/pandas/core/nanops.py

from distutils.version import LooseVersion
import functools
import itertools
import operator
import warnings

import numpy as np

from pandas._libs import iNaT, lib, tslibs
import pandas.compat as compat

from pandas.core.dtypes.cast import _int64_max, maybe_upcast_putmask
from pandas.core.dtypes.common import (
    _get_dtype, is_any_int_dtype, is_bool_dtype, is_complex, is_complex_dtype,
    is_datetime64_dtype, is_datetime64tz_dtype, is_datetime_or_timedelta_dtype,
    is_float, is_float_dtype, is_integer, is_integer_dtype, is_numeric_dtype,
    is_object_dtype, is_scalar, is_timedelta64_dtype, pandas_dtype)
from pandas.core.dtypes.dtypes import DatetimeTZDtype
from pandas.core.dtypes.missing import isna, na_value_for_dtype, notna

import pandas.core.common as com
from pandas.core.config import get_option

_BOTTLENECK_INSTALLED = False
_MIN_BOTTLENECK_VERSION = '1.0.0'

try:
    import bottleneck as bn
    ver = bn.__version__
    _BOTTLENECK_INSTALLED = (LooseVersion(ver) >=
                             LooseVersion(_MIN_BOTTLENECK_VERSION))

    if not _BOTTLENECK_INSTALLED:
        warnings.warn(
            "The installed version of bottleneck {ver} is not supported "
            "in pandas and will be not be used\nThe minimum supported "
            "version is {min_ver}\n".format(
                ver=ver, min_ver=_MIN_BOTTLENECK_VERSION), UserWarning)

except ImportError:  # pragma: no cover
    pass


_USE_BOTTLENECK = False


def set_use_bottleneck(v=True):
    # set/unset to use bottleneck
    global _USE_BOTTLENECK
    if _BOTTLENECK_INSTALLED:
        _USE_BOTTLENECK = v


set_use_bottleneck(get_option('compute.use_bottleneck'))


class disallow(object):

    def __init__(self, *dtypes):
        super(disallow, self).__init__()
        self.dtypes = tuple(pandas_dtype(dtype).type for dtype in dtypes)

    def check(self, obj):
        return hasattr(obj, 'dtype') and issubclass(obj.dtype.type,
                                                    self.dtypes)

    def __call__(self, f):
        @functools.wraps(f)
        def _f(*args, **kwargs):
            obj_iter = itertools.chain(args, compat.itervalues(kwargs))
            if any(self.check(obj) for obj in obj_iter):
                msg = 'reduction operation {name!r} not allowed for this dtype'
                raise TypeError(msg.format(name=f.__name__.replace('nan', '')))
            try:
                with np.errstate(invalid='ignore'):
                    return f(*args, **kwargs)
            except ValueError as e:
                # we want to transform an object array
                # ValueError message to the more typical TypeError
                # e.g. this is normally a disallowed function on
                # object arrays that contain strings
                if is_object_dtype(args[0]):
                    raise TypeError(e)
                raise

        return _f


class bottleneck_switch(object):

    def __init__(self, **kwargs):
        self.kwargs = kwargs

    def __call__(self, alt):
        bn_name = alt.__name__

        try:
            bn_func = getattr(bn, bn_name)
        except (AttributeError, NameError):  # pragma: no cover
            bn_func = None

        @functools.wraps(alt)
        def f(values, axis=None, skipna=True, **kwds):
            if len(self.kwargs) > 0:
                for k, v in compat.iteritems(self.kwargs):
                    if k not in kwds:
                        kwds[k] = v
            try:
                if values.size == 0 and kwds.get('min_count') is None:
                    # We are empty, returning NA for our type
                    # Only applies for the default `min_count` of None
                    # since that affects how empty arrays are handled.
                    # TODO(GH-18976) update all the nanops methods to
                    # correctly handle empty inputs and remove this check.
                    # It *may* just be `var`
                    return _na_for_min_count(values, axis)

                if (_USE_BOTTLENECK and skipna and
                        _bn_ok_dtype(values.dtype, bn_name)):
                    result = bn_func(values, axis=axis, **kwds)

