lib.pyx

from collections import abc
from decimal import Decimal
import warnings

import cython
from cython import Py_ssize_t

from cpython.datetime cimport (
    PyDate_Check,
    PyDateTime_Check,
    PyDateTime_IMPORT,
    PyDelta_Check,
    PyTime_Check,
)
from cpython.iterator cimport PyIter_Check
from cpython.number cimport PyNumber_Check
from cpython.object cimport (
    Py_EQ,
    PyObject_RichCompareBool,
)
from cpython.ref cimport Py_INCREF
from cpython.sequence cimport PySequence_Check
from cpython.tuple cimport (
    PyTuple_New,
    PyTuple_SET_ITEM,
)

PyDateTime_IMPORT

import numpy as np

cimport numpy as cnp
from numpy cimport (
    NPY_OBJECT,
    PyArray_Check,
    PyArray_GETITEM,
    PyArray_ITER_DATA,
    PyArray_ITER_NEXT,
    PyArray_IterNew,
    complex128_t,
    flatiter,
    float32_t,
    float64_t,
    int64_t,
    intp_t,
    ndarray,
    uint8_t,
    uint64_t,
)

cnp.import_array()

cdef extern from "numpy/arrayobject.h":
    # cython's numpy.dtype specification is incorrect, which leads to
    # errors in issubclass(self.dtype.type, np.bool_), so we directly
    # include the correct version
    # https://github.com/cython/cython/issues/2022

    ctypedef class numpy.dtype [object PyArray_Descr]:
        # Use PyDataType_* macros when possible, however there are no macros
        # for accessing some of the fields, so some are defined. Please
        # ask on cython-dev if you need more.
        cdef:
            int type_num
            int itemsize "elsize"
            char byteorder
            object fields
            tuple names


cdef extern from "src/parse_helper.h":
    int floatify(object, float64_t *result, int *maybe_int) except -1

from pandas._libs cimport util
from pandas._libs.util cimport (
    INT64_MAX,
    INT64_MIN,
    UINT64_MAX,
    is_nan,
)

from pandas._libs.tslib import array_to_datetime
from pandas._libs.tslibs.period import Period

from pandas._libs.missing cimport (
    C_NA,
    checknull,
    is_matching_na,
    is_null_datetime64,
    is_null_timedelta64,
    isnaobj,
)
from pandas._libs.tslibs.conversion cimport convert_to_tsobject
from pandas._libs.tslibs.nattype cimport (
    NPY_NAT,
    c_NaT as NaT,
    checknull_with_nat,
)
from pandas._libs.tslibs.offsets cimport is_offset_object
from pandas._libs.tslibs.period cimport is_period_object
from pandas._libs.tslibs.timedeltas cimport convert_to_timedelta64
from pandas._libs.tslibs.timezones cimport tz_compare

# constants that will be compared to potentially arbitrarily large
# python int
cdef:
    object oINT64_MAX = <int64_t>INT64_MAX
    object oINT64_MIN = <int64_t>INT64_MIN
    object oUINT64_MAX = <uint64_t>UINT64_MAX

    float64_t NaN = <float64_t>np.NaN


@cython.wraparound(False)
@cython.boundscheck(False)
def memory_usage_of_objects(arr: object[:]) -> int64_t:
    """
    Return the memory usage of an object array in bytes.

    Does not include the actual bytes of the pointers
    """
    i: Py_ssize_t
    n: Py_ssize_t
    size: int64_t

    size = 0
    n = len(arr)
    for i in range(n):
        size += arr[i].__sizeof__()
    return size


# ----------------------------------------------------------------------


def is_scalar(val: object) -> bool:
    """
    Return True if given object is scalar.

    Parameters
    ----------
    val : object
        This includes:

        - numpy array scalar (e.g. np.int64)
        - Python builtin numerics
        - Python builtin byte arrays and strings
        - None
        - datetime.datetime
        - datetime.timedelta
        - Period
        - decimal.Decimal
        - Interval
        - DateOffset
        - Fraction
        - Number.

    Returns
    -------
    bool
        Return True if given object is scalar.

    Examples
    --------
    >>> dt = datetime.datetime(2018, 10, 3)
    >>> pd.api.types.is_scalar(dt)
    True

    >>> pd.api.types.is_scalar([2, 3])
    False

    >>> pd.api.types.is_scalar({0: 1, 2: 3})
    False

    >>> pd.api.types.is_scalar((0, 2))
    False

    pandas supports PEP 3141 numbers:

    >>> from fractions import Fraction
    >>> pd.api.types.is_scalar(Fraction(3, 5))
    True
    """

    # Start with C-optimized checks
    if (cnp.PyArray_IsAnyScalar(val)
            # PyArray_IsAnyScalar is always False for bytearrays on Py3
            or PyDate_Check(val)
            or PyDelta_Check(val)
            or PyTime_Check(val)
            # We differ from numpy, which claims that None is not scalar;
            # see np.isscalar
            or val is C_NA
            or val is None):
        return True

    # Next use C-optimized checks to exclude common non-scalars before falling
    #  back to non-optimized checks.
    if PySequence_Check(val):
        # e.g. list, tuple
        # includes np.ndarray, Series which PyNumber_Check can return True for
        return False

    # Note: PyNumber_Check check includes Decimal, Fraction, numbers.Number
    return (PyNumber_Check(val)
            or is_period_object(val)
            or is_interval(val)
            or is_offset_object(val))


def is_iterator(obj: object) -> bool:
    """
    Check if the object is an iterator.

    This is intended for generators, not list-like objects.

    Parameters
    ----------
    obj : The object to check

    Returns
    -------
    is_iter : bool
        Whether `obj` is an iterator.

    Examples
    --------
    >>> is_iterator((x for x in []))
    True
    >>> is_iterator([1, 2, 3])
    False
    >>> is_iterator(datetime(2017, 1, 1))
    False
    >>> is_iterator("foo")
    False
    >>> is_iterator(1)
    False
    """
    return PyIter_Check(obj)


def item_from_zerodim(val: object) -> object:
    """
    If the value is a zerodim array, return the item it contains.

    Parameters
    ----------
    val : object

    Returns
    -------
    object

    Examples
    --------
    >>> item_from_zerodim(1)
    1
    >>> item_from_zerodim('foobar')
    'foobar'
    >>> item_from_zerodim(np.array(1))
    1
    >>> item_from_zerodim(np.array([1]))
    array([1])
    """
    if cnp.PyArray_IsZeroDim(val):
        return cnp.PyArray_ToScalar(cnp.PyArray_DATA(val), val)
    return val


@cython.wraparound(False)
@cython.boundscheck(False)
def fast_unique_multiple(list arrays, sort: bool = True) -> list:
    """
    Generate a list of unique values from a list of arrays.

    Parameters
    ----------
    list : array-like
        List of array-like objects.
    sort : bool
        Whether or not to sort the resulting unique list.

    Returns
    -------
    list of unique values
    """
    cdef:
        ndarray[object] buf
        Py_ssize_t k = len(arrays)
        Py_ssize_t i, j, n
        list uniques = []
        dict table = {}
        object val, stub = 0

    for i in range(k):
        buf = arrays[i]
        n = len(buf)
        for j in range(n):
            val = buf[j]
            if val not in table:
                table[val] = stub
                uniques.append(val)

    if sort is None:
        try:
            uniques.sort()
        except TypeError:
            warnings.warn(
                "The values in the array are unorderable. "
                "Pass `sort=False` to suppress this warning.",
                RuntimeWarning,
            )
            pass

    return uniques


@cython.wraparound(False)
@cython.boundscheck(False)
def fast_unique_multiple_list(lists: list, sort: bool = True) -> list:
    cdef:
        list buf
        Py_ssize_t k = len(lists)
        Py_ssize_t i, j, n
        list uniques = []
        dict table = {}
        object val, stub = 0

    for i in range(k):
        buf = lists[i]
        n = len(buf)
        for j in range(n):
            val = buf[j]
            if val not in table:
                table[val] = stub
                uniques.append(val)
    if sort:
        try:
            uniques.sort()
        except TypeError:
            pass

    return uniques


@cython.wraparound(False)
@cython.boundscheck(False)
def fast_unique_multiple_list_gen(object gen, bint sort=True) -> list:
    """
    Generate a list of unique values from a generator of lists.

    Parameters
    ----------
    gen : generator object
        Generator of lists from which the unique list is created.
    sort : bool
        Whether or not to sort the resulting unique list.

