algorithms.py

"""
Generic data algorithms. This module is experimental at the moment and not
intended for public consumption
"""
from __future__ import annotations

import operator
from textwrap import dedent
from typing import (
    TYPE_CHECKING,
    Literal,
    cast,
)
import warnings

import numpy as np

from pandas._libs import (
    algos,
    hashtable as htable,
    iNaT,
    lib,
)
from pandas._typing import (
    AnyArrayLike,
    ArrayLike,
    AxisInt,
    DtypeObj,
    TakeIndexer,
    npt,
)
from pandas.util._decorators import doc
from pandas.util._exceptions import find_stack_level

from pandas.core.dtypes.cast import (
    construct_1d_object_array_from_listlike,
    np_find_common_type,
)
from pandas.core.dtypes.common import (
    ensure_float64,
    ensure_object,
    ensure_platform_int,
    is_array_like,
    is_bool_dtype,
    is_complex_dtype,
    is_dict_like,
    is_extension_array_dtype,
    is_float_dtype,
    is_integer,
    is_integer_dtype,
    is_list_like,
    is_object_dtype,
    is_signed_integer_dtype,
    needs_i8_conversion,
)
from pandas.core.dtypes.concat import concat_compat
from pandas.core.dtypes.dtypes import (
    BaseMaskedDtype,
    CategoricalDtype,
    ExtensionDtype,
    NumpyEADtype,
)
from pandas.core.dtypes.generic import (
    ABCDatetimeArray,
    ABCExtensionArray,
    ABCIndex,
    ABCMultiIndex,
    ABCSeries,
    ABCTimedeltaArray,
)
from pandas.core.dtypes.missing import (
    isna,
    na_value_for_dtype,
)

from pandas.core.array_algos.take import take_nd
from pandas.core.construction import (
    array as pd_array,
    ensure_wrapped_if_datetimelike,
    extract_array,
)
from pandas.core.indexers import validate_indices

if TYPE_CHECKING:
    from pandas._typing import (
        ListLike,
        NumpySorter,
        NumpyValueArrayLike,
    )

    from pandas import (
        Categorical,
        Index,
        Series,
    )
    from pandas.core.arrays import (
        BaseMaskedArray,
        ExtensionArray,
    )


# --------------- #
# dtype access    #
# --------------- #
def _ensure_data(values: ArrayLike) -> np.ndarray:
    """
    routine to ensure that our data is of the correct
    input dtype for lower-level routines

    This will coerce:
    - ints -> int64
    - uint -> uint64
    - bool -> uint8
    - datetimelike -> i8
    - datetime64tz -> i8 (in local tz)
    - categorical -> codes

    Parameters
    ----------
    values : np.ndarray or ExtensionArray

    Returns
    -------
    np.ndarray
    """

    if not isinstance(values, ABCMultiIndex):
        # extract_array would raise
        values = extract_array(values, extract_numpy=True)

    if is_object_dtype(values.dtype):
        return ensure_object(np.asarray(values))

    elif isinstance(values.dtype, BaseMaskedDtype):
        # i.e. BooleanArray, FloatingArray, IntegerArray
        values = cast("BaseMaskedArray", values)
        if not values._hasna:
            # No pd.NAs -> We can avoid an object-dtype cast (and copy) GH#41816
            #  recurse to avoid re-implementing logic for eg bool->uint8
            return _ensure_data(values._data)
        return np.asarray(values)

    elif isinstance(values.dtype, CategoricalDtype):
        # NB: cases that go through here should NOT be using _reconstruct_data
        #  on the back-end.
        values = cast("Categorical", values)
        return values.codes

    elif is_bool_dtype(values.dtype):
        if isinstance(values, np.ndarray):
            # i.e. actually dtype == np.dtype("bool")
            return np.asarray(values).view("uint8")
        else:
            # e.g. Sparse[bool, False]  # TODO: no test cases get here
            return np.asarray(values).astype("uint8", copy=False)

    elif is_integer_dtype(values.dtype):
        return np.asarray(values)

    elif is_float_dtype(values.dtype):
        # Note: checking `values.dtype == "float128"` raises on Windows and 32bit
        # error: Item "ExtensionDtype" of "Union[Any, ExtensionDtype, dtype[Any]]"
        # has no attribute "itemsize"
        if values.dtype.itemsize in [2, 12, 16]:  # type: ignore[union-attr]
            # we dont (yet) have float128 hashtable support
            return ensure_float64(values)
        return np.asarray(values)

    elif is_complex_dtype(values.dtype):
        return cast(np.ndarray, values)

    # datetimelike
    elif needs_i8_conversion(values.dtype):
        npvalues = values.view("i8")
        npvalues = cast(np.ndarray, npvalues)
        return npvalues

    # we have failed, return object
    values = np.asarray(values, dtype=object)
    return ensure_object(values)


def _reconstruct_data(
    values: ArrayLike, dtype: DtypeObj, original: AnyArrayLike
) -> ArrayLike:
    """
    reverse of _ensure_data

    Parameters
    ----------
    values : np.ndarray or ExtensionArray
    dtype : np.dtype or ExtensionDtype
    original : AnyArrayLike

    Returns
    -------
    ExtensionArray or np.ndarray
    """
    if isinstance(values, ABCExtensionArray) and values.dtype == dtype:
        # Catch DatetimeArray/TimedeltaArray
        return values

    if not isinstance(dtype, np.dtype):
        # i.e. ExtensionDtype; note we have ruled out above the possibility
        #  that values.dtype == dtype
        cls = dtype.construct_array_type()

        values = cls._from_sequence(values, dtype=dtype)

    else:
        values = values.astype(dtype, copy=False)

    return values


def _ensure_arraylike(values, func_name: str) -> ArrayLike:
    """
    ensure that we are arraylike if not already
    """
    if not isinstance(values, (ABCIndex, ABCSeries, ABCExtensionArray, np.ndarray)):
        # GH#52986
        if func_name != "isin-targets":
            # Make an exception for the comps argument in isin.
            warnings.warn(
                f"{func_name} with argument that is not not a Series, Index, "
                "ExtensionArray, or np.ndarray is deprecated and will raise in a "
                "future version.",
                FutureWarning,
                stacklevel=find_stack_level(),
            )

        inferred = lib.infer_dtype(values, skipna=False)
        if inferred in ["mixed", "string", "mixed-integer"]:
            # "mixed-integer" to ensure we do not cast ["ss", 42] to str GH#22160
            if isinstance(values, tuple):
                values = list(values)
            values = construct_1d_object_array_from_listlike(values)
        else:
            values = np.asarray(values)
    return values


_hashtables = {
    "complex128": htable.Complex128HashTable,
    "complex64": htable.Complex64HashTable,
    "float64": htable.Float64HashTable,
    "float32": htable.Float32HashTable,
    "uint64": htable.UInt64HashTable,
    "uint32": htable.UInt32HashTable,
    "uint16": htable.UInt16HashTable,
    "uint8": htable.UInt8HashTable,
    "int64": htable.Int64HashTable,
    "int32": htable.Int32HashTable,
    "int16": htable.Int16HashTable,
    "int8": htable.Int8HashTable,
    "string": htable.StringHashTable,
    "object": htable.PyObjectHashTable,
}


def _get_hashtable_algo(values: np.ndarray):
    """
    Parameters
    ----------
    values : np.ndarray

    Returns
    -------
    htable : HashTable subclass
    values : ndarray
    """
    values = _ensure_data(values)

    ndtype = _check_object_for_strings(values)
    hashtable = _hashtables[ndtype]
    return hashtable, values


def _check_object_for_strings(values: np.ndarray) -> str:
    """
    Check if we can use string hashtable instead of object hashtable.

