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cast.py
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cast.py
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"""
Routines for casting.
"""
from __future__ import annotations
import datetime as dt
import functools
from typing import (
TYPE_CHECKING,
Any,
Literal,
TypeVar,
cast,
overload,
)
import warnings
import numpy as np
from pandas._config import using_pyarrow_string_dtype
from pandas._libs import (
Interval,
Period,
lib,
)
from pandas._libs.missing import (
NA,
NAType,
checknull,
)
from pandas._libs.tslibs import (
NaT,
OutOfBoundsDatetime,
OutOfBoundsTimedelta,
Timedelta,
Timestamp,
is_supported_dtype,
)
from pandas._libs.tslibs.timedeltas import array_to_timedelta64
from pandas.compat.numpy import np_version_gt2
from pandas.errors import (
IntCastingNaNError,
LossySetitemError,
)
from pandas.core.dtypes.common import (
ensure_int8,
ensure_int16,
ensure_int32,
ensure_int64,
ensure_object,
ensure_str,
is_bool,
is_complex,
is_float,
is_integer,
is_object_dtype,
is_scalar,
is_string_dtype,
pandas_dtype as pandas_dtype_func,
)
from pandas.core.dtypes.dtypes import (
ArrowDtype,
BaseMaskedDtype,
CategoricalDtype,
DatetimeTZDtype,
ExtensionDtype,
IntervalDtype,
PandasExtensionDtype,
PeriodDtype,
)
from pandas.core.dtypes.generic import (
ABCExtensionArray,
ABCIndex,
ABCSeries,
)
from pandas.core.dtypes.inference import is_list_like
from pandas.core.dtypes.missing import (
is_valid_na_for_dtype,
isna,
na_value_for_dtype,
notna,
)
from pandas.io._util import _arrow_dtype_mapping
if TYPE_CHECKING:
from collections.abc import (
Sequence,
Sized,
)
from pandas._typing import (
ArrayLike,
Dtype,
DtypeObj,
NumpyIndexT,
Scalar,
npt,
)
from pandas import Index
from pandas.core.arrays import (
Categorical,
DatetimeArray,
ExtensionArray,
IntervalArray,
PeriodArray,
TimedeltaArray,
)
_int8_max = np.iinfo(np.int8).max
_int16_max = np.iinfo(np.int16).max
_int32_max = np.iinfo(np.int32).max
_dtype_obj = np.dtype(object)
NumpyArrayT = TypeVar("NumpyArrayT", bound=np.ndarray)
def maybe_convert_platform(
values: list | tuple | range | np.ndarray | ExtensionArray,
) -> ArrayLike:
"""try to do platform conversion, allow ndarray or list here"""
arr: ArrayLike
if isinstance(values, (list, tuple, range)):
arr = construct_1d_object_array_from_listlike(values)
else:
# The caller is responsible for ensuring that we have np.ndarray
# or ExtensionArray here.
arr = values
if arr.dtype == _dtype_obj:
arr = cast(np.ndarray, arr)
arr = lib.maybe_convert_objects(arr)
return arr
def is_nested_object(obj) -> bool:
"""
return a boolean if we have a nested object, e.g. a Series with 1 or
more Series elements
This may not be necessarily be performant.
"""
return bool(
isinstance(obj, ABCSeries)
and is_object_dtype(obj.dtype)
and any(isinstance(v, ABCSeries) for v in obj._values)
)
def maybe_box_datetimelike(value: Scalar, dtype: Dtype | None = None) -> Scalar:
"""
Cast scalar to Timestamp or Timedelta if scalar is datetime-like
and dtype is not object.
Parameters
----------
value : scalar
dtype : Dtype, optional
Returns
-------
scalar
"""
if dtype == _dtype_obj:
pass
elif isinstance(value, (np.datetime64, dt.datetime)):
value = Timestamp(value)
elif isinstance(value, (np.timedelta64, dt.timedelta)):
value = Timedelta(value)
return value
def maybe_box_native(value: Scalar | None | NAType) -> Scalar | None | NAType:
"""
If passed a scalar cast the scalar to a python native type.
Parameters
----------
value : scalar or Series
Returns
-------
scalar or Series
"""
if is_float(value):
value = float(value)
elif is_integer(value):
value = int(value)
elif is_bool(value):
value = bool(value)
elif isinstance(value, (np.datetime64, np.timedelta64)):
value = maybe_box_datetimelike(value)
elif value is NA:
value = None
return value
def _maybe_unbox_datetimelike(value: Scalar, dtype: DtypeObj) -> Scalar:
"""
Convert a Timedelta or Timestamp to timedelta64 or datetime64 for setting
into a numpy array. Failing to unbox would risk dropping nanoseconds.
