- NumPy is part of SciPy.
- NumPy defines arrays along with some basic numerical functions like indexing, sorting, reshaping, etc.
- SciPy implements computations such as numerical integration, optimization and machine learning using NumPy’s functionality.
NumPy and SciPy are closely related libraries in Python, often used in conjunction with each other for scientific computing and data analysis tasks. Here’s how they are related:
- NumPy (Numerical Python): NumPy is a fundamental package for scientific computing with Python. It provides support for multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently. NumPy forms the foundation for numerical computation in Python and is widely used for tasks like array manipulation, linear algebra operations, Fourier transform, random number generation, and more.
- SciPy (Scientific Python): SciPy is built on top of NumPy and extends its capabilities with additional high-level algorithms and functions for scientific computing. It provides modules for optimization, integration, interpolation, linear algebra, signal and image processing, statistics, and much more. SciPy is designed to be a comprehensive library for scientific computing in Python, offering functionality beyond what NumPy provides directly.
In summary, NumPy provides the basic array data structure and mathematical operations, while SciPy builds upon NumPy by adding more advanced scientific computing capabilities and algorithms. They are often used together, with SciPy relying heavily on NumPy arrays for data manipulation and computation.