    Parameters
    ----------
    values : ndarray

    Returns
    -------
    str
    """
    ndtype = values.dtype.name
    if ndtype == "object":
        # it's cheaper to use a String Hash Table than Object; we infer
        # including nulls because that is the only difference between
        # StringHashTable and ObjectHashtable
        if lib.is_string_array(values, skipna=False):
            ndtype = "string"
    return ndtype


# --------------- #
# top-level algos #
# --------------- #


def unique(values):
    """
    Return unique values based on a hash table.

    Uniques are returned in order of appearance. This does NOT sort.

    Significantly faster than numpy.unique for long enough sequences.
    Includes NA values.

    Parameters
    ----------
    values : 1d array-like

    Returns
    -------
    numpy.ndarray or ExtensionArray

        The return can be:

        * Index : when the input is an Index
        * Categorical : when the input is a Categorical dtype
        * ndarray : when the input is a Series/ndarray

        Return numpy.ndarray or ExtensionArray.

    See Also
    --------
    Index.unique : Return unique values from an Index.
    Series.unique : Return unique values of Series object.

    Examples
    --------
    >>> pd.unique(pd.Series([2, 1, 3, 3]))
    array([2, 1, 3])

    >>> pd.unique(pd.Series([2] + [1] * 5))
    array([2, 1])

    >>> pd.unique(pd.Series([pd.Timestamp("20160101"), pd.Timestamp("20160101")]))
    array(['2016-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

    >>> pd.unique(
    ...     pd.Series(
    ...         [
    ...             pd.Timestamp("20160101", tz="US/Eastern"),
    ...             pd.Timestamp("20160101", tz="US/Eastern"),
    ...         ]
    ...     )
    ... )
    <DatetimeArray>
    ['2016-01-01 00:00:00-05:00']
    Length: 1, dtype: datetime64[ns, US/Eastern]

    >>> pd.unique(
    ...     pd.Index(
    ...         [
    ...             pd.Timestamp("20160101", tz="US/Eastern"),
    ...             pd.Timestamp("20160101", tz="US/Eastern"),
    ...         ]
    ...     )
    ... )
    DatetimeIndex(['2016-01-01 00:00:00-05:00'],
            dtype='datetime64[ns, US/Eastern]',
            freq=None)

    >>> pd.unique(np.array(list("baabc"), dtype="O"))
    array(['b', 'a', 'c'], dtype=object)

    An unordered Categorical will return categories in the
    order of appearance.

    >>> pd.unique(pd.Series(pd.Categorical(list("baabc"))))
    ['b', 'a', 'c']
    Categories (3, object): ['a', 'b', 'c']

    >>> pd.unique(pd.Series(pd.Categorical(list("baabc"), categories=list("abc"))))
    ['b', 'a', 'c']
    Categories (3, object): ['a', 'b', 'c']

    An ordered Categorical preserves the category ordering.

    >>> pd.unique(
    ...     pd.Series(
    ...         pd.Categorical(list("baabc"), categories=list("abc"), ordered=True)
    ...     )
    ... )
    ['b', 'a', 'c']
    Categories (3, object): ['a' < 'b' < 'c']

    An array of tuples

    >>> pd.unique(pd.Series([("a", "b"), ("b", "a"), ("a", "c"), ("b", "a")]).values)
    array([('a', 'b'), ('b', 'a'), ('a', 'c')], dtype=object)
    """
    return unique_with_mask(values)


def nunique_ints(values: ArrayLike) -> int:
    """
    Return the number of unique values for integer array-likes.

    Significantly faster than pandas.unique for long enough sequences.
    No checks are done to ensure input is integral.

    Parameters
    ----------
    values : 1d array-like

    Returns
    -------
    int : The number of unique values in ``values``
    """
    if len(values) == 0:
        return 0
    values = _ensure_data(values)
    # bincount requires intp
    result = (np.bincount(values.ravel().astype("intp")) != 0).sum()
    return result


def unique_with_mask(values, mask: npt.NDArray[np.bool_] | None = None):
    """See algorithms.unique for docs. Takes a mask for masked arrays."""
    values = _ensure_arraylike(values, func_name="unique")

    if isinstance(values.dtype, ExtensionDtype):
        # Dispatch to extension dtype's unique.
        return values.unique()

    original = values
    hashtable, values = _get_hashtable_algo(values)

    table = hashtable(len(values))
    if mask is None:
        uniques = table.unique(values)
        uniques = _reconstruct_data(uniques, original.dtype, original)
        return uniques

    else:
        uniques, mask = table.unique(values, mask=mask)
        uniques = _reconstruct_data(uniques, original.dtype, original)
        assert mask is not None  # for mypy
        return uniques, mask.astype("bool")


unique1d = unique


_MINIMUM_COMP_ARR_LEN = 1_000_000


def isin(comps: ListLike, values: ListLike) -> npt.NDArray[np.bool_]:
    """
    Compute the isin boolean array.

    Parameters
    ----------
    comps : list-like
    values : list-like

    Returns
    -------
    ndarray[bool]
        Same length as `comps`.
    """
    if not is_list_like(comps):
        raise TypeError(
            "only list-like objects are allowed to be passed "
            f"to isin(), you passed a `{type(comps).__name__}`"
        )
    if not is_list_like(values):
        raise TypeError(
            "only list-like objects are allowed to be passed "
            f"to isin(), you passed a `{type(values).__name__}`"
        )

    if not isinstance(values, (ABCIndex, ABCSeries, ABCExtensionArray, np.ndarray)):
        orig_values = list(values)
        values = _ensure_arraylike(orig_values, func_name="isin-targets")

        if (
            len(values) > 0
            and values.dtype.kind in "iufcb"
            and not is_signed_integer_dtype(comps)
        ):
            # GH#46485 Use object to avoid upcast to float64 later
            # TODO: Share with _find_common_type_compat
            values = construct_1d_object_array_from_listlike(orig_values)

    elif isinstance(values, ABCMultiIndex):
        # Avoid raising in extract_array
        values = np.array(values)
    else:
        values = extract_array(values, extract_numpy=True, extract_range=True)

    comps_array = _ensure_arraylike(comps, func_name="isin")
    comps_array = extract_array(comps_array, extract_numpy=True)
    if not isinstance(comps_array, np.ndarray):
        # i.e. Extension Array
        return comps_array.isin(values)

    elif needs_i8_conversion(comps_array.dtype):
        # Dispatch to DatetimeLikeArrayMixin.isin
        return pd_array(comps_array).isin(values)
    elif needs_i8_conversion(values.dtype) and not is_object_dtype(comps_array.dtype):
        # e.g. comps_array are integers and values are datetime64s
        return np.zeros(comps_array.shape, dtype=bool)
        # TODO: not quite right ... Sparse/Categorical
    elif needs_i8_conversion(values.dtype):
        return isin(comps_array, values.astype(object))

    elif isinstance(values.dtype, ExtensionDtype):
        return isin(np.asarray(comps_array), np.asarray(values))

