What is SciPy?

SciPy is an open-source library of scientific algorithms for Python. It is organized into subpackages—each focused on a domain—while sharing NumPy's array model.

Core subpackages

• scipy.stats — probability distributions and statistical tests
• scipy.optimize — function minimization and root finding
• scipy.linalg — dense linear algebra (extends NumPy)
• scipy.sparse — sparse matrix formats and solvers
• scipy.integrate — quadrature and ODE solvers
• scipy.signal — filtering, convolution, spectral analysis
• scipy.interpolate — splines and grid interpolation
• scipy.special — special mathematical functions

Typical use cases

• A/B test analysis with hypothesis tests
• Fitting model parameters to experimental data
• Solving linear systems in engineering simulations
• Processing sensor time series with FFT and filters
• Sparse graph and network computations

Import conventions

import numpy as np
from scipy import stats, optimize, linalg
import scipy.sparse as sp

x = np.linspace(0, 1, 5)
print('stats normal pdf at 0:', stats.norm.pdf(0.0))

Important interview questions and answers

1. Q: SciPy vs NumPy?
   A: NumPy = ndarray foundation; SciPy = specialized algorithms on those arrays.
2. Q: Why submodule imports?
   A: Keeps namespaces clear—import only what you need (from scipy import stats).

Self-check

1. Name four SciPy subpackages and one use case each.
2. What array type do SciPy functions expect?

Tip: Import submodules you need (stats, optimize)—avoid wildcard imports in production.