Address remaining functions

Author: rhshadrach

pandas/_libs/lib.pyx

@@ -41,6 +41,7 @@ from cython cimport (
 from pandas._config import using_string_dtype
 
 from pandas._libs.missing import check_na_tuples_nonequal
+from pandas.util._decorators import set_module
 
 import_datetime()
 
@@ -154,6 +155,7 @@ def memory_usage_of_objects(arr: object[:]) -> int64_t:
 # ----------------------------------------------------------------------
 
 
+@set_module("pandas.api.types")
 def is_scalar(val: object) -> bool:
     """
     Return True if given object is scalar.
@@ -255,6 +257,7 @@ cdef int64_t get_itemsize(object val):
         return -1
 
 
+@set_module("pandas.api.types")
 def is_iterator(obj: object) -> bool:
     """
     Check if the object is an iterator.
@@ -1095,6 +1098,7 @@ def indices_fast(ndarray[intp_t, ndim=1] index, const int64_t[:] labels, list ke
 
 # core.common import for fast inference checks
 
+@set_module("pandas.api.types")
 def is_float(obj: object) -> bool:
     """
     Return True if given object is float.
@@ -1128,6 +1132,7 @@ def is_float(obj: object) -> bool:
     return util.is_float_object(obj)
 
 
+@set_module("pandas.api.types")
 def is_integer(obj: object) -> bool:
     """
     Return True if given object is integer.
@@ -1172,6 +1177,7 @@ def is_int_or_none(obj) -> bool:
     return obj is None or util.is_integer_object(obj)
 
 
+@set_module("pandas.api.types")
 def is_bool(obj: object) -> bool:
     """
     Return True if given object is boolean.
@@ -1202,6 +1208,7 @@ def is_bool(obj: object) -> bool:
     return util.is_bool_object(obj)
 
 
+@set_module("pandas.api.types")
 def is_complex(obj: object) -> bool:
     """
     Return True if given object is complex.
@@ -1237,6 +1244,7 @@ cpdef bint is_decimal(object obj):
     return isinstance(obj, Decimal)
 
 
+@set_module("pandas.api.types")
 def is_list_like(obj: object, allow_sets: bool = True) -> bool:
     """
     Check if the object is list-like.
@@ -1520,6 +1528,7 @@ cdef object _try_infer_map(object dtype):
     return None
 
 
+@set_module("pandas.api.types")
 def infer_dtype(value: object, skipna: bool = True) -> str:
     """
     Return a string label of the type of the elements in a list-like input.

pandas/core/dtypes/common.py

@@ -22,6 +22,7 @@
 )
 from pandas._libs.tslibs import conversion
 from pandas.errors import Pandas4Warning
+from pandas.util._decorators import set_module
 from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.base import _registry as registry
@@ -138,6 +139,7 @@ def _classes_and_not_datetimelike(*klasses) -> Callable:
     )
 
 
+@set_module("pandas.api.types")
 def is_object_dtype(arr_or_dtype) -> bool:
     """
     Check whether an array-like or dtype is of the object dtype.
@@ -183,6 +185,7 @@ def is_object_dtype(arr_or_dtype) -> bool:
     return _is_dtype_type(arr_or_dtype, classes(np.object_))
 
 
+@set_module("pandas.api.types")
 def is_sparse(arr) -> bool:
     """
     Check whether an array-like is a 1-D pandas sparse array.
@@ -282,6 +285,7 @@ def is_scipy_sparse(arr) -> bool:
     return _is_scipy_sparse(arr)
 
 
+@set_module("pandas.api.types")
 def is_datetime64_dtype(arr_or_dtype) -> bool:
     """
     Check whether an array-like or dtype is of the datetime64 dtype.
@@ -323,6 +327,7 @@ def is_datetime64_dtype(arr_or_dtype) -> bool:
     return _is_dtype_type(arr_or_dtype, classes(np.datetime64))
 
 
+@set_module("pandas.api.types")
 def is_datetime64tz_dtype(arr_or_dtype) -> bool:
     """
     Check whether an array-like or dtype is of a DatetimeTZDtype dtype.
@@ -384,6 +389,7 @@ def is_datetime64tz_dtype(arr_or_dtype) -> bool:
     return DatetimeTZDtype.is_dtype(arr_or_dtype)
 
 
+@set_module("pandas.api.types")
 def is_timedelta64_dtype(arr_or_dtype) -> bool:
     """
     Check whether an array-like or dtype is of the timedelta64 dtype.
@@ -426,6 +432,7 @@ def is_timedelta64_dtype(arr_or_dtype) -> bool:
     return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))
 
 
+@set_module("pandas.api.types")
 def is_period_dtype(arr_or_dtype) -> bool:
     """
     Check whether an array-like or dtype is of the Period dtype.
@@ -479,6 +486,7 @@ def is_period_dtype(arr_or_dtype) -> bool:
     return PeriodDtype.is_dtype(arr_or_dtype)
 
