self, offset: BaseOffset) -> Self:
                 # "dtype[Any] | type[Any] | _SupportsDType[dtype[Any]]"
                 res_values = res_values.view(values.dtype)  # type: ignore[arg-type]
         except NotImplementedError:
-            if _get_option("performance_warnings"):
+            if get_option("performance_warnings"):
                 warnings.warn(
                     "Non-vectorized DateOffset being applied to Series or "
                     "DatetimeIndex.",

pandas/core/arrays/datetimes.py

@@ -18,7 +18,7 @@
 
 import numpy as np
 
-from pandas._config.config import _get_option
+from pandas._config.config import get_option
 
 from pandas._libs import lib
 import pandas._libs.sparse as splib
@@ -1158,7 +1158,7 @@ def searchsorted(
         side: Literal["left", "right"] = "left",
         sorter: NumpySorter | None = None,
     ) -> npt.NDArray[np.intp] | np.intp:
-        if _get_option("performance_warnings"):
+        if get_option("performance_warnings"):
             msg = "searchsorted requires high memory usage."
             warnings.warn(msg, PerformanceWarning, stacklevel=find_stack_level())
         v = np.asarray(v)

pandas/core/arrays/sparse/array.py

@@ -12,7 +12,7 @@
 
 import numpy as np
 
-from pandas._config.config import _get_option
+from pandas._config.config import get_option
 
 from pandas._libs import (
     lib,
@@ -345,7 +345,7 @@ def _str_contains(
         self, pat, case: bool = True, flags: int = 0, na=np.nan, regex: bool = True
     ):
         if flags:
-            if _get_option("mode.performance_warnings"):
+            if get_option("mode.performance_warnings"):
                 fallback_performancewarning()
             return super()._str_contains(pat, case, flags, na, regex)
 
@@ -406,7 +406,7 @@ def _str_replace(
         regex: bool = True,
     ):
         if isinstance(pat, re.Pattern) or callable(repl) or not case or flags:
-            if _get_option("mode.performance_warnings"):
+            if get_option("mode.performance_warnings"):
                 fallback_performancewarning()
             return super()._str_replace(pat, repl, n, case, flags, regex)
 

pandas/core/arrays/string_arrow.py

@@ -15,7 +15,7 @@
 
 import numpy as np
 
-from pandas._config.config import _get_option
+from pandas._config.config import get_option
 
 from pandas.errors import PerformanceWarning
 from pandas.util._exceptions import find_stack_level
@@ -127,7 +127,7 @@ def _align_core(terms):
 
                 ordm = np.log10(max(1, abs(reindexer_size - term_axis_size)))
                 if (
-                    _get_option("performance_warnings")
+                    get_option("performance_warnings")
                     and ordm >= 1
                     and reindexer_size >= 10000
                 ):

pandas/core/computation/align.py

@@ -21,7 +21,7 @@
 import numpy as np
 import pytz
 
-from pandas._config.config import _get_option
+from pandas._config.config import get_option
 
 from pandas._libs import (
     lib,
@@ -2030,7 +2030,7 @@ def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
 
         # np.nan isn't a singleton, so we may end up with multiple
         # NaNs here, so we ignore the all NA case too.
-        if _get_option("performance_warnings") and (
+        if get_option("performance_warnings") and (
             not (len(set(fill_values)) == 1 or isna(fill_values).all())
         ):
             warnings.warn(

pandas/core/dtypes/dtypes.py

@@ -6506,15 +6506,15 @@ def drop_duplicates(
         DataFrame or None
             DataFrame with duplicates removed or None if ``inplace=True``.
 
-        Notes
-        -------
-        This method requires columns specified by ``subset`` to be of hashable type.
-        Passing unhashable columns will raise a ``TypeError``.
-
         See Also
         --------
         DataFrame.value_counts: Count unique combinations of columns.
 
+        Notes
+        -----
+        This method requires columns specified by ``subset`` to be of hashable type.
+        Passing unhashable columns will raise a ``TypeError``.
+
         Examples
         --------
         Consider dataset containing ramen rating.

pandas/core/frame.py

@@ -21,7 +21,6 @@
 import numpy as np
 
 from pandas._config import get_option
-from pandas._config.config import _get_option
 
 from pandas._libs import (
     algos as libalgos,
@@ -2380,7 +2379,7 @@ def drop(  # type: ignore[override]
                     step = loc.step if loc.step is not None else 1
                     inds.extend(range(loc.start, loc.stop, step))
                 elif com.is_bool_indexer(loc):
-                    if _get_option("performance_warnings") and self._lexsort_depth == 0:
+                    if get_option("performance_warnings") and self._lexsort_depth == 0:
                         warnings.warn(
                             "dropping on a non-lexsorted multi-index "
                             "without a level parameter may impact performance.",
@@ -3042,7 +3041,7 @@ def _maybe_to_slice(loc):
         if not follow_key:
             return slice(start, stop)
 
