common.py

"""
Common type operations.
"""

from typing import Any, Callable, Union
import warnings

import numpy as np

from pandas._libs import Interval, Period, algos
from pandas._libs.tslibs import conversion
from pandas._typing import ArrayLike, DtypeObj

from pandas.core.dtypes.base import registry
from pandas.core.dtypes.dtypes import (
    CategoricalDtype,
    DatetimeTZDtype,
    ExtensionDtype,
    IntervalDtype,
    PeriodDtype,
)
from pandas.core.dtypes.generic import ABCCategorical, ABCIndexClass
from pandas.core.dtypes.inference import (  # noqa:F401
    is_array_like,
    is_bool,
    is_complex,
    is_dataclass,
    is_decimal,
    is_dict_like,
    is_file_like,
    is_float,
    is_hashable,
    is_integer,
    is_interval,
    is_iterator,
    is_list_like,
    is_named_tuple,
    is_nested_list_like,
    is_number,
    is_re,
    is_re_compilable,
    is_scalar,
    is_sequence,
)

_POSSIBLY_CAST_DTYPES = {
    np.dtype(t).name
    for t in [
        "O",
        "int8",
        "uint8",
        "int16",
        "uint16",
        "int32",
        "uint32",
        "int64",
        "uint64",
    ]
}

DT64NS_DTYPE = conversion.DT64NS_DTYPE
TD64NS_DTYPE = conversion.TD64NS_DTYPE
INT64_DTYPE = np.dtype(np.int64)

# oh the troubles to reduce import time
_is_scipy_sparse = None

ensure_float64 = algos.ensure_float64
ensure_float32 = algos.ensure_float32


def ensure_float(arr):
    """
    Ensure that an array object has a float dtype if possible.

    Parameters
    ----------
    arr : array-like
        The array whose data type we want to enforce as float.

    Returns
    -------
    float_arr : The original array cast to the float dtype if
                possible. Otherwise, the original array is returned.
    """
    if issubclass(arr.dtype.type, (np.integer, np.bool_)):
        arr = arr.astype(float)
    return arr


ensure_uint64 = algos.ensure_uint64
ensure_int64 = algos.ensure_int64
ensure_int32 = algos.ensure_int32
ensure_int16 = algos.ensure_int16
ensure_int8 = algos.ensure_int8
ensure_platform_int = algos.ensure_platform_int
ensure_object = algos.ensure_object


def ensure_str(value: Union[bytes, Any]) -> str:
    """
    Ensure that bytes and non-strings get converted into ``str`` objects.
    """
    if isinstance(value, bytes):
        value = value.decode("utf-8")
    elif not isinstance(value, str):
        value = str(value)
    return value


def ensure_int_or_float(arr: ArrayLike, copy: bool = False) -> np.array:
    """
    Ensure that an dtype array of some integer dtype
    has an int64 dtype if possible.
    If it's not possible, potentially because of overflow,
    convert the array to float64 instead.

    Parameters
    ----------
    arr : array-like
          The array whose data type we want to enforce.
    copy: bool
          Whether to copy the original array or reuse
          it in place, if possible.

    Returns
    -------
    out_arr : The input array cast as int64 if
              possible without overflow.
              Otherwise the input array cast to float64.

    Notes
    -----
    If the array is explicitly of type uint64 the type
    will remain unchanged.
    """
    # TODO: GH27506 potential bug with ExtensionArrays
    try:
        return arr.astype("int64", copy=copy, casting="safe")  # type: ignore
    except TypeError:
        pass
    try:
        return arr.astype("uint64", copy=copy, casting="safe")  # type: ignore
    except TypeError:
        if is_extension_array_dtype(arr.dtype):
            return arr.to_numpy(dtype="float64", na_value=np.nan)
        return arr.astype("float64", copy=copy)


def ensure_python_int(value: Union[int, np.integer]) -> int:
    """
    Ensure that a value is a python int.

    Parameters
    ----------
    value: int or numpy.integer

    Returns
    -------
    int

    Raises
    ------
    TypeError: if the value isn't an int or can't be converted to one.
    """
    if not is_scalar(value):
        raise TypeError(
            f"Value needs to be a scalar value, was type {type(value).__name__}"
        )
    try:
        new_value = int(value)
        assert new_value == value
    except (TypeError, ValueError, AssertionError) as err:
        raise TypeError(f"Wrong type {type(value)} for value {value}") from err
    return new_value


def classes(*klasses) -> Callable:
    """ evaluate if the tipo is a subclass of the klasses """
    return lambda tipo: issubclass(tipo, klasses)


def classes_and_not_datetimelike(*klasses) -> Callable:
    """
    evaluate if the tipo is a subclass of the klasses
    and not a datetimelike
    """
    return lambda tipo: (
        issubclass(tipo, klasses)
        and not issubclass(tipo, (np.datetime64, np.timedelta64))
    )


def is_object_dtype(arr_or_dtype) -> bool:
    """
    Check whether an array-like or dtype is of the object dtype.

    Parameters
    ----------
    arr_or_dtype : array-like
        The array-like or dtype to check.

    Returns
    -------
    boolean
        Whether or not the array-like or dtype is of the object dtype.

    Examples
    --------
    >>> is_object_dtype(object)
    True
    >>> is_object_dtype(int)
    False
    >>> is_object_dtype(np.array([], dtype=object))
    True
    >>> is_object_dtype(np.array([], dtype=int))
    False
    >>> is_object_dtype([1, 2, 3])
    False
    """
    return _is_dtype_type(arr_or_dtype, classes(np.object_))


def is_sparse(arr) -> bool:
    """
    Check whether an array-like is a 1-D pandas sparse array.

    Check that the one-dimensional array-like is a pandas sparse array.
    Returns True if it is a pandas sparse array, not another type of
    sparse array.

    Parameters
    ----------
    arr : array-like
        Array-like to check.

    Returns
    -------
    bool
        Whether or not the array-like is a pandas sparse array.

    Examples
    --------
    Returns `True` if the parameter is a 1-D pandas sparse array.

    >>> is_sparse(pd.arrays.SparseArray([0, 0, 1, 0]))
    True
    >>> is_sparse(pd.Series(pd.arrays.SparseArray([0, 0, 1, 0])))
    True

    Returns `False` if the parameter is not sparse.