                    # prefer to treat inf/-inf as NA, but must compute the func
                    # twice :(
                    if _has_infs(result):
                        result = alt(values, axis=axis, skipna=skipna, **kwds)
                else:
                    result = alt(values, axis=axis, skipna=skipna, **kwds)
            except Exception:
                try:
                    result = alt(values, axis=axis, skipna=skipna, **kwds)
                except ValueError as e:
                    # we want to transform an object array
                    # ValueError message to the more typical TypeError
                    # e.g. this is normally a disallowed function on
                    # object arrays that contain strings

                    if is_object_dtype(values):
                        raise TypeError(e)
                    raise

            return result

        return f


def _bn_ok_dtype(dt, name):
    # Bottleneck chokes on datetime64
    if (not is_object_dtype(dt) and
            not (is_datetime_or_timedelta_dtype(dt) or
                 is_datetime64tz_dtype(dt))):

        # GH 15507
        # bottleneck does not properly upcast during the sum
        # so can overflow

        # GH 9422
        # further we also want to preserve NaN when all elements
        # are NaN, unlinke bottleneck/numpy which consider this
        # to be 0
        if name in ['nansum', 'nanprod']:
            return False

        return True
    return False


def _has_infs(result):
    if isinstance(result, np.ndarray):
        if result.dtype == 'f8':
            return lib.has_infs_f8(result.ravel())
        elif result.dtype == 'f4':
            return lib.has_infs_f4(result.ravel())
    try:
        return np.isinf(result).any()
    except (TypeError, NotImplementedError):
        # if it doesn't support infs, then it can't have infs
        return False


def _get_fill_value(dtype, fill_value=None, fill_value_typ=None):
    """ return the correct fill value for the dtype of the values """
    if fill_value is not None:
        return fill_value
    if _na_ok_dtype(dtype):
        if fill_value_typ is None:
            return np.nan
        else:
            if fill_value_typ == '+inf':
                return np.inf
            else:
                return -np.inf
    else:
        if fill_value_typ is None:
            return tslibs.iNaT
        else:
            if fill_value_typ == '+inf':
                # need the max int here
                return _int64_max
            else:
                return tslibs.iNaT


def _get_values(values, skipna, fill_value=None, fill_value_typ=None,
                isfinite=False, copy=True, mask=None):
    """ utility to get the values view, mask, dtype
    if necessary copy and mask using the specified fill_value
    copy = True will force the copy
    """

    if is_datetime64tz_dtype(values):
        # com.values_from_object returns M8[ns] dtype instead of tz-aware,
        #  so this case must be handled separately from the rest
        dtype = values.dtype
        values = getattr(values, "_values", values)
    else:
        values = com.values_from_object(values)
        dtype = values.dtype

    if mask is None:
        if isfinite:
            mask = _isfinite(values)
        else:
            mask = isna(values)

    if is_datetime_or_timedelta_dtype(values) or is_datetime64tz_dtype(values):
        # changing timedelta64/datetime64 to int64 needs to happen after
        #  finding `mask` above
        values = getattr(values, "asi8", values)
        values = values.view(np.int64)

    dtype_ok = _na_ok_dtype(dtype)

    # get our fill value (in case we need to provide an alternative
    # dtype for it)
    fill_value = _get_fill_value(dtype, fill_value=fill_value,
                                 fill_value_typ=fill_value_typ)

    if skipna:
        if copy:
            values = values.copy()
        if dtype_ok:
            np.putmask(values, mask, fill_value)

        # promote if needed
        else:
            values, changed = maybe_upcast_putmask(values, mask, fill_value)

    elif copy:
        values = values.copy()

    # return a platform independent precision dtype
    dtype_max = dtype
    if is_integer_dtype(dtype) or is_bool_dtype(dtype):
        dtype_max = np.int64
    elif is_float_dtype(dtype):
        dtype_max = np.float64

    return values, mask, dtype, dtype_max, fill_value


def _isfinite(values):
    if is_datetime_or_timedelta_dtype(values):
        return isna(values)
    if (is_complex_dtype(values) or is_float_dtype(values) or
            is_integer_dtype(values) or is_bool_dtype(values)):
        return ~np.isfinite(values)
    return ~np.isfinite(values.astype('float64'))


def _na_ok_dtype(dtype):
    # TODO: what about datetime64tz?  PeriodDtype?
    return not issubclass(dtype.type,
                          (np.integer, np.timedelta64, np.datetime64))


def _wrap_results(result, dtype, fill_value=None):
    """ wrap our results if needed """

    if is_datetime64_dtype(dtype) or is_datetime64tz_dtype(dtype):
        if fill_value is None:
            # GH#24293
            fill_value = iNaT
        if not isinstance(result, np.ndarray):
            tz = getattr(dtype, 'tz', None)
            assert not isna(fill_value), "Expected non-null fill_value"
            if result == fill_value:
                result = np.nan
            result = tslibs.Timestamp(result, tz=tz)
        else:
            result = result.view(dtype)
    elif is_timedelta64_dtype(dtype):
        if not isinstance(result, np.ndarray):
            if result == fill_value:
                result = np.nan