    Returns
    -------
    list of unique values
    """
    cdef:
        list buf
        Py_ssize_t j, n
        list uniques = []
        dict table = {}
        object val, stub = 0

    for buf in gen:
        n = len(buf)
        for j in range(n):
            val = buf[j]
            if val not in table:
                table[val] = stub
                uniques.append(val)
    if sort:
        try:
            uniques.sort()
        except TypeError:
            pass

    return uniques


@cython.wraparound(False)
@cython.boundscheck(False)
def dicts_to_array(dicts: list, columns: list):
    cdef:
        Py_ssize_t i, j, k, n
        ndarray[object, ndim=2] result
        dict row
        object col, onan = np.nan

    k = len(columns)
    n = len(dicts)

    result = np.empty((n, k), dtype='O')

    for i in range(n):
        row = dicts[i]
        for j in range(k):
            col = columns[j]
            if col in row:
                result[i, j] = row[col]
            else:
                result[i, j] = onan

    return result


def fast_zip(list ndarrays) -> ndarray[object]:
    """
    For zipping multiple ndarrays into an ndarray of tuples.
    """
    cdef:
        Py_ssize_t i, j, k, n
        ndarray[object] result
        flatiter it
        object val, tup

    k = len(ndarrays)
    n = len(ndarrays[0])

    result = np.empty(n, dtype=object)

    # initialize tuples on first pass
    arr = ndarrays[0]
    it = <flatiter>PyArray_IterNew(arr)
    for i in range(n):
        val = PyArray_GETITEM(arr, PyArray_ITER_DATA(it))
        tup = PyTuple_New(k)

        PyTuple_SET_ITEM(tup, 0, val)
        Py_INCREF(val)
        result[i] = tup
        PyArray_ITER_NEXT(it)

    for j in range(1, k):
        arr = ndarrays[j]
        it = <flatiter>PyArray_IterNew(arr)
        if len(arr) != n:
            raise ValueError("all arrays must be same length")

        for i in range(n):
            val = PyArray_GETITEM(arr, PyArray_ITER_DATA(it))
            PyTuple_SET_ITEM(result[i], j, val)
            Py_INCREF(val)
            PyArray_ITER_NEXT(it)

    return result


def get_reverse_indexer(const int64_t[:] indexer, Py_ssize_t length):
    """
    Reverse indexing operation.

    Given `indexer`, make `indexer_inv` of it, such that::

        indexer_inv[indexer[x]] = x

    .. note:: If indexer is not unique, only first occurrence is accounted.
    """
    cdef:
        Py_ssize_t i, n = len(indexer)
        ndarray[int64_t] rev_indexer
        int64_t idx

    rev_indexer = np.empty(length, dtype=np.int64)
    rev_indexer[:] = -1
    for i in range(n):
        idx = indexer[i]
        if idx != -1:
            rev_indexer[idx] = i

    return rev_indexer


@cython.wraparound(False)
@cython.boundscheck(False)
def has_infs_f4(const float32_t[:] arr) -> bool:
    cdef:
        Py_ssize_t i, n = len(arr)
        float32_t inf, neginf, val

    inf = np.inf
    neginf = -inf

    for i in range(n):
        val = arr[i]
        if val == inf or val == neginf:
            return True
    return False


@cython.wraparound(False)
@cython.boundscheck(False)
def has_infs_f8(const float64_t[:] arr) -> bool:
    cdef:
        Py_ssize_t i, n = len(arr)
        float64_t inf, neginf, val

    inf = np.inf
    neginf = -inf

    for i in range(n):
        val = arr[i]
        if val == inf or val == neginf:
            return True
    return False


def maybe_indices_to_slice(ndarray[intp_t] indices, int max_len):
    cdef:
        Py_ssize_t i, n = len(indices)
        int k, vstart, vlast, v

    if n == 0:
        return slice(0, 0)

    vstart = indices[0]
    if vstart < 0 or max_len <= vstart:
        return indices

    if n == 1:
        return slice(vstart, vstart + 1)

    vlast = indices[n - 1]
    if vlast < 0 or max_len <= vlast:
        return indices

    k = indices[1] - indices[0]
    if k == 0:
        return indices
    else:
        for i in range(2, n):
            v = indices[i]
            if v - indices[i - 1] != k:
                return indices

        if k > 0:
            return slice(vstart, vlast + 1, k)
        else:
            if vlast == 0:
                return slice(vstart, None, k)
            else:
                return slice(vstart, vlast - 1, k)


@cython.wraparound(False)
@cython.boundscheck(False)
def maybe_booleans_to_slice(ndarray[uint8_t] mask):
    cdef:
        Py_ssize_t i, n = len(mask)
        Py_ssize_t start = 0, end = 0
        bint started = False, finished = False

    for i in range(n):
        if mask[i]:
            if finished:
                return mask.view(np.bool_)
            if not started:
                started = True
                start = i
        else:
            if finished:
                continue

            if started:
                end = i
                finished = True

    if not started:
        return slice(0, 0)
    if not finished:
        return slice(start, None)
    else:
        return slice(start, end)


@cython.wraparound(False)
@cython.boundscheck(False)
def array_equivalent_object(left: object[:], right: object[:]) -> bool:
    """
    Perform an element by element comparison on 1-d object arrays
    taking into account nan positions.
    """
    cdef:
        Py_ssize_t i, n = left.shape[0]
        object x, y

    for i in range(n):
        x = left[i]
        y = right[i]

        # we are either not equal or both nan
        # I think None == None will be true here
        try:
            if PyArray_Check(x) and PyArray_Check(y):
                if not array_equivalent_object(x, y):
                    return False
            elif (x is C_NA) ^ (y is C_NA):
                return False
            elif not (
                PyObject_RichCompareBool(x, y, Py_EQ)
                or is_matching_na(x, y, nan_matches_none=True)
            ):
                return False
        except ValueError:
            # Avoid raising ValueError when comparing Numpy arrays to other types
            if cnp.PyArray_IsAnyScalar(x) != cnp.PyArray_IsAnyScalar(y):
                # Only compare scalars to scalars and non-scalars to non-scalars
                return False
            elif (not (cnp.PyArray_IsPythonScalar(x) or cnp.PyArray_IsPythonScalar(y))
                  and not (isinstance(x, type(y)) or isinstance(y, type(x)))):
                # Check if non-scalars have the same type
                return False
            raise
    return True


@cython.wraparound(False)
@cython.boundscheck(False)
def astype_intsafe(ndarray[object] arr, new_dtype) -> ndarray:
    cdef:
        Py_ssize_t i, n = len(arr)
        object val
        bint is_datelike
        ndarray result

    is_datelike = new_dtype == 'm8[ns]'
    result = np.empty(n, dtype=new_dtype)
    for i in range(n):
        val = arr[i]
        if is_datelike and checknull(val):
            result[i] = NPY_NAT
        else:
            result[i] = val

    return result


@cython.wraparound(False)
@cython.boundscheck(False)
cpdef ndarray[object] ensure_string_array(
        arr,
        object na_value=np.nan,
        bint convert_na_value=True,
        bint copy=True,
        bint skipna=True,
):
    """Returns a new numpy array with object dtype and only strings and na values.

    Parameters
    ----------
    arr : array-like
        The values to be converted to str, if needed.
    na_value : Any, default np.nan
        The value to use for na. For example, np.nan or pd.NA.
    convert_na_value : bool, default True
        If False, existing na values will be used unchanged in the new array.
    copy : bool, default True
        Whether to ensure that a new array is returned.
    skipna : bool, default True
        Whether or not to coerce nulls to their stringified form
        (e.g. if False, NaN becomes 'nan').

