ut
    contains integers or floats smaller than ``float64``, the output
    data-type is ``float64``. Otherwise, the output data-type is the
    same as that of the input. If `out` is specified, that array is
    returned instead.

See Also
--------
quantile
nanmean, nanmedian
nanmedian : equivalent to ``nanquantile(..., 0.5)``
nanpercentile : same as nanquantile, but with q in the range [0, 100].

Notes
-----
The behavior of `numpy.nanquantile` is the same as that of
`numpy.quantile` (ignoring nan values).
For more information, please see `numpy.quantile`.

Examples
--------
>>> import numpy as np
>>> a = np.array([[10., 7., 4.], [3., 2., 1.]])
>>> a[0][1] = np.nan
>>> a
array([[10.,  nan,   4.],
      [ 3.,   2.,   1.]])
>>> np.quantile(a, 0.5)
np.float64(nan)
>>> np.nanquantile(a, 0.5)
3.0
>>> np.nanquantile(a, 0.5, axis=0)
array([6.5, 2. , 2.5])
>>> np.nanquantile(a, 0.5, axis=1, keepdims=True)
array([[7.],
       [2.]])
>>> m = np.nanquantile(a, 0.5, axis=0)
>>> out = np.zeros_like(m)
>>> np.nanquantile(a, 0.5, axis=0, out=out)
array([6.5, 2. , 2.5])
>>> m
array([6.5,  2. ,  2.5])
>>> b = a.copy()
>>> np.nanquantile(b, 0.5, axis=1, overwrite_input=True)
array([7., 2.])
>>> assert not np.all(a==b)

References
----------
.. [1] R. J. Hyndman and Y. Fan,
   "Sample quantiles in statistical packages,"
   The American Statistician, 50(4), pp. 361-365, 1996

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