            # raise if we have a timedelta64[ns] which is too large
            if np.fabs(result) > _int64_max:
                raise ValueError("overflow in timedelta operation")

            result = tslibs.Timedelta(result, unit='ns')
        else:
            result = result.astype('i8').view(dtype)

    return result


def _na_for_min_count(values, axis):
    """Return the missing value for `values`

    Parameters
    ----------
    values : ndarray
    axis : int or None
        axis for the reduction

    Returns
    -------
    result : scalar or ndarray
        For 1-D values, returns a scalar of the correct missing type.
        For 2-D values, returns a 1-D array where each element is missing.
    """
    # we either return np.nan or pd.NaT
    if is_numeric_dtype(values):
        values = values.astype('float64')
    fill_value = na_value_for_dtype(values.dtype)

    if values.ndim == 1:
        return fill_value
    else:
        result_shape = (values.shape[:axis] +
                        values.shape[axis + 1:])
        result = np.empty(result_shape, dtype=values.dtype)
        result.fill(fill_value)
        return result


def nanany(values, axis=None, skipna=True, mask=None):
    """
    Check if any elements along an axis evaluate to True.

    Parameters
    ----------
    values : ndarray
    axis : int, optional
    skipna : bool, default True
    mask : ndarray[bool], optional
        nan-mask if known

    Returns
    -------
    result : bool

    Examples
    --------
    >>> import pandas.core.nanops as nanops
    >>> s = pd.Series([1, 2])
    >>> nanops.nanany(s)
    True

    >>> import pandas.core.nanops as nanops
    >>> s = pd.Series([np.nan])
    >>> nanops.nanany(s)
    False
    """
    values, mask, dtype, _, _ = _get_values(values, skipna, False, copy=skipna,
                                            mask=mask)
    return values.any(axis)


def nanall(values, axis=None, skipna=True, mask=None):
    """
    Check if all elements along an axis evaluate to True.

    Parameters
    ----------
    values : ndarray
    axis: int, optional
    skipna : bool, default True
    mask : ndarray[bool], optional
        nan-mask if known

    Returns
    -------
    result : bool

    Examples
    --------
    >>> import pandas.core.nanops as nanops
    >>> s = pd.Series([1, 2, np.nan])
    >>> nanops.nanall(s)
    True

    >>> import pandas.core.nanops as nanops
    >>> s = pd.Series([1, 0])
    >>> nanops.nanall(s)
    False
    """
    values, mask, dtype, _, _ = _get_values(values, skipna, True, copy=skipna,
                                            mask=mask)
    return values.all(axis)


@disallow('M8')
def nansum(values, axis=None, skipna=True, min_count=0, mask=None):
    """
    Sum the elements along an axis ignoring NaNs

    Parameters
    ----------
    values : ndarray[dtype]
    axis: int, optional
    skipna : bool, default True
    min_count: int, default 0
    mask : ndarray[bool], optional
        nan-mask if known

    Returns
    -------
    result : dtype

    Examples
    --------
    >>> import pandas.core.nanops as nanops
    >>> s = pd.Series([1, 2, np.nan])
    >>> nanops.nansum(s)
    3.0
    """
    values, mask, dtype, dtype_max, _ = _get_values(values,
                                                    skipna, 0, mask=mask)
    dtype_sum = dtype_max
    if is_float_dtype(dtype):
        dtype_sum = dtype
    elif is_timedelta64_dtype(dtype):
        dtype_sum = np.float64
    the_sum = values.sum(axis, dtype=dtype_sum)
    the_sum = _maybe_null_out(the_sum, axis, mask, min_count=min_count)

    return _wrap_results(the_sum, dtype)


@disallow('M8', DatetimeTZDtype)
@bottleneck_switch()
def nanmean(values, axis=None, skipna=True, mask=None):
    """
    Compute the mean of the element along an axis ignoring NaNs

    Parameters
    ----------
    values : ndarray
    axis: int, optional
    skipna : bool, default True
    mask : ndarray[bool], optional
        nan-mask if known

    Returns
    -------
    result : float
        Unless input is a float array, in which case use the same
        precision as the input array.