    Returns
    -------
    ndarray
        An array with the input array's elements casted to str or nan-like.
    """
    cdef:
        Py_ssize_t i = 0, n = len(arr)

    if hasattr(arr, "to_numpy"):
        arr = arr.to_numpy()
    elif not isinstance(arr, np.ndarray):
        arr = np.array(arr, dtype="object")

    result = np.asarray(arr, dtype="object")

    if copy and result is arr:
        result = result.copy()

    for i in range(n):
        val = arr[i]

        if isinstance(val, str):
            continue

        if not checknull(val):
            result[i] = str(val)
        else:
            if convert_na_value:
                val = na_value
            if skipna:
                result[i] = val
            else:
                result[i] = str(val)

    return result


@cython.wraparound(False)
@cython.boundscheck(False)
def clean_index_list(obj: list):
    """
    Utility used in ``pandas.core.indexes.api.ensure_index``.
    """
    cdef:
        Py_ssize_t i, n = len(obj)
        object val
        bint all_arrays = True

    for i in range(n):
        val = obj[i]
        if not (isinstance(val, list) or
                util.is_array(val) or hasattr(val, '_data')):
            all_arrays = False
            break

    if all_arrays:
        return obj, all_arrays

    # don't force numpy coerce with nan's
    inferred = infer_dtype(obj, skipna=False)
    if inferred in ['string', 'bytes', 'mixed', 'mixed-integer']:
        return np.asarray(obj, dtype=object), 0
    elif inferred in ['integer']:
        # TODO: we infer an integer but it *could* be a uint64
        try:
            return np.asarray(obj, dtype='int64'), 0
        except OverflowError:
            return np.asarray(obj, dtype='object'), 0

    return np.asarray(obj), 0


# ------------------------------------------------------------------------------
# Groupby-related functions

# TODO: could do even better if we know something about the data. eg, index has
# 1-min data, binner has 5-min data, then bins are just strides in index. This
# is a general, O(max(len(values), len(binner))) method.
@cython.boundscheck(False)
@cython.wraparound(False)
def generate_bins_dt64(ndarray[int64_t] values, const int64_t[:] binner,
                       object closed='left', bint hasnans=False):
    """
    Int64 (datetime64) version of generic python version in ``groupby.py``.
    """
    cdef:
        Py_ssize_t lenidx, lenbin, i, j, bc, vc
        ndarray[int64_t] bins
        int64_t l_bin, r_bin, nat_count
        bint right_closed = closed == 'right'

    nat_count = 0
    if hasnans:
        mask = values == NPY_NAT
        nat_count = np.sum(mask)
        values = values[~mask]

    lenidx = len(values)
    lenbin = len(binner)

    if lenidx <= 0 or lenbin <= 0:
        raise ValueError("Invalid length for values or for binner")

    # check binner fits data
    if values[0] < binner[0]:
        raise ValueError("Values falls before first bin")

    if values[lenidx - 1] > binner[lenbin - 1]:
        raise ValueError("Values falls after last bin")

    bins = np.empty(lenbin - 1, dtype=np.int64)

    j = 0  # index into values
    bc = 0  # bin count

    # linear scan
    if right_closed:
        for i in range(0, lenbin - 1):
            r_bin = binner[i + 1]
            # count values in current bin, advance to next bin
            while j < lenidx and values[j] <= r_bin:
                j += 1
            bins[bc] = j
            bc += 1
    else:
        for i in range(0, lenbin - 1):
            r_bin = binner[i + 1]
            # count values in current bin, advance to next bin
            while j < lenidx and values[j] < r_bin:
                j += 1
            bins[bc] = j
            bc += 1

    if nat_count > 0:
        # shift bins by the number of NaT
        bins = bins + nat_count
        bins = np.insert(bins, 0, nat_count)

    return bins


@cython.boundscheck(False)
@cython.wraparound(False)
def get_level_sorter(const int64_t[:] label, const int64_t[:] starts):
    """
    Argsort for a single level of a multi-index, keeping the order of higher
    levels unchanged. `starts` points to starts of same-key indices w.r.t
    to leading levels; equivalent to:
        np.hstack([label[starts[i]:starts[i+1]].argsort(kind='mergesort')
            + starts[i] for i in range(len(starts) - 1)])
    """
    cdef:
        int64_t l, r
        Py_ssize_t i
        ndarray[int64_t, ndim=1] out = np.empty(len(label), dtype=np.int64)
        ndarray[int64_t, ndim=1] label_arr = np.asarray(label)

    for i in range(len(starts) - 1):
        l, r = starts[i], starts[i + 1]
        out[l:r] = l + label_arr[l:r].argsort(kind='mergesort')

    return out


@cython.boundscheck(False)
@cython.wraparound(False)
def count_level_2d(ndarray[uint8_t, ndim=2, cast=True] mask,
                   const int64_t[:] labels,
                   Py_ssize_t max_bin,
                   int axis):
    cdef:
        Py_ssize_t i, j, k, n
        ndarray[int64_t, ndim=2] counts

    assert (axis == 0 or axis == 1)
    n, k = (<object>mask).shape

    if axis == 0:
        counts = np.zeros((max_bin, k), dtype='i8')
        with nogil:
            for i in range(n):
                for j in range(k):
                    if mask[i, j]:
                        counts[labels[i], j] += 1

    else:  # axis == 1
        counts = np.zeros((n, max_bin), dtype='i8')
        with nogil:
            for i in range(n):
                for j in range(k):
                    if mask[i, j]:
                        counts[i, labels[j]] += 1

    return counts


def generate_slices(const int64_t[:] labels, Py_ssize_t ngroups):
    cdef:
        Py_ssize_t i, group_size, n, start
        int64_t lab
        object slobj
        ndarray[int64_t] starts, ends

    n = len(labels)

    starts = np.zeros(ngroups, dtype=np.int64)
    ends = np.zeros(ngroups, dtype=np.int64)

    start = 0
    group_size = 0
    for i in range(n):
        lab = labels[i]
        if lab < 0:
            start += 1
        else:
            group_size += 1
            if i == n - 1 or lab != labels[i + 1]:
                starts[lab] = start
                ends[lab] = start + group_size
                start += group_size
                group_size = 0

    return starts, ends


def indices_fast(ndarray index, const int64_t[:] labels, list keys,
                 list sorted_labels) -> dict:
    """
    Parameters
    ----------
    index : ndarray
    labels : ndarray[int64]
    keys : list
    sorted_labels : list[ndarray[int64]]
    """
    cdef:
        Py_ssize_t i, j, k, lab, cur, start, n = len(labels)
        dict result = {}
        object tup

    k = len(keys)

    # Start at the first non-null entry
    j = 0
    for j in range(0, n):
        if labels[j] != -1:
            break
    else:
        return result
    cur = labels[j]
    start = j

    for i in range(j+1, n):
        lab = labels[i]

        if lab != cur:
            if lab != -1:
                if k == 1:
                    # When k = 1 we do not want to return a tuple as key
                    tup = keys[0][sorted_labels[0][i - 1]]
                else:
                    tup = PyTuple_New(k)
                    for j in range(k):
                        val = keys[j][sorted_labels[j][i - 1]]
                        PyTuple_SET_ITEM(tup, j, val)
                        Py_INCREF(val)
                result[tup] = index[start:i]
            start = i
        cur = lab

    if k == 1:
        # When k = 1 we do not want to return a tuple as key
        tup = keys[0][sorted_labels[0][n - 1]]
    else:
        tup = PyTuple_New(k)
        for j in range(k):
            val = keys[j][sorted_labels[j][n - 1]]
            PyTuple_SET_ITEM(tup, j, val)
            Py_INCREF(val)
    result[tup] = index[start:]

    return result


# core.common import for fast inference checks

def is_float(obj: object) -> bool:
    """
    Return True if given object is float.

    Returns
    -------
    bool
    """
    return util.is_float_object(obj)


def is_integer(obj: object) -> bool:
    """
    Return True if given object is integer.

    Returns
    -------
    bool
    """
    return util.is_integer_object(obj)


def is_bool(obj: object) -> bool:
    """
    Return True if given object is boolean.

    Returns
    -------
    bool
    """
    return util.is_bool_object(obj)


def is_complex(obj: object) -> bool:
    """
    Return True if given object is complex.

    Returns
    -------
    bool
    """
    return util.is_complex_object(obj)


cpdef bint is_decimal(object obj):
    return isinstance(obj, Decimal)


cpdef bint is_interval(object obj):
    return getattr(obj, '_typ', '_typ') == 'interval'


def is_period(val: object) -> bool:
    """
    Return True if given object is Period.

    Returns
    -------
    bool
    """
    return is_period_object(val)


def is_list_like(obj: object, allow_sets: bool = True) -> bool:
    """
    Check if the object is list-like.

    Objects that are considered list-like are for example Python
    lists, tuples, sets, NumPy arrays, and Pandas Series.

    Strings and datetime objects, however, are not considered list-like.

    Parameters
    ----------
    obj : object
        Object to check.
    allow_sets : bool, default True
        If this parameter is False, sets will not be considered list-like.

        .. versionadded:: 0.24.0

    Returns
    -------
    bool
        Whether `obj` has list-like properties.

