Introduction to NumPy

NumPy (Numerical Python) is the fundamental library for scientific computing in Python. It provides powerful tools for working with arrays - collections of numbers that can be manipulated incredibly fast.

Why NumPy Matters for Finance

Imagine calculating daily returns for a stock over 10 years. That's roughly 2,500 trading days. With standard Python lists, you'd need to loop through each day individually. With NumPy, you perform the calculation on all 2,500 days simultaneously - often 10 to 100 times faster.

Creating NumPy Arrays

NumPy arrays are similar to lists but with superpowers for numerical operations:

Array vs List: The Speed Difference

Vectorized Operations

The real power of NumPy - perform operations on entire arrays at once:

Practice: Create Price Arrays

Practice: Calculate Returns

Key Takeaways

• NumPy arrays are optimized for numerical operations
• Use np.array() to create arrays from lists
• np.zeros(), np.ones(), np.arange(), np.linspace() create special arrays
• Vectorized operations work on entire arrays at once
• NumPy is 10-100x faster than Python lists for numerical work

Next, we'll explore the essential NumPy functions used in financial analysis!