    >>> is_sparse(np.array([0, 0, 1, 0]))
    False
    >>> is_sparse(pd.Series([0, 1, 0, 0]))
    False

    Returns `False` if the parameter is not a pandas sparse array.

    >>> from scipy.sparse import bsr_matrix
    >>> is_sparse(bsr_matrix([0, 1, 0, 0]))
    False

    Returns `False` if the parameter has more than one dimension.
    """
    from pandas.core.arrays.sparse import SparseDtype

    dtype = getattr(arr, "dtype", arr)
    return isinstance(dtype, SparseDtype)


def is_scipy_sparse(arr) -> bool:
    """
    Check whether an array-like is a scipy.sparse.spmatrix instance.

    Parameters
    ----------
    arr : array-like
        The array-like to check.

    Returns
    -------
    boolean
        Whether or not the array-like is a scipy.sparse.spmatrix instance.

    Notes
    -----
    If scipy is not installed, this function will always return False.

    Examples
    --------
    >>> from scipy.sparse import bsr_matrix
    >>> is_scipy_sparse(bsr_matrix([1, 2, 3]))
    True
    >>> is_scipy_sparse(pd.arrays.SparseArray([1, 2, 3]))
    False
    """
    global _is_scipy_sparse

    if _is_scipy_sparse is None:
        try:
            from scipy.sparse import issparse as _is_scipy_sparse
        except ImportError:
            _is_scipy_sparse = lambda _: False

    assert _is_scipy_sparse is not None
    return _is_scipy_sparse(arr)


def is_categorical(arr) -> bool:
    """
    Check whether an array-like is a Categorical instance.

    Parameters
    ----------
    arr : array-like
        The array-like to check.

    Returns
    -------
    boolean
        Whether or not the array-like is of a Categorical instance.

    Examples
    --------
    >>> is_categorical([1, 2, 3])
    False

    Categoricals, Series Categoricals, and CategoricalIndex will return True.

    >>> cat = pd.Categorical([1, 2, 3])
    >>> is_categorical(cat)
    True
    >>> is_categorical(pd.Series(cat))
    True
    >>> is_categorical(pd.CategoricalIndex([1, 2, 3]))
    True
    """
    warnings.warn(
        "is_categorical is deprecated and will be removed in a future version.  "
        "Use is_categorical_dtype instead",
        FutureWarning,
        stacklevel=2,
    )
    return isinstance(arr, ABCCategorical) or is_categorical_dtype(arr)


def is_datetime64_dtype(arr_or_dtype) -> bool:
    """
    Check whether an array-like or dtype is of the datetime64 dtype.

    Parameters
    ----------
    arr_or_dtype : array-like
        The array-like or dtype to check.

    Returns
    -------
    boolean
        Whether or not the array-like or dtype is of the datetime64 dtype.

    Examples
    --------
    >>> is_datetime64_dtype(object)
    False
    >>> is_datetime64_dtype(np.datetime64)
    True
    >>> is_datetime64_dtype(np.array([], dtype=int))
    False
    >>> is_datetime64_dtype(np.array([], dtype=np.datetime64))
    True
    >>> is_datetime64_dtype([1, 2, 3])
    False
    """
    if isinstance(arr_or_dtype, np.dtype):
        # GH#33400 fastpath for dtype object
        return arr_or_dtype.kind == "M"
    return _is_dtype_type(arr_or_dtype, classes(np.datetime64))


def is_datetime64tz_dtype(arr_or_dtype) -> bool:
    """
    Check whether an array-like or dtype is of a DatetimeTZDtype dtype.

    Parameters
    ----------
    arr_or_dtype : array-like
        The array-like or dtype to check.

    Returns
    -------
    boolean
        Whether or not the array-like or dtype is of a DatetimeTZDtype dtype.

    Examples
    --------
    >>> is_datetime64tz_dtype(object)
    False
    >>> is_datetime64tz_dtype([1, 2, 3])
    False
    >>> is_datetime64tz_dtype(pd.DatetimeIndex([1, 2, 3]))  # tz-naive
    False
    >>> is_datetime64tz_dtype(pd.DatetimeIndex([1, 2, 3], tz="US/Eastern"))
    True

    >>> dtype = DatetimeTZDtype("ns", tz="US/Eastern")
    >>> s = pd.Series([], dtype=dtype)
    >>> is_datetime64tz_dtype(dtype)
    True
    >>> is_datetime64tz_dtype(s)
    True
    """
    if isinstance(arr_or_dtype, ExtensionDtype):
        # GH#33400 fastpath for dtype object
        return arr_or_dtype.kind == "M"

    if arr_or_dtype is None:
        return False
    return DatetimeTZDtype.is_dtype(arr_or_dtype)


def is_timedelta64_dtype(arr_or_dtype) -> bool:
    """
    Check whether an array-like or dtype is of the timedelta64 dtype.

    Parameters
    ----------
    arr_or_dtype : array-like
        The array-like or dtype to check.

    Returns
    -------
    boolean
        Whether or not the array-like or dtype is of the timedelta64 dtype.

    Examples
    --------
    >>> is_timedelta64_dtype(object)
    False
    >>> is_timedelta64_dtype(np.timedelta64)
    True
    >>> is_timedelta64_dtype([1, 2, 3])
    False
    >>> is_timedelta64_dtype(pd.Series([], dtype="timedelta64[ns]"))
    True
    >>> is_timedelta64_dtype('0 days')
    False
    """
    if isinstance(arr_or_dtype, np.dtype):
        # GH#33400 fastpath for dtype object
        return arr_or_dtype.kind == "m"

    return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))


def is_period_dtype(arr_or_dtype) -> bool:
    """
    Check whether an array-like or dtype is of the Period dtype.

    Parameters
    ----------
    arr_or_dtype : array-like
        The array-like or dtype to check.

    Returns
    -------
    boolean
        Whether or not the array-like or dtype is of the Period dtype.

