xorbits.numpy.nanquantile#

xorbits.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(numpy).

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(numpy): 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(numpy).

Returns

quantile – 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.

Return type

scalar or ndarray

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

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)

References

1

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

Warning

This method has not been implemented yet. Xorbits will try to execute it with numpy.

This docstring was copied from numpy.