Notes
-----
Caller is responsible for checking dtype.kind in "mM"
"""
if is_valid_na_for_dtype(value, dtype):
# GH#36541: can't fill array directly with pd.NaT
# > np.empty(10, dtype="datetime64[ns]").fill(pd.NaT)
# ValueError: cannot convert float NaN to integer
value = dtype.type("NaT", "ns")
elif isinstance(value, Timestamp):
if value.tz is None:
value = value.to_datetime64()
elif not isinstance(dtype, DatetimeTZDtype):
raise TypeError("Cannot unbox tzaware Timestamp to tznaive dtype")
elif isinstance(value, Timedelta):
value = value.to_timedelta64()
_disallow_mismatched_datetimelike(value, dtype)
return value
def _disallow_mismatched_datetimelike(value, dtype: DtypeObj) -> None:
"""
numpy allows np.array(dt64values, dtype="timedelta64[ns]") and
vice-versa, but we do not want to allow this, so we need to
check explicitly
"""
vdtype = getattr(value, "dtype", None)
if vdtype is None:
return
elif (vdtype.kind == "m" and dtype.kind == "M") or (
vdtype.kind == "M" and dtype.kind == "m"
):
raise TypeError(f"Cannot cast {value!r} to {dtype}")
@overload
def maybe_downcast_to_dtype(result: np.ndarray, dtype: str | np.dtype) -> np.ndarray:
...
@overload
def maybe_downcast_to_dtype(result: ExtensionArray, dtype: str | np.dtype) -> ArrayLike:
...
def maybe_downcast_to_dtype(result: ArrayLike, dtype: str | np.dtype) -> ArrayLike:
"""
try to cast to the specified dtype (e.g. convert back to bool/int
or could be an astype of float64->float32
"""
if isinstance(result, ABCSeries):
result = result._values
do_round = False
if isinstance(dtype, str):
if dtype == "infer":
inferred_type = lib.infer_dtype(result, skipna=False)
if inferred_type == "boolean":
dtype = "bool"
elif inferred_type == "integer":
dtype = "int64"
elif inferred_type == "datetime64":
dtype = "datetime64[ns]"
elif inferred_type in ["timedelta", "timedelta64"]:
dtype = "timedelta64[ns]"
# try to upcast here
elif inferred_type == "floating":
dtype = "int64"
if issubclass(result.dtype.type, np.number):
do_round = True
else:
# TODO: complex? what if result is already non-object?
dtype = "object"
dtype = np.dtype(dtype)
if not isinstance(dtype, np.dtype):
# enforce our signature annotation
raise TypeError(dtype) # pragma: no cover
converted = maybe_downcast_numeric(result, dtype, do_round)
if converted is not result:
return converted
# a datetimelike
# GH12821, iNaT is cast to float
if dtype.kind in "mM" and result.dtype.kind in "if":
result = result.astype(dtype)
elif dtype.kind == "m" and result.dtype == _dtype_obj:
# test_where_downcast_to_td64
result = cast(np.ndarray, result)
result = array_to_timedelta64(result)
elif dtype == np.dtype("M8[ns]") and result.dtype == _dtype_obj:
result = cast(np.ndarray, result)
return np.asarray(maybe_cast_to_datetime(result, dtype=dtype))
return result
@overload
def maybe_downcast_numeric(
result: np.ndarray, dtype: np.dtype, do_round: bool = False
) -> np.ndarray:
...
@overload
def maybe_downcast_numeric(
result: ExtensionArray, dtype: DtypeObj, do_round: bool = False
) -> ArrayLike:
...
def maybe_downcast_numeric(
result: ArrayLike, dtype: DtypeObj, do_round: bool = False
) -> ArrayLike:
"""
Subset of maybe_downcast_to_dtype restricted to numeric dtypes.
