https://github.com/aqueeqazam/numpy-for-mathematical-computing

You're at the perfect spot if you're interested in developing your career in data science or machine learning, or if you just enjoy having fun with mathematical operations.
https://github.com/aqueeqazam/numpy-for-mathematical-computing

mathematics numpy numpy-arrays

Last synced: 11 days ago
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You're at the perfect spot if you're interested in developing your career in data science or machine learning, or if you just enjoy having fun with mathematical operations.

• Host: GitHub
• URL: https://github.com/aqueeqazam/numpy-for-mathematical-computing
• Owner: AqueeqAzam
• Created: 2024-02-04T14:00:12.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2024-06-09T15:25:30.000Z (8 months ago)
• Last Synced: 2024-11-14T16:43:23.141Z (2 months ago)
• Topics: mathematics, numpy, numpy-arrays
• Language: Python
• Homepage:
• Size: 26.4 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
Numerical Python, or NumPy for short, is a core library for scientific and mathematical computing in Python. It provides a range of capabilities that make data science, machine learning, and other computationally demanding professions easy to use and indispensable.

Multidimensional arrays: The nd array, a potent n-dimensional array object that effectively stores and manipulates huge datasets, is the fundamental component of NumPy. Compared to Python's built-in lists, which are less effective for numerical operations, this is a major benefit.

Functions for mathematics: NumPy offers an extensive collection of functions for math operations on arrays. These functions encompass a wide range of topics, including random number generation, Fourier transformations, and linear algebra.

NumPy's powerful broadcasting system enables, under certain circumstances, operations to be performed on arrays of various shapes. It lessens the requirement for explicit loops and streamlines computations.

Integration with other libraries: Pandas (data analysis), SciPy (scientific computing), Matplotlib (visualization), and scikit-learn (machine learning) are just a few of the scientific Python libraries that rely on NumPy as their base. These libraries make use of NumPy's arrays to perform computations quickly.

In conclusion, NumPy gives Python the means to do effective numerical computations, enabling it to serve as a foundation for a wide range of data-driven and scientific applications.