Book Image

Functional Python Programming - Second Edition

By : Steven F. Lott
Book Image

Functional Python Programming - Second Edition

By: Steven F. Lott

Overview of this book

If you’re a Python developer who wants to discover how to take the power of functional programming (FP) and bring it into your own programs, then this book is essential for you, even if you know next to nothing about the paradigm. Starting with a general overview of functional concepts, you’ll explore common functional features such as first-class and higher-order functions, pure functions, and more. You’ll see how these are accomplished in Python 3.6 to give you the core foundations you’ll build upon. After that, you’ll discover common functional optimizations for Python to help your apps reach even higher speeds. You’ll learn FP concepts such as lazy evaluation using Python’s generator functions and expressions. Moving forward, you’ll learn to design and implement decorators to create composite functions. You'll also explore data preparation techniques and data exploration in depth, and see how the Python standard library fits the functional programming model. Finally, to top off your journey into the world of functional Python, you’ll at look at the PyMonad project and some larger examples to put everything into perspective.
Table of Contents (22 chapters)
Title Page
Packt Upsell

What concurrency really means

In a small computer, with a single processor and a single core, all evaluations are serialized through the one and only core of the processor. The use OS throughout will interleave multiple processes and multiple threads through clever time-slicing arrangements.

On a computer with multiple CPUs or multiple cores in a single CPU, there can be some actual concurrent processing of CPU instructions. All other concurrency is simulated through time slicing at the OS level. A macOS X laptop can have 200 concurrent processes that share the CPU; this is many more processes than the number of available cores. From this, we can see that OS time slicing is responsible for most of the apparently concurrent behavior of the system as a whole.

The boundary conditions

Let's consider a hypothetical algorithm that has a complexity described by 

. Assume that there is an inner loop that involves 1,000 bytes of Python code. When processing 10,000 objects, we're executing 100 billion...