    # GH16012
    # Ensure np.isin doesn't get object types or it *may* throw an exception
    # Albeit hashmap has O(1) look-up (vs. O(logn) in sorted array),
    # isin is faster for small sizes
    if (
        len(comps_array) > _MINIMUM_COMP_ARR_LEN
        and len(values) <= 26
        and comps_array.dtype != object
    ):
        # If the values include nan we need to check for nan explicitly
        # since np.nan it not equal to np.nan
        if isna(values).any():

            def f(c, v):
                return np.logical_or(np.isin(c, v).ravel(), np.isnan(c))

        else:
            f = lambda a, b: np.isin(a, b).ravel()

    else:
        common = np_find_common_type(values.dtype, comps_array.dtype)
        values = values.astype(common, copy=False)
        comps_array = comps_array.astype(common, copy=False)
        f = htable.ismember

    return f(comps_array, values)


def factorize_array(
    values: np.ndarray,
    use_na_sentinel: bool = True,
    size_hint: int | None = None,
    na_value: object = None,
    mask: npt.NDArray[np.bool_] | None = None,
) -> tuple[npt.NDArray[np.intp], np.ndarray]:
    """
    Factorize a numpy array to codes and uniques.

    This doesn't do any coercion of types or unboxing before factorization.

    Parameters
    ----------
    values : ndarray
    use_na_sentinel : bool, default True
        If True, the sentinel -1 will be used for NaN values. If False,
        NaN values will be encoded as non-negative integers and will not drop the
        NaN from the uniques of the values.
    size_hint : int, optional
        Passed through to the hashtable's 'get_labels' method
    na_value : object, optional
        A value in `values` to consider missing. Note: only use this
        parameter when you know that you don't have any values pandas would
        consider missing in the array (NaN for float data, iNaT for
        datetimes, etc.).
    mask : ndarray[bool], optional
        If not None, the mask is used as indicator for missing values
        (True = missing, False = valid) instead of `na_value` or
        condition "val != val".

    Returns
    -------
    codes : ndarray[np.intp]
    uniques : ndarray
    """
    original = values
    if values.dtype.kind in "mM":
        # _get_hashtable_algo will cast dt64/td64 to i8 via _ensure_data, so we
        #  need to do the same to na_value. We are assuming here that the passed
        #  na_value is an appropriately-typed NaT.
        # e.g. test_where_datetimelike_categorical
        na_value = iNaT

    hash_klass, values = _get_hashtable_algo(values)

    table = hash_klass(size_hint or len(values))
    uniques, codes = table.factorize(
        values,
        na_sentinel=-1,
        na_value=na_value,
        mask=mask,
        ignore_na=use_na_sentinel,
    )

    # re-cast e.g. i8->dt64/td64, uint8->bool
    uniques = _reconstruct_data(uniques, original.dtype, original)

    codes = ensure_platform_int(codes)
    return codes, uniques


@doc(
    values=dedent(
        """\
    values : sequence
        A 1-D sequence. Sequences that aren't pandas objects are
        coerced to ndarrays before factorization.
    """
    ),
    sort=dedent(
        """\
    sort : bool, default False
        Sort `uniques` and shuffle `codes` to maintain the
        relationship.
    """
    ),
    size_hint=dedent(
        """\
    size_hint : int, optional
        Hint to the hashtable sizer.
    """
    ),
)
def factorize(
    values,
    sort: bool = False,
    use_na_sentinel: bool = True,
    size_hint: int | None = None,
) -> tuple[np.ndarray, np.ndarray | Index]:
    """
    Encode the object as an enumerated type or categorical variable.

    This method is useful for obtaining a numeric representation of an
    array when all that matters is identifying distinct values. `factorize`
    is available as both a top-level function :func:`pandas.factorize`,
    and as a method :meth:`Series.factorize` and :meth:`Index.factorize`.

    Parameters
    ----------
    {values}{sort}
    use_na_sentinel : bool, default True
        If True, the sentinel -1 will be used for NaN values. If False,
        NaN values will be encoded as non-negative integers and will not drop the
        NaN from the uniques of the values.

        .. versionadded:: 1.5.0
    {size_hint}\

    Returns
    -------
    codes : ndarray
        An integer ndarray that's an indexer into `uniques`.
        ``uniques.take(codes)`` will have the same values as `values`.
    uniques : ndarray, Index, or Categorical
        The unique valid values. When `values` is Categorical, `uniques`
        is a Categorical. When `values` is some other pandas object, an
        `Index` is returned. Otherwise, a 1-D ndarray is returned.

        .. note::

           Even if there's a missing value in `values`, `uniques` will
           *not* contain an entry for it.

    See Also
    --------
    cut : Discretize continuous-valued array.
    unique : Find the unique value in an array.

    Notes
    -----
    Reference :ref:`the user guide <reshaping.factorize>` for more examples.

    Examples
    --------
    These examples all show factorize as a top-level method like
    ``pd.factorize(values)``. The results are identical for methods like
    :meth:`Series.factorize`.

    >>> codes, uniques = pd.factorize(np.array(['b', 'b', 'a', 'c', 'b'], dtype="O"))
    >>> codes
    array([0, 0, 1, 2, 0])
    >>> uniques
    array(['b', 'a', 'c'], dtype=object)

    With ``sort=True``, the `uniques` will be sorted, and `codes` will be
    shuffled so that the relationship is the maintained.

    >>> codes, uniques = pd.factorize(np.array(['b', 'b', 'a', 'c', 'b'], dtype="O"),
    ...                               sort=True)
    >>> codes
    array([1, 1, 0, 2, 1])
    >>> uniques
    array(['a', 'b', 'c'], dtype=object)

    When ``use_na_sentinel=True`` (the default), missing values are indicated in
    the `codes` with the sentinel value ``-1`` and missing values are not
    included in `uniques`.

    >>> codes, uniques = pd.factorize(np.array(['b', None, 'a', 'c', 'b'], dtype="O"))
    >>> codes
    array([ 0, -1,  1,  2,  0])
    >>> uniques
    array(['b', 'a', 'c'], dtype=object)

    Thus far, we've only factorized lists (which are internally coerced to
    NumPy arrays). When factorizing pandas objects, the type of `uniques`
    will differ. For Categoricals, a `Categorical` is returned.

    >>> cat = pd.Categorical(['a', 'a', 'c'], categories=['a', 'b', 'c'])
    >>> codes, uniques = pd.factorize(cat)
    >>> codes
    array([0, 0, 1])
    >>> uniques
    ['a', 'c']
    Categories (3, object): ['a', 'b', 'c']

    Notice that ``'b'`` is in ``uniques.categories``, despite not being
    present in ``cat.values``.

