Complete Guide to NumPy Functions, Operations, and Visualizations
This repository offers a comprehensive guide to using the NumPy library for numerical computing, covering a wide range of functions, operations, and techniques. With a focus on real-world applications, this repo explores the full scope of NumPy's array manipulation capabilities and demonstrates data visualizations based on a dataset.
Key Components:
- Array Creation and Manipulation: Detailed examples on creating and reshaping arrays, indexing, slicing, broadcasting, and vectorization techniques.
- Mathematical Functions: Coverage of universal functions (ufuncs), including element-wise operations, aggregation functions (e.g., sum, mean, min, max), and trigonometric functions.
- Linear Algebra: Applications of matrix operations such as dot products, transpositions, inverses, eigenvalues, and decomposition (e.g., Singular Value Decomposition, or SVD).
- Random Sampling: Use of the numpy.random module for generating random numbers, distributions, and simulations.
- Statistical Operations: Calculation of statistical measures such as variance, standard deviation, and correlation coefficients.
- Data Visualizations: Visualization examples using libraries like Matplotlib and Seaborn to illustrate the effects of transformations and array manipulations.
Perfect for data scientists, analysts, and anyone aiming to deepen their understanding of NumPy, this repository provides a solid foundation in handling multidimensional arrays and optimizing computational efficiency using Python’s most powerful numerical library.