Difference between NumPy and SciPy in Python
Last Updated :
05 Jul, 2025
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.
Types of Differences | NumPy | SciPy |
|---|
Primary Focus | Efficient array handling, basic math operations | Advanced scientific and technical computing functions |
Use Cases | Array manipulation, basic linear algebra, simple math, random numbers | Complex tasks: optimization, integration, solving equations, signal processing |
Module Structure | Single core library | Modular library with subpackages (optimize, integrate, signal, stats, etc.) |
Capabilities | - Efficient storage of data
- Vectorization arithmetic
- Broadcasting mechanisms to handle arrays of different shapes during mathematical operations.
| - Multidimensional image processing.
- Advanced optimization routines using "optimize".
- special functions through its "special module.
|
Domain | - Elementary linear algebra.
- Basic statistical functions.
- Fourier analysis.
- Random number capabilities.
| - Spatial data structure and algorithm
- Interpolation functions with interpolate.
- Eigenvalue problems and matrix functions.
- Sparse matrix computations.
|
Evolution | NumPy is originated from the older Numeric and Numarray libraries. It was designed to provide an efficient array computing utility for Python. | Scipy is started with Travis Oliphant wanting to combine the functionalities of Numeric and another library called "scipy.base". The result was the more comprehensive and integrated library we know today. |
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:
Python
import numpy as np
import scipy.optimize as opt