This repository contains my personal practice of NumPy, one of the core libraries for numerical computing in Python. The notebook covers:
- Creating 1D, 2D, and 3D arrays
- Array operations (ones, zeros, arange, full, ravel, eye, flatten, random, diag, sqrt, mean, sum, max, min, argmax, len, shape, dimension, log, pi, transpose, char, arccos, angle, reshape, slicing, sorting)
- Use of self-created mini-datasets to simulate real-world use cases
- Simple data visualizations using Matplotlib (
plotetc.) - Mathematical and statistical operations
- Performance comparison between Python lists and NumPy arrays
- Hands-on examples with comments and markdown explanations
I created this to strengthen my foundational understanding of NumPy before moving into advanced data science and machine learning topics. It’s structured to be beginner-friendly and easy to revisit later.
Just open the notebook with Jupyter and run the cells:
Numpy.ipynb