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Module « scipy.stats.mstats »

Fonction hdquantiles - module scipy.stats.mstats

Signature de la fonction hdquantiles

def hdquantiles(data, prob=(0.25, 0.5, 0.75), axis=None, var=False) 

Description

help(scipy.stats.mstats.hdquantiles)

Computes quantile estimates with the Harrell-Davis method.

The quantile estimates are calculated as a weighted linear combination
of order statistics.

Parameters
----------
data : array_like
    Data array.
prob : sequence, optional
    Sequence of probabilities at which to compute the quantiles.
axis : int or None, optional
    Axis along which to compute the quantiles. If None, use a flattened
    array.
var : bool, optional
    Whether to return the variance of the estimate.

Returns
-------
hdquantiles : MaskedArray
    A (p,) array of quantiles (if `var` is False), or a (2,p) array of
    quantiles and variances (if `var` is True), where ``p`` is the
    number of quantiles.

See Also
--------
hdquantiles_sd

Examples
--------
>>> import numpy as np
>>> from scipy.stats.mstats import hdquantiles
>>>
>>> # Sample data
>>> data = np.array([1.2, 2.5, 3.7, 4.0, 5.1, 6.3, 7.0, 8.2, 9.4])
>>>
>>> # Probabilities at which to compute quantiles
>>> probabilities = [0.25, 0.5, 0.75]
>>>
>>> # Compute Harrell-Davis quantile estimates
>>> quantile_estimates = hdquantiles(data, prob=probabilities)
>>>
>>> # Display the quantile estimates
>>> for i, quantile in enumerate(probabilities):
...     print(f"{int(quantile * 100)}th percentile: {quantile_estimates[i]}")
25th percentile: 3.1505820231763066 # may vary
50th percentile: 5.194344084883956
75th percentile: 7.430626414674935

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