ENH: Use find_stack_level in pandas.core

pandas/core/accessor.py

@@ -9,6 +9,7 @@
 import warnings
 
 from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
 
 
 class DirNamesMixin:
@@ -267,7 +268,7 @@ def decorator(accessor):
                 f"{repr(name)} for type {repr(cls)} is overriding a preexisting "
                 f"attribute with the same name.",
                 UserWarning,
-                stacklevel=2,
+                stacklevel=find_stack_level(),
             )
         setattr(cls, name, CachedAccessor(name, accessor))
         cls._accessors.add(name)

pandas/core/arraylike.py

@@ -11,6 +11,7 @@
 import numpy as np
 
 from pandas._libs import lib
+from pandas.util._exceptions import find_stack_level
 
 from pandas.core.construction import extract_array
 from pandas.core.ops import (
@@ -210,7 +211,7 @@ def _maybe_fallback(ufunc: np.ufunc, method: str, *inputs: Any, **kwargs: Any):
                 "or align manually (eg 'df1, df2 = df1.align(df2)') before passing to "
                 "the ufunc to obtain the future behaviour and silence this warning.",
                 FutureWarning,
-                stacklevel=4,
+                stacklevel=find_stack_level(),
             )
 
             # keep the first dataframe of the inputs, other DataFrame/Series is

pandas/core/arrays/categorical.py

@@ -390,7 +390,7 @@ def __init__(
                 "Allowing scalars in the Categorical constructor is deprecated "
                 "and will raise in a future version.  Use `[value]` instead",
                 FutureWarning,
-                stacklevel=2,
+                stacklevel=find_stack_level(),
             )
             values = [values]
 
@@ -945,7 +945,7 @@ def set_categories(
                 "a future version. Removing unused categories will always "
                 "return a new Categorical object.",
                 FutureWarning,
-                stacklevel=2,
+                stacklevel=find_stack_level(),
             )
         else:
             inplace = False
@@ -1045,7 +1045,7 @@ def rename_categories(self, new_categories, inplace=no_default):
                 "a future version. Removing unused categories will always "
                 "return a new Categorical object.",
                 FutureWarning,
-                stacklevel=2,
+                stacklevel=find_stack_level(),
             )
         else:
             inplace = False
@@ -1177,7 +1177,7 @@ def add_categories(self, new_categories, inplace=no_default):
                 "a future version. Removing unused categories will always "
                 "return a new Categorical object.",
                 FutureWarning,
-                stacklevel=2,
+                stacklevel=find_stack_level(),
             )
         else:
             inplace = False
@@ -1252,7 +1252,7 @@ def remove_categories(self, removals, inplace=no_default):
                 "a future version. Removing unused categories will always "
                 "return a new Categorical object.",
                 FutureWarning,
-                stacklevel=2,
+                stacklevel=find_stack_level(),
             )
         else:
             inplace = False
@@ -1327,7 +1327,7 @@ def remove_unused_categories(self, inplace=no_default):
                 "remove_unused_categories is deprecated and "
                 "will be removed in a future version.",
                 FutureWarning,
-                stacklevel=2,
+                stacklevel=find_stack_level(),
             )
         else:
             inplace = False
@@ -1884,7 +1884,7 @@ def to_dense(self) -> np.ndarray:
             "Categorical.to_dense is deprecated and will be removed in "
             "a future version.  Use np.asarray(cat) instead.",
             FutureWarning,
-            stacklevel=2,
+            stacklevel=find_stack_level(),
         )
         return np.asarray(self)
 
@@ -1901,7 +1901,7 @@ def _codes(self, value: np.ndarray):
             "Setting the codes on a Categorical is deprecated and will raise in "
             "a future version. Create a new Categorical object instead",
             FutureWarning,
-            stacklevel=2,
+            stacklevel=find_stack_level(),
         )  # GH#40606
         NDArrayBacked.__init__(self, value, self.dtype)
 
@@ -1924,7 +1924,7 @@ def take_nd(self, indexer, allow_fill: bool = False, fill_value=None):
         warn(
             "Categorical.take_nd is deprecated, use Categorical.take instead",
             FutureWarning,
-            stacklevel=2,
+            stacklevel=find_stack_level(),
         )
         return self.take(indexer, allow_fill=allow_fill, fill_value=fill_value)
 
@@ -2344,7 +2344,7 @@ def is_dtype_equal(self, other) -> bool:
             "Categorical.is_dtype_equal is deprecated and will be removed "
             "in a future version",
             FutureWarning,
-            stacklevel=2,
+            stacklevel=find_stack_level(),
         )
         try:
             return self._categories_match_up_to_permutation(other)

pandas/core/arrays/datetimes.py

@@ -1206,7 +1206,7 @@ def to_perioddelta(self, freq) -> TimedeltaArray:
             "Use `dtindex - dtindex.to_period(freq).to_timestamp()` instead.",
             FutureWarning,
             # stacklevel chosen to be correct for when called from DatetimeIndex
-            stacklevel=3,
+            stacklevel=find_stack_level(),
         )
         from pandas.core.arrays.timedeltas import TimedeltaArray
 
