Difference between NumPy and SciPy in Python

In Python scientific computing, NumPy provides the core tools for numerical operations and array handling, while SciPy builds on NumPy to offer advanced scientific functions like integration, optimization and signal processing. Simply put, NumPy handles the numbers and SciPy solves the problems.

What is NumPy?

NumPy (Numerical Python) is the core library for numerical computing in Python. It provides:

• Powerful n-dimensional array objects
• Vectorized operations (fast math on arrays without writing loops)
• Basic linear algebra, statistics, and random number capabilities

Many other libraries (like SciPy, pandas, scikit-learn) are built on top of NumPy.

What is SciPy?

SciPy (Scientific Python) is built on top of NumPy and extends its functionality. It offers:

• Advanced scientific tools (optimization, integration, interpolation, signal processing, statistics, etc.)
• A large collection of algorithms for scientific and technical computing
• Specialized submodules (e.g., scipy.optimize, scipy.signal, scipy.stats, etc.)

Difference between NumPy and SciPy

Knowing the difference helps you pick the right tool, whether you need to work with numbers quickly or solve harder problems like finding the best solution or calculating areas.

Which one should you choose?

Use NumPy when you:

• Need fast array/matrix manipulation
• Perform basic linear algebra or math
• Handle data storage and broadcasting

Use SciPy when you:

• Solve complex equations (e.g., differential equations, root finding)
• Perform optimization or integration
• Conduct advanced statistical analysis or signal processing

Often, you will need use both together, NumPy for managing data and SciPy for advanced computations:
Here's how to import them:

import numpy as np
import scipy.optimize as opt