    Examples
    --------
    >>> is_list_like([1, 2, 3])
    True
    >>> is_list_like({1, 2, 3})
    True
    >>> is_list_like(datetime(2017, 1, 1))
    False
    >>> is_list_like("foo")
    False
    >>> is_list_like(1)
    False
    >>> is_list_like(np.array([2]))
    True
    >>> is_list_like(np.array(2))
    False
    """
    return c_is_list_like(obj, allow_sets)


cdef inline bint c_is_list_like(object obj, bint allow_sets) except -1:
    return (
        # equiv: `isinstance(obj, abc.Iterable)`
        hasattr(obj, "__iter__") and not isinstance(obj, type)
        # we do not count strings/unicode/bytes as list-like
        and not isinstance(obj, (str, bytes))
        # exclude zero-dimensional numpy arrays, effectively scalars
        and not cnp.PyArray_IsZeroDim(obj)
        # exclude sets if allow_sets is False
        and not (allow_sets is False and isinstance(obj, abc.Set))
    )


_TYPE_MAP = {
    "categorical": "categorical",
    "category": "categorical",
    "int8": "integer",
    "int16": "integer",
    "int32": "integer",
    "int64": "integer",
    "i": "integer",
    "uint8": "integer",
    "uint16": "integer",
    "uint32": "integer",
    "uint64": "integer",
    "u": "integer",
    "float32": "floating",
    "float64": "floating",
    "f": "floating",
    "complex64": "complex",
    "complex128": "complex",
    "c": "complex",
    "string": "string",
    "S": "bytes",
    "U": "string",
    "bool": "boolean",
    "b": "boolean",
    "datetime64[ns]": "datetime64",
    "M": "datetime64",
    "timedelta64[ns]": "timedelta64",
    "m": "timedelta64",
    "interval": "interval",
    Period: "period",
}

# types only exist on certain platform
try:
    np.float128
    _TYPE_MAP['float128'] = 'floating'
except AttributeError:
    pass
try:
    np.complex256
    _TYPE_MAP['complex256'] = 'complex'
except AttributeError:
    pass
try:
    np.float16
    _TYPE_MAP['float16'] = 'floating'
except AttributeError:
    pass


cdef class Seen:
    """
    Class for keeping track of the types of elements
    encountered when trying to perform type conversions.
    """

    cdef:
        bint int_             # seen_int
        bint nat_             # seen nat
        bint bool_            # seen_bool
        bint null_            # seen_null
        bint nan_             # seen_np.nan
        bint uint_            # seen_uint (unsigned integer)
        bint sint_            # seen_sint (signed integer)
        bint float_           # seen_float
        bint object_          # seen_object
        bint complex_         # seen_complex
        bint datetime_        # seen_datetime
        bint coerce_numeric   # coerce data to numeric
        bint timedelta_       # seen_timedelta
        bint datetimetz_      # seen_datetimetz

    def __cinit__(self, bint coerce_numeric=False):
        """
        Initialize a Seen instance.

        Parameters
        ----------
        coerce_numeric : bool, default False
            Whether or not to force conversion to a numeric data type if
            initial methods to convert to numeric fail.
        """
        self.int_ = False
        self.nat_ = False
        self.bool_ = False
        self.null_ = False
        self.nan_ = False
        self.uint_ = False
        self.sint_ = False
        self.float_ = False
        self.object_ = False
        self.complex_ = False
        self.datetime_ = False
        self.timedelta_ = False
        self.datetimetz_ = False
        self.coerce_numeric = coerce_numeric

    cdef inline bint check_uint64_conflict(self) except -1:
        """
        Check whether we can safely convert a uint64 array to a numeric dtype.

        There are two cases when conversion to numeric dtype with a uint64
        array is not safe (and will therefore not be performed)

        1) A NaN element is encountered.

           uint64 cannot be safely cast to float64 due to truncation issues
           at the extreme ends of the range.

        2) A negative number is encountered.

           There is no numerical dtype that can hold both negative numbers
           and numbers greater than INT64_MAX. Hence, at least one number
           will be improperly cast if we convert to a numeric dtype.

        Returns
        -------
        bool
            Whether or not we should return the original input array to avoid
            data truncation.

        Raises
        ------
        ValueError
            uint64 elements were detected, and at least one of the
            two conflict cases was also detected. However, we are
            trying to force conversion to a numeric dtype.
        """
        return (self.uint_ and (self.null_ or self.sint_)
                and not self.coerce_numeric)

    cdef inline saw_null(self):
        """
        Set flags indicating that a null value was encountered.
        """
        self.null_ = True
        self.float_ = True

    cdef saw_int(self, object val):
        """
        Set flags indicating that an integer value was encountered.

        In addition to setting a flag that an integer was seen, we
        also set two flags depending on the type of integer seen:

        1) sint_ : a negative (signed) number in the
                   range of [-2**63, 0) was encountered
        2) uint_ : a positive number in the range of
                   [2**63, 2**64) was encountered

        Parameters
        ----------
        val : Python int
            Value with which to set the flags.
        """
        self.int_ = True
        self.sint_ = self.sint_ or (oINT64_MIN <= val < 0)
        self.uint_ = self.uint_ or (oINT64_MAX < val <= oUINT64_MAX)

    @property
    def numeric_(self):
        return self.complex_ or self.float_ or self.int_

    @property
    def is_bool(self):
        return not (self.datetime_ or self.numeric_ or self.timedelta_
                    or self.nat_)

    @property
    def is_float_or_complex(self):
        return not (self.bool_ or self.datetime_ or self.timedelta_
                    or self.nat_)


cdef object _try_infer_map(object dtype):
    """
    If its in our map, just return the dtype.
    """
    cdef:
        object val
        str attr
    for attr in ["name", "kind", "base", "type"]:
        val = getattr(dtype, attr, None)
        if val in _TYPE_MAP:
            return _TYPE_MAP[val]
    return None


def infer_dtype(value: object, skipna: bool = True) -> str:
    """
    Efficiently infer the type of a passed val, or list-like
    array of values. Return a string describing the type.

    Parameters
    ----------
    value : scalar, list, ndarray, or pandas type
    skipna : bool, default True
        Ignore NaN values when inferring the type.

    Returns
    -------
    str
        Describing the common type of the input data.
    Results can include:

    - string
    - bytes
    - floating
    - integer
    - mixed-integer
    - mixed-integer-float
    - decimal
    - complex
    - categorical
    - boolean
    - datetime64
    - datetime
    - date
    - timedelta64
    - timedelta
    - time
    - period
    - mixed
    - unknown-array

    Raises
    ------
    TypeError
        If ndarray-like but cannot infer the dtype

    Notes
    -----
    - 'mixed' is the catchall for anything that is not otherwise
      specialized
    - 'mixed-integer-float' are floats and integers
    - 'mixed-integer' are integers mixed with non-integers
    - 'unknown-array' is the catchall for something that *is* an array (has
      a dtype attribute), but has a dtype unknown to pandas (e.g. external
      extension array)

    Examples
    --------
    >>> infer_dtype(['foo', 'bar'])
    'string'

    >>> infer_dtype(['a', np.nan, 'b'], skipna=True)
    'string'

    >>> infer_dtype(['a', np.nan, 'b'], skipna=False)
    'mixed'

    >>> infer_dtype([b'foo', b'bar'])
    'bytes'

    >>> infer_dtype([1, 2, 3])
    'integer'

    >>> infer_dtype([1, 2, 3.5])
    'mixed-integer-float'

    >>> infer_dtype([1.0, 2.0, 3.5])
    'floating'

    >>> infer_dtype(['a', 1])
    'mixed-integer'

    >>> infer_dtype([Decimal(1), Decimal(2.0)])
    'decimal'

    >>> infer_dtype([True, False])
    'boolean'

    >>> infer_dtype([True, False, np.nan])
    'boolean'

    >>> infer_dtype([pd.Timestamp('20130101')])
    'datetime'

    >>> infer_dtype([datetime.date(2013, 1, 1)])
    'date'

    >>> infer_dtype([np.datetime64('2013-01-01')])
    'datetime64'

    >>> infer_dtype([datetime.timedelta(0, 1, 1)])
    'timedelta'

    >>> infer_dtype(pd.Series(list('aabc')).astype('category'))
    'categorical'
    """
    cdef:
        Py_ssize_t i, n
        object val
        ndarray values
        bint seen_pdnat = False
        bint seen_val = False

    if util.is_array(value):
        values = value
    elif hasattr(value, "inferred_type") and skipna is False:
        # Index, use the cached attribute if possible, populate the cache otherwise
        return value.inferred_type
    elif hasattr(value, "dtype"):
        # this will handle ndarray-like
        # e.g. categoricals
        dtype = value.dtype
        if not isinstance(dtype, np.dtype):
            inferred = _try_infer_map(value.dtype)
            if inferred is not None:
                return inferred
            return "unknown-array"