 
+@set_module("pandas.api.types")
 def is_interval_dtype(arr_or_dtype) -> bool:
     """
     Check whether an array-like or dtype is of the Interval dtype.
@@ -537,6 +545,7 @@ def is_interval_dtype(arr_or_dtype) -> bool:
     return IntervalDtype.is_dtype(arr_or_dtype)
 
 
+@set_module("pandas.api.types")
 def is_categorical_dtype(arr_or_dtype) -> bool:
     """
     Check whether an array-like or dtype is of the Categorical dtype.
@@ -598,6 +607,7 @@ def is_string_or_object_np_dtype(dtype: np.dtype) -> bool:
     return dtype == object or dtype.kind in "SU"
 
 
+@set_module("pandas.api.types")
 def is_string_dtype(arr_or_dtype) -> bool:
     """
     Check whether the provided array or dtype is of the string dtype.
@@ -650,6 +660,7 @@ def condition(dtype) -> bool:
     return _is_dtype(arr_or_dtype, condition)
 
 
+@set_module("pandas.api.types")
 def is_dtype_equal(source, target) -> bool:
     """
     Check if two dtypes are equal.
@@ -714,6 +725,7 @@ def is_dtype_equal(source, target) -> bool:
         return False
 
 
+@set_module("pandas.api.types")
 def is_integer_dtype(arr_or_dtype) -> bool:
     """
     Check whether the provided array or dtype is of an integer dtype.
@@ -780,6 +792,7 @@ def is_integer_dtype(arr_or_dtype) -> bool:
     )
 
 
+@set_module("pandas.api.types")
 def is_signed_integer_dtype(arr_or_dtype) -> bool:
     """
     Check whether the provided array or dtype is of a signed integer dtype.
@@ -848,6 +861,7 @@ def is_signed_integer_dtype(arr_or_dtype) -> bool:
     )
 
 
+@set_module("pandas.api.types")
 def is_unsigned_integer_dtype(arr_or_dtype) -> bool:
     """
     Check whether the provided array or dtype is of an unsigned integer dtype.
@@ -907,6 +921,7 @@ def is_unsigned_integer_dtype(arr_or_dtype) -> bool:
     )
 
 
+@set_module("pandas.api.types")
 def is_int64_dtype(arr_or_dtype) -> bool:
     """
     Check whether the provided array or dtype is of the int64 dtype.
@@ -980,6 +995,7 @@ def is_int64_dtype(arr_or_dtype) -> bool:
     return _is_dtype_type(arr_or_dtype, classes(np.int64))
 
 
+@set_module("pandas.api.types")
 def is_datetime64_any_dtype(arr_or_dtype) -> bool:
     """
     Check whether the provided array or dtype is of the datetime64 dtype.
@@ -1042,6 +1058,7 @@ def is_datetime64_any_dtype(arr_or_dtype) -> bool:
     )
 
 
+@set_module("pandas.api.types")
 def is_datetime64_ns_dtype(arr_or_dtype) -> bool:
     """
     Check whether the provided array or dtype is of the datetime64[ns] dtype.
@@ -1097,6 +1114,7 @@ def is_datetime64_ns_dtype(arr_or_dtype) -> bool:
     )
 
 
+@set_module("pandas.api.types")
 def is_timedelta64_ns_dtype(arr_or_dtype) -> bool:
     """
     Check whether the provided array or dtype is of the timedelta64[ns] dtype.
@@ -1224,6 +1242,7 @@ def needs_i8_conversion(dtype: DtypeObj | None) -> bool:
     return isinstance(dtype, (PeriodDtype, DatetimeTZDtype))
 
 
+@set_module("pandas.api.types")
 def is_numeric_dtype(arr_or_dtype) -> bool:
     """
     Check whether the provided array or dtype is of a numeric dtype.
@@ -1278,6 +1297,7 @@ def is_numeric_dtype(arr_or_dtype) -> bool:
     )
 
 
+@set_module("pandas.api.types")
 def is_any_real_numeric_dtype(arr_or_dtype) -> bool:
     """
     Check whether the provided array or dtype is of a real number dtype.
@@ -1321,6 +1341,7 @@ def is_any_real_numeric_dtype(arr_or_dtype) -> bool:
     )
 
 
+@set_module("pandas.api.types")
 def is_float_dtype(arr_or_dtype) -> bool:
     """
     Check whether the provided array or dtype is of a float dtype.
@@ -1368,6 +1389,7 @@ def is_float_dtype(arr_or_dtype) -> bool:
     )
 
 
+@set_module("pandas.api.types")
 def is_bool_dtype(arr_or_dtype) -> bool:
     """
     Check whether the provided array or dtype is of a boolean dtype.
@@ -1455,6 +1477,7 @@ def is_1d_only_ea_dtype(dtype: DtypeObj | None) -> bool:
     return isinstance(dtype, ExtensionDtype) and not dtype._supports_2d
 
