nanquantile(a, q, axis=None, out=None, overwrite_input=False, method='linear', keepdims=<no value>, *, interpolation=None)
For more information please see numpy.quantile
Input array or object that can be converted to an array, containing nan values to be ignored
Quantile or sequence of quantiles to compute, which must be between 0 and 1 inclusive.
Axis or axes along which the quantiles are computed. The default is to compute the quantile(s) along a flattened version of the array.
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.
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.
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 are:
'inverted_cdf'
'averaged_inverted_cdf'
'closest_observation'
'interpolated_inverted_cdf'
'hazen'
'weibull'
'linear' (default)
'median_unbiased'
'normal_unbiased'
The first three methods are discontiuous. NumPy further defines the following discontinuous variations of the default 'linear' (7.) option:
'lower'
'higher',
'midpoint'
'nearest'
This argument was previously called "interpolation" and only offered the "linear" default and last four options.
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 :None:None:`keepdims` this will raise a RuntimeError.
Deprecated name for the method keyword argument.
If q is a single percentile and :None:None:`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 :None:None:`out` is specified, that array is returned instead.
Compute the qth quantile of the data along the specified axis, while ignoring nan values. Returns the qth quantile(s) of the array elements.
nanmedian
equivalent to nanquantile(..., 0.5)
nanpercentile
same as nanquantile, but with q in the range [0, 100].
>>> 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)See :
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.quantile
numpy.lib.nanfunctions
numpy.nanpercentile
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