nanpercentile(a, q, axis=None, out=None, overwrite_input=False, method='linear', keepdims=<no value>, *, interpolation=None)
Returns the qth percentile(s) of the array elements.
For more information please see numpy.percentile
Input array or object that can be converted to an array, containing nan values to be ignored.
Percentile or sequence of percentiles to compute, which must be between 0 and 100 inclusive.
Axis or axes along which the percentiles are computed. The default is to compute the percentile(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 percentile. 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 percentiles are given, first axis of the result corresponds to the percentiles. 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 percentile of the data along the specified axis, while ignoring nan values.
nanmedian
equivalent to nanpercentile(..., 50)
nanquantile
equivalent to nanpercentile, except q in range [0, 1].
>>> a = np.array([[10., 7., 4.], [3., 2., 1.]])
... a[0][1] = np.nan
... a array([[10., nan, 4.], [ 3., 2., 1.]])
>>> np.percentile(a, 50) nan
>>> np.nanpercentile(a, 50) 3.0
>>> np.nanpercentile(a, 50, axis=0) array([6.5, 2. , 2.5])
>>> np.nanpercentile(a, 50, axis=1, keepdims=True) array([[7.], [2.]])
>>> m = np.nanpercentile(a, 50, axis=0)
... out = np.zeros_like(m)
... np.nanpercentile(a, 50, axis=0, out=out) array([6.5, 2. , 2.5])
>>> m array([6.5, 2. , 2.5])
>>> b = a.copy()
... np.nanpercentile(b, 50, 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.nanquantile
numpy.percentile
numpy.lib.nanfunctions
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them