Parameters
----------
result : ndarray or ExtensionArray
dtype : np.dtype or ExtensionDtype
do_round : bool
Returns
-------
ndarray or ExtensionArray
"""
if not isinstance(dtype, np.dtype) or not isinstance(result.dtype, np.dtype):
# e.g. SparseDtype has no itemsize attr
return result
def trans(x):
if do_round:
return x.round()
return x
if dtype.kind == result.dtype.kind:
# don't allow upcasts here (except if empty)
if result.dtype.itemsize <= dtype.itemsize and result.size:
return result
if dtype.kind in "biu":
if not result.size:
# if we don't have any elements, just astype it
return trans(result).astype(dtype)
if isinstance(result, np.ndarray):
element = result.item(0)
else:
element = result.iloc[0]
if not isinstance(element, (np.integer, np.floating, int, float, bool)):
# a comparable, e.g. a Decimal may slip in here
return result
if (
issubclass(result.dtype.type, (np.object_, np.number))
and notna(result).all()
):
new_result = trans(result).astype(dtype)
if new_result.dtype.kind == "O" or result.dtype.kind == "O":
# np.allclose may raise TypeError on object-dtype
if (new_result == result).all():
return new_result
else:
if np.allclose(new_result, result, rtol=0):
return new_result
elif (
issubclass(dtype.type, np.floating)
and result.dtype.kind != "b"
and not is_string_dtype(result.dtype)
):
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", "overflow encountered in cast", RuntimeWarning
)
new_result = result.astype(dtype)
# Adjust tolerances based on floating point size
size_tols = {4: 5e-4, 8: 5e-8, 16: 5e-16}
atol = size_tols.get(new_result.dtype.itemsize, 0.0)
# Check downcast float values are still equal within 7 digits when
# converting from float64 to float32
if np.allclose(new_result, result, equal_nan=True, rtol=0.0, atol=atol):
return new_result
elif dtype.kind == result.dtype.kind == "c":
new_result = result.astype(dtype)
if np.array_equal(new_result, result, equal_nan=True):
# TODO: use tolerance like we do for float?
return new_result
return result
def maybe_upcast_numeric_to_64bit(arr: NumpyIndexT) -> NumpyIndexT:
"""
If array is a int/uint/float bit size lower than 64 bit, upcast it to 64 bit.
Parameters
----------
arr : ndarray or ExtensionArray
Returns
-------
ndarray or ExtensionArray
"""
dtype = arr.dtype
if dtype.kind == "i" and dtype != np.int64:
return arr.astype(np.int64)
elif dtype.kind == "u" and dtype != np.uint64:
return arr.astype(np.uint64)
elif dtype.kind == "f" and dtype != np.float64:
return arr.astype(np.float64)
else:
return arr
def maybe_cast_pointwise_result(
result: ArrayLike,
dtype: DtypeObj,
numeric_only: bool = False,
same_dtype: bool = True,
) -> ArrayLike:
"""
Try casting result of a pointwise operation back to the original dtype if
appropriate.
Parameters
----------
result : array-like
Result to cast.
dtype : np.dtype or ExtensionDtype
Input Series from which result was calculated.
numeric_only : bool, default False
Whether to cast only numerics or datetimes as well.
same_dtype : bool, default True
Specify dtype when calling _from_sequence
Returns
-------
result : array-like
result maybe casted to the dtype.
"""
if isinstance(dtype, ExtensionDtype):
cls = dtype.construct_array_type()
if same_dtype:
result = _maybe_cast_to_extension_array(cls, result, dtype=dtype)
else:
result = _maybe_cast_to_extension_array(cls, result)
elif (numeric_only and dtype.kind in "iufcb") or not numeric_only:
result = maybe_downcast_to_dtype(result, dtype)
return result
def _maybe_cast_to_extension_array(
cls: type[ExtensionArray], obj: ArrayLike, dtype: ExtensionDtype | None = None
) -> ArrayLike:
"""
Call to `_from_sequence` that returns the object unchanged on Exception.
Parameters
----------
cls : class, subclass of ExtensionArray
obj : arraylike
Values to pass to cls._from_sequence
dtype : ExtensionDtype, optional
Returns
-------
ExtensionArray or obj
"""
result: ArrayLike
if dtype is not None:
try:
result = cls._from_scalars(obj, dtype=dtype)
except (TypeError, ValueError):
return obj
return result
try:
result = cls._from_sequence(obj, dtype=dtype)
except Exception:
# We can't predict what downstream EA constructors may raise
result = obj
return result
@overload
def ensure_dtype_can_hold_na(dtype: np.dtype) -> np.dtype:
...
@overload
def ensure_dtype_can_hold_na(dtype: ExtensionDtype) -> ExtensionDtype:
...
def ensure_dtype_can_hold_na(dtype: DtypeObj) -> DtypeObj:
"""
If we have a dtype that cannot hold NA values, find the best match that can.
