I’ve started using numpy more frequently in my own work.

Problem: I think of np.array like a Python list. But that’s not right.

This visualization guide helped me think of them differently.

Covers:

• arrays
• creating arrays (I didn’t know about np.ones(), np.zeros(), or np.random.random(), so cool)
• array arithmetic
• indexing and slicing
• aggregation with min, max, sum, mean, prod, etc.
• matrices : 2D arrays
• matrix arithmetic
• dot product (with visuals, it takes seconds to understand)
• matrix indexing and slicing
• matrix aggregation (both all entries and column or row with axis parameter)
• transposing and reshaping
• ndarray: n-dimensional arrays
• transforming mathematical formulas to numpy syntax
• data representation
• All with excellent drawings to help visualize the concept.

#numpy