    Examples
    --------
    >>> is_period_dtype(object)
    False
    >>> is_period_dtype(PeriodDtype(freq="D"))
    True
    >>> is_period_dtype([1, 2, 3])
    False
    >>> is_period_dtype(pd.Period("2017-01-01"))
    False
    >>> is_period_dtype(pd.PeriodIndex([], freq="A"))
    True
    """
    if isinstance(arr_or_dtype, ExtensionDtype):
        # GH#33400 fastpath for dtype object
        return arr_or_dtype.type is Period

    if arr_or_dtype is None:
        return False
    return PeriodDtype.is_dtype(arr_or_dtype)


def is_interval_dtype(arr_or_dtype) -> bool:
    """
    Check whether an array-like or dtype is of the Interval dtype.

    Parameters
    ----------
    arr_or_dtype : array-like
        The array-like or dtype to check.

    Returns
    -------
    boolean
        Whether or not the array-like or dtype is of the Interval dtype.

    Examples
    --------
    >>> is_interval_dtype(object)
    False
    >>> is_interval_dtype(IntervalDtype())
    True
    >>> is_interval_dtype([1, 2, 3])
    False
    >>>
    >>> interval = pd.Interval(1, 2, closed="right")
    >>> is_interval_dtype(interval)
    False
    >>> is_interval_dtype(pd.IntervalIndex([interval]))
    True
    """
    if isinstance(arr_or_dtype, ExtensionDtype):
        # GH#33400 fastpath for dtype object
        return arr_or_dtype.type is Interval

    if arr_or_dtype is None:
        return False
    return IntervalDtype.is_dtype(arr_or_dtype)


def is_categorical_dtype(arr_or_dtype) -> bool:
    """
    Check whether an array-like or dtype is of the Categorical dtype.

    Parameters
    ----------
    arr_or_dtype : array-like
        The array-like or dtype to check.

    Returns
    -------
    boolean
        Whether or not the array-like or dtype is of the Categorical dtype.

    Examples
    --------
    >>> is_categorical_dtype(object)
    False
    >>> is_categorical_dtype(CategoricalDtype())
    True
    >>> is_categorical_dtype([1, 2, 3])
    False
    >>> is_categorical_dtype(pd.Categorical([1, 2, 3]))
    True
    >>> is_categorical_dtype(pd.CategoricalIndex([1, 2, 3]))
    True
    """
    if isinstance(arr_or_dtype, ExtensionDtype):
        # GH#33400 fastpath for dtype object
        return arr_or_dtype.name == "category"

    if arr_or_dtype is None:
        return False
    return CategoricalDtype.is_dtype(arr_or_dtype)


def is_string_dtype(arr_or_dtype) -> bool:
    """
    Check whether the provided array or dtype is of the string dtype.

    Parameters
    ----------
    arr_or_dtype : array-like
        The array or dtype to check.

    Returns
    -------
    boolean
        Whether or not the array or dtype is of the string dtype.

    Examples
    --------
    >>> is_string_dtype(str)
    True
    >>> is_string_dtype(object)
    True
    >>> is_string_dtype(int)
    False
    >>>
    >>> is_string_dtype(np.array(['a', 'b']))
    True
    >>> is_string_dtype(pd.Series([1, 2]))
    False
    """
    # TODO: gh-15585: consider making the checks stricter.
    def condition(dtype) -> bool:
        return dtype.kind in ("O", "S", "U") and not is_excluded_dtype(dtype)

    def is_excluded_dtype(dtype) -> bool:
        """
        These have kind = "O" but aren't string dtypes so need to be explicitly excluded
        """
        is_excluded_checks = (is_period_dtype, is_interval_dtype, is_categorical_dtype)
        return any(is_excluded(dtype) for is_excluded in is_excluded_checks)

    return _is_dtype(arr_or_dtype, condition)


def is_dtype_equal(source, target) -> bool:
    """
    Check if two dtypes are equal.

    Parameters
    ----------
    source : The first dtype to compare
    target : The second dtype to compare

    Returns
    -------
    boolean
        Whether or not the two dtypes are equal.

    Examples
    --------
    >>> is_dtype_equal(int, float)
    False
    >>> is_dtype_equal("int", int)
    True
    >>> is_dtype_equal(object, "category")
    False
    >>> is_dtype_equal(CategoricalDtype(), "category")
    True
    >>> is_dtype_equal(DatetimeTZDtype(tz="UTC"), "datetime64")
    False
    """
    try:
        source = _get_dtype(source)
        target = _get_dtype(target)
        return source == target
    except (TypeError, AttributeError):

        # invalid comparison
        # object == category will hit this
        return False


def is_any_int_dtype(arr_or_dtype) -> bool:
    """
    Check whether the provided array or dtype is of an integer dtype.

    In this function, timedelta64 instances are also considered "any-integer"
    type objects and will return True.

    This function is internal and should not be exposed in the public API.

    .. versionchanged:: 0.24.0

       The nullable Integer dtypes (e.g. pandas.Int64Dtype) are also considered
       as integer by this function.

    Parameters
    ----------
    arr_or_dtype : array-like
        The array or dtype to check.

    Returns
    -------
    boolean
        Whether or not the array or dtype is of an integer dtype.

    Examples
    --------
    >>> is_any_int_dtype(str)
    False
    >>> is_any_int_dtype(int)
    True
    >>> is_any_int_dtype(float)
    False
    >>> is_any_int_dtype(np.uint64)
    True
    >>> is_any_int_dtype(np.datetime64)
    False
    >>> is_any_int_dtype(np.timedelta64)
    True
    >>> is_any_int_dtype(np.array(['a', 'b']))
    False
    >>> is_any_int_dtype(pd.Series([1, 2]))
    True
    >>> is_any_int_dtype(np.array([], dtype=np.timedelta64))
    True
    >>> is_any_int_dtype(pd.Index([1, 2.]))  # float
    False
    """
    return _is_dtype_type(arr_or_dtype, classes(np.integer, np.timedelta64))


def is_integer_dtype(arr_or_dtype) -> bool:
    """
    Check whether the provided array or dtype is of an integer dtype.

    Unlike in `in_any_int_dtype`, timedelta64 instances will return False.

    .. versionchanged:: 0.24.0

       The nullable Integer dtypes (e.g. pandas.Int64Dtype) are also considered
       as integer by this function.

    Parameters
    ----------
    arr_or_dtype : array-like
        The array or dtype to check.