@@ -1373,7 +1373,7 @@ def weekofyear(self):
             "weekofyear and return an Index, you may call "
             "pd.Int64Index(idx.isocalendar().week)",
             FutureWarning,
-            stacklevel=3,
+            stacklevel=find_stack_level(),
         )
         week_series = self.isocalendar().week
         if week_series.hasnans:

pandas/core/arrays/sparse/array.py

@@ -467,7 +467,7 @@ def __init__(
                         "loses timezone information. Cast to object before "
                         "sparse to retain timezone information.",
                         UserWarning,
-                        stacklevel=2,
+                        stacklevel=find_stack_level(),
                     )
                     data = np.asarray(data, dtype="datetime64[ns]")
                     if fill_value is NaT:
@@ -1089,7 +1089,7 @@ def searchsorted(
     ) -> npt.NDArray[np.intp] | np.intp:
 
         msg = "searchsorted requires high memory usage."
-        warnings.warn(msg, PerformanceWarning, stacklevel=2)
+        warnings.warn(msg, PerformanceWarning, stacklevel=find_stack_level())
         if not is_scalar(v):
             v = np.asarray(v)
         v = np.asarray(v)

pandas/core/arrays/sparse/dtype.py

@@ -16,6 +16,7 @@
     type_t,
 )
 from pandas.errors import PerformanceWarning
+from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.base import (
     ExtensionDtype,
@@ -389,7 +390,7 @@ def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
                 f"values: '{fill_values}'. Picking the first and "
                 "converting the rest.",
                 PerformanceWarning,
-                stacklevel=6,
+                stacklevel=find_stack_level(),
             )
 
         np_dtypes = [x.subtype if isinstance(x, SparseDtype) else x for x in dtypes]

pandas/core/common.py

@@ -36,6 +36,7 @@
     Scalar,
     T,
 )
+from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
 from pandas.core.dtypes.common import (
@@ -175,7 +176,7 @@ def cast_scalar_indexer(val, warn_float: bool = False):
                 "Indexing with a float is deprecated, and will raise an IndexError "
                 "in pandas 2.0. You can manually convert to an integer key instead.",
                 FutureWarning,
-                stacklevel=3,
+                stacklevel=find_stack_level(),
             )
         return int(val)
     return val

pandas/core/computation/align.py

@@ -16,6 +16,7 @@
 import numpy as np
 
 from pandas.errors import PerformanceWarning
+from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.generic import (
     ABCDataFrame,
@@ -126,7 +127,9 @@ def _align_core(terms):
                         f"than an order of magnitude on term {repr(terms[i].name)}, "
                         f"by more than {ordm:.4g}; performance may suffer."
                     )
-                    warnings.warn(w, category=PerformanceWarning, stacklevel=6)
+                    warnings.warn(
+                        w, category=PerformanceWarning, stacklevel=find_stack_level()
+                    )
 
                 f = partial(ti.reindex, reindexer, axis=axis, copy=False)
 

pandas/core/computation/eval.py

@@ -7,6 +7,7 @@
 import warnings
 
 from pandas._libs.lib import no_default
+from pandas.util._exceptions import find_stack_level
 from pandas.util._validators import validate_bool_kwarg
 
 from pandas.core.computation.engines import ENGINES
@@ -308,7 +309,7 @@ def eval(
                 "will be removed in a future version."
             ),
             FutureWarning,
-            stacklevel=2,
+            stacklevel=find_stack_level(),
         )
 
     exprs: list[str | BinOp]

pandas/core/config_init.py

@@ -25,6 +25,8 @@
     is_text,
 )
 
+from pandas.util._exceptions import find_stack_level
+
 # compute
 
 use_bottleneck_doc = """
@@ -373,7 +375,7 @@ def _deprecate_negative_int_max_colwidth(key):
                 "will not be supported in future version. Instead, use None "
                 "to not limit the column width.",
                 FutureWarning,
-                stacklevel=4,
+                stacklevel=find_stack_level(),
             )
 
     cf.register_option(

pandas/core/construction.py

@@ -25,6 +25,7 @@
     DtypeObj,
 )
 from pandas.errors import IntCastingNaNError
+from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.base import (
     ExtensionDtype,
@@ -538,7 +539,7 @@ def sanitize_array(
                         "if they cannot be cast losslessly (matching Series behavior). "
                         "To retain the old behavior, use DataFrame(data).astype(dtype)",
                         FutureWarning,
-                        stacklevel=4,
+                        stacklevel=find_stack_level(),
                     )
                     # GH#40110 until the deprecation is enforced, we _dont_
                     #  ignore the dtype for DataFrame, and _do_ cast even though
@@ -777,7 +778,7 @@ def _try_cast(
                 "passed to 'DataFrame', either all columns will be cast to that "
                 "dtype, or a TypeError will be raised.",
                 FutureWarning,
-                stacklevel=7,
+                stacklevel=find_stack_level(),
             )
             subarr = np.array(arr, dtype=object, copy=copy)
     return subarr