"""
if isinstance(dtype, ExtensionDtype):
if dtype._can_hold_na:
return dtype
elif isinstance(dtype, IntervalDtype):
# TODO(GH#45349): don't special-case IntervalDtype, allow
# overriding instead of returning object below.
return IntervalDtype(np.float64, closed=dtype.closed)
return _dtype_obj
elif dtype.kind == "b":
return _dtype_obj
elif dtype.kind in "iu":
return np.dtype(np.float64)
return dtype
_canonical_nans = {
np.datetime64: np.datetime64("NaT", "ns"),
np.timedelta64: np.timedelta64("NaT", "ns"),
type(np.nan): np.nan,
}
def maybe_promote(dtype: np.dtype, fill_value=np.nan):
"""
Find the minimal dtype that can hold both the given dtype and fill_value.
Parameters
----------
dtype : np.dtype
fill_value : scalar, default np.nan
Returns
-------
dtype
Upcasted from dtype argument if necessary.
fill_value
Upcasted from fill_value argument if necessary.
Raises
------
ValueError
If fill_value is a non-scalar and dtype is not object.
"""
orig = fill_value
orig_is_nat = False
if checknull(fill_value):
# https://github.com/pandas-dev/pandas/pull/39692#issuecomment-1441051740
# avoid cache misses with NaN/NaT values that are not singletons
if fill_value is not NA:
try:
orig_is_nat = np.isnat(fill_value)
except TypeError:
pass
fill_value = _canonical_nans.get(type(fill_value), fill_value)
# for performance, we are using a cached version of the actual implementation
# of the function in _maybe_promote. However, this doesn't always work (in case
# of non-hashable arguments), so we fallback to the actual implementation if needed
try:
# error: Argument 3 to "__call__" of "_lru_cache_wrapper" has incompatible type
# "Type[Any]"; expected "Hashable" [arg-type]
dtype, fill_value = _maybe_promote_cached(
dtype,
fill_value,
type(fill_value), # type: ignore[arg-type]
)
except TypeError:
# if fill_value is not hashable (required for caching)
dtype, fill_value = _maybe_promote(dtype, fill_value)
if (dtype == _dtype_obj and orig is not None) or (
orig_is_nat and np.datetime_data(orig)[0] != "ns"
):
# GH#51592,53497 restore our potentially non-canonical fill_value
fill_value = orig
return dtype, fill_value
@functools.lru_cache
def _maybe_promote_cached(dtype, fill_value, fill_value_type):
# The cached version of _maybe_promote below
# This also use fill_value_type as (unused) argument to use this in the
# cache lookup -> to differentiate 1 and True
return _maybe_promote(dtype, fill_value)
def _maybe_promote(dtype: np.dtype, fill_value=np.nan):
# The actual implementation of the function, use `maybe_promote` above for
# a cached version.
if not is_scalar(fill_value):
# with object dtype there is nothing to promote, and the user can
# pass pretty much any weird fill_value they like
if dtype != object:
# with object dtype there is nothing to promote, and the user can
# pass pretty much any weird fill_value they like
raise ValueError("fill_value must be a scalar")
dtype = _dtype_obj
return dtype, fill_value
if is_valid_na_for_dtype(fill_value, dtype) and dtype.kind in "iufcmM":
dtype = ensure_dtype_can_hold_na(dtype)
fv = na_value_for_dtype(dtype)
return dtype, fv
elif isinstance(dtype, CategoricalDtype):
if fill_value in dtype.categories or isna(fill_value):
return dtype, fill_value
else:
return object, ensure_object(fill_value)
elif isna(fill_value):
dtype = _dtype_obj
if fill_value is None:
# but we retain e.g. pd.NA
fill_value = np.nan
return dtype, fill_value
# returns tuple of (dtype, fill_value)
if issubclass(dtype.type, np.datetime64):
