numpy.nanquantile#
- numpy.nanquantile(a, q, axis=None, out=None, overwrite_input=False, method='linear', keepdims=<no value>, *, weights=None, interpolation=None)[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
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.
- 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.
- methodstr, 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:
‘inverted_cdf’
‘averaged_inverted_cdf’
‘closest_observation’
‘interpolated_inverted_cdf’
‘hazen’
‘weibull’
‘linear’ (default)
‘median_unbiased’
‘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.
- 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 andmean
does not have the kwarg keepdims this will raise a RuntimeError.- weightsarray_like, optional
An array of weights associated with the values in a. Each value in a contributes to the quantile according to its associated weight. The weights array can either be 1-D (in which case its length must be the size of a along the given axis) or of the same shape as a. If weights=None, then all data in a are assumed to have a weight equal to one. Only method=”inverted_cdf” supports weights.
New in version 2.0.0.
- interpolationstr, optional
Deprecated name for the method keyword argument.
Deprecated since version 1.22.0.
- Returns:
- quantilescalar 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 isfloat64
. 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) 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)