    Examples
    --------
    >>> import pandas.core.nanops as nanops
    >>> s = pd.Series([1, 2, np.nan])
    >>> nanops.nanmean(s)
    1.5
    """
    values, mask, dtype, dtype_max, _ = _get_values(
        values, skipna, 0, mask=mask)
    dtype_sum = dtype_max
    dtype_count = np.float64
    if (is_integer_dtype(dtype) or is_timedelta64_dtype(dtype) or
            is_datetime64_dtype(dtype) or is_datetime64tz_dtype(dtype)):
        dtype_sum = np.float64
    elif is_float_dtype(dtype):
        dtype_sum = dtype
        dtype_count = dtype
    count = _get_counts(mask, axis, dtype=dtype_count)
    the_sum = _ensure_numeric(values.sum(axis, dtype=dtype_sum))

    if axis is not None and getattr(the_sum, 'ndim', False):
        with np.errstate(all="ignore"):
            # suppress division by zero warnings
            the_mean = the_sum / count
        ct_mask = count == 0
        if ct_mask.any():
            the_mean[ct_mask] = np.nan
    else:
        the_mean = the_sum / count if count > 0 else np.nan

    return _wrap_results(the_mean, dtype)


@disallow('M8')
@bottleneck_switch()
def nanmedian(values, axis=None, skipna=True, mask=None):
    """
    Parameters
    ----------
    values : ndarray
    axis: int, optional
    skipna : bool, default True
    mask : ndarray[bool], optional
        nan-mask if known

    Returns
    -------
    result : float
        Unless input is a float array, in which case use the same
        precision as the input array.

    Examples
    --------
    >>> import pandas.core.nanops as nanops
    >>> s = pd.Series([1, np.nan, 2, 2])
    >>> nanops.nanmedian(s)
    2.0
    """
    def get_median(x):
        mask = notna(x)
        if not skipna and not mask.all():
            return np.nan
        return np.nanmedian(x[mask])

    values, mask, dtype, dtype_max, _ = _get_values(values, skipna, mask=mask)
    if not is_float_dtype(values):
        values = values.astype('f8')
        values[mask] = np.nan

    if axis is None:
        values = values.ravel()

    notempty = values.size

    # an array from a frame
    if values.ndim > 1:

        # there's a non-empty array to apply over otherwise numpy raises
        if notempty:
            if not skipna:
                return _wrap_results(
                    np.apply_along_axis(get_median, axis, values), dtype)

            # fastpath for the skipna case
            return _wrap_results(np.nanmedian(values, axis), dtype)

        # must return the correct shape, but median is not defined for the
        # empty set so return nans of shape "everything but the passed axis"
        # since "axis" is where the reduction would occur if we had a nonempty
        # array
        shp = np.array(values.shape)
        dims = np.arange(values.ndim)
        ret = np.empty(shp[dims != axis])
        ret.fill(np.nan)
        return _wrap_results(ret, dtype)

    # otherwise return a scalar value
    return _wrap_results(get_median(values) if notempty else np.nan, dtype)


def _get_counts_nanvar(mask, axis, ddof, dtype=float):
    dtype = _get_dtype(dtype)
    count = _get_counts(mask, axis, dtype=dtype)
    d = count - dtype.type(ddof)

    # always return NaN, never inf
    if is_scalar(count):
        if count <= ddof:
            count = np.nan
            d = np.nan
    else:
        mask2 = count <= ddof
        if mask2.any():
            np.putmask(d, mask2, np.nan)
            np.putmask(count, mask2, np.nan)
    return count, d


@disallow('M8')
@bottleneck_switch(ddof=1)
def nanstd(values, axis=None, skipna=True, ddof=1, mask=None):
    """
    Compute the standard deviation along given axis while ignoring NaNs

    Parameters
    ----------
    values : ndarray
    axis: int, optional
    skipna : bool, default True
    ddof : int, default 1
        Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
        where N represents the number of elements.
    mask : ndarray[bool], optional
        nan-mask if known

    Returns
    -------
    result : float
        Unless input is a float array, in which case use the same
        precision as the input array.

    Examples
    --------
    >>> import pandas.core.nanops as nanops
    >>> s = pd.Series([1, np.nan, 2, 3])
    >>> nanops.nanstd(s)
    1.0
    """
    result = np.sqrt(nanvar(values, axis=axis, skipna=skipna, ddof=ddof,
                            mask=mask))
    return _wrap_results(result, values.dtype)


@disallow('M8')
@bottleneck_switch(ddof=1)
def nanvar(values, axis=None, skipna=True, ddof=1, mask=None):
    """
    Compute the variance along given axis while ignoring NaNs

    Parameters
    ----------
    values : ndarray
    axis: int, optional
    skipna : bool, default True
    ddof : int, default 1
        Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
        where N represents the number of elements.
    mask : ndarray[bool], optional
        nan-mask if known

    Returns
    -------
    result : float
        Unless input is a float array, in which case use the same
        precision as the input array.

