Scipy Basics

Introduction

SciPy provides scientific computing algorithms built on NumPy.

Optimization

from scipy.optimize import minimize, root

# Minimize function
def objective(x):
    return (x[0] - 2)**2 + (x[1] - 3)**2

result = minimize(objective, x0=[0, 0])
print(result.x)  # [2, 3]

Integration

from scipy.integrate import quad, trapz

# Definite integral
result, error = quad(lambda x: x**2, 0, 1)
print(result)  # 0.333...

# Numerical integration
x = np.linspace(0, 1, 100)
y = x**2
trapz(y, x)  # 0.333...

Statistics

from scipy import stats

# Distribution functions
stats.norm.pdf(x)        # PDF
stats.norm.cdf(x)        # CDF
stats.norm.ppf(0.95)     # Inverse CDF
stats.norm.rvs(0, 1)     # Random sample

Practice Problems

1. Find minimum of Rosenbrock function
2. Solve equation root
3. Calculate probabilities with normal distribution
4. Perform curve fitting
5. Calculate confidence intervals