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Optimization

minimize() scalar objective bounds in optimization constraints in minimize() least_squares() curve_fit() root() nonlinear equations linprog() linear programming minimize_scalar()

Integration

quad() single integral dblquad() double integral cumulative_trapezoid() solve_ivp() ODE ODE event detection dense ODE output simpson() sampled integration fixed_quad() Gaussian quadrature

Statistics

stats.norm distribution t-test one sample t-test independent samples Mann–Whitney U test chi-square test Pearson correlation Spearman correlation bootstrap confidence interval

Signal Processing

find_peaks() butter() filter design sosfilt() welch() power spectrum correlate() resample() signal savgol_filter() smoothing spectrogram()

Linear Algebra & Sparse

linalg.solve() LU factorization QR decomposition SVD sparse csr_matrix sparse linear solve eigs() sparse eigenvalues distance matrix

Spatial & Interpolation

KDTree nearest neighbors ConvexHull Delaunay triangulation interp1d() CubicSpline RegularGridInterpolator griddata() distance.cdist()

SciPy / Statistics

bootstrap confidence interval

Learn bootstrap confidence interval in SciPy with a dedicated explanation, a concept-specific example, practical cautions, and a mastery check.

12 min read • Intermediate

scipy-024.py

python

import numpy as np
from scipy.stats import bootstrap
data=(np.array([8,9,10,12,13,15]),)
res=bootstrap(data,np.mean,confidence_level=.95,random_state=1)
print(res.confidence_interval)

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bootstrap confidence interval Mastery Check →

Spearman correlation