    For all other pandas objects, an Index of the appropriate type is
    returned.

    >>> cat = pd.Series(['a', 'a', 'c'])
    >>> codes, uniques = pd.factorize(cat)
    >>> codes
    array([0, 0, 1])
    >>> uniques
    Index(['a', 'c'], dtype='object')

    If NaN is in the values, and we want to include NaN in the uniques of the
    values, it can be achieved by setting ``use_na_sentinel=False``.

    >>> values = np.array([1, 2, 1, np.nan])
    >>> codes, uniques = pd.factorize(values)  # default: use_na_sentinel=True
    >>> codes
    array([ 0,  1,  0, -1])
    >>> uniques
    array([1., 2.])

    >>> codes, uniques = pd.factorize(values, use_na_sentinel=False)
    >>> codes
    array([0, 1, 0, 2])
    >>> uniques
    array([ 1.,  2., nan])
    """
    # Implementation notes: This method is responsible for 3 things
    # 1.) coercing data to array-like (ndarray, Index, extension array)
    # 2.) factorizing codes and uniques
    # 3.) Maybe boxing the uniques in an Index
    #
    # Step 2 is dispatched to extension types (like Categorical). They are
    # responsible only for factorization. All data coercion, sorting and boxing
    # should happen here.
    if isinstance(values, (ABCIndex, ABCSeries)):
        return values.factorize(sort=sort, use_na_sentinel=use_na_sentinel)

    values = _ensure_arraylike(values, func_name="factorize")
    original = values

    if (
        isinstance(values, (ABCDatetimeArray, ABCTimedeltaArray))
        and values.freq is not None
    ):
        # The presence of 'freq' means we can fast-path sorting and know there
        #  aren't NAs
        codes, uniques = values.factorize(sort=sort)
        return codes, uniques

    elif not isinstance(values, np.ndarray):
        # i.e. ExtensionArray
        codes, uniques = values.factorize(use_na_sentinel=use_na_sentinel)

    else:
        values = np.asarray(values)  # convert DTA/TDA/MultiIndex

        if not use_na_sentinel and values.dtype == object:
            # factorize can now handle differentiating various types of null values.
            # These can only occur when the array has object dtype.
            # However, for backwards compatibility we only use the null for the
            # provided dtype. This may be revisited in the future, see GH#48476.
            null_mask = isna(values)
            if null_mask.any():
                na_value = na_value_for_dtype(values.dtype, compat=False)
                # Don't modify (potentially user-provided) array
                values = np.where(null_mask, na_value, values)

        codes, uniques = factorize_array(
            values,
            use_na_sentinel=use_na_sentinel,
            size_hint=size_hint,
        )

    if sort and len(uniques) > 0:
        uniques, codes = safe_sort(
            uniques,
            codes,
            use_na_sentinel=use_na_sentinel,
            assume_unique=True,
            verify=False,
        )

    uniques = _reconstruct_data(uniques, original.dtype, original)

    return codes, uniques


def value_counts(
    values,
    sort: bool = True,
    ascending: bool = False,
    normalize: bool = False,
    bins=None,
    dropna: bool = True,
) -> Series:
    """
    Compute a histogram of the counts of non-null values.

    Parameters
    ----------
    values : ndarray (1-d)
    sort : bool, default True
        Sort by values
    ascending : bool, default False
        Sort in ascending order
    normalize: bool, default False
        If True then compute a relative histogram
    bins : integer, optional
        Rather than count values, group them into half-open bins,
        convenience for pd.cut, only works with numeric data
    dropna : bool, default True
        Don't include counts of NaN

    Returns
    -------
    Series
    """
    warnings.warn(
        # GH#53493
        "pandas.value_counts is deprecated and will be removed in a "
        "future version. Use pd.Series(obj).value_counts() instead.",
        FutureWarning,
        stacklevel=find_stack_level(),
    )
    return value_counts_internal(
        values,
        sort=sort,
        ascending=ascending,
        normalize=normalize,
        bins=bins,
        dropna=dropna,
    )


def value_counts_internal(
    values,
    sort: bool = True,
    ascending: bool = False,
    normalize: bool = False,
    bins=None,
    dropna: bool = True,
) -> Series:
    from pandas import (
        Index,
        Series,
    )

    index_name = getattr(values, "name", None)
    name = "proportion" if normalize else "count"

    if bins is not None:
        from pandas.core.reshape.tile import cut

        if isinstance(values, Series):
            values = values._values

        try:
            ii = cut(values, bins, include_lowest=True)
        except TypeError as err:
            raise TypeError("bins argument only works with numeric data.") from err

        # count, remove nulls (from the index), and but the bins
        result = ii.value_counts(dropna=dropna)
        result.name = name
        result = result[result.index.notna()]
        result.index = result.index.astype("interval")
        result = result.sort_index()

        # if we are dropna and we have NO values
        if dropna and (result._values == 0).all():
            result = result.iloc[0:0]

        # normalizing is by len of all (regardless of dropna)
        counts = np.array([len(ii)])

    else:
        if is_extension_array_dtype(values):
            # handle Categorical and sparse,
            result = Series(values, copy=False)._values.value_counts(dropna=dropna)
            result.name = name
            result.index.name = index_name
            counts = result._values
            if not isinstance(counts, np.ndarray):
                # e.g. ArrowExtensionArray
                counts = np.asarray(counts)

        elif isinstance(values, ABCMultiIndex):
            # GH49558
            levels = list(range(values.nlevels))
            result = (
                Series(index=values, name=name)
                .groupby(level=levels, dropna=dropna)
                .size()
            )
            result.index.names = values.names
            counts = result._values

        else:
            values = _ensure_arraylike(values, func_name="value_counts")
            keys, counts, _ = value_counts_arraylike(values, dropna)
            if keys.dtype == np.float16:
                keys = keys.astype(np.float32)

            # For backwards compatibility, we let Index do its normal type
            #  inference, _except_ for if if infers from object to bool.
            idx = Index(keys)
            if idx.dtype == bool and keys.dtype == object:
                idx = idx.astype(object)
            elif (
                idx.dtype != keys.dtype  # noqa: PLR1714  # # pylint: disable=R1714
                and idx.dtype != "string[pyarrow_numpy]"
            ):
                warnings.warn(
                    # GH#56161
                    "The behavior of value_counts with object-dtype is deprecated. "
                    "In a future version, this will *not* perform dtype inference "
                    "on the resulting index. To retain the old behavior, use "
                    "`result.index = result.index.infer_objects()`",
                    FutureWarning,
                    stacklevel=find_stack_level(),
                )
            idx.name = index_name

            result = Series(counts, index=idx, name=name, copy=False)

    if sort:
        result = result.sort_values(ascending=ascending)

    if normalize:
        result = result / counts.sum()

    return result


# Called once from SparseArray, otherwise could be private
def value_counts_arraylike(
    values: np.ndarray, dropna: bool, mask: npt.NDArray[np.bool_] | None = None
) -> tuple[ArrayLike, npt.NDArray[np.int64], int]:
    """
    Parameters
    ----------
    values : np.ndarray
    dropna : bool
    mask : np.ndarray[bool] or None, default None

    Returns
    -------
    uniques : np.ndarray
    counts : np.ndarray[np.int64]
    """
    original = values
    values = _ensure_data(values)

    keys, counts, na_counter = htable.value_count(values, dropna, mask=mask)

    if needs_i8_conversion(original.dtype):
        # datetime, timedelta, or period

        if dropna:
            mask = keys != iNaT
            keys, counts = keys[mask], counts[mask]

    res_keys = _reconstruct_data(keys, original.dtype, original)
    return res_keys, counts, na_counter


def duplicated(
    values: ArrayLike,
    keep: Literal["first", "last", False] = "first",
    mask: npt.NDArray[np.bool_] | None = None,
) -> npt.NDArray[np.bool_]:
    """
    Return boolean ndarray denoting duplicate values.