    Returns
    -------
    boolean
        Whether or not the array or dtype is of an integer dtype and
        not an instance of timedelta64.

    Examples
    --------
    >>> is_integer_dtype(str)
    False
    >>> is_integer_dtype(int)
    True
    >>> is_integer_dtype(float)
    False
    >>> is_integer_dtype(np.uint64)
    True
    >>> is_integer_dtype('int8')
    True
    >>> is_integer_dtype('Int8')
    True
    >>> is_integer_dtype(pd.Int8Dtype)
    True
    >>> is_integer_dtype(np.datetime64)
    False
    >>> is_integer_dtype(np.timedelta64)
    False
    >>> is_integer_dtype(np.array(['a', 'b']))
    False
    >>> is_integer_dtype(pd.Series([1, 2]))
    True
    >>> is_integer_dtype(np.array([], dtype=np.timedelta64))
    False
    >>> is_integer_dtype(pd.Index([1, 2.]))  # float
    False
    """
    return _is_dtype_type(arr_or_dtype, classes_and_not_datetimelike(np.integer))


def is_signed_integer_dtype(arr_or_dtype) -> bool:
    """
    Check whether the provided array or dtype is of a signed integer dtype.

    Unlike in `in_any_int_dtype`, timedelta64 instances will return False.

    .. versionchanged:: 0.24.0

       The nullable Integer dtypes (e.g. pandas.Int64Dtype) are also considered
       as integer by this function.

    Parameters
    ----------
    arr_or_dtype : array-like
        The array or dtype to check.

    Returns
    -------
    boolean
        Whether or not the array or dtype is of a signed integer dtype
        and not an instance of timedelta64.

    Examples
    --------
    >>> is_signed_integer_dtype(str)
    False
    >>> is_signed_integer_dtype(int)
    True
    >>> is_signed_integer_dtype(float)
    False
    >>> is_signed_integer_dtype(np.uint64)  # unsigned
    False
    >>> is_signed_integer_dtype('int8')
    True
    >>> is_signed_integer_dtype('Int8')
    True
    >>> is_signed_integer_dtype(pd.Int8Dtype)
    True
    >>> is_signed_integer_dtype(np.datetime64)
    False
    >>> is_signed_integer_dtype(np.timedelta64)
    False
    >>> is_signed_integer_dtype(np.array(['a', 'b']))
    False
    >>> is_signed_integer_dtype(pd.Series([1, 2]))
    True
    >>> is_signed_integer_dtype(np.array([], dtype=np.timedelta64))
    False
    >>> is_signed_integer_dtype(pd.Index([1, 2.]))  # float
    False
    >>> is_signed_integer_dtype(np.array([1, 2], dtype=np.uint32))  # unsigned
    False
    """
    return _is_dtype_type(arr_or_dtype, classes_and_not_datetimelike(np.signedinteger))


def is_unsigned_integer_dtype(arr_or_dtype) -> bool:
    """
    Check whether the provided array or dtype is of an unsigned integer dtype.

    .. versionchanged:: 0.24.0

       The nullable Integer dtypes (e.g. pandas.UInt64Dtype) are also
       considered as integer by this function.

    Parameters
    ----------
    arr_or_dtype : array-like
        The array or dtype to check.

    Returns
    -------
    boolean
        Whether or not the array or dtype is of an unsigned integer dtype.

    Examples
    --------
    >>> is_unsigned_integer_dtype(str)
    False
    >>> is_unsigned_integer_dtype(int)  # signed
    False
    >>> is_unsigned_integer_dtype(float)
    False
    >>> is_unsigned_integer_dtype(np.uint64)
    True
    >>> is_unsigned_integer_dtype('uint8')
    True
    >>> is_unsigned_integer_dtype('UInt8')
    True
    >>> is_unsigned_integer_dtype(pd.UInt8Dtype)
    True
    >>> is_unsigned_integer_dtype(np.array(['a', 'b']))
    False
    >>> is_unsigned_integer_dtype(pd.Series([1, 2]))  # signed
    False
    >>> is_unsigned_integer_dtype(pd.Index([1, 2.]))  # float
    False
    >>> is_unsigned_integer_dtype(np.array([1, 2], dtype=np.uint32))
    True
    """
    return _is_dtype_type(
        arr_or_dtype, classes_and_not_datetimelike(np.unsignedinteger)
    )


def is_int64_dtype(arr_or_dtype) -> bool:
    """
    Check whether the provided array or dtype is of the int64 dtype.

    Parameters
    ----------
    arr_or_dtype : array-like
        The array or dtype to check.

    Returns
    -------
    boolean
        Whether or not the array or dtype is of the int64 dtype.

    Notes
    -----
    Depending on system architecture, the return value of `is_int64_dtype(
    int)` will be True if the OS uses 64-bit integers and False if the OS
    uses 32-bit integers.

    Examples
    --------
    >>> is_int64_dtype(str)
    False
    >>> is_int64_dtype(np.int32)
    False
    >>> is_int64_dtype(np.int64)
    True
    >>> is_int64_dtype('int8')
    False
    >>> is_int64_dtype('Int8')
    False
    >>> is_int64_dtype(pd.Int64Dtype)
    True
    >>> is_int64_dtype(float)
    False
    >>> is_int64_dtype(np.uint64)  # unsigned
    False
    >>> is_int64_dtype(np.array(['a', 'b']))
    False
    >>> is_int64_dtype(np.array([1, 2], dtype=np.int64))
    True
    >>> is_int64_dtype(pd.Index([1, 2.]))  # float
    False
    >>> is_int64_dtype(np.array([1, 2], dtype=np.uint32))  # unsigned
    False
    """
    return _is_dtype_type(arr_or_dtype, classes(np.int64))


def is_datetime64_any_dtype(arr_or_dtype) -> bool:
    """
    Check whether the provided array or dtype is of the datetime64 dtype.

    Parameters
    ----------
    arr_or_dtype : array-like
        The array or dtype to check.

    Returns
    -------
    bool
        Whether or not the array or dtype is of the datetime64 dtype.