inferred, fv = infer_dtype_from_scalar(fill_value)
if inferred == dtype:
return dtype, fv
from pandas.core.arrays import DatetimeArray
dta = DatetimeArray._from_sequence([], dtype="M8[ns]")
try:
fv = dta._validate_setitem_value(fill_value)
return dta.dtype, fv
except (ValueError, TypeError):
return _dtype_obj, fill_value
elif issubclass(dtype.type, np.timedelta64):
inferred, fv = infer_dtype_from_scalar(fill_value)
if inferred == dtype:
return dtype, fv
elif inferred.kind == "m":
# different unit, e.g. passed np.timedelta64(24, "h") with dtype=m8[ns]
# see if we can losslessly cast it to our dtype
unit = np.datetime_data(dtype)[0]
try:
td = Timedelta(fill_value).as_unit(unit, round_ok=False)
except OutOfBoundsTimedelta:
return _dtype_obj, fill_value
else:
return dtype, td.asm8
return _dtype_obj, fill_value
elif is_float(fill_value):
if issubclass(dtype.type, np.bool_):
dtype = np.dtype(np.object_)
elif issubclass(dtype.type, np.integer):
dtype = np.dtype(np.float64)
elif dtype.kind == "f":
mst = np.min_scalar_type(fill_value)
if mst > dtype:
# e.g. mst is np.float64 and dtype is np.float32
dtype = mst
elif dtype.kind == "c":
mst = np.min_scalar_type(fill_value)
dtype = np.promote_types(dtype, mst)
elif is_bool(fill_value):
if not issubclass(dtype.type, np.bool_):
dtype = np.dtype(np.object_)
elif is_integer(fill_value):
if issubclass(dtype.type, np.bool_):
dtype = np.dtype(np.object_)
elif issubclass(dtype.type, np.integer):
if not np_can_cast_scalar(fill_value, dtype): # type: ignore[arg-type]
# upcast to prevent overflow
mst = np.min_scalar_type(fill_value)
dtype = np.promote_types(dtype, mst)
if dtype.kind == "f":
# Case where we disagree with numpy
dtype = np.dtype(np.object_)
elif is_complex(fill_value):
if issubclass(dtype.type, np.bool_):
dtype = np.dtype(np.object_)
elif issubclass(dtype.type, (np.integer, np.floating)):
mst = np.min_scalar_type(fill_value)
dtype = np.promote_types(dtype, mst)
elif dtype.kind == "c":
mst = np.min_scalar_type(fill_value)
if mst > dtype:
# e.g. mst is np.complex128 and dtype is np.complex64
dtype = mst
else:
dtype = np.dtype(np.object_)
# in case we have a string that looked like a number
if issubclass(dtype.type, (bytes, str)):
dtype = np.dtype(np.object_)
fill_value = _ensure_dtype_type(fill_value, dtype)
return dtype, fill_value
def _ensure_dtype_type(value, dtype: np.dtype):
"""
Ensure that the given value is an instance of the given dtype.
e.g. if out dtype is np.complex64_, we should have an instance of that
as opposed to a python complex object.
Parameters
----------
value : object
dtype : np.dtype
Returns
-------
object
"""
# Start with exceptions in which we do _not_ cast to numpy types
if dtype == _dtype_obj:
return value
# Note: before we get here we have already excluded isna(value)
return dtype.type(value)
def infer_dtype_from(val) -> tuple[DtypeObj, Any]:
"""
Interpret the dtype from a scalar or array.
Parameters
----------
val : object
"""
if not is_list_like(val):
return infer_dtype_from_scalar(val)
return infer_dtype_from_array(val)
def infer_dtype_from_scalar(val) -> tuple[DtypeObj, Any]:
"""
Interpret the dtype from a scalar.
Parameters
----------
val : object
"""
dtype: DtypeObj = _dtype_obj
# a 1-element ndarray
if isinstance(val, np.ndarray):
if val.ndim != 0:
msg = "invalid ndarray passed to infer_dtype_from_scalar"
raise ValueError(msg)
dtype = val.dtype
val = lib.item_from_zerodim(val)
elif isinstance(val, str):
