pearsonr — SciPy v1.15.2 Manual
pearsonr# scipy.stats.pearsonr(x, y, *, alternative='two-sided', method=None, axis=0)[source]# Pearson correlation coefficient and p-value for testing non-correlation. The Pearson correlation coefficient [1] measures the linear relationship between two datasets. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an
ttest_ind — SciPy v1.16.0 Manual
ttest_ind# scipy.stats.ttest_ind(a, b, *, axis=0, equal_var=True, nan_policy='propagate', permutations=None, random_state=None, alternative='two-sided', trim=0, method=None, keepdims=False)[source]# Calculate the T-test for the means of two independent samples of scores. This is a test for the null hypothesis that 2 independent samples have identical average (expected) values. This test assumes th
Interpolation (scipy.interpolate) — SciPy v1.1.0 Reference Guide
There are several general interpolation facilities available in SciPy, for data in 1, 2, and higher dimensions: A class representing an interpolant (interp1d) in 1-D, offering several interpolation methods. Convenience function griddata offering a simple interface to interpolation in N dimensions (N = 1, 2, 3, 4, …). Object-oriented interface for the underlying routines is also available. Function
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File IO (scipy.io) — SciPy v1.15.2 Manual
fcluster — SciPy v1.15.0 Manual
kendalltau — SciPy v1.15.2 Manual
spearmanr — SciPy v1.15.2 Manual
numpy.random.mtrand.RandomState.randint — NumPy v1.17 Manual
numpy.random.mtrand.RandomState.randint — NumPy v1.17 Manual
numpy.reshape — NumPy v2.2 Manual
numpy.mean — NumPy v2.2 Manual
numpy.absolute — NumPy v2.2 Manual
zoom — SciPy v1.15.2 Manual
affine_transform — SciPy v1.15.2 Manual
rotate — SciPy v1.15.2 Manual
shift — SciPy v1.15.2 Manual
numpy.zeros — NumPy v2.2 Manual
maximum — SciPy v1.15.2 Manual
distance_transform_edt — SciPy v1.15.2 Manual
center_of_mass — SciPy v1.15.2 Manual