    Examples
    --------
    >>> import pandas.core.nanops as nanops
    >>> s = pd.Series([1, np.nan, 2, 3])
    >>> nanops.nanvar(s)
    1.0
    """
    values = com.values_from_object(values)
    dtype = values.dtype
    if mask is None:
        mask = isna(values)
    if is_any_int_dtype(values):
        values = values.astype('f8')
        values[mask] = np.nan

    if is_float_dtype(values):
        count, d = _get_counts_nanvar(mask, axis, ddof, values.dtype)
    else:
        count, d = _get_counts_nanvar(mask, axis, ddof)

    if skipna:
        values = values.copy()
        np.putmask(values, mask, 0)

    # xref GH10242
    # Compute variance via two-pass algorithm, which is stable against
    # cancellation errors and relatively accurate for small numbers of
    # observations.
    #
    # See https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
    avg = _ensure_numeric(values.sum(axis=axis, dtype=np.float64)) / count
    if axis is not None:
        avg = np.expand_dims(avg, axis)
    sqr = _ensure_numeric((avg - values) ** 2)
    np.putmask(sqr, mask, 0)
    result = sqr.sum(axis=axis, dtype=np.float64) / d

    # Return variance as np.float64 (the datatype used in the accumulator),
    # unless we were dealing with a float array, in which case use the same
    # precision as the original values array.
    if is_float_dtype(dtype):
        result = result.astype(dtype)
    return _wrap_results(result, values.dtype)


@disallow('M8', 'm8')
def nansem(values, axis=None, skipna=True, ddof=1, mask=None):
    """
    Compute the standard error in the mean along given axis while ignoring NaNs

    Parameters
    ----------
    values : ndarray
    axis: int, optional
    skipna : bool, default True
    ddof : int, default 1
        Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
        where N represents the number of elements.
    mask : ndarray[bool], optional
        nan-mask if known

    Returns
    -------
    result : float64
        Unless input is a float array, in which case use the same
        precision as the input array.

    Examples
    --------
    >>> import pandas.core.nanops as nanops
    >>> s = pd.Series([1, np.nan, 2, 3])
    >>> nanops.nansem(s)
     0.5773502691896258
    """

    # This checks if non-numeric-like data is passed with numeric_only=False
    # and raises a TypeError otherwise
    nanvar(values, axis, skipna, ddof=ddof, mask=mask)

    if mask is None:
        mask = isna(values)
    if not is_float_dtype(values.dtype):
        values = values.astype('f8')
    count, _ = _get_counts_nanvar(mask, axis, ddof, values.dtype)
    var = nanvar(values, axis, skipna, ddof=ddof)

    return np.sqrt(var) / np.sqrt(count)


def _nanminmax(meth, fill_value_typ):
    @bottleneck_switch()
    def reduction(values, axis=None, skipna=True, mask=None):

        values, mask, dtype, dtype_max, fill_value = _get_values(
            values, skipna, fill_value_typ=fill_value_typ, mask=mask)

        if ((axis is not None and values.shape[axis] == 0) or
                values.size == 0):
            try:
                result = getattr(values, meth)(axis, dtype=dtype_max)
                result.fill(np.nan)
            except (AttributeError, TypeError,
                    ValueError, np.core._internal.AxisError):
                result = np.nan
        else:
            result = getattr(values, meth)(axis)

        result = _wrap_results(result, dtype, fill_value)
        return _maybe_null_out(result, axis, mask)

    reduction.__name__ = 'nan' + meth
    return reduction


nanmin = _nanminmax('min', fill_value_typ='+inf')
nanmax = _nanminmax('max', fill_value_typ='-inf')


@disallow('O')
def nanargmax(values, axis=None, skipna=True, mask=None):
    """
    Parameters
    ----------
    values : ndarray
    axis: int, optional
    skipna : bool, default True
    mask : ndarray[bool], optional
        nan-mask if known

    Returns
    --------
    result : int
        The index of max value in specified axis or -1 in the NA case

