This repository contains my practice notebooks for learning NumPy, a Python library for numerical computing. The notebooks are created in Google Colab and cover array operations, slicing, and linear algebra.
- Array Creation:
np.zeros,np.arange,np.linspace. - Slicing: Extract rows, columns, and conditional elements.
- Operations: Element-wise arithmetic and statistics.
- Linear Algebra: Matrix multiplication and determinants.
- Broadcasting: Operations with arrays of different shapes.
- Array Manipulation: Reshaping, concatenation, and splitting.
- Random Numbers: Uniform, normal, and integer arrays; shuffling.
- Data Analysis: Statistics and filtering on a simulated dataset.
- Vectorization: Comparing loops vs. vectorized operations and using np.where for conditional logic.
- Master NumPy fundamentals.
- Apply NumPy to data analysis and small projects.
- Share progress and get feedback.
- Interested in Data Science