        # Unwrap Series/Index
        values = np.asarray(value)

    else:
        if not isinstance(value, list):
            value = list(value)

        from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
        values = construct_1d_object_array_from_listlike(value)

    # make contiguous
    # for f-contiguous array 1000 x 1000, passing order="K" gives 5000x speedup
    values = values.ravel(order="K")

    val = _try_infer_map(values.dtype)
    if val is not None:
        return val

    if values.dtype != np.object_:
        values = values.astype("O")

    if skipna:
        values = values[~isnaobj(values)]

    n = len(values)
    if n == 0:
        return "empty"

    # try to use a valid value
    for i in range(n):
        val = values[i]

        # do not use is_nul_datetimelike to keep
        # np.datetime64('nat') and np.timedelta64('nat')
        if val is None or util.is_nan(val):
            pass
        elif val is NaT:
            seen_pdnat = True
        else:
            seen_val = True
            break

    # if all values are nan/NaT
    if seen_val is False and seen_pdnat is True:
        return "datetime"
        # float/object nan is handled in latter logic

    if util.is_datetime64_object(val):
        if is_datetime64_array(values):
            return "datetime64"

    elif is_timedelta(val):
        if is_timedelta_or_timedelta64_array(values):
            return "timedelta"

    elif util.is_integer_object(val):
        # ordering matters here; this check must come after the is_timedelta
        #  check otherwise numpy timedelta64 objects would come through here

        if is_integer_array(values):
            return "integer"
        elif is_integer_float_array(values):
            if is_integer_na_array(values):
                return "integer-na"
            else:
                return "mixed-integer-float"
        return "mixed-integer"

    elif PyDateTime_Check(val):
        if is_datetime_array(values, skipna=skipna):
            return "datetime"
        elif is_date_array(values, skipna=skipna):
            return "date"

    elif PyDate_Check(val):
        if is_date_array(values, skipna=skipna):
            return "date"

    elif PyTime_Check(val):
        if is_time_array(values, skipna=skipna):
            return "time"

    elif is_decimal(val):
        if is_decimal_array(values):
            return "decimal"

    elif util.is_complex_object(val):
        if is_complex_array(values):
            return "complex"

    elif util.is_float_object(val):
        if is_float_array(values):
            return "floating"
        elif is_integer_float_array(values):
            if is_integer_na_array(values):
                return "integer-na"
            else:
                return "mixed-integer-float"

    elif util.is_bool_object(val):
        if is_bool_array(values, skipna=skipna):
            return "boolean"

    elif isinstance(val, str):
        if is_string_array(values, skipna=skipna):
            return "string"

    elif isinstance(val, bytes):
        if is_bytes_array(values, skipna=skipna):
            return "bytes"

    elif is_period_object(val):
        if is_period_array(values):
            return "period"

    elif is_interval(val):
        if is_interval_array(values):
            return "interval"

    for i in range(n):
        val = values[i]
        if (util.is_integer_object(val) and
                not util.is_timedelta64_object(val) and
                not util.is_datetime64_object(val)):
            return "mixed-integer"

    return "mixed"


def infer_datetimelike_array(arr: ndarray[object]) -> str:
    """
    Infer if we have a datetime or timedelta array.
    - date: we have *only* date and maybe strings, nulls
    - datetime: we have *only* datetimes and maybe strings, nulls
    - timedelta: we have *only* timedeltas and maybe strings, nulls
    - nat: we do not have *any* date, datetimes or timedeltas, but do have
      at least a NaT
    - mixed: other objects (strings, a mix of tz-aware and tz-naive, or
                            actual objects)

    Parameters
    ----------
    arr : ndarray[object]

    Returns
    -------
    str: {datetime, timedelta, date, nat, mixed}
    """
    cdef:
        Py_ssize_t i, n = len(arr)
        bint seen_timedelta = False, seen_date = False, seen_datetime = False
        bint seen_tz_aware = False, seen_tz_naive = False
        bint seen_nat = False
        list objs = []
        object v

    for i in range(n):
        v = arr[i]
        if isinstance(v, str):
            objs.append(v)

            if len(objs) == 3:
                break

        elif v is None or util.is_nan(v):
            # nan or None
            pass
        elif v is NaT:
            seen_nat = True
        elif PyDateTime_Check(v):
            # datetime
            seen_datetime = True

            # disambiguate between tz-naive and tz-aware
            if v.tzinfo is None:
                seen_tz_naive = True
            else:
                seen_tz_aware = True

            if seen_tz_naive and seen_tz_aware:
                return "mixed"
        elif util.is_datetime64_object(v):
            # np.datetime64
            seen_datetime = True
        elif PyDate_Check(v):
            seen_date = True
        elif is_timedelta(v):
            # timedelta, or timedelta64
            seen_timedelta = True
        else:
            return "mixed"

    if seen_date and not (seen_datetime or seen_timedelta):
        return "date"
    elif seen_datetime and not seen_timedelta:
        return "datetime"
    elif seen_timedelta and not seen_datetime:
        return "timedelta"
    elif seen_nat:
        return "nat"

    # short-circuit by trying to
    # actually convert these strings
    # this is for performance as we don't need to try
    # convert *every* string array
    if len(objs):
        try:
            array_to_datetime(objs, errors="raise")
            return "datetime"
        except (ValueError, TypeError):
            pass

        # we are *not* going to infer from strings
        # for timedelta as too much ambiguity

    return 'mixed'


cdef inline bint is_timedelta(object o):
    return PyDelta_Check(o) or util.is_timedelta64_object(o)


cdef class Validator:

    cdef:
        Py_ssize_t n
        dtype dtype
        bint skipna

    def __cinit__(self, Py_ssize_t n, dtype dtype=np.dtype(np.object_),
                  bint skipna=False):
        self.n = n
        self.dtype = dtype
        self.skipna = skipna

    cdef bint validate(self, ndarray values) except -1:
        if not self.n:
            return False

        if self.is_array_typed():
            return True
        elif self.dtype.type_num == NPY_OBJECT:
            if self.skipna:
                return self._validate_skipna(values)
            else:
                return self._validate(values)
        else:
            return False

    @cython.wraparound(False)
    @cython.boundscheck(False)
    cdef bint _validate(self, ndarray values) except -1:
        cdef:
            Py_ssize_t i
            Py_ssize_t n = self.n

        for i in range(n):
            if not self.is_valid(values[i]):
                return False

        return self.finalize_validate()

    @cython.wraparound(False)
    @cython.boundscheck(False)
    cdef bint _validate_skipna(self, ndarray values) except -1:
        cdef:
            Py_ssize_t i
            Py_ssize_t n = self.n

        for i in range(n):
            if not self.is_valid_skipna(values[i]):
                return False

        return self.finalize_validate_skipna()

    cdef bint is_valid(self, object value) except -1:
        return self.is_value_typed(value)

    cdef bint is_valid_skipna(self, object value) except -1:
        return self.is_valid(value) or self.is_valid_null(value)

    cdef bint is_value_typed(self, object value) except -1:
        raise NotImplementedError(f"{type(self).__name__} child class "
                                  "must define is_value_typed")

    cdef bint is_valid_null(self, object value) except -1:
        return value is None or value is C_NA or util.is_nan(value)

    cdef bint is_array_typed(self) except -1:
        return False

    cdef inline bint finalize_validate(self):
        return True

    cdef bint finalize_validate_skipna(self):
        # TODO(phillipc): Remove the existing validate methods and replace them
        # with the skipna versions upon full deprecation of skipna=False
        return True


cdef class BoolValidator(Validator):
    cdef inline bint is_value_typed(self, object value) except -1:
        return util.is_bool_object(value)

    cdef inline bint is_array_typed(self) except -1:
        return issubclass(self.dtype.type, np.bool_)


cpdef bint is_bool_array(ndarray values, bint skipna=False):
    cdef:
        BoolValidator validator = BoolValidator(len(values),
                                                values.dtype,
                                                skipna=skipna)
    return validator.validate(values)


cdef class IntegerValidator(Validator):
    cdef inline bint is_value_typed(self, object value) except -1:
        return util.is_integer_object(value)

    cdef inline bint is_array_typed(self) except -1:
        return issubclass(self.dtype.type, np.integer)


cpdef bint is_integer_array(ndarray values):
    cdef:
        IntegerValidator validator = IntegerValidator(len(values),
                                                      values.dtype)
    return validator.validate(values)


