A Visual Introduction to NumPy helped me think of np.array differently a Python list

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.

Learn numpy features to see why you should use numpy - high performance, multidimensional container, broadcasting functions, working with varied databases

Learn the uses of numpy - Alternate for lists in python, multi dimensional array, mathematical operations. See numpy applications with python libraries.

Learn NumPy Copy and View - Deep Copy, shallow copy and No copy in NumPy, NumPy view creation and types with examples, NumPy View vs Copy

Python is an open-source object-oriented language. It has many features of which one is the wide range of external packages. There are a lot of packages for installation and use for expanding functionalities. These packages are a repository of functions in python script. NumPy is one such package to ease array computations.

Learn about NumPy Array, NumPy Array creation, various array functions, array indexing & Slicing, array operations, methods and dimensions,It also includes array splitting, reshaping, and joining of arrays. Even the other external libraries in Python relate to NumPy arrays.