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quantile(a, q, axis=None, out=None, overwrite_input=False, method='linear', keepdims=False, *, interpolation=None)
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Notes

Given a vector V of length N , the q-th quantile of V is the value q of the way from the minimum to the maximum in a sorted copy of V . The values and distances of the two nearest neighbors as well as the :None:None:`method` parameter will determine the quantile if the normalized ranking does not match the location of q exactly. This function is the same as the median if q=0.5 , the same as the minimum if q=0.0 and the same as the maximum if q=1.0 .

This optional :None:None:`method` parameter specifies the method to use when the desired quantile lies between two data points i < j . If g is the fractional part of the index surrounded by i and alpha and beta are correction constants modifying i and j.

$$i + g = (q - alpha) / ( n - alpha - beta + 1 )$$

The different methods then work as follows

inverted_cdf:

method 1 of H&F . This method gives discontinuous results: * if g > 0 ; then take j * if g = 0 ; then take i

averaged_inverted_cdf:

method 2 of H&F . This method give discontinuous results: * if g > 0 ; then take j * if g = 0 ; then average between bounds

closest_observation:

method 3 of H&F . This method give discontinuous results: * if g > 0 ; then take j * if g = 0 and index is odd ; then take j * if g = 0 and index is even ; then take i

interpolated_inverted_cdf:

method 4 of H&F . This method give continuous results using: * alpha = 0 * beta = 1

hazen:

method 5 of H&F . This method give continuous results using: * alpha = 1/2 * beta = 1/2

weibull:

method 6 of H&F . This method give continuous results using: * alpha = 0 * beta = 0

linear:

method 7 of H&F . This method give continuous results using: * alpha = 1 * beta = 1

median_unbiased:

method 8 of H&F . This method is probably the best method if the sample distribution function is unknown (see reference). This method give continuous results using: * alpha = 1/3 * beta = 1/3

normal_unbiased:

method 9 of H&F . This method is probably the best method if the sample distribution function is known to be normal. This method give continuous results using: * alpha = 3/8 * beta = 3/8

lower:

NumPy method kept for backwards compatibility. Takes i as the interpolation point.

higher:

NumPy method kept for backwards compatibility. Takes j as the interpolation point.

nearest:

NumPy method kept for backwards compatibility. Takes i or j , whichever is nearest.

midpoint:

NumPy method kept for backwards compatibility. Uses (i + j) / 2 .

Parameters

a : array_like

Input array or object that can be converted to an array.

q : array_like of float

Quantile or sequence of quantiles to compute, which 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 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 discontiuous. NumPy further defines the following discontinuous variations of the default 'linear' (7.) option:

  • 'lower'

  • 'higher',

  • 'midpoint'

  • 'nearest'

versionchanged

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.

interpolation : str, optional

Deprecated name for the method keyword argument.

deprecated

Returns

quantile : scalar or ndarray

If q is a single quantile 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 q-th quantile of the data along the specified axis.

See Also

mean
median

equivalent to quantile(..., 0.5)

nanquantile
percentile

equivalent to quantile, but with q in the range [0, 100].

Examples

>>> a = np.array([[10, 7, 4], [3, 2, 1]])
... a array([[10, 7, 4], [ 3, 2, 1]])
>>> np.quantile(a, 0.5)
3.5
>>> np.quantile(a, 0.5, axis=0)
array([6.5, 4.5, 2.5])
>>> np.quantile(a, 0.5, axis=1)
array([7.,  2.])
>>> np.quantile(a, 0.5, axis=1, keepdims=True)
array([[7.],
       [2.]])
>>> m = np.quantile(a, 0.5, axis=0)
... out = np.zeros_like(m)
... np.quantile(a, 0.5, axis=0, out=out) array([6.5, 4.5, 2.5])
>>> m
array([6.5, 4.5, 2.5])
>>> b = a.copy()
... np.quantile(b, 0.5, axis=1, overwrite_input=True) array([7., 2.])
>>> assert not np.all(a == b)

See also numpy.percentile for a visualization of most methods.

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

numpy.nanquantile numpy.percentile

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