cdef class IntegerNaValidator(Validator):
    cdef inline bint is_value_typed(self, object value) except -1:
        return (util.is_integer_object(value)
                or (util.is_nan(value) and util.is_float_object(value)))


cdef bint is_integer_na_array(ndarray values):
    cdef:
        IntegerNaValidator validator = IntegerNaValidator(len(values),
                                                          values.dtype)
    return validator.validate(values)


cdef class IntegerFloatValidator(Validator):
    cdef inline bint is_value_typed(self, object value) except -1:
        return util.is_integer_object(value) or util.is_float_object(value)

    cdef inline bint is_array_typed(self) except -1:
        return issubclass(self.dtype.type, np.integer)


cdef bint is_integer_float_array(ndarray values):
    cdef:
        IntegerFloatValidator validator = IntegerFloatValidator(len(values),
                                                                values.dtype)
    return validator.validate(values)


cdef class FloatValidator(Validator):
    cdef inline bint is_value_typed(self, object value) except -1:
        return util.is_float_object(value)

    cdef inline bint is_array_typed(self) except -1:
        return issubclass(self.dtype.type, np.floating)


cpdef bint is_float_array(ndarray values):
    cdef:
        FloatValidator validator = FloatValidator(len(values), values.dtype)
    return validator.validate(values)


cdef class ComplexValidator(Validator):
    cdef inline bint is_value_typed(self, object value) except -1:
        return (
            util.is_complex_object(value)
            or (util.is_float_object(value) and is_nan(value))
        )

    cdef inline bint is_array_typed(self) except -1:
        return issubclass(self.dtype.type, np.complexfloating)


cdef bint is_complex_array(ndarray values):
    cdef:
        ComplexValidator validator = ComplexValidator(len(values), values.dtype)
    return validator.validate(values)


cdef class DecimalValidator(Validator):
    cdef inline bint is_value_typed(self, object value) except -1:
        return is_decimal(value)


cdef bint is_decimal_array(ndarray values):
    cdef:
        DecimalValidator validator = DecimalValidator(len(values), values.dtype)
    return validator.validate(values)


cdef class StringValidator(Validator):
    cdef inline bint is_value_typed(self, object value) except -1:
        return isinstance(value, str)

    cdef inline bint is_array_typed(self) except -1:
        return issubclass(self.dtype.type, np.str_)

    cdef bint is_valid_null(self, object value) except -1:
        # We deliberately exclude None / NaN here since StringArray uses NA
        return value is C_NA


cpdef bint is_string_array(ndarray values, bint skipna=False):
    cdef:
        StringValidator validator = StringValidator(len(values),
                                                    values.dtype,
                                                    skipna=skipna)
    return validator.validate(values)


cdef class BytesValidator(Validator):
    cdef inline bint is_value_typed(self, object value) except -1:
        return isinstance(value, bytes)

    cdef inline bint is_array_typed(self) except -1:
        return issubclass(self.dtype.type, np.bytes_)


cdef bint is_bytes_array(ndarray values, bint skipna=False):
    cdef:
        BytesValidator validator = BytesValidator(len(values), values.dtype,
                                                  skipna=skipna)
    return validator.validate(values)


cdef class TemporalValidator(Validator):
    cdef:
        Py_ssize_t generic_null_count

    def __cinit__(self, Py_ssize_t n, dtype dtype=np.dtype(np.object_),
                  bint skipna=False):
        self.n = n
        self.dtype = dtype
        self.skipna = skipna
        self.generic_null_count = 0

    cdef inline bint is_valid(self, object value) except -1:
        return self.is_value_typed(value) or self.is_valid_null(value)

    cdef bint is_valid_null(self, object value) except -1:
        raise NotImplementedError(f"{type(self).__name__} child class "
                                  "must define is_valid_null")

    cdef inline bint is_valid_skipna(self, object value) except -1:
        cdef:
            bint is_typed_null = self.is_valid_null(value)
            bint is_generic_null = value is None or util.is_nan(value)
        self.generic_null_count += is_typed_null and is_generic_null
        return self.is_value_typed(value) or is_typed_null or is_generic_null

    cdef inline bint finalize_validate_skipna(self):
        return self.generic_null_count != self.n


cdef class DatetimeValidator(TemporalValidator):
    cdef bint is_value_typed(self, object value) except -1:
        return PyDateTime_Check(value)

    cdef inline bint is_valid_null(self, object value) except -1:
        return is_null_datetime64(value)


cpdef bint is_datetime_array(ndarray values, bint skipna=True):
    cdef:
        DatetimeValidator validator = DatetimeValidator(len(values),
                                                        skipna=skipna)
    return validator.validate(values)


cdef class Datetime64Validator(DatetimeValidator):
    cdef inline bint is_value_typed(self, object value) except -1:
        return util.is_datetime64_object(value)


cpdef bint is_datetime64_array(ndarray values):
    cdef:
        Datetime64Validator validator = Datetime64Validator(len(values),
                                                            skipna=True)
    return validator.validate(values)


# TODO: only non-here use is in test
def is_datetime_with_singletz_array(values: ndarray) -> bool:
    """
    Check values have the same tzinfo attribute.
    Doesn't check values are datetime-like types.
    """
    cdef:
        Py_ssize_t i = 0, j, n = len(values)
        object base_val, base_tz, val, tz

    if n == 0:
        return False
    # Get a reference timezone to compare with the rest of the tzs in the array
    for i in range(n):
        base_val = values[i]
        if base_val is not NaT:
            base_tz = getattr(base_val, 'tzinfo', None)
            break

    for j in range(i, n):
        # Compare val's timezone with the reference timezone
        # NaT can coexist with tz-aware datetimes, so skip if encountered
        val = values[j]
        if val is not NaT:
            tz = getattr(val, 'tzinfo', None)
            if not tz_compare(base_tz, tz):
                return False

    return True


cdef class TimedeltaValidator(TemporalValidator):
    cdef bint is_value_typed(self, object value) except -1:
        return PyDelta_Check(value)

    cdef inline bint is_valid_null(self, object value) except -1:
        return is_null_timedelta64(value)


cdef class AnyTimedeltaValidator(TimedeltaValidator):
    cdef inline bint is_value_typed(self, object value) except -1:
        return is_timedelta(value)


# TODO: only non-here use is in test
cpdef bint is_timedelta_or_timedelta64_array(ndarray values):
    """
    Infer with timedeltas and/or nat/none.
    """
    cdef:
        AnyTimedeltaValidator validator = AnyTimedeltaValidator(len(values),
                                                                skipna=True)
    return validator.validate(values)


cdef class DateValidator(Validator):
    cdef inline bint is_value_typed(self, object value) except -1:
        return PyDate_Check(value)


cpdef bint is_date_array(ndarray values, bint skipna=False):
    cdef:
        DateValidator validator = DateValidator(len(values), skipna=skipna)
    return validator.validate(values)


cdef class TimeValidator(Validator):
    cdef inline bint is_value_typed(self, object value) except -1:
        return PyTime_Check(value)


cpdef bint is_time_array(ndarray values, bint skipna=False):
    cdef:
        TimeValidator validator = TimeValidator(len(values), skipna=skipna)
    return validator.validate(values)


cdef class PeriodValidator(TemporalValidator):
    cdef inline bint is_value_typed(self, object value) except -1:
        return is_period_object(value)

    cdef inline bint is_valid_null(self, object value) except -1:
        return checknull_with_nat(value)


cpdef bint is_period_array(ndarray values):
    cdef:
        PeriodValidator validator = PeriodValidator(len(values), skipna=True)
    return validator.validate(values)


cdef class IntervalValidator(Validator):
    cdef inline bint is_value_typed(self, object value) except -1:
        return is_interval(value)


cpdef bint is_interval_array(ndarray values):
    cdef:
        IntervalValidator validator = IntervalValidator(len(values),
                                                        skipna=True)
    return validator.validate(values)


@cython.boundscheck(False)
@cython.wraparound(False)
def maybe_convert_numeric(
    ndarray[object] values,
    set na_values,
    bint convert_empty=True,
    bint coerce_numeric=False,
) -> ndarray:
    """
    Convert object array to a numeric array if possible.

    Parameters
    ----------
    values : ndarray[object]
        Array of object elements to convert.
    na_values : set
        Set of values that should be interpreted as NaN.
    convert_empty : bool, default True
        If an empty array-like object is encountered, whether to interpret
        that element as NaN or not. If set to False, a ValueError will be
        raised if such an element is encountered and 'coerce_numeric' is False.
    coerce_numeric : bool, default False
        If initial attempts to convert to numeric have failed, whether to
        force conversion to numeric via alternative methods or by setting the
        element to NaN. Otherwise, an Exception will be raised when such an
        element is encountered.

