Arrray-Oriented Programming with NumPy (Deitel Ch 7)¶

In this chapter, you’ll:

Learn what arrays are and how they differ from lists.
Use the numpy module’s highperformance ndarrays.
Compare list and ndarray performance with the IPython %timeit magic.
Use ndarrays to store and retrieve data efficiently.
Create and initialize ndarrays.
Refer to individual ndarray elements.
Iterate through ndarrays.
Create and manipulate multidimensional ndarrays.
Perform common ndarray manipulations.
Create and manipulate pandas one-dimensional Series and two-dimensional DataFrames.

First appeared in 2006 and is the preferred Python array implementation.
High-performance, richly functional n-dimensional array type called ndarray.
Written in C and up to 100 times faster than lists.
Critical in big-data processing, AI applications and much more.
According to libraries.io, over 450 Python libraries depend on NumPy.
Many popular data science libraries such as Pandas, SciPy (Scientific Python) and Keras (for deep learning) are built on or depend on NumPy.