    Examples
    --------
    >>> is_datetime64_any_dtype(str)
    False
    >>> is_datetime64_any_dtype(int)
    False
    >>> is_datetime64_any_dtype(np.datetime64)  # can be tz-naive
    True
    >>> is_datetime64_any_dtype(DatetimeTZDtype("ns", "US/Eastern"))
    True
    >>> is_datetime64_any_dtype(np.array(['a', 'b']))
    False
    >>> is_datetime64_any_dtype(np.array([1, 2]))
    False
    >>> is_datetime64_any_dtype(np.array([], dtype="datetime64[ns]"))
    True
    >>> is_datetime64_any_dtype(pd.DatetimeIndex([1, 2, 3], dtype="datetime64[ns]"))
    True
    """
    if isinstance(arr_or_dtype, (np.dtype, ExtensionDtype)):
        # GH#33400 fastpath for dtype object
        return arr_or_dtype.kind == "M"

    if arr_or_dtype is None:
        return False
    return is_datetime64_dtype(arr_or_dtype) or is_datetime64tz_dtype(arr_or_dtype)


def is_datetime64_ns_dtype(arr_or_dtype) -> bool:
    """
    Check whether the provided array or dtype is of the datetime64[ns] dtype.

    Parameters
    ----------
    arr_or_dtype : array-like
        The array or dtype to check.

    Returns
    -------
    bool
        Whether or not the array or dtype is of the datetime64[ns] dtype.

    Examples
    --------
    >>> is_datetime64_ns_dtype(str)
    False
    >>> is_datetime64_ns_dtype(int)
    False
    >>> is_datetime64_ns_dtype(np.datetime64)  # no unit
    False
    >>> is_datetime64_ns_dtype(DatetimeTZDtype("ns", "US/Eastern"))
    True
    >>> is_datetime64_ns_dtype(np.array(['a', 'b']))
    False
    >>> is_datetime64_ns_dtype(np.array([1, 2]))
    False
    >>> is_datetime64_ns_dtype(np.array([], dtype="datetime64"))  # no unit
    False
    >>> is_datetime64_ns_dtype(np.array([], dtype="datetime64[ps]"))  # wrong unit
    False
    >>> is_datetime64_ns_dtype(pd.DatetimeIndex([1, 2, 3], dtype="datetime64[ns]"))
    True
    """
    if arr_or_dtype is None:
        return False
    try:
        tipo = _get_dtype(arr_or_dtype)
    except TypeError:
        if is_datetime64tz_dtype(arr_or_dtype):
            tipo = _get_dtype(arr_or_dtype.dtype)
        else:
            return False
    return tipo == DT64NS_DTYPE or getattr(tipo, "base", None) == DT64NS_DTYPE


def is_timedelta64_ns_dtype(arr_or_dtype) -> bool:
    """
    Check whether the provided array or dtype is of the timedelta64[ns] dtype.

    This is a very specific dtype, so generic ones like `np.timedelta64`
    will return False if passed into this function.

    Parameters
    ----------
    arr_or_dtype : array-like
        The array or dtype to check.

    Returns
    -------
    boolean
        Whether or not the array or dtype is of the timedelta64[ns] dtype.

    Examples
    --------
    >>> is_timedelta64_ns_dtype(np.dtype('m8[ns]'))
    True
    >>> is_timedelta64_ns_dtype(np.dtype('m8[ps]'))  # Wrong frequency
    False
    >>> is_timedelta64_ns_dtype(np.array([1, 2], dtype='m8[ns]'))
    True
    >>> is_timedelta64_ns_dtype(np.array([1, 2], dtype=np.timedelta64))
    False
    """
    return _is_dtype(arr_or_dtype, lambda dtype: dtype == TD64NS_DTYPE)


def is_datetime_or_timedelta_dtype(arr_or_dtype) -> bool:
    """
    Check whether the provided array or dtype is of
    a timedelta64 or datetime64 dtype.

    Parameters
    ----------
    arr_or_dtype : array-like
        The array or dtype to check.

    Returns
    -------
    boolean
        Whether or not the array or dtype is of a timedelta64,
        or datetime64 dtype.

    Examples
    --------
    >>> is_datetime_or_timedelta_dtype(str)
    False
    >>> is_datetime_or_timedelta_dtype(int)
    False
    >>> is_datetime_or_timedelta_dtype(np.datetime64)
    True
    >>> is_datetime_or_timedelta_dtype(np.timedelta64)
    True
    >>> is_datetime_or_timedelta_dtype(np.array(['a', 'b']))
    False
    >>> is_datetime_or_timedelta_dtype(pd.Series([1, 2]))
    False
    >>> is_datetime_or_timedelta_dtype(np.array([], dtype=np.timedelta64))
    True
    >>> is_datetime_or_timedelta_dtype(np.array([], dtype=np.datetime64))
    True
    """
    return _is_dtype_type(arr_or_dtype, classes(np.datetime64, np.timedelta64))


# This exists to silence numpy deprecation warnings, see GH#29553
def is_numeric_v_string_like(a, b):
    """
    Check if we are comparing a string-like object to a numeric ndarray.
    NumPy doesn't like to compare such objects, especially numeric arrays
    and scalar string-likes.

    Parameters
    ----------
    a : array-like, scalar
        The first object to check.
    b : array-like, scalar
        The second object to check.

    Returns
    -------
    boolean
        Whether we return a comparing a string-like object to a numeric array.

    Examples
    --------
    >>> is_numeric_v_string_like(1, 1)
    False
    >>> is_numeric_v_string_like("foo", "foo")
    False
    >>> is_numeric_v_string_like(1, "foo")  # non-array numeric
    False
    >>> is_numeric_v_string_like(np.array([1]), "foo")
    True
    >>> is_numeric_v_string_like("foo", np.array([1]))  # symmetric check
    True
    >>> is_numeric_v_string_like(np.array([1, 2]), np.array(["foo"]))
    True
    >>> is_numeric_v_string_like(np.array(["foo"]), np.array([1, 2]))
    True
    >>> is_numeric_v_string_like(np.array([1]), np.array([2]))
    False
    >>> is_numeric_v_string_like(np.array(["foo"]), np.array(["foo"]))
    False
    """
    is_a_array = isinstance(a, np.ndarray)
    is_b_array = isinstance(b, np.ndarray)

    is_a_numeric_array = is_a_array and is_numeric_dtype(a)
    is_b_numeric_array = is_b_array and is_numeric_dtype(b)
    is_a_string_array = is_a_array and is_string_like_dtype(a)
    is_b_string_array = is_b_array and is_string_like_dtype(b)

    is_a_scalar_string_like = not is_a_array and isinstance(a, str)
    is_b_scalar_string_like = not is_b_array and isinstance(b, str)

    return (
        (is_a_numeric_array and is_b_scalar_string_like)
        or (is_b_numeric_array and is_a_scalar_string_like)
        or (is_a_numeric_array and is_b_string_array)
        or (is_b_numeric_array and is_a_string_array)
    )


# This exists to silence numpy deprecation warnings, see GH#29553
def is_datetimelike_v_numeric(a, b):
    """
    Check if we are comparing a datetime-like object to a numeric object.
    By "numeric," we mean an object that is either of an int or float dtype.

