Book Image

Learn Web Development with Python

By : Fabrizio Romano, Gaston C. Hillar, Arun Ravindran
Book Image

Learn Web Development with Python

By: Fabrizio Romano, Gaston C. Hillar, Arun Ravindran

Overview of this book

If you want to develop complete Python web apps with Django, this Learning Path is for you. It will walk you through Python programming techniques and guide you in implementing them when creating 4 professional Django projects, teaching you how to solve common problems and develop RESTful web services with Django and Python. You will learn how to build a blog application, a social image bookmarking website, an online shop, and an e-learning platform. Learn Web Development with Python will get you started with Python programming techniques, show you how to enhance your applications with AJAX, create RESTful APIs, and set up a production environment for your Django projects. Last but not least, you’ll learn the best practices for creating real-world applications. By the end of this Learning Path, you will have a full understanding of how Django works and how to use it to build web applications from scratch. This Learning Path includes content from the following Packt products: • Learn Python Programming by Fabrizio Romano • Django RESTful Web Services by Gastón C. Hillar • Django Design Patterns and Best Practices by Arun Ravindran
Table of Contents (33 chapters)
Title Page
About Packt
Contributors
Preface
Index

The map, zip, and filter functions


We'll start by reviewing map, filter, and zip, which are the main built-in functions one can employ when handling collections, and then we'll learn how to achieve the same results using two very important constructs: comprehensions and generators. Fasten your seatbelt!

map

According to the official Python documentation:

map(function, iterable, ...) returns an iterator that applies function to every item of iterable, yielding the results. If additional iterable arguments are passed, function must take that many arguments and is applied to the items from all iterables in parallel. With multiple iterables, the iterator stops when the shortest iterable is exhausted.

We will explain the concept of yielding later on in the chapter. For now, let's translate this into code—we'll use a lambda function that takes a variable number of positional arguments, and just returns them as a tuple:

# map.example.py
>>> map(lambda *a: a, range(3))  # 1 iterable
<map...