# numpy.nanquantile

numpy.nanquantile(a, q, axis=None, out=None, overwrite_input=False, method='linear', keepdims=<no value>, *, interpolation=None)

  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:
    a array_like

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

    q array_like of float

      Probability or sequence of probabilities for the quantiles to compute. Values 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.

    out ndarray, 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_input bool, 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.

    method str, optional

      This parameter specifies the method to use for estimating the quantile. There are many different methods, some unique to NumPy. See the notes for explanation. The options sorted by their R type as summarized in the H&F paper [1] are:

      1. ‘inverted_cdf’

      2. ‘averaged_inverted_cdf’

      3. ‘closest_observation’

      4. ‘interpolated_inverted_cdf’

      5. ‘hazen’

      6. ‘weibull’

      7. ‘linear’ (default)

      8. ‘median_unbiased’

      9. ‘normal_unbiased’

      The first three methods are discontinuous. NumPy further defines the following discontinuous variations of the default ‘linear’ (7.) option:

      - ‘lower’

      - ‘higher’,

      - ‘midpoint’

      - ‘nearest’

      Changed in version 1.22.0: This argument was previously called “interpolation” and only offered the “linear” default and last four options.

    keepdims bool, 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.

    interpolation str, optional

      Deprecated name for the method keyword argument.

      Deprecated since version 1.22.0.

  Returns:
    quantile scalar or ndarray

      If q is a single probability and axis=None, then the result is a scalar. If multiple probability levels 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.

  See also

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

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

  `nanpercentile`

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

  Notes

  For more information please see `numpy.quantile`

  References

  [1]

  R. J. Hyndman and Y. Fan, “Sample quantiles in statistical packages,” The American Statistician, 50(4), pp. 361-365, 1996

  Examples

      >>> 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)
      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)