    Parameters
    ----------
    a : array-like, scalar
        The first object to check.
    b : array-like, scalar
        The second object to check.

    Returns
    -------
    boolean
        Whether we return a comparing a datetime-like to a numeric object.

    Examples
    --------
    >>> from datetime import datetime
    >>> dt = np.datetime64(datetime(2017, 1, 1))
    >>>
    >>> is_datetimelike_v_numeric(1, 1)
    False
    >>> is_datetimelike_v_numeric(dt, dt)
    False
    >>> is_datetimelike_v_numeric(1, dt)
    True
    >>> is_datetimelike_v_numeric(dt, 1)  # symmetric check
    True
    >>> is_datetimelike_v_numeric(np.array([dt]), 1)
    True
    >>> is_datetimelike_v_numeric(np.array([1]), dt)
    True
    >>> is_datetimelike_v_numeric(np.array([dt]), np.array([1]))
    True
    >>> is_datetimelike_v_numeric(np.array([1]), np.array([2]))
    False
    >>> is_datetimelike_v_numeric(np.array([dt]), np.array([dt]))
    False
    """
    if not hasattr(a, "dtype"):
        a = np.asarray(a)
    if not hasattr(b, "dtype"):
        b = np.asarray(b)

    def is_numeric(x):
        """
        Check if an object has a numeric dtype (i.e. integer or float).
        """
        return is_integer_dtype(x) or is_float_dtype(x)

    return (needs_i8_conversion(a) and is_numeric(b)) or (
        needs_i8_conversion(b) and is_numeric(a)
    )


def needs_i8_conversion(arr_or_dtype) -> bool:
    """
    Check whether the array or dtype should be converted to int64.

    An array-like or dtype "needs" such a conversion if the array-like
    or dtype is of a datetime-like dtype

    Parameters
    ----------
    arr_or_dtype : array-like
        The array or dtype to check.

    Returns
    -------
    boolean
        Whether or not the array or dtype should be converted to int64.

    Examples
    --------
    >>> needs_i8_conversion(str)
    False
    >>> needs_i8_conversion(np.int64)
    False
    >>> needs_i8_conversion(np.datetime64)
    True
    >>> needs_i8_conversion(np.array(['a', 'b']))
    False
    >>> needs_i8_conversion(pd.Series([1, 2]))
    False
    >>> needs_i8_conversion(pd.Series([], dtype="timedelta64[ns]"))
    True
    >>> needs_i8_conversion(pd.DatetimeIndex([1, 2, 3], tz="US/Eastern"))
    True
    """
    if arr_or_dtype is None:
        return False
    return (
        is_datetime_or_timedelta_dtype(arr_or_dtype)
        or is_datetime64tz_dtype(arr_or_dtype)
        or is_period_dtype(arr_or_dtype)
    )


def is_numeric_dtype(arr_or_dtype) -> bool:
    """
    Check whether the provided array or dtype is of a numeric dtype.

    Parameters
    ----------
    arr_or_dtype : array-like
        The array or dtype to check.

    Returns
    -------
    boolean
        Whether or not the array or dtype is of a numeric dtype.

    Examples
    --------
    >>> is_numeric_dtype(str)
    False
    >>> is_numeric_dtype(int)
    True
    >>> is_numeric_dtype(float)
    True
    >>> is_numeric_dtype(np.uint64)
    True
    >>> is_numeric_dtype(np.datetime64)
    False
    >>> is_numeric_dtype(np.timedelta64)
    False
    >>> is_numeric_dtype(np.array(['a', 'b']))
    False
    >>> is_numeric_dtype(pd.Series([1, 2]))
    True
    >>> is_numeric_dtype(pd.Index([1, 2.]))
    True
    >>> is_numeric_dtype(np.array([], dtype=np.timedelta64))
    False
    """
    return _is_dtype_type(
        arr_or_dtype, classes_and_not_datetimelike(np.number, np.bool_)
    )


def is_string_like_dtype(arr_or_dtype) -> bool:
    """
    Check whether the provided array or dtype is of a string-like dtype.

    Unlike `is_string_dtype`, the object dtype is excluded because it
    is a mixed dtype.

    Parameters
    ----------
    arr_or_dtype : array-like
        The array or dtype to check.

    Returns
    -------
    boolean
        Whether or not the array or dtype is of the string dtype.

    Examples
    --------
    >>> is_string_like_dtype(str)
    True
    >>> is_string_like_dtype(object)
    False
    >>> is_string_like_dtype(np.array(['a', 'b']))
    True
    >>> is_string_like_dtype(pd.Series([1, 2]))
    False
    """
    return _is_dtype(arr_or_dtype, lambda dtype: dtype.kind in ("S", "U"))


def is_float_dtype(arr_or_dtype) -> bool:
    """
    Check whether the provided array or dtype is of a float dtype.

    This function is internal and should not be exposed in the public API.

    Parameters
    ----------
    arr_or_dtype : array-like
        The array or dtype to check.

    Returns
    -------
    boolean
        Whether or not the array or dtype is of a float dtype.

    Examples
    --------
    >>> is_float_dtype(str)
    False
    >>> is_float_dtype(int)
    False
    >>> is_float_dtype(float)
    True
    >>> is_float_dtype(np.array(['a', 'b']))
    False
    >>> is_float_dtype(pd.Series([1, 2]))
    False
    >>> is_float_dtype(pd.Index([1, 2.]))
    True
    """
    return _is_dtype_type(arr_or_dtype, classes(np.floating))


def is_bool_dtype(arr_or_dtype) -> bool:
    """
    Check whether the provided array or dtype is of a boolean dtype.