 
+@set_module("pandas.api.types")
 def is_extension_array_dtype(arr_or_dtype) -> bool:
     """
     Check if an object is a pandas extension array type.
@@ -1532,6 +1555,7 @@ def is_ea_or_datetimelike_dtype(dtype: DtypeObj | None) -> bool:
     return isinstance(dtype, ExtensionDtype) or (lib.is_np_dtype(dtype, "mM"))
 
 
+@set_module("pandas.api.types")
 def is_complex_dtype(arr_or_dtype) -> bool:
     """
     Check whether the provided array or dtype is of a complex dtype.
@@ -1794,6 +1818,7 @@ def validate_all_hashable(*args, error_name: str | None = None) -> None:
         raise TypeError("All elements must be hashable")
 
 
+@set_module("pandas.api.types")
 def pandas_dtype(dtype) -> DtypeObj:
     """
     Convert input into a pandas only dtype object or a numpy dtype object.

pandas/core/dtypes/concat.py

@@ -12,6 +12,7 @@
 import numpy as np
 
 from pandas._libs import lib
+from pandas.util._decorators import set_module
 
 from pandas.core.dtypes.astype import astype_array
 from pandas.core.dtypes.cast import (
@@ -168,6 +169,7 @@ def _get_result_dtype(
     return any_ea, kinds, target_dtype
 
 
+@set_module("pandas.api.types")
 def union_categoricals(
     to_union, sort_categories: bool = False, ignore_order: bool = False
 ) -> Categorical:

pandas/core/dtypes/inference.py

@@ -14,6 +14,7 @@
 import numpy as np
 
 from pandas._libs import lib
+from pandas.util._decorators import set_module
 
 if TYPE_CHECKING:
     from collections.abc import Hashable
@@ -35,6 +36,7 @@
 is_iterator = lib.is_iterator
 
 
+@set_module("pandas.api.types")
 def is_number(obj: object) -> TypeGuard[Number | np.number]:
     """
     Check if the object is a number.
@@ -101,6 +103,7 @@ def iterable_not_string(obj: object) -> bool:
     return isinstance(obj, abc.Iterable) and not isinstance(obj, str)
 
 
+@set_module("pandas.api.types")
 def is_file_like(obj: object) -> bool:
     """
     Check if the object is a file-like object.
@@ -148,6 +151,7 @@ def is_file_like(obj: object) -> bool:
     return bool(hasattr(obj, "__iter__"))
 
 
+@set_module("pandas.api.types")
 def is_re(obj: object) -> TypeGuard[Pattern]:
     """
     Check if the object is a regex pattern instance.
@@ -184,6 +188,7 @@ def is_re(obj: object) -> TypeGuard[Pattern]:
     return isinstance(obj, Pattern)
 
 
+@set_module("pandas.api.types")
 def is_re_compilable(obj: object) -> bool:
     """
     Check if the object can be compiled into a regex pattern instance.
@@ -218,6 +223,7 @@ def is_re_compilable(obj: object) -> bool:
         return True
 
 
+@set_module("pandas.api.types")
 def is_array_like(obj: object) -> bool:
     """
     Check if the object is array-like.
@@ -297,6 +303,7 @@ def is_nested_list_like(obj: object) -> bool:
     )
 
 
+@set_module("pandas.api.types")
 def is_dict_like(obj: object) -> bool:
     """
     Check if the object is dict-like.
@@ -339,6 +346,7 @@ def is_dict_like(obj: object) -> bool:
     )
 
 
+@set_module("pandas.api.types")
 def is_named_tuple(obj: object) -> bool:
     """
     Check if the object is a named tuple.
@@ -376,6 +384,7 @@ def is_named_tuple(obj: object) -> bool:
     return isinstance(obj, abc.Sequence) and hasattr(obj, "_fields")
 
 
+@set_module("pandas.api.types")
 def is_hashable(obj: object) -> TypeGuard[Hashable]:
     """
     Return True if hash(obj) will succeed, False otherwise.

pandas/core/indexers/utils.py

@@ -12,6 +12,7 @@
 import numpy as np
 
 from pandas._libs import lib
+from pandas.util._decorators import set_module
 
 from pandas.core.dtypes.common import (
     is_array_like,
@@ -417,6 +418,7 @@ def unpack_tuple_and_ellipses(item: tuple):
 # Public indexer validation
 
 
+@set_module("pandas.api.indexers")
 def check_array_indexer(array: AnyArrayLike, indexer: Any) -> Any:
     """
     Check if `indexer` is a valid array indexer for `array`.

pandas/core/interchange/from_dataframe.py

@@ -12,6 +12,7 @@
 from pandas._config import using_string_dtype
 
 from pandas.compat._optional import import_optional_dependency
+from pandas.util._decorators import set_module
 
 import pandas as pd
 from pandas.core.interchange.dataframe_protocol import (
@@ -34,6 +35,7 @@
 }
 
 
+@set_module("pandas.api.interchange")
 def from_dataframe(df, allow_copy: bool = True) -> pd.DataFrame:
     """
     Build a ``pd.DataFrame`` from any DataFrame supporting the interchange protocol.