# If we create an empty array using a string to infer
# the dtype, NumPy will only allocate one character per entry
# so this is kind of bad. Alternately we could use np.repeat
# instead of np.empty (but then you still don't want things
# coming out as np.str_!
dtype = _dtype_obj
if using_pyarrow_string_dtype():
from pandas.core.arrays.string_ import StringDtype
dtype = StringDtype(storage="pyarrow_numpy")
elif isinstance(val, (np.datetime64, dt.datetime)):
try:
val = Timestamp(val)
except OutOfBoundsDatetime:
return _dtype_obj, val
if val is NaT or val.tz is None:
val = val.to_datetime64()
dtype = val.dtype
# TODO: test with datetime(2920, 10, 1) based on test_replace_dtypes
else:
dtype = DatetimeTZDtype(unit=val.unit, tz=val.tz)
elif isinstance(val, (np.timedelta64, dt.timedelta)):
try:
val = Timedelta(val)
except (OutOfBoundsTimedelta, OverflowError):
dtype = _dtype_obj
else:
if val is NaT:
val = np.timedelta64("NaT", "ns")
else:
val = val.asm8
dtype = val.dtype
elif is_bool(val):
dtype = np.dtype(np.bool_)
elif is_integer(val):
if isinstance(val, np.integer):
dtype = np.dtype(type(val))
else:
dtype = np.dtype(np.int64)
try:
np.array(val, dtype=dtype)
except OverflowError:
dtype = np.array(val).dtype
elif is_float(val):
if isinstance(val, np.floating):
dtype = np.dtype(type(val))
else:
dtype = np.dtype(np.float64)
elif is_complex(val):
dtype = np.dtype(np.complex128)
if isinstance(val, Period):
dtype = PeriodDtype(freq=val.freq)
elif isinstance(val, Interval):
subtype = infer_dtype_from_scalar(val.left)[0]
dtype = IntervalDtype(subtype=subtype, closed=val.closed)
return dtype, val
def dict_compat(d: dict[Scalar, Scalar]) -> dict[Scalar, Scalar]:
"""
Convert datetimelike-keyed dicts to a Timestamp-keyed dict.
Parameters
----------
d: dict-like object
Returns
-------
dict
"""
return {maybe_box_datetimelike(key): value for key, value in d.items()}
def infer_dtype_from_array(arr) -> tuple[DtypeObj, ArrayLike]:
"""
Infer the dtype from an array.
Parameters
----------
arr : array
Returns
-------
tuple (pandas-compat dtype, array)
Examples
--------
>>> np.asarray([1, '1'])
array(['1', '1'], dtype='<U21')
>>> infer_dtype_from_array([1, '1'])
(dtype('O'), [1, '1'])
"""
if isinstance(arr, np.ndarray):
return arr.dtype, arr
if not is_list_like(arr):
raise TypeError("'arr' must be list-like")
arr_dtype = getattr(arr, "dtype", None)
if isinstance(arr_dtype, ExtensionDtype):
return arr.dtype, arr
elif isinstance(arr, ABCSeries):
return arr.dtype, np.asarray(arr)
# don't force numpy coerce with nan's
inferred = lib.infer_dtype(arr, skipna=False)
if inferred in ["string", "bytes", "mixed", "mixed-integer"]:
return (np.dtype(np.object_), arr)
arr = np.asarray(arr)
return arr.dtype, arr
def _maybe_infer_dtype_type(element):
"""
Try to infer an object's dtype, for use in arithmetic ops.
Uses `element.dtype` if that's available.
Objects implementing the iterator protocol are cast to a NumPy array,
and from there the array's type is used.
Parameters
----------
element : object
Possibly has a `.dtype` attribute, and possibly the iterator
protocol.
Returns
-------
tipo : type
Examples
--------
>>> from collections import namedtuple
>>> Foo = namedtuple("Foo", "dtype")
>>> _maybe_infer_dtype_type(Foo(np.dtype("i8")))
dtype('int64')
"""
tipo = None
if hasattr(element, "dtype"):
tipo = element.dtype
elif is_list_like(element):
element = np.asarray(element)
tipo = element.dtype
return tipo
def invalidate_string_dtypes(dtype_set: set[DtypeObj]) -> None:
"""
Change string like dtypes to object for
``DataFrame.select_dtypes()``.
"""
# error: Argument 1 to <set> has incompatible type "Type[generic]"; expected
# "Union[dtype[Any], ExtensionDtype, None]"
# error: Argument 2 to <set> has incompatible type "Type[generic]"; expected
# "Union[dtype[Any], ExtensionDtype, None]"
non_string_dtypes = dtype_set - {
np.dtype("S").type, # type: ignore[arg-type]
np.dtype("<U").type, # type: ignore[arg-type]
}
if non_string_dtypes != dtype_set:
raise TypeError("string dtypes are not allowed, use 'object' instead")
def coerce_indexer_dtype(indexer, categories) -> np.ndarray:
"""coerce the indexer input array to the smallest dtype possible"""
length = len(categories)
if length < _int8_max:
return ensure_int8(indexer)
elif length < _int16_max:
return ensure_int16(indexer)
elif length < _int32_max:
return ensure_int32(indexer)
return ensure_int64(indexer)
def convert_dtypes(
input_array: ArrayLike,
convert_string: bool = True,
convert_integer: bool = True,
convert_boolean: bool = True,
convert_floating: bool = True,
infer_objects: bool = False,
dtype_backend: Literal["numpy_nullable", "pyarrow"] = "numpy_nullable",
) -> DtypeObj:
"""
Convert objects to best possible type, and optionally,
to types supporting ``pd.NA``.
Parameters