    Examples
    --------
    >>> import pandas.core.nanops as nanops
    >>> s = pd.Series([1, 2, 3, np.nan, 4])
    >>> nanops.nanargmax(s)
    4
    """
    values, mask, dtype, _, _ = _get_values(
        values, skipna, fill_value_typ='-inf', mask=mask)
    result = values.argmax(axis)
    result = _maybe_arg_null_out(result, axis, mask, skipna)
    return result


@disallow('O')
def nanargmin(values, axis=None, skipna=True, mask=None):
    """
    Parameters
    ----------
    values : ndarray
    axis: int, optional
    skipna : bool, default True
    mask : ndarray[bool], optional
        nan-mask if known

    Returns
    --------
    result : int
        The index of min value in specified axis or -1 in the NA case

    Examples
    --------
    >>> import pandas.core.nanops as nanops
    >>> s = pd.Series([1, 2, 3, np.nan, 4])
    >>> nanops.nanargmin(s)
    0
    """
    values, mask, dtype, _, _ = _get_values(
        values, skipna, fill_value_typ='+inf', mask=mask)
    result = values.argmin(axis)
    result = _maybe_arg_null_out(result, axis, mask, skipna)
    return result


@disallow('M8', 'm8')
def nanskew(values, axis=None, skipna=True, mask=None):
    """ Compute the sample skewness.

    The statistic computed here is the adjusted Fisher-Pearson standardized
    moment coefficient G1. The algorithm computes this coefficient directly
    from the second and third central moment.

    Parameters
    ----------
    values : ndarray
    axis: int, optional
    skipna : bool, default True
    mask : ndarray[bool], optional
        nan-mask if known

    Returns
    -------
    result : float64
        Unless input is a float array, in which case use the same
        precision as the input array.

    Examples
    --------
    >>> import pandas.core.nanops as nanops
    >>> s = pd.Series([1,np.nan, 1, 2])
    >>> nanops.nanskew(s)
    1.7320508075688787
    """
    values = com.values_from_object(values)
    if mask is None:
        mask = isna(values)
    if not is_float_dtype(values.dtype):
        values = values.astype('f8')
        count = _get_counts(mask, axis)
    else:
        count = _get_counts(mask, axis, dtype=values.dtype)

    if skipna:
        values = values.copy()
        np.putmask(values, mask, 0)

    mean = values.sum(axis, dtype=np.float64) / count
    if axis is not None:
        mean = np.expand_dims(mean, axis)

    adjusted = values - mean
    if skipna:
        np.putmask(adjusted, mask, 0)
    adjusted2 = adjusted ** 2
    adjusted3 = adjusted2 * adjusted
    m2 = adjusted2.sum(axis, dtype=np.float64)
    m3 = adjusted3.sum(axis, dtype=np.float64)

    # floating point error
    #
    # #18044 in _libs/windows.pyx calc_skew follow this behavior
    # to fix the fperr to treat m2 <1e-14 as zero
    m2 = _zero_out_fperr(m2)
    m3 = _zero_out_fperr(m3)

    with np.errstate(invalid='ignore', divide='ignore'):
        result = (count * (count - 1) ** 0.5 / (count - 2)) * (m3 / m2 ** 1.5)

    dtype = values.dtype
    if is_float_dtype(dtype):
        result = result.astype(dtype)

    if isinstance(result, np.ndarray):
        result = np.where(m2 == 0, 0, result)
        result[count < 3] = np.nan
        return result
    else:
        result = 0 if m2 == 0 else result
        if count < 3:
            return np.nan
        return result


@disallow('M8', 'm8')
def nankurt(values, axis=None, skipna=True, mask=None):
    """
    Compute the sample excess kurtosis

    The statistic computed here is the adjusted Fisher-Pearson standardized
    moment coefficient G2, computed directly from the second and fourth
    central moment.

    Parameters
    ----------
    values : ndarray
    axis: int, optional
    skipna : bool, default True
    mask : ndarray[bool], optional
        nan-mask if known

    Returns
    -------
    result : float64
        Unless input is a float array, in which case use the same
        precision as the input array.

