pyinterp.DescriptiveStatistics

pyinterp.DescriptiveStatistics#

pyinterp.DescriptiveStatistics(values: object, weights: object | None = None, axis: collections.abc.Sequence[int] | None = None, dtype: object | None = None) → object#

Univariate descriptive statistics.

Computes statistics using numerically stable algorithms that support parallel and online computation with arbitrary weights.

Reference: https://doi.org/10.1007/s00180-015-0637-z

Parameters:

  • values – Input array of values.

  • weights – Optional array of weights (same shape as values).

  • axis – Optional axis or axes along which to compute statistics.

  • dtype – Data type for internal storage, either ‘float32’ or ‘float64’. Determines precision and memory usage. Defaults to ‘float64’.

Examples

>>> import numpy as np
>>> import pyinterp

Compute statistics for a 1D array with float64 (default)

>>> data = np.random.randn(100)
>>> stats = pyinterp.DescriptiveStatistics(data)
>>> print(f"Mean: {stats.mean()}, Std: {np.sqrt(stats.variance())}")

Compute statistics with float32 for reduced memory usage

>>> data = data.astype('float32')
>>> stats = pyinterp.DescriptiveStatistics(data, dtype='float32')

Compute along a specific axis

>>> data = np.random.randn(100, 50)
>>> stats_axis = pyinterp.DescriptiveStatistics(data, axis=[0])
>>> print(f"Means shape: {stats_axis.mean().shape}")

Compute with weights

>>> weights = np.random.rand(100, 50)
>>> stats_weighted = pyinterp.DescriptiveStatistics(data, weights=weights)
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