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You will learn that NumPy has very efficient arrays that are easy to use due to the powerful indexing mechanism. This book describes some of the more advanced and tricky indexing techniques. This weekend, we will cover many fundamental operations of the NumPy. New NumPy-related developments seem to come to our attention every week, or maybe even daily.
Also, what will you learn? You will learn that NumPy has very efficient arrays that are easy to use due to the powerful indexing mechanism. This chapter describes some of the more advanced and tricky indexing techniques. Also we will try to make an attempt to document the most essential methods that every NumPy user should know. NumPy has many methods—too many to even mention in this book!
Although this book/sheet is talking about NumPy, yet this package does not need an introduction, as many readers read this to learn more about NumPy not to know what NumPy is. But still let’s have a small introduction to be fair, just in case you missed something about NumPy. NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical methods to operate on these arrays. NumPy is famous for its efficient arrays. This fame is partly due to the ease of indexing. We will demonstrate advanced indexing tricks using images. Before diving into indexing, we will install the necessary software—SciPy and PIL.
What will we cover in this weekend?
Introduction &amp Installation (5 steps   3 steps)
Images Resizing (4 steps)
Copy vs. View (3 steps)
Images Flipping (3 steps)
Fancy Indexing (2 steps)
Indexing with a List (2 steps)
Boolean Indexing (2 steps)
Stride for Sudoku (3 steps)
Broadcasting Arrays (5 steps)
Checkpoint
Summing Fibonacci (6 steps)
Prime Factors (4 steps)
Palindromic Numbers (2 steps)
The Steady State (8 steps)
Power Law (4 steps)
Trading Periodically on Dips (3 steps)
Simulating Trading (4 steps)
Sieving Integers with the Sieve of Eratosthenes (2 steps)