    Parameters
    ----------
    values : np.ndarray or ExtensionArray
        Array over which to check for duplicate values.
    keep : {'first', 'last', False}, default 'first'
        - ``first`` : Mark duplicates as ``True`` except for the first
          occurrence.
        - ``last`` : Mark duplicates as ``True`` except for the last
          occurrence.
        - False : Mark all duplicates as ``True``.
    mask : ndarray[bool], optional
        array indicating which elements to exclude from checking

    Returns
    -------
    duplicated : ndarray[bool]
    """
    values = _ensure_data(values)
    return htable.duplicated(values, keep=keep, mask=mask)


def mode(
    values: ArrayLike, dropna: bool = True, mask: npt.NDArray[np.bool_] | None = None
) -> ArrayLike:
    """
    Returns the mode(s) of an array.

    Parameters
    ----------
    values : array-like
        Array over which to check for duplicate values.
    dropna : bool, default True
        Don't consider counts of NaN/NaT.

    Returns
    -------
    np.ndarray or ExtensionArray
    """
    values = _ensure_arraylike(values, func_name="mode")
    original = values

    if needs_i8_conversion(values.dtype):
        # Got here with ndarray; dispatch to DatetimeArray/TimedeltaArray.
        values = ensure_wrapped_if_datetimelike(values)
        values = cast("ExtensionArray", values)
        return values._mode(dropna=dropna)

    values = _ensure_data(values)

    npresult, res_mask = htable.mode(values, dropna=dropna, mask=mask)
    if res_mask is not None:
        return npresult, res_mask  # type: ignore[return-value]

    try:
        npresult = np.sort(npresult)
    except TypeError as err:
        warnings.warn(
            f"Unable to sort modes: {err}",
            stacklevel=find_stack_level(),
        )

    result = _reconstruct_data(npresult, original.dtype, original)
    return result


def rank(
    values: ArrayLike,
    axis: AxisInt = 0,
    method: str = "average",
    na_option: str = "keep",
    ascending: bool = True,
    pct: bool = False,
) -> npt.NDArray[np.float64]:
    """
    Rank the values along a given axis.

    Parameters
    ----------
    values : np.ndarray or ExtensionArray
        Array whose values will be ranked. The number of dimensions in this
        array must not exceed 2.
    axis : int, default 0
        Axis over which to perform rankings.
    method : {'average', 'min', 'max', 'first', 'dense'}, default 'average'
        The method by which tiebreaks are broken during the ranking.
    na_option : {'keep', 'top'}, default 'keep'
        The method by which NaNs are placed in the ranking.
        - ``keep``: rank each NaN value with a NaN ranking
        - ``top``: replace each NaN with either +/- inf so that they
                   there are ranked at the top
    ascending : bool, default True
        Whether or not the elements should be ranked in ascending order.
    pct : bool, default False
        Whether or not to the display the returned rankings in integer form
        (e.g. 1, 2, 3) or in percentile form (e.g. 0.333..., 0.666..., 1).
    """
    is_datetimelike = needs_i8_conversion(values.dtype)
    values = _ensure_data(values)

    if values.ndim == 1:
        ranks = algos.rank_1d(
            values,
            is_datetimelike=is_datetimelike,
            ties_method=method,
            ascending=ascending,
            na_option=na_option,
            pct=pct,
        )
    elif values.ndim == 2:
        ranks = algos.rank_2d(
            values,
            axis=axis,
            is_datetimelike=is_datetimelike,
            ties_method=method,
            ascending=ascending,
            na_option=na_option,
            pct=pct,
        )
    else:
        raise TypeError("Array with ndim > 2 are not supported.")

    return ranks


def checked_add_with_arr(
    arr: npt.NDArray[np.int64],
    b: int | npt.NDArray[np.int64],
    arr_mask: npt.NDArray[np.bool_] | None = None,
    b_mask: npt.NDArray[np.bool_] | None = None,
) -> npt.NDArray[np.int64]:
    """
    Perform array addition that checks for underflow and overflow.

    Performs the addition of an int64 array and an int64 integer (or array)
    but checks that they do not result in overflow first. For elements that
    are indicated to be NaN, whether or not there is overflow for that element
    is automatically ignored.

    Parameters
    ----------
    arr : np.ndarray[int64] addend.
    b : array or scalar addend.
    arr_mask : np.ndarray[bool] or None, default None
        array indicating which elements to exclude from checking
    b_mask : np.ndarray[bool] or None, default None
        array or scalar indicating which element(s) to exclude from checking

    Returns
    -------
    sum : An array for elements x + b for each element x in arr if b is
          a scalar or an array for elements x + y for each element pair
          (x, y) in (arr, b).

    Raises
    ------
    OverflowError if any x + y exceeds the maximum or minimum int64 value.
    """
    # For performance reasons, we broadcast 'b' to the new array 'b2'
    # so that it has the same size as 'arr'.
    b2 = np.broadcast_to(b, arr.shape)
    if b_mask is not None:
        # We do the same broadcasting for b_mask as well.
        b2_mask = np.broadcast_to(b_mask, arr.shape)
    else:
        b2_mask = None

    # For elements that are NaN, regardless of their value, we should
    # ignore whether they overflow or not when doing the checked add.
    if arr_mask is not None and b2_mask is not None:
        not_nan = np.logical_not(arr_mask | b2_mask)
    elif arr_mask is not None:
        not_nan = np.logical_not(arr_mask)
    elif b_mask is not None:
        # error: Argument 1 to "__call__" of "_UFunc_Nin1_Nout1" has
        # incompatible type "Optional[ndarray[Any, dtype[bool_]]]";
        # expected "Union[_SupportsArray[dtype[Any]], _NestedSequence
        # [_SupportsArray[dtype[Any]]], bool, int, float, complex, str
        # , bytes, _NestedSequence[Union[bool, int, float, complex, str
        # , bytes]]]"
        not_nan = np.logical_not(b2_mask)  # type: ignore[arg-type]
    else:
        not_nan = np.empty(arr.shape, dtype=bool)
        not_nan.fill(True)

