# numpy.nanquantile¶

`numpy.``nanquantile`(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=<no value>)[source]

Compute the qth quantile of the data along the specified axis, while ignoring nan values. Returns the qth quantile(s) of the array elements.

New in version 1.15.0.

Parameters
aarray_like

Input array or object that can be converted to an array, containing nan values to be ignored

qarray_like of float

Quantile or sequence of quantiles to compute, which must be between 0 and 1 inclusive.

axis{int, tuple of int, None}, optional

Axis or axes along which the quantiles are computed. The default is to compute the quantile(s) along a flattened version of the array.

outndarray, optional

Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output, but the type (of the output) will be cast if necessary.

overwrite_inputbool, optional

If True, then allow the input array a to be modified by intermediate calculations, to save memory. In this case, the contents of the input a after this function completes is undefined.

interpolation{‘linear’, ‘lower’, ‘higher’, ‘midpoint’, ‘nearest’}

This optional parameter specifies the interpolation method to use when the desired quantile lies between two data points `i < j`:

• linear: `i + (j - i) * fraction`, where `fraction` is the fractional part of the index surrounded by `i` and `j`.

• lower: `i`.

• higher: `j`.

• nearest: `i` or `j`, whichever is nearest.

• midpoint: `(i + j) / 2`.

keepdimsbool, optional

If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original array a.

If this is anything but the default value it will be passed through (in the special case of an empty array) to the `mean` function of the underlying array. If the array is a sub-class and `mean` does not have the kwarg keepdims this will raise a RuntimeError.

Returns
quantilescalar or ndarray

If q is a single percentile and axis=None, then the result is a scalar. If multiple quantiles are given, first axis of the result corresponds to the quantiles. The other axes are the axes that remain after the reduction of a. If the input 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.

`quantile`
`nanmean`, `nanmedian`
`nanmedian`

equivalent to `nanquantile(..., 0.5)`

`nanpercentile`

same as nanquantile, but with q in the range [0, 100].

Examples

```>>> a = np.array([[10., 7., 4.], [3., 2., 1.]])
>>> a = np.nan
>>> a
array([[10.,  nan,   4.],
[ 3.,   2.,   1.]])
>>> np.quantile(a, 0.5)
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)
```