-        if _get_option("performance_warnings"):
+        if get_option("performance_warnings"):
             warnings.warn(
                 "indexing past lexsort depth may impact performance.",
                 PerformanceWarning,

pandas/core/indexes/multi.py

@@ -18,7 +18,7 @@
 
 import numpy as np
 
-from pandas._config.config import _get_option
+from pandas._config.config import get_option
 
 from pandas._libs import (
     algos as libalgos,
@@ -1529,7 +1529,7 @@ def insert(self, loc: int, item: Hashable, value: ArrayLike, refs=None) -> None:
         self._known_consolidated = False
 
         if (
-            _get_option("performance_warnings")
+            get_option("performance_warnings")
             and sum(not block.is_extension for block in self.blocks) > 100
         ):
             warnings.warn(

pandas/core/internals/managers.py

@@ -10,7 +10,7 @@
 
 import numpy as np
 
-from pandas._config.config import _get_option
+from pandas._config.config import get_option
 
 import pandas._libs.reshape as libreshape
 from pandas.errors import PerformanceWarning
@@ -146,7 +146,7 @@ def __init__(
         num_cells = num_rows * num_columns
 
         # GH 26314: Previous ValueError raised was too restrictive for many users.
-        if _get_option("performance_warnings") and num_cells > np.iinfo(np.int32).max:
+        if get_option("performance_warnings") and num_cells > np.iinfo(np.int32).max:
             warnings.warn(
                 f"The following operation may generate {num_cells} cells "
                 f"in the resulting pandas object.",

pandas/core/reshape/reshape.py

@@ -1131,7 +1131,7 @@ def __new__(
                     ext = "xlsx"
 
                 try:
-                    engine = config.get_option(f"io.excel.{ext}.writer", silent=True)
+                    engine = config.get_option(f"io.excel.{ext}.writer")
                     if engine == "auto":
                         engine = get_default_engine(ext, mode="writer")
                 except KeyError as err:
@@ -1552,7 +1552,7 @@ def __init__(
                         "an engine manually."
                     )
 
-            engine = config.get_option(f"io.excel.{ext}.reader", silent=True)
+            engine = config.get_option(f"io.excel.{ext}.reader")
             if engine == "auto":
                 engine = get_default_engine(ext, mode="reader")
 

pandas/io/excel/_base.py

@@ -622,7 +622,7 @@ def __init__(
     @property
     def max_rows(self) -> int:
         """Maximum info rows to be displayed."""
-        return get_option("display.max_info_rows", len(self.data) + 1)
+        return get_option("display.max_info_rows")
 
     @property
     def exceeds_info_cols(self) -> bool:
@@ -641,7 +641,7 @@ def col_count(self) -> int:
 
     def _initialize_max_cols(self, max_cols: int | None) -> int:
         if max_cols is None:
-            return get_option("display.max_info_columns", self.col_count + 1)
+            return get_option("display.max_info_columns")
         return max_cols
 
     def _initialize_show_counts(self, show_counts: bool | None) -> bool:

pandas/io/formats/info.py

@@ -31,7 +31,6 @@
     get_option,
     using_pyarrow_string_dtype,
 )
-from pandas._config.config import _get_option
 
 from pandas._libs import (
     lib,
@@ -3149,7 +3148,7 @@ def write_array(
                 pass
             elif inferred_type == "string":
                 pass
-            elif _get_option("performance_warnings"):
+            elif get_option("performance_warnings"):
                 ws = performance_doc % (inferred_type, key, items)
                 warnings.warn(ws, PerformanceWarning, stacklevel=find_stack_level())
 

pandas/io/pytables.py

@@ -395,7 +395,7 @@ def f3(key):
         assert cf.get_option("a") == 500
 
         cf.reset_option("a")
-        assert options.a == cf.get_option("a", 0)
+        assert options.a == cf.get_option("a")
 
         msg = "You can only set the value of existing options"
         with pytest.raises(OptionError, match=msg):

pandas/tests/config/test_config.py

@@ -114,18 +114,17 @@ def test_matplotlib_formatters(self):
 
     def test_option_no_warning(self):
         pytest.importorskip("matplotlib.pyplot")
-        ctx = cf.option_context("plotting.matplotlib.register_converters", False)
         plt = pytest.importorskip("matplotlib.pyplot")
         s = Series(range(12), index=date_range("2017", periods=12))
         _, ax = plt.subplots()
 
         # Test without registering first, no warning
-        with ctx:
+        with cf.option_context("plotting.matplotlib.register_converters", False):
             ax.plot(s.index, s.values)
 
         # Now test with registering
         register_matplotlib_converters()
-        with ctx:
+        with cf.option_context("plotting.matplotlib.register_converters", False):
             ax.plot(s.index, s.values)
         plt.close()