    Parameters
    ----------
    arr_or_dtype : array-like
        The array or dtype to check.

    Returns
    -------
    boolean
        Whether or not the array or dtype is of a boolean dtype.

    Notes
    -----
    An ExtensionArray is considered boolean when the ``_is_boolean``
    attribute is set to True.

    Examples
    --------
    >>> is_bool_dtype(str)
    False
    >>> is_bool_dtype(int)
    False
    >>> is_bool_dtype(bool)
    True
    >>> is_bool_dtype(np.bool_)
    True
    >>> is_bool_dtype(np.array(['a', 'b']))
    False
    >>> is_bool_dtype(pd.Series([1, 2]))
    False
    >>> is_bool_dtype(np.array([True, False]))
    True
    >>> is_bool_dtype(pd.Categorical([True, False]))
    True
    >>> is_bool_dtype(pd.arrays.SparseArray([True, False]))
    True
    """
    if arr_or_dtype is None:
        return False
    try:
        dtype = _get_dtype(arr_or_dtype)
    except TypeError:
        return False

    if isinstance(arr_or_dtype, CategoricalDtype):
        arr_or_dtype = arr_or_dtype.categories
        # now we use the special definition for Index

    if isinstance(arr_or_dtype, ABCIndexClass):

        # TODO(jreback)
        # we don't have a boolean Index class
        # so its object, we need to infer to
        # guess this
        return arr_or_dtype.is_object and arr_or_dtype.inferred_type == "boolean"
    elif is_extension_array_dtype(arr_or_dtype):
        dtype = getattr(arr_or_dtype, "dtype", arr_or_dtype)
        return dtype._is_boolean

    return issubclass(dtype.type, np.bool_)


def is_extension_type(arr) -> bool:
    """
    Check whether an array-like is of a pandas extension class instance.

    .. deprecated:: 1.0.0
        Use ``is_extension_array_dtype`` instead.

    Extension classes include categoricals, pandas sparse objects (i.e.
    classes represented within the pandas library and not ones external
    to it like scipy sparse matrices), and datetime-like arrays.

    Parameters
    ----------
    arr : array-like
        The array-like to check.

    Returns
    -------
    boolean
        Whether or not the array-like is of a pandas extension class instance.

    Examples
    --------
    >>> is_extension_type([1, 2, 3])
    False
    >>> is_extension_type(np.array([1, 2, 3]))
    False
    >>>
    >>> cat = pd.Categorical([1, 2, 3])
    >>>
    >>> is_extension_type(cat)
    True
    >>> is_extension_type(pd.Series(cat))
    True
    >>> is_extension_type(pd.arrays.SparseArray([1, 2, 3]))
    True
    >>> from scipy.sparse import bsr_matrix
    >>> is_extension_type(bsr_matrix([1, 2, 3]))
    False
    >>> is_extension_type(pd.DatetimeIndex([1, 2, 3]))
    False
    >>> is_extension_type(pd.DatetimeIndex([1, 2, 3], tz="US/Eastern"))
    True
    >>>
    >>> dtype = DatetimeTZDtype("ns", tz="US/Eastern")
    >>> s = pd.Series([], dtype=dtype)
    >>> is_extension_type(s)
    True
    """
    warnings.warn(
        "'is_extension_type' is deprecated and will be removed in a future "
        "version.  Use 'is_extension_array_dtype' instead.",
        FutureWarning,
        stacklevel=2,
    )

    if is_categorical_dtype(arr):
        return True
    elif is_sparse(arr):
        return True
    elif is_datetime64tz_dtype(arr):
        return True
    return False


def is_extension_array_dtype(arr_or_dtype) -> bool:
    """
    Check if an object is a pandas extension array type.

    See the :ref:`Use Guide <extending.extension-types>` for more.

    Parameters
    ----------
    arr_or_dtype : object
        For array-like input, the ``.dtype`` attribute will
        be extracted.

    Returns
    -------
    bool
        Whether the `arr_or_dtype` is an extension array type.

    Notes
    -----
    This checks whether an object implements the pandas extension
    array interface. In pandas, this includes:

    * Categorical
    * Sparse
    * Interval
    * Period
    * DatetimeArray
    * TimedeltaArray

    Third-party libraries may implement arrays or types satisfying
    this interface as well.

    Examples
    --------
    >>> from pandas.api.types import is_extension_array_dtype
    >>> arr = pd.Categorical(['a', 'b'])
    >>> is_extension_array_dtype(arr)
    True
    >>> is_extension_array_dtype(arr.dtype)
    True

    >>> arr = np.array(['a', 'b'])
    >>> is_extension_array_dtype(arr.dtype)
    False
    """
    dtype = getattr(arr_or_dtype, "dtype", arr_or_dtype)
    return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None


def is_complex_dtype(arr_or_dtype) -> bool:
    """
    Check whether the provided array or dtype is of a complex dtype.

    Parameters
    ----------
    arr_or_dtype : array-like
        The array or dtype to check.

    Returns
    -------
    boolean
        Whether or not the array or dtype is of a complex dtype.

    Examples
    --------
    >>> is_complex_dtype(str)
    False
    >>> is_complex_dtype(int)
    False
    >>> is_complex_dtype(np.complex_)
    True
    >>> is_complex_dtype(np.array(['a', 'b']))
    False
    >>> is_complex_dtype(pd.Series([1, 2]))
    False
    >>> is_complex_dtype(np.array([1 + 1j, 5]))
    True
    """
    return _is_dtype_type(arr_or_dtype, classes(np.complexfloating))


def _is_dtype(arr_or_dtype, condition) -> bool:
    """
    Return a boolean if the condition is satisfied for the arr_or_dtype.

    Parameters
    ----------
    arr_or_dtype : array-like, str, np.dtype, or ExtensionArrayType
        The array-like or dtype object whose dtype we want to extract.
    condition : callable[Union[np.dtype, ExtensionDtype]]

    Returns
    -------
    bool

    """
    if arr_or_dtype is None:
        return False
    try:
        dtype = _get_dtype(arr_or_dtype)
    except (TypeError, ValueError, UnicodeEncodeError):
        return False
    return condition(dtype)


def _get_dtype(arr_or_dtype) -> DtypeObj:
    """
    Get the dtype instance associated with an array
    or dtype object.