        This boolean also has an impact on how conversion behaves when a
        numeric array has no suitable numerical dtype to return (i.e. uint64,
        int32, uint8). If set to False, the original object array will be
        returned. Otherwise, a ValueError will be raised.

    Returns
    -------
    np.ndarray
        Array of converted object values to numerical ones.
    """
    if len(values) == 0:
        return np.array([], dtype='i8')

    # fastpath for ints - try to convert all based on first value
    cdef:
        object val = values[0]

    if util.is_integer_object(val):
        try:
            maybe_ints = values.astype('i8')
            if (maybe_ints == values).all():
                return maybe_ints
        except (ValueError, OverflowError, TypeError):
            pass

    # Otherwise, iterate and do full inference.
    cdef:
        int status, maybe_int
        Py_ssize_t i, n = values.size
        Seen seen = Seen(coerce_numeric)
        ndarray[float64_t] floats = np.empty(n, dtype='f8')
        ndarray[complex128_t] complexes = np.empty(n, dtype='c16')
        ndarray[int64_t] ints = np.empty(n, dtype='i8')
        ndarray[uint64_t] uints = np.empty(n, dtype='u8')
        ndarray[uint8_t] bools = np.empty(n, dtype='u1')
        float64_t fval

    for i in range(n):
        val = values[i]

        if val.__hash__ is not None and val in na_values:
            seen.saw_null()
            floats[i] = complexes[i] = NaN
        elif util.is_float_object(val):
            fval = val
            if fval != fval:
                seen.null_ = True

            floats[i] = complexes[i] = fval
            seen.float_ = True
        elif util.is_integer_object(val):
            floats[i] = complexes[i] = val

            val = int(val)
            seen.saw_int(val)

            if val >= 0:
                if val <= oUINT64_MAX:
                    uints[i] = val
                else:
                    seen.float_ = True

            if oINT64_MIN <= val <= oINT64_MAX:
                ints[i] = val

            if val < oINT64_MIN or (seen.sint_ and seen.uint_):
                seen.float_ = True

        elif util.is_bool_object(val):
            floats[i] = uints[i] = ints[i] = bools[i] = val
            seen.bool_ = True
        elif val is None or val is C_NA:
            seen.saw_null()
            floats[i] = complexes[i] = NaN
        elif hasattr(val, '__len__') and len(val) == 0:
            if convert_empty or seen.coerce_numeric:
                seen.saw_null()
                floats[i] = complexes[i] = NaN
            else:
                raise ValueError("Empty string encountered")
        elif util.is_complex_object(val):
            complexes[i] = val
            seen.complex_ = True
        elif is_decimal(val):
            floats[i] = complexes[i] = val
            seen.float_ = True
        else:
            try:
                status = floatify(val, &fval, &maybe_int)

                if fval in na_values:
                    seen.saw_null()
                    floats[i] = complexes[i] = NaN
                else:
                    if fval != fval:
                        seen.null_ = True

                    floats[i] = fval

                if maybe_int:
                    as_int = int(val)

                    if as_int in na_values:
                        seen.saw_null()
                    else:
                        seen.saw_int(as_int)

                    if as_int not in na_values:
                        if as_int < oINT64_MIN or as_int > oUINT64_MAX:
                            if seen.coerce_numeric:
                                seen.float_ = True
                            else:
                                raise ValueError("Integer out of range.")
                        else:
                            if as_int >= 0:
                                uints[i] = as_int

                            if as_int <= oINT64_MAX:
                                ints[i] = as_int

                    seen.float_ = seen.float_ or (seen.uint_ and seen.sint_)
                else:
                    seen.float_ = True
            except (TypeError, ValueError) as err:
                if not seen.coerce_numeric:
                    raise type(err)(f"{err} at position {i}")

                seen.saw_null()
                floats[i] = NaN

    if seen.check_uint64_conflict():
        return values

    if seen.complex_:
        return complexes
    elif seen.float_:
        return floats
    elif seen.int_:
        if seen.uint_:
            return uints
        else:
            return ints
    elif seen.bool_:
        return bools.view(np.bool_)
    elif seen.uint_:
        return uints
    return ints


@cython.boundscheck(False)
@cython.wraparound(False)
def maybe_convert_objects(ndarray[object] objects, bint try_float=False,
                          bint safe=False, bint convert_datetime=False,
                          bint convert_timedelta=False,
                          bint convert_to_nullable_integer=False) -> "ArrayLike":
    """
    Type inference function-- convert object array to proper dtype

    Parameters
    ----------
    values : ndarray[object]
        Array of object elements to convert.
    try_float : bool, default False
        If an array-like object contains only float or NaN values is
        encountered, whether to convert and return an array of float dtype.
    safe : bool, default False
        Whether to upcast numeric type (e.g. int cast to float). If set to
        True, no upcasting will be performed.
    convert_datetime : bool, default False
        If an array-like object contains only datetime values or NaT is
        encountered, whether to convert and return an array of M8[ns] dtype.
    convert_timedelta : bool, default False
        If an array-like object contains only timedelta values or NaT is
        encountered, whether to convert and return an array of m8[ns] dtype.
    convert_to_nullable_integer : bool, default False
        If an array-like object contains only integer values (and NaN) is
        encountered, whether to convert and return an IntegerArray.

    Returns
    -------
    np.ndarray or ExtensionArray
        Array of converted object values to more specific dtypes if applicable.
    """
    cdef:
        Py_ssize_t i, n
        ndarray[float64_t] floats
        ndarray[complex128_t] complexes
        ndarray[int64_t] ints
        ndarray[uint64_t] uints
        ndarray[uint8_t] bools
        int64_t[:]  idatetimes
        int64_t[:] itimedeltas
        Seen seen = Seen()
        object val
        float64_t fval, fnan

    n = len(objects)

    floats = np.empty(n, dtype='f8')
    complexes = np.empty(n, dtype='c16')
    ints = np.empty(n, dtype='i8')
    uints = np.empty(n, dtype='u8')
    bools = np.empty(n, dtype=np.uint8)
    mask = np.full(n, False)

    if convert_datetime:
        datetimes = np.empty(n, dtype='M8[ns]')
        idatetimes = datetimes.view(np.int64)

    if convert_timedelta:
        timedeltas = np.empty(n, dtype='m8[ns]')
        itimedeltas = timedeltas.view(np.int64)

    fnan = np.nan

    for i in range(n):
        val = objects[i]

        if val is None:
            seen.null_ = True
            floats[i] = complexes[i] = fnan
            mask[i] = True
        elif val is NaT:
            seen.nat_ = True
            if convert_datetime:
                idatetimes[i] = NPY_NAT
            if convert_timedelta:
                itimedeltas[i] = NPY_NAT
            if not (convert_datetime or convert_timedelta):
                seen.object_ = True
                break
        elif val is np.nan:
            seen.nan_ = True
            mask[i] = True
            floats[i] = complexes[i] = val
        elif util.is_bool_object(val):
            seen.bool_ = True
            bools[i] = val
        elif util.is_float_object(val):
            floats[i] = complexes[i] = val
            seen.float_ = True
        elif util.is_datetime64_object(val):
            if convert_datetime:
                idatetimes[i] = convert_to_tsobject(
                    val, None, None, 0, 0).value
                seen.datetime_ = True
            else:
                seen.object_ = True
                break
        elif is_timedelta(val):
            if convert_timedelta:
                itimedeltas[i] = convert_to_timedelta64(val, "ns").view("i8")
                seen.timedelta_ = True
            else:
                seen.object_ = True
                break
        elif util.is_integer_object(val):
            seen.int_ = True
            floats[i] = <float64_t>val
            complexes[i] = <double complex>val
            if not seen.null_:
                val = int(val)
                seen.saw_int(val)

                if ((seen.uint_ and seen.sint_) or
                        val > oUINT64_MAX or val < oINT64_MIN):
                    seen.object_ = True
                    break

                if seen.uint_:
                    uints[i] = val
                elif seen.sint_:
                    ints[i] = val
                else:
                    uints[i] = val
                    ints[i] = val

        elif util.is_complex_object(val):
            complexes[i] = val
            seen.complex_ = True
        elif PyDateTime_Check(val) or util.is_datetime64_object(val):

