Welcome to the NumPy and SciPy Learning Center! NumPy and SciPy are two of the most popular Python libraries for numerical computing. This guide will help you get started with these powerful tools.

What is NumPy?

NumPy is a fundamental package for scientific computing with Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.

Key Features of NumPy:

  • Arrays: NumPy provides support for arrays of different data types and dimensions.
  • Functions: A wide range of mathematical functions for operations on arrays.
  • Efficiency: NumPy is highly optimized for performance.

What is SciPy?

SciPy is an open-source scientific computing library for Python. It builds on the NumPy array object and extends it with additional functionality for scientific computing.

Key Features of SciPy:

  • Optimization: Functions for optimization and root finding.
  • Integration: Functions for integration and interpolation.
  • Signal Processing: Functions for signal processing and filtering.
  • Statistics: Functions for statistical analysis.

Getting Started

To get started with NumPy and SciPy, you can install them using pip:

pip install numpy scipy

Example: Creating a NumPy Array

Here's a simple example of creating a NumPy array:

import numpy as np

# Create a 1D array
array_1d = np.array([1, 2, 3, 4, 5])

# Create a 2D array
array_2d = np.array([[1, 2, 3], [4, 5, 6]])

print(array_1d)
print(array_2d)

Further Reading

For more information on NumPy and SciPy, you can visit the following resources:

NumPy and SciPy