    # gh-14324: For each element in 'arr' and its corresponding element
    # in 'b2', we check the sign of the element in 'b2'. If it is positive,
    # we then check whether its sum with the element in 'arr' exceeds
    # np.iinfo(np.int64).max. If so, we have an overflow error. If it
    # it is negative, we then check whether its sum with the element in
    # 'arr' exceeds np.iinfo(np.int64).min. If so, we have an overflow
    # error as well.
    i8max = lib.i8max
    i8min = iNaT

    mask1 = b2 > 0
    mask2 = b2 < 0

    if not mask1.any():
        to_raise = ((i8min - b2 > arr) & not_nan).any()
    elif not mask2.any():
        to_raise = ((i8max - b2 < arr) & not_nan).any()
    else:
        to_raise = ((i8max - b2[mask1] < arr[mask1]) & not_nan[mask1]).any() or (
            (i8min - b2[mask2] > arr[mask2]) & not_nan[mask2]
        ).any()

    if to_raise:
        raise OverflowError("Overflow in int64 addition")

    result = arr + b
    if arr_mask is not None or b2_mask is not None:
        np.putmask(result, ~not_nan, iNaT)

    return result


# ---- #
# take #
# ---- #


def take(
    arr,
    indices: TakeIndexer,
    axis: AxisInt = 0,
    allow_fill: bool = False,
    fill_value=None,
):
    """
    Take elements from an array.

    Parameters
    ----------
    arr : array-like or scalar value
        Non array-likes (sequences/scalars without a dtype) are coerced
        to an ndarray.

        .. deprecated:: 2.1.0
            Passing an argument other than a numpy.ndarray, ExtensionArray,
            Index, or Series is deprecated.

    indices : sequence of int or one-dimensional np.ndarray of int
        Indices to be taken.
    axis : int, default 0
        The axis over which to select values.
    allow_fill : bool, default False
        How to handle negative values in `indices`.

        * False: negative values in `indices` indicate positional indices
          from the right (the default). This is similar to :func:`numpy.take`.

        * True: negative values in `indices` indicate
          missing values. These values are set to `fill_value`. Any other
          negative values raise a ``ValueError``.

    fill_value : any, optional
        Fill value to use for NA-indices when `allow_fill` is True.
        This may be ``None``, in which case the default NA value for
        the type (``self.dtype.na_value``) is used.

        For multi-dimensional `arr`, each *element* is filled with
        `fill_value`.

    Returns
    -------
    ndarray or ExtensionArray
        Same type as the input.

    Raises
    ------
    IndexError
        When `indices` is out of bounds for the array.
    ValueError
        When the indexer contains negative values other than ``-1``
        and `allow_fill` is True.

    Notes
    -----
    When `allow_fill` is False, `indices` may be whatever dimensionality
    is accepted by NumPy for `arr`.

    When `allow_fill` is True, `indices` should be 1-D.

    See Also
    --------
    numpy.take : Take elements from an array along an axis.

    Examples
    --------
    >>> import pandas as pd

    With the default ``allow_fill=False``, negative numbers indicate
    positional indices from the right.

    >>> pd.api.extensions.take(np.array([10, 20, 30]), [0, 0, -1])
    array([10, 10, 30])

    Setting ``allow_fill=True`` will place `fill_value` in those positions.

    >>> pd.api.extensions.take(np.array([10, 20, 30]), [0, 0, -1], allow_fill=True)
    array([10., 10., nan])

    >>> pd.api.extensions.take(np.array([10, 20, 30]), [0, 0, -1], allow_fill=True,
    ...      fill_value=-10)
    array([ 10,  10, -10])
    """
    if not isinstance(arr, (np.ndarray, ABCExtensionArray, ABCIndex, ABCSeries)):
        # GH#52981
        warnings.warn(
            "pd.api.extensions.take accepting non-standard inputs is deprecated "
            "and will raise in a future version. Pass either a numpy.ndarray, "
            "ExtensionArray, Index, or Series instead.",
            FutureWarning,
            stacklevel=find_stack_level(),
        )

    if not is_array_like(arr):
        arr = np.asarray(arr)

    indices = ensure_platform_int(indices)

    if allow_fill:
        # Pandas style, -1 means NA
        validate_indices(indices, arr.shape[axis])
        result = take_nd(
            arr, indices, axis=axis, allow_fill=True, fill_value=fill_value
        )
    else:
        # NumPy style
        result = arr.take(indices, axis=axis)
    return result


# ------------ #
# searchsorted #
# ------------ #


def searchsorted(
    arr: ArrayLike,
    value: NumpyValueArrayLike | ExtensionArray,
    side: Literal["left", "right"] = "left",
    sorter: NumpySorter | None = None,
) -> npt.NDArray[np.intp] | np.intp:
    """
    Find indices where elements should be inserted to maintain order.

    Find the indices into a sorted array `arr` (a) such that, if the
    corresponding elements in `value` were inserted before the indices,
    the order of `arr` would be preserved.

    Assuming that `arr` is sorted:

    ======  ================================
    `side`  returned index `i` satisfies
    ======  ================================
    left    ``arr[i-1] < value <= self[i]``
    right   ``arr[i-1] <= value < self[i]``
    ======  ================================

    Parameters
    ----------
    arr: np.ndarray, ExtensionArray, Series
        Input array. If `sorter` is None, then it must be sorted in
        ascending order, otherwise `sorter` must be an array of indices
        that sort it.
    value : array-like or scalar
        Values to insert into `arr`.
    side : {'left', 'right'}, optional
        If 'left', the index of the first suitable location found is given.
        If 'right', return the last such index.  If there is no suitable
        index, return either 0 or N (where N is the length of `self`).
    sorter : 1-D array-like, optional
        Optional array of integer indices that sort array a into ascending
        order. They are typically the result of argsort.

    Returns
    -------
    array of ints or int
        If value is array-like, array of insertion points.
        If value is scalar, a single integer.

    See Also
    --------
    numpy.searchsorted : Similar method from NumPy.
    """
    if sorter is not None:
        sorter = ensure_platform_int(sorter)

    if (
        isinstance(arr, np.ndarray)
        and arr.dtype.kind in "iu"
        and (is_integer(value) or is_integer_dtype(value))
    ):
        # if `arr` and `value` have different dtypes, `arr` would be
        # recast by numpy, causing a slow search.
        # Before searching below, we therefore try to give `value` the
        # same dtype as `arr`, while guarding against integer overflows.
        iinfo = np.iinfo(arr.dtype.type)
        value_arr = np.array([value]) if is_integer(value) else np.array(value)
        if (value_arr >= iinfo.min).all() and (value_arr <= iinfo.max).all():
            # value within bounds, so no overflow, so can convert value dtype
            # to dtype of arr
            dtype = arr.dtype
        else:
            dtype = value_arr.dtype

        if is_integer(value):
            # We know that value is int
            value = cast(int, dtype.type(value))
        else:
            value = pd_array(cast(ArrayLike, value), dtype=dtype)
    else:
        # E.g. if `arr` is an array with dtype='datetime64[ns]'
        # and `value` is a pd.Timestamp, we may need to convert value
        arr = ensure_wrapped_if_datetimelike(arr)

    # Argument 1 to "searchsorted" of "ndarray" has incompatible type
    # "Union[NumpyValueArrayLike, ExtensionArray]"; expected "NumpyValueArrayLike"
    return arr.searchsorted(value, side=side, sorter=sorter)  # type: ignore[arg-type]


# ---- #
# diff #
# ---- #

_diff_special = {"float64", "float32", "int64", "int32", "int16", "int8"}


def diff(arr, n: int, axis: AxisInt = 0):
    """
    difference of n between self,
    analogous to s-s.shift(n)

    Parameters
    ----------
    arr : ndarray or ExtensionArray
    n : int
        number of periods
    axis : {0, 1}
        axis to shift on
    stacklevel : int, default 3
        The stacklevel for the lost dtype warning.

