scipy

SciPy - Scientific Computing

Advanced scientific computing library built on NumPy, providing algorithms for optimization, integration, interpolation, and more.

When to Use

• Integrating functions (numerical integration, ODEs)
• Optimizing functions (minimization, root finding, curve fitting)
• Interpolating data (1D, 2D, splines)
• Advanced linear algebra (sparse matrices, decompositions)
• Signal processing (filtering, Fourier transforms, wavelets)
• Statistical analysis (distributions, hypothesis tests)
• Image processing (filters, morphology, measurements)
• Spatial algorithms (distance matrices, clustering, Voronoi)
• Special mathematical functions (Bessel, gamma, error functions)
• Solving differential equations (ODEs, PDEs)

Reference Documentation

Official docs: https://docs.scipy.org/
Search patterns: scipy.integrate.quad, scipy.optimize.minimize, scipy.interpolate, scipy.stats, scipy.signal

Core Principles

Use SciPy For

Task	Module	Example
Integration	integrate	quad(f, 0, 1)
Optimization	optimize	minimize(f, x0)
Interpolation	interpolate	interp1d(x, y)
Linear algebra	linalg	linalg.solve(A, b)
Signal processing	signal	signal.butter(4, 0.5)
Statistics	stats	stats.norm.pdf(x)
ODEs	integrate	solve_ivp(f, t_span, y0)
FFT	fft	fft.fft(signal)

Do NOT Use For

• Basic array operations (use NumPy)
• Machine learning (use scikit-learn)
• Deep learning (use PyTorch, TensorFlow)
• Symbolic mathematics (use SymPy)
• Data manipulation (use pandas)

Quick Reference

Installation

# pip
pip install scipy

# conda
conda install scipy

# With NumPy
pip install numpy scipy

Standard Imports

import numpy as np
from scipy import integrate, optimize, interpolate
from scipy import linalg, signal, stats
from scipy.integrate import odeint, solve_ivp
from scipy.optimize import minimize, root
from scipy.interpolate import interp1d, UnivariateSpline