    Examples
    --------
    >>> import pandas.core.nanops as nanops
    >>> s = pd.Series([1,np.nan, 1, 3, 2])
    >>> nanops.nankurt(s)
    -1.2892561983471076
    """
    values = com.values_from_object(values)
    if mask is None:
        mask = isna(values)
    if not is_float_dtype(values.dtype):
        values = values.astype('f8')
        count = _get_counts(mask, axis)
    else:
        count = _get_counts(mask, axis, dtype=values.dtype)

    if skipna:
        values = values.copy()
        np.putmask(values, mask, 0)

    mean = values.sum(axis, dtype=np.float64) / count
    if axis is not None:
        mean = np.expand_dims(mean, axis)

    adjusted = values - mean
    if skipna:
        np.putmask(adjusted, mask, 0)
    adjusted2 = adjusted ** 2
    adjusted4 = adjusted2 ** 2
    m2 = adjusted2.sum(axis, dtype=np.float64)
    m4 = adjusted4.sum(axis, dtype=np.float64)

    with np.errstate(invalid='ignore', divide='ignore'):
        adj = 3 * (count - 1) ** 2 / ((count - 2) * (count - 3))
        numer = count * (count + 1) * (count - 1) * m4
        denom = (count - 2) * (count - 3) * m2 ** 2

    # floating point error
    #
    # #18044 in _libs/windows.pyx calc_kurt follow this behavior
    # to fix the fperr to treat denom <1e-14 as zero
    numer = _zero_out_fperr(numer)
    denom = _zero_out_fperr(denom)

    if not isinstance(denom, np.ndarray):
        # if ``denom`` is a scalar, check these corner cases first before
        # doing division
        if count < 4:
            return np.nan
        if denom == 0:
            return 0

    with np.errstate(invalid='ignore', divide='ignore'):
        result = numer / denom - adj

    dtype = values.dtype
    if is_float_dtype(dtype):
        result = result.astype(dtype)

    if isinstance(result, np.ndarray):
        result = np.where(denom == 0, 0, result)
        result[count < 4] = np.nan

    return result


@disallow('M8', 'm8')
def nanprod(values, axis=None, skipna=True, min_count=0, mask=None):
    """
    Parameters
    ----------
    values : ndarray[dtype]
    axis: int, optional
    skipna : bool, default True
    min_count: int, default 0
    mask : ndarray[bool], optional
        nan-mask if known

    Returns
    -------
    result : dtype

    Examples
    --------
    >>> import pandas.core.nanops as nanops
    >>> s = pd.Series([1, 2, 3, np.nan])
    >>> nanops.nanprod(s)
    6.0

    Returns
    --------
    The product of all elements on a given axis. ( NaNs are treated as 1)
    """
    if mask is None:
        mask = isna(values)
    if skipna and not is_any_int_dtype(values):
        values = values.copy()
        values[mask] = 1
    result = values.prod(axis)
    return _maybe_null_out(result, axis, mask, min_count=min_count)


def _maybe_arg_null_out(result, axis, mask, skipna):
    # helper function for nanargmin/nanargmax
    if axis is None or not getattr(result, 'ndim', False):
        if skipna:
            if mask.all():
                result = -1
        else:
            if mask.any():
                result = -1
    else:
        if skipna:
            na_mask = mask.all(axis)
        else:
            na_mask = mask.any(axis)
        if na_mask.any():
            result[na_mask] = -1
    return result


def _get_counts(mask, axis, dtype=float):
    dtype = _get_dtype(dtype)
    if axis is None:
        return dtype.type(mask.size - mask.sum())

    count = mask.shape[axis] - mask.sum(axis)
    if is_scalar(count):
        return dtype.type(count)
    try:
        return count.astype(dtype)
    except AttributeError:
        return np.array(count, dtype=dtype)


def _maybe_null_out(result, axis, mask, min_count=1):
    if axis is not None and getattr(result, 'ndim', False):
        null_mask = (mask.shape[axis] - mask.sum(axis) - min_count) < 0
        if np.any(null_mask):
            if is_numeric_dtype(result):
                if np.iscomplexobj(result):
                    result = result.astype('c16')
                else:
                    result = result.astype('f8')
                result[null_mask] = np.nan
            else:
                # GH12941, use None to auto cast null
                result[null_mask] = None
    elif result is not tslibs.NaT:
        null_mask = mask.size - mask.sum()
        if null_mask < min_count:
            result = np.nan

    return result


def _zero_out_fperr(arg):
    # #18044 reference this behavior to fix rolling skew/kurt issue
    if isinstance(arg, np.ndarray):
        with np.errstate(invalid='ignore'):
            return np.where(np.abs(arg) < 1e-14, 0, arg)
    else:
        return arg.dtype.type(0) if np.abs(arg) < 1e-14 else arg