    Returns
    -------
    shifted
    """

    n = int(n)
    na = np.nan
    dtype = arr.dtype

    is_bool = is_bool_dtype(dtype)
    if is_bool:
        op = operator.xor
    else:
        op = operator.sub

    if isinstance(dtype, NumpyEADtype):
        # NumpyExtensionArray cannot necessarily hold shifted versions of itself.
        arr = arr.to_numpy()
        dtype = arr.dtype

    if not isinstance(arr, np.ndarray):
        # i.e ExtensionArray
        if hasattr(arr, f"__{op.__name__}__"):
            if axis != 0:
                raise ValueError(f"cannot diff {type(arr).__name__} on axis={axis}")
            return op(arr, arr.shift(n))
        else:
            raise TypeError(
                f"{type(arr).__name__} has no 'diff' method. "
                "Convert to a suitable dtype prior to calling 'diff'."
            )

    is_timedelta = False
    if arr.dtype.kind in "mM":
        dtype = np.int64
        arr = arr.view("i8")
        na = iNaT
        is_timedelta = True

    elif is_bool:
        # We have to cast in order to be able to hold np.nan
        dtype = np.object_

    elif dtype.kind in "iu":
        # We have to cast in order to be able to hold np.nan

        # int8, int16 are incompatible with float64,
        # see https://github.com/cython/cython/issues/2646
        if arr.dtype.name in ["int8", "int16"]:
            dtype = np.float32
        else:
            dtype = np.float64

    orig_ndim = arr.ndim
    if orig_ndim == 1:
        # reshape so we can always use algos.diff_2d
        arr = arr.reshape(-1, 1)
        # TODO: require axis == 0

    dtype = np.dtype(dtype)
    out_arr = np.empty(arr.shape, dtype=dtype)

    na_indexer = [slice(None)] * 2
    na_indexer[axis] = slice(None, n) if n >= 0 else slice(n, None)
    out_arr[tuple(na_indexer)] = na

    if arr.dtype.name in _diff_special:
        # TODO: can diff_2d dtype specialization troubles be fixed by defining
        #  out_arr inside diff_2d?
        algos.diff_2d(arr, out_arr, n, axis, datetimelike=is_timedelta)
    else:
        # To keep mypy happy, _res_indexer is a list while res_indexer is
        #  a tuple, ditto for lag_indexer.
        _res_indexer = [slice(None)] * 2
        _res_indexer[axis] = slice(n, None) if n >= 0 else slice(None, n)
        res_indexer = tuple(_res_indexer)

        _lag_indexer = [slice(None)] * 2
        _lag_indexer[axis] = slice(None, -n) if n > 0 else slice(-n, None)
        lag_indexer = tuple(_lag_indexer)

        out_arr[res_indexer] = op(arr[res_indexer], arr[lag_indexer])

    if is_timedelta:
        out_arr = out_arr.view("timedelta64[ns]")

    if orig_ndim == 1:
        out_arr = out_arr[:, 0]
    return out_arr


# --------------------------------------------------------------------
# Helper functions


# Note: safe_sort is in algorithms.py instead of sorting.py because it is
#  low-dependency, is used in this module, and used private methods from
#  this module.
def safe_sort(
    values: Index | ArrayLike,
    codes: npt.NDArray[np.intp] | None = None,
    use_na_sentinel: bool = True,
    assume_unique: bool = False,
    verify: bool = True,
) -> AnyArrayLike | tuple[AnyArrayLike, np.ndarray]:
    """
    Sort ``values`` and reorder corresponding ``codes``.

    ``values`` should be unique if ``codes`` is not None.
    Safe for use with mixed types (int, str), orders ints before strs.

    Parameters
    ----------
    values : list-like
        Sequence; must be unique if ``codes`` is not None.
    codes : np.ndarray[intp] or None, default None
        Indices to ``values``. All out of bound indices are treated as
        "not found" and will be masked with ``-1``.
    use_na_sentinel : bool, default True
        If True, the sentinel -1 will be used for NaN values. If False,
        NaN values will be encoded as non-negative integers and will not drop the
        NaN from the uniques of the values.
    assume_unique : bool, default False
        When True, ``values`` are assumed to be unique, which can speed up
        the calculation. Ignored when ``codes`` is None.
    verify : bool, default True
        Check if codes are out of bound for the values and put out of bound
        codes equal to ``-1``. If ``verify=False``, it is assumed there
        are no out of bound codes. Ignored when ``codes`` is None.

    Returns
    -------
    ordered : AnyArrayLike
        Sorted ``values``
    new_codes : ndarray
        Reordered ``codes``; returned when ``codes`` is not None.

    Raises
    ------
    TypeError
        * If ``values`` is not list-like or if ``codes`` is neither None
        nor list-like
        * If ``values`` cannot be sorted
    ValueError
        * If ``codes`` is not None and ``values`` contain duplicates.
    """
    if not isinstance(values, (np.ndarray, ABCExtensionArray, ABCIndex)):
        raise TypeError(
            "Only np.ndarray, ExtensionArray, and Index objects are allowed to "
            "be passed to safe_sort as values"
        )

    sorter = None
    ordered: AnyArrayLike

    if (
        not isinstance(values.dtype, ExtensionDtype)
        and lib.infer_dtype(values, skipna=False) == "mixed-integer"
    ):
        ordered = _sort_mixed(values)
    else:
        try:
            sorter = values.argsort()
            ordered = values.take(sorter)
        except TypeError:
            # Previous sorters failed or were not applicable, try `_sort_mixed`
            # which would work, but which fails for special case of 1d arrays
            # with tuples.
            if values.size and isinstance(values[0], tuple):
                # error: Argument 1 to "_sort_tuples" has incompatible type
                # "Union[Index, ExtensionArray, ndarray[Any, Any]]"; expected
                # "ndarray[Any, Any]"
                ordered = _sort_tuples(values)  # type: ignore[arg-type]
            else:
                ordered = _sort_mixed(values)

