SCIPY Tutorial

Introduction to SciPy

SciPy (Scientific Python) is a powerful open-source Python library used for scientific computing and technical computing. It builds on top of NumPy and provides additional functionality for optimization, integration, interpolation, eigenvalue problems, algebraic equations, and more.

Why Use SciPy?

• Provides high-level functions for numerical integration and optimization.
• Supports linear algebra, signal processing, and statistics.
• Extends NumPy with more complex and domain-specific functionality.
• Widely used in machine learning and data science projects.

Installing SciPy

You can install SciPy using pip:

pip install scipy

Basic Example

Here's a simple example using SciPy to compute the integration of a function:

 from scipy import integrate # Define a function to integrate def f(x): return x**2 # Integrate from 0 to 1 result, error = integrate.quad(f, 0, 1) print("Integration result:", result) 

Common SciPy Modules

Live Preview

You can run SciPy code using any Python interpreter. Here's an interactive version in Jupyter Notebook or Google Colab:

Tips

• Use NumPy arrays with SciPy for best performance.
• Explore SciPy's documentation for detailed use-cases: SciPy Docs
• Use SciPy in combination with Matplotlib for data visualization.