@disallow('M8', 'm8')
def nancorr(a, b, method='pearson', min_periods=None):
    """
    a, b: ndarrays
    """
    if len(a) != len(b):
        raise AssertionError('Operands to nancorr must have same size')

    if min_periods is None:
        min_periods = 1

    valid = notna(a) & notna(b)
    if not valid.all():
        a = a[valid]
        b = b[valid]

    if len(a) < min_periods:
        return np.nan

    f = get_corr_func(method)
    return f(a, b)


def get_corr_func(method):
    if method in ['kendall', 'spearman']:
        from scipy.stats import kendalltau, spearmanr
    elif callable(method):
        return method

    def _pearson(a, b):
        return np.corrcoef(a, b)[0, 1]

    def _kendall(a, b):
        rs = kendalltau(a, b)
        if isinstance(rs, tuple):
            return rs[0]
        return rs

    def _spearman(a, b):
        return spearmanr(a, b)[0]

    _cor_methods = {
        'pearson': _pearson,
        'kendall': _kendall,
        'spearman': _spearman
    }
    return _cor_methods[method]


@disallow('M8', 'm8')
def nancov(a, b, min_periods=None):
    if len(a) != len(b):
        raise AssertionError('Operands to nancov must have same size')

    if min_periods is None:
        min_periods = 1

    valid = notna(a) & notna(b)
    if not valid.all():
        a = a[valid]
        b = b[valid]

    if len(a) < min_periods:
        return np.nan

    return np.cov(a, b)[0, 1]


def _ensure_numeric(x):
    if isinstance(x, np.ndarray):
        if is_integer_dtype(x) or is_bool_dtype(x):
            x = x.astype(np.float64)
        elif is_object_dtype(x):
            try:
                x = x.astype(np.complex128)
            except (TypeError, ValueError):
                x = x.astype(np.float64)
            else:
                if not np.any(x.imag):
                    x = x.real
    elif not (is_float(x) or is_integer(x) or is_complex(x)):
        try:
            x = float(x)
        except Exception:
            try:
                x = complex(x)
            except Exception:
                raise TypeError('Could not convert {value!s} to numeric'
                                .format(value=x))
    return x

# NA-friendly array comparisons


def make_nancomp(op):
    def f(x, y):
        xmask = isna(x)
        ymask = isna(y)
        mask = xmask | ymask

        with np.errstate(all='ignore'):
            result = op(x, y)

        if mask.any():
            if is_bool_dtype(result):
                result = result.astype('O')
            np.putmask(result, mask, np.nan)

        return result

    return f


nangt = make_nancomp(operator.gt)
nange = make_nancomp(operator.ge)
nanlt = make_nancomp(operator.lt)
nanle = make_nancomp(operator.le)
naneq = make_nancomp(operator.eq)
nanne = make_nancomp(operator.ne)


def _nanpercentile_1d(values, mask, q, na_value, interpolation):
    """
    Wraper for np.percentile that skips missing values, specialized to
    1-dimensional case.

    Parameters
    ----------
    values : array over which to find quantiles
    mask : ndarray[bool]
        locations in values that should be considered missing
    q : scalar or array of quantile indices to find
    na_value : scalar
        value to return for empty or all-null values
    interpolation : str

    Returns
    -------
    quantiles : scalar or array
    """
    # mask is Union[ExtensionArray, ndarray]
    values = values[~mask]

    if len(values) == 0:
        if lib.is_scalar(q):
            return na_value
        else:
            return np.array([na_value] * len(q),
                            dtype=values.dtype)

    return np.percentile(values, q, interpolation=interpolation)


def nanpercentile(values, q, axis, na_value, mask, ndim, interpolation):
    """
    Wraper for np.percentile that skips missing values.

    Parameters
    ----------
    values : array over which to find quantiles
    q : scalar or array of quantile indices to find
    axis : {0, 1}
    na_value : scalar
        value to return for empty or all-null values
    mask : ndarray[bool]
        locations in values that should be considered missing
    ndim : {1, 2}
    interpolation : str

    Returns
    -------
    quantiles : scalar or array
    """
    if not lib.is_scalar(mask) and mask.any():
        if ndim == 1:
            return _nanpercentile_1d(values, mask, q, na_value,
                                     interpolation=interpolation)
        else:
            # for nonconsolidatable blocks mask is 1D, but values 2D
            if mask.ndim < values.ndim:
                mask = mask.reshape(values.shape)
            if axis == 0:
                values = values.T
                mask = mask.T
            result = [_nanpercentile_1d(val, m, q, na_value,
                                        interpolation=interpolation)
                      for (val, m) in zip(list(values), list(mask))]
            result = np.array(result, dtype=values.dtype, copy=False).T
            return result
    else:
        return np.percentile(values, q, axis=axis, interpolation=interpolation)