            # if we have an tz's attached then return the objects
            if convert_datetime:
                if getattr(val, 'tzinfo', None) is not None:
                    seen.datetimetz_ = True
                    break
                else:
                    seen.datetime_ = True
                    idatetimes[i] = convert_to_tsobject(
                        val, None, None, 0, 0).value
            else:
                seen.object_ = True
                break
        elif try_float and not isinstance(val, str):
            # this will convert Decimal objects
            try:
                floats[i] = float(val)
                complexes[i] = complex(val)
                seen.float_ = True
            except (ValueError, TypeError):
                seen.object_ = True
                break
        else:
            seen.object_ = True
            break

    # we try to coerce datetime w/tz but must all have the same tz
    if seen.datetimetz_:
        if is_datetime_with_singletz_array(objects):
            from pandas import DatetimeIndex
            dti = DatetimeIndex(objects)

            # unbox to DatetimeArray
            return dti._data
        seen.object_ = True

    if not seen.object_:
        if not safe:
            if seen.null_ or seen.nan_:
                if seen.is_float_or_complex:
                    if seen.complex_:
                        return complexes
                    elif seen.float_:
                        return floats
                    elif seen.int_:
                        if convert_to_nullable_integer:
                            from pandas.core.arrays import IntegerArray
                            return IntegerArray(ints, mask)
                        else:
                            return floats
                    elif seen.nan_:
                        return floats
            else:
                if not seen.bool_:
                    if seen.datetime_:
                        if not seen.numeric_ and not seen.timedelta_:
                            return datetimes
                    elif seen.timedelta_:
                        if not seen.numeric_:
                            return timedeltas
                    elif seen.nat_:
                        if not seen.numeric_:
                            if convert_datetime and convert_timedelta:
                                # TODO: array full of NaT ambiguity resolve here needed
                                pass
                            elif convert_datetime:
                                return datetimes
                            elif convert_timedelta:
                                return timedeltas
                    else:
                        if seen.complex_:
                            return complexes
                        elif seen.float_:
                            return floats
                        elif seen.int_:
                            if seen.uint_:
                                return uints
                            else:
                                return ints
                elif seen.is_bool:
                    return bools.view(np.bool_)

        else:
            # don't cast int to float, etc.
            if seen.null_:
                if seen.is_float_or_complex:
                    if seen.complex_:
                        if not seen.int_:
                            return complexes
                    elif seen.float_ or seen.nan_:
                        if not seen.int_:
                            return floats
            else:
                if not seen.bool_:
                    if seen.datetime_:
                        if not seen.numeric_ and not seen.timedelta_:
                            return datetimes
                    elif seen.timedelta_:
                        if not seen.numeric_:
                            return timedeltas
                    elif seen.nat_:
                        if not seen.numeric_:
                            if convert_datetime and convert_timedelta:
                                # TODO: array full of NaT ambiguity resolve here needed
                                pass
                            elif convert_datetime:
                                return datetimes
                            elif convert_timedelta:
                                return timedeltas
                    else:
                        if seen.complex_:
                            if not seen.int_:
                                return complexes
                        elif seen.float_ or seen.nan_:
                            if not seen.int_:
                                return floats
                        elif seen.int_:
                            if seen.uint_:
                                return uints
                            else:
                                return ints
                elif seen.is_bool and not seen.nan_:
                    return bools.view(np.bool_)

    return objects


# Note: no_default is exported to the public API in pandas.api.extensions
no_default = object()  # Sentinel indicating the default value.


@cython.boundscheck(False)
@cython.wraparound(False)
def map_infer_mask(ndarray arr, object f, const uint8_t[:] mask, bint convert=True,
                   object na_value=no_default, object dtype=object) -> "ArrayLike":
    """
    Substitute for np.vectorize with pandas-friendly dtype inference.

    Parameters
    ----------
    arr : ndarray
    f : function
    mask : ndarray
        uint8 dtype ndarray indicating values not to apply `f` to.
    convert : bool, default True
        Whether to call `maybe_convert_objects` on the resulting ndarray
    na_value : Any, optional
        The result value to use for masked values. By default, the
        input value is used
    dtype : numpy.dtype
        The numpy dtype to use for the result ndarray.

    Returns
    -------
    ndarray
    """
    cdef:
        Py_ssize_t i, n
        ndarray result
        object val

    n = len(arr)
    result = np.empty(n, dtype=dtype)
    for i in range(n):
        if mask[i]:
            if na_value is no_default:
                val = arr[i]
            else:
                val = na_value
        else:
            val = f(arr[i])

            if cnp.PyArray_IsZeroDim(val):
                # unbox 0-dim arrays, GH#690
                val = val.item()

        result[i] = val

    if convert:
        return maybe_convert_objects(result,
                                     try_float=False,
                                     convert_datetime=False,
                                     convert_timedelta=False)

    return result


@cython.boundscheck(False)
@cython.wraparound(False)
def map_infer(
    ndarray arr, object f, bint convert=True, bint ignore_na=False
) -> "ArrayLike":
    """
    Substitute for np.vectorize with pandas-friendly dtype inference.

    Parameters
    ----------
    arr : ndarray
    f : function
    convert : bint
    ignore_na : bint
        If True, NA values will not have f applied

    Returns
    -------
    np.ndarray or ExtensionArray
    """
    cdef:
        Py_ssize_t i, n
        ndarray[object] result
        object val

    n = len(arr)
    result = np.empty(n, dtype=object)
    for i in range(n):
        if ignore_na and checknull(arr[i]):
            result[i] = arr[i]
            continue
        val = f(arr[i])

        if cnp.PyArray_IsZeroDim(val):
            # unbox 0-dim arrays, GH#690
            val = val.item()

        result[i] = val

    if convert:
        return maybe_convert_objects(result,
                                     try_float=False,
                                     convert_datetime=False,
                                     convert_timedelta=False)

    return result


def to_object_array(rows: object, min_width: int = 0) -> ndarray:
    """
    Convert a list of lists into an object array.

    Parameters
    ----------
    rows : 2-d array (N, K)
        List of lists to be converted into an array.
    min_width : int
        Minimum width of the object array. If a list
        in `rows` contains fewer than `width` elements,
        the remaining elements in the corresponding row
        will all be `NaN`.

    Returns
    -------
    np.ndarray[object, ndim=2]
    """
    cdef:
        Py_ssize_t i, j, n, k, tmp
        ndarray[object, ndim=2] result
        list row

    rows = list(rows)
    n = len(rows)

    k = min_width
    for i in range(n):
        tmp = len(rows[i])
        if tmp > k:
            k = tmp

    result = np.empty((n, k), dtype=object)

    for i in range(n):
        row = list(rows[i])

        for j in range(len(row)):
            result[i, j] = row[j]

    return result


def tuples_to_object_array(ndarray[object] tuples):
    cdef:
        Py_ssize_t i, j, n, k, tmp
        ndarray[object, ndim=2] result
        tuple tup

    n = len(tuples)
    k = len(tuples[0])
    result = np.empty((n, k), dtype=object)
    for i in range(n):
        tup = tuples[i]
        for j in range(k):
            result[i, j] = tup[j]

    return result


def to_object_array_tuples(rows: object):
    """
    Convert a list of tuples into an object array. Any subclass of
    tuple in `rows` will be casted to tuple.

    Parameters
    ----------
    rows : 2-d array (N, K)
        List of tuples to be converted into an array.

    Returns
    -------
    numpy array of the object dtype.
    """
    cdef:
        Py_ssize_t i, j, n, k, tmp
        ndarray[object, ndim=2] result
        tuple row

    rows = list(rows)
    n = len(rows)

    k = 0
    for i in range(n):
        tmp = 1 if checknull(rows[i]) else len(rows[i])
        if tmp > k:
            k = tmp

    result = np.empty((n, k), dtype=object)

    try:
        for i in range(n):
            row = rows[i]
            for j in range(len(row)):
                result[i, j] = row[j]
    except TypeError:
        # e.g. "Expected tuple, got list"
        # upcast any subclasses to tuple
        for i in range(n):
            row = (rows[i],) if checknull(rows[i]) else tuple(rows[i])
            for j in range(len(row)):
                result[i, j] = row[j]

    return result


@cython.wraparound(False)
@cython.boundscheck(False)
def fast_multiget(dict mapping, ndarray keys, default=np.nan) -> "ArrayLike":
    cdef:
        Py_ssize_t i, n = len(keys)
        object val
        ndarray[object] output = np.empty(n, dtype='O')

    if n == 0:
        # kludge, for Series
        return np.empty(0, dtype='f8')

    for i in range(n):
        val = keys[i]
        if val in mapping:
            output[i] = mapping[val]
        else:
            output[i] = default

    return maybe_convert_objects(output)