    # codes:

    if codes is None:
        return ordered

    if not is_list_like(codes):
        raise TypeError(
            "Only list-like objects or None are allowed to "
            "be passed to safe_sort as codes"
        )
    codes = ensure_platform_int(np.asarray(codes))

    if not assume_unique and not len(unique(values)) == len(values):
        raise ValueError("values should be unique if codes is not None")

    if sorter is None:
        # mixed types
        # error: Argument 1 to "_get_hashtable_algo" has incompatible type
        # "Union[Index, ExtensionArray, ndarray[Any, Any]]"; expected
        # "ndarray[Any, Any]"
        hash_klass, values = _get_hashtable_algo(values)  # type: ignore[arg-type]
        t = hash_klass(len(values))
        t.map_locations(values)
        sorter = ensure_platform_int(t.lookup(ordered))

    if use_na_sentinel:
        # take_nd is faster, but only works for na_sentinels of -1
        order2 = sorter.argsort()
        if verify:
            mask = (codes < -len(values)) | (codes >= len(values))
            codes[mask] = 0
        else:
            mask = None
        new_codes = take_nd(order2, codes, fill_value=-1)
    else:
        reverse_indexer = np.empty(len(sorter), dtype=int)
        reverse_indexer.put(sorter, np.arange(len(sorter)))
        # Out of bound indices will be masked with `-1` next, so we
        # may deal with them here without performance loss using `mode='wrap'`
        new_codes = reverse_indexer.take(codes, mode="wrap")

        if use_na_sentinel:
            mask = codes == -1
            if verify:
                mask = mask | (codes < -len(values)) | (codes >= len(values))

    if use_na_sentinel and mask is not None:
        np.putmask(new_codes, mask, -1)

    return ordered, ensure_platform_int(new_codes)


def _sort_mixed(values) -> AnyArrayLike:
    """order ints before strings before nulls in 1d arrays"""
    str_pos = np.array([isinstance(x, str) for x in values], dtype=bool)
    null_pos = np.array([isna(x) for x in values], dtype=bool)
    num_pos = ~str_pos & ~null_pos
    str_argsort = np.argsort(values[str_pos])
    num_argsort = np.argsort(values[num_pos])
    # convert boolean arrays to positional indices, then order by underlying values
    str_locs = str_pos.nonzero()[0].take(str_argsort)
    num_locs = num_pos.nonzero()[0].take(num_argsort)
    null_locs = null_pos.nonzero()[0]
    locs = np.concatenate([num_locs, str_locs, null_locs])
    return values.take(locs)


def _sort_tuples(values: np.ndarray) -> np.ndarray:
    """
    Convert array of tuples (1d) to array of arrays (2d).
    We need to keep the columns separately as they contain different types and
    nans (can't use `np.sort` as it may fail when str and nan are mixed in a
    column as types cannot be compared).
    """
    from pandas.core.internals.construction import to_arrays
    from pandas.core.sorting import lexsort_indexer

    arrays, _ = to_arrays(values, None)
    indexer = lexsort_indexer(arrays, orders=True)
    return values[indexer]


def union_with_duplicates(
    lvals: ArrayLike | Index, rvals: ArrayLike | Index
) -> ArrayLike | Index:
    """
    Extracts the union from lvals and rvals with respect to duplicates and nans in
    both arrays.

    Parameters
    ----------
    lvals: np.ndarray or ExtensionArray
        left values which is ordered in front.
    rvals: np.ndarray or ExtensionArray
        right values ordered after lvals.

    Returns
    -------
    np.ndarray or ExtensionArray
        Containing the unsorted union of both arrays.

    Notes
    -----
    Caller is responsible for ensuring lvals.dtype == rvals.dtype.
    """
    from pandas import Series

    with warnings.catch_warnings():
        # filter warning from object dtype inference; we will end up discarding
        # the index here, so the deprecation does not affect the end result here.
        warnings.filterwarnings(
            "ignore",
            "The behavior of value_counts with object-dtype is deprecated",
            category=FutureWarning,
        )
        l_count = value_counts_internal(lvals, dropna=False)
        r_count = value_counts_internal(rvals, dropna=False)
    l_count, r_count = l_count.align(r_count, fill_value=0)
    final_count = np.maximum(l_count.values, r_count.values)
    final_count = Series(final_count, index=l_count.index, dtype="int", copy=False)
    if isinstance(lvals, ABCMultiIndex) and isinstance(rvals, ABCMultiIndex):
        unique_vals = lvals.append(rvals).unique()
    else:
        if isinstance(lvals, ABCIndex):
            lvals = lvals._values
        if isinstance(rvals, ABCIndex):
            rvals = rvals._values
        # error: List item 0 has incompatible type "Union[ExtensionArray,
        # ndarray[Any, Any], Index]"; expected "Union[ExtensionArray,
        # ndarray[Any, Any]]"
        combined = concat_compat([lvals, rvals])  # type: ignore[list-item]
        unique_vals = unique(combined)
        unique_vals = ensure_wrapped_if_datetimelike(unique_vals)
    repeats = final_count.reindex(unique_vals).values
    return np.repeat(unique_vals, repeats)


def map_array(
    arr: ArrayLike,
    mapper,
    na_action: Literal["ignore"] | None = None,
    convert: bool = True,
) -> np.ndarray | ExtensionArray | Index:
    """
    Map values using an input mapping or function.

    Parameters
    ----------
    mapper : function, dict, or Series
        Mapping correspondence.
    na_action : {None, 'ignore'}, default None
        If 'ignore', propagate NA values, without passing them to the
        mapping correspondence.
    convert : bool, default True
        Try to find better dtype for elementwise function results. If
        False, leave as dtype=object.

    Returns
    -------
    Union[ndarray, Index, ExtensionArray]
        The output of the mapping function applied to the array.
        If the function returns a tuple with more than one element
        a MultiIndex will be returned.
    """
    if na_action not in (None, "ignore"):
        msg = f"na_action must either be 'ignore' or None, {na_action} was passed"
        raise ValueError(msg)

    # we can fastpath dict/Series to an efficient map
    # as we know that we are not going to have to yield
    # python types
    if is_dict_like(mapper):
        if isinstance(mapper, dict) and hasattr(mapper, "__missing__"):
            # If a dictionary subclass defines a default value method,
            # convert mapper to a lookup function (GH #15999).
            dict_with_default = mapper
            mapper = lambda x: dict_with_default[
                np.nan if isinstance(x, float) and np.isnan(x) else x
            ]
        else:
            # Dictionary does not have a default. Thus it's safe to
            # convert to an Series for efficiency.
            # we specify the keys here to handle the
            # possibility that they are tuples

            # The return value of mapping with an empty mapper is
            # expected to be pd.Series(np.nan, ...). As np.nan is
            # of dtype float64 the return value of this method should
            # be float64 as well
            from pandas import Series

            if len(mapper) == 0:
                mapper = Series(mapper, dtype=np.float64)
            else:
                mapper = Series(mapper)

    if isinstance(mapper, ABCSeries):
        if na_action == "ignore":
            mapper = mapper[mapper.index.notna()]

        # Since values were input this means we came from either
        # a dict or a series and mapper should be an index
        indexer = mapper.index.get_indexer(arr)
        new_values = take_nd(mapper._values, indexer)

        return new_values

    if not len(arr):
        return arr.copy()

    # we must convert to python types
    values = arr.astype(object, copy=False)
    if na_action is None:
        return lib.map_infer(values, mapper, convert=convert)
    else:
        return lib.map_infer_mask(
            values, mapper, mask=isna(values).view(np.uint8), convert=convert
        )