SciPy Cheat Sheet
SciPy scientific computing reference covering statistics, optimization, linear algebra, and interpolation for numerical Python workflows.
2 PagesIntermediateMar 28, 2026
scipy.stats
Distributions and hypothesis tests.
python
from scipy import statst_stat, p_value = stats.ttest_ind(group_a, group_b) # two-sample t-testr, p = stats.pearsonr(x, y) # Pearson correlationdist = stats.norm(loc=0, scale=1)print(dist.pdf(0), dist.cdf(1.96))z_scores = stats.zscore(data)
scipy.optimize
Minimization and root finding.
python
from scipy import optimizedef f(x): return (x[0] - 3) ** 2 + (x[1] + 1) ** 2result = optimize.minimize(f, x0=[0, 0], method="BFGS")print(result.x, result.fun)root = optimize.brentq(lambda x: x ** 2 - 4, 0, 5) # find root in [0, 5]
Linear Algebra & Interpolation
Solve systems and interpolate data.
python
from scipy import linalg, interpolateA = [[2, 1], [1, 3]]b = [3, 5]x = linalg.solve(A, b) # solve Ax = beigvals, eigvecs = linalg.eig(A)f = interpolate.interp1d(x_known, y_known, kind="cubic")y_new = f(x_query)
Key Submodules
Major areas of SciPy's functionality.
- scipy.stats- probability distributions and hypothesis tests
- scipy.optimize- minimization, curve fitting, root finding
- scipy.linalg- linear algebra beyond NumPy (LU, eig, solve)
- scipy.sparse- sparse matrix formats and operations
- scipy.signal- filtering, convolution, spectral analysis
- scipy.spatial- distance metrics, KD-trees, Voronoi diagrams
- scipy.integrate- numerical integration and ODE solvers
- scipy.interpolate- 1D/2D interpolation of data points
Pro Tip
scipy.optimize.curve_fit is often simpler than minimize for fitting a parametric function to data — it wraps least-squares fitting and returns both the parameters and their covariance matrix for uncertainty estimates.
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