- Introduction: Numpy provides high-performance operations on arrays and matrices, crucial for data science.
- Basic Operations: Demonstrates array creation, and simple arithmetic operations like doubling values and comparing list performance with Numpy arrays.
- Advanced Operations: Showcases statistical functions, boolean indexing for filtering, and performance comparisons between Numpy arrays and Python lists.
- Array Manipulation: Covers slicing, reshaping, and aggregating data with functions like sum, mean, and standard deviation.
- String Operations: Utilizes Numpy's `np.char` module for efficient string processing across arrays.
- Statistical Analysis: Applies Numpy for calculating mean, median, mode, and standard deviation across datasets.
- Investment Analysis: Uses Numpy for projecting investment growth over time with compound interest calculations.
- Data Manipulation Tasks: Includes tasks like generating even numbers, filtering based on conditions, and exploring 3D arrays for deeper data analysis.