    Parameters
    ----------
    arr_or_dtype : array-like
        The array-like or dtype object whose dtype we want to extract.

    Returns
    -------
    obj_dtype : The extract dtype instance from the
                passed in array or dtype object.

    Raises
    ------
    TypeError : The passed in object is None.
    """
    if arr_or_dtype is None:
        raise TypeError("Cannot deduce dtype from null object")

    # fastpath
    elif isinstance(arr_or_dtype, np.dtype):
        return arr_or_dtype
    elif isinstance(arr_or_dtype, type):
        return np.dtype(arr_or_dtype)

    # if we have an array-like
    elif hasattr(arr_or_dtype, "dtype"):
        arr_or_dtype = arr_or_dtype.dtype

    return pandas_dtype(arr_or_dtype)


def _is_dtype_type(arr_or_dtype, condition) -> bool:
    """
    Return a boolean if the condition is satisfied for the arr_or_dtype.

    Parameters
    ----------
    arr_or_dtype : array-like
        The array-like or dtype object whose dtype we want to extract.
    condition : callable[Union[np.dtype, ExtensionDtypeType]]

    Returns
    -------
    bool : if the condition is satisfied for the arr_or_dtype
    """
    if arr_or_dtype is None:
        return condition(type(None))

    # fastpath
    if isinstance(arr_or_dtype, np.dtype):
        return condition(arr_or_dtype.type)
    elif isinstance(arr_or_dtype, type):
        if issubclass(arr_or_dtype, ExtensionDtype):
            arr_or_dtype = arr_or_dtype.type
        return condition(np.dtype(arr_or_dtype).type)

    # if we have an array-like
    if hasattr(arr_or_dtype, "dtype"):
        arr_or_dtype = arr_or_dtype.dtype

    # we are not possibly a dtype
    elif is_list_like(arr_or_dtype):
        return condition(type(None))

    try:
        tipo = pandas_dtype(arr_or_dtype).type
    except (TypeError, ValueError, UnicodeEncodeError):
        if is_scalar(arr_or_dtype):
            return condition(type(None))

        return False

    return condition(tipo)


def infer_dtype_from_object(dtype):
    """
    Get a numpy dtype.type-style object for a dtype object.

    This methods also includes handling of the datetime64[ns] and
    datetime64[ns, TZ] objects.

    If no dtype can be found, we return ``object``.

    Parameters
    ----------
    dtype : dtype, type
        The dtype object whose numpy dtype.type-style
        object we want to extract.

    Returns
    -------
    dtype_object : The extracted numpy dtype.type-style object.
    """
    if isinstance(dtype, type) and issubclass(dtype, np.generic):
        # Type object from a dtype
        return dtype
    elif isinstance(dtype, (np.dtype, ExtensionDtype)):
        # dtype object
        try:
            _validate_date_like_dtype(dtype)
        except TypeError:
            # Should still pass if we don't have a date-like
            pass
        return dtype.type

    try:
        dtype = pandas_dtype(dtype)
    except TypeError:
        pass

    if is_extension_array_dtype(dtype):
        return dtype.type
    elif isinstance(dtype, str):

        # TODO(jreback)
        # should deprecate these
        if dtype in ["datetimetz", "datetime64tz"]:
            return DatetimeTZDtype.type
        elif dtype in ["period"]:
            raise NotImplementedError

        if dtype == "datetime" or dtype == "timedelta":
            dtype += "64"
        try:
            return infer_dtype_from_object(getattr(np, dtype))
        except (AttributeError, TypeError):
            # Handles cases like _get_dtype(int) i.e.,
            # Python objects that are valid dtypes
            # (unlike user-defined types, in general)
            #
            # TypeError handles the float16 type code of 'e'
            # further handle internal types
            pass

    return infer_dtype_from_object(np.dtype(dtype))


def _validate_date_like_dtype(dtype) -> None:
    """
    Check whether the dtype is a date-like dtype. Raises an error if invalid.

    Parameters
    ----------
    dtype : dtype, type
        The dtype to check.

    Raises
    ------
    TypeError : The dtype could not be casted to a date-like dtype.
    ValueError : The dtype is an illegal date-like dtype (e.g. the
                 the frequency provided is too specific)
    """
    try:
        typ = np.datetime_data(dtype)[0]
    except ValueError as e:
        raise TypeError(e) from e
    if typ != "generic" and typ != "ns":
        raise ValueError(
            f"{repr(dtype.name)} is too specific of a frequency, "
            f"try passing {repr(dtype.type.__name__)}"
        )


def pandas_dtype(dtype) -> DtypeObj:
    """
    Convert input into a pandas only dtype object or a numpy dtype object.

    Parameters
    ----------
    dtype : object to be converted

    Returns
    -------
    np.dtype or a pandas dtype

    Raises
    ------
    TypeError if not a dtype
    """
    # short-circuit
    if isinstance(dtype, np.ndarray):
        return dtype.dtype
    elif isinstance(dtype, (np.dtype, ExtensionDtype)):
        return dtype

    # registered extension types
    result = registry.find(dtype)
    if result is not None:
        return result

    # try a numpy dtype
    # raise a consistent TypeError if failed
    try:
        npdtype = np.dtype(dtype)
    except SyntaxError as err:
        # np.dtype uses `eval` which can raise SyntaxError
        raise TypeError(f"data type '{dtype}' not understood") from err

    # Any invalid dtype (such as pd.Timestamp) should raise an error.
    # np.dtype(invalid_type).kind = 0 for such objects. However, this will
    # also catch some valid dtypes such as object, np.object_ and 'object'
    # which we safeguard against by catching them earlier and returning
    # np.dtype(valid_dtype) before this condition is evaluated.
    if is_hashable(dtype) and dtype in [object, np.object_, "object", "O"]:
        # check hashability to avoid errors/DeprecationWarning when we get
        # here and `dtype` is an array
        return npdtype
    elif npdtype.kind == "O":
        raise TypeError(f"dtype '{dtype}' not understood")

    return npdtype