pandas/core/describe.py

@@ -23,6 +23,7 @@
 
 from pandas._libs.tslibs import Timestamp
 from pandas._typing import NDFrameT
+from pandas.util._exceptions import find_stack_level
 from pandas.util._validators import validate_percentile
 
 from pandas.core.dtypes.common import (
@@ -377,7 +378,7 @@ def select_describe_func(
                 "version of pandas. Specify `datetime_is_numeric=True` to "
                 "silence this warning and adopt the future behavior now.",
                 FutureWarning,
-                stacklevel=5,
+                stacklevel=find_stack_level(),
             )
             return describe_timestamp_as_categorical_1d
     elif is_timedelta64_dtype(data.dtype):

pandas/core/dtypes/cast.py

@@ -563,7 +563,7 @@ def _maybe_promote(dtype: np.dtype, fill_value=np.nan):
                     "dtype is deprecated. In a future version, this will be cast "
                     "to object dtype. Pass `fill_value=Timestamp(date_obj)` instead.",
                     FutureWarning,
-                    stacklevel=8,
+                    stacklevel=find_stack_level(),
                 )
                 return dtype, fv
         elif isinstance(fill_value, str):
@@ -1133,7 +1133,7 @@ def astype_nansafe(
                 "Use .view(...) instead.",
                 FutureWarning,
                 # stacklevel chosen to be correct when reached via Series.astype
-                stacklevel=7,
+                stacklevel=find_stack_level(),
             )
             if isna(arr).any():
                 raise ValueError("Cannot convert NaT values to integer")
@@ -1155,7 +1155,7 @@ def astype_nansafe(
                 "Use .view(...) instead.",
                 FutureWarning,
                 # stacklevel chosen to be correct when reached via Series.astype
-                stacklevel=7,
+                stacklevel=find_stack_level(),
             )
             if isna(arr).any():
                 raise ValueError("Cannot convert NaT values to integer")
@@ -1651,7 +1651,7 @@ def maybe_cast_to_datetime(
                                 "`pd.Series(values).dt.tz_localize(None)` "
                                 "instead.",
                                 FutureWarning,
-                                stacklevel=8,
+                                stacklevel=find_stack_level(),
                             )
                             # equiv: dta.view(dtype)
                             # Note: NOT equivalent to dta.astype(dtype)
@@ -1691,7 +1691,7 @@ def maybe_cast_to_datetime(
                                     ".tz_localize('UTC').tz_convert(dtype.tz) "
                                     "or pd.Series(data.view('int64'), dtype=dtype)",
                                     FutureWarning,
-                                    stacklevel=5,
+                                    stacklevel=find_stack_level(),
                                 )
 
                             value = dta.tz_localize("UTC").tz_convert(dtype.tz)
@@ -1859,7 +1859,7 @@ def construct_2d_arraylike_from_scalar(
     shape = (length, width)
 
     if dtype.kind in ["m", "M"]:
-        value = maybe_unbox_datetimelike_tz_deprecation(value, dtype, stacklevel=4)
+        value = maybe_unbox_datetimelike_tz_deprecation(value, dtype)
     # error: Non-overlapping equality check (left operand type: "dtype[Any]", right
     # operand type: "Type[object]")
     elif dtype == object:  # type: ignore[comparison-overlap]
@@ -1932,9 +1932,7 @@ def construct_1d_arraylike_from_scalar(
     return subarr
 
 
-def maybe_unbox_datetimelike_tz_deprecation(
-    value: Scalar, dtype: DtypeObj, stacklevel: int = 5
-):
+def maybe_unbox_datetimelike_tz_deprecation(value: Scalar, dtype: DtypeObj):
     """
     Wrap maybe_unbox_datetimelike with a check for a timezone-aware Timestamp
     along with a timezone-naive datetime64 dtype, which is deprecated.
@@ -1963,7 +1961,7 @@ def maybe_unbox_datetimelike_tz_deprecation(
                 "`pd.Series(values).dt.tz_localize(None)` "
                 "instead.",
                 FutureWarning,
-                stacklevel=stacklevel,
+                stacklevel=find_stack_level(),
             )
             new_value = value.tz_localize(None)
             return maybe_unbox_datetimelike(new_value, dtype)

pandas/core/dtypes/common.py

@@ -22,6 +22,7 @@
     ArrayLike,
     DtypeObj,
 )
+from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.base import _registry as registry
 from pandas.core.dtypes.dtypes import (
@@ -304,7 +305,7 @@ def is_categorical(arr) -> bool:
         "is_categorical is deprecated and will be removed in a future version. "
         "Use is_categorical_dtype instead.",
         FutureWarning,
-        stacklevel=2,
+        stacklevel=find_stack_level(),
     )
     return isinstance(arr, ABCCategorical) or is_categorical_dtype(arr)
 
@@ -1378,7 +1379,7 @@ def is_extension_type(arr) -> bool:
         "'is_extension_type' is deprecated and will be removed in a future "
         "version.  Use 'is_extension_array_dtype' instead.",
         FutureWarning,
-        stacklevel=2,
+        stacklevel=find_stack_level(),
     )
 
     if is_categorical_dtype(arr):