5. Saving Time and Memory | Learning Python

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Learning Python

By : Romano

4.1 (21)

Learning Python

4.1 (21)

By: Romano

Overview of this book

Learning Python has a dynamic and varied nature. It reads easily and lays a good foundation for those who are interested in digging deeper. It has a practical and example-oriented approach through which both the introductory and the advanced topics are explained. Starting with the fundamentals of programming and Python, it ends by exploring very different topics, like GUIs, web apps and data science. The book takes you all the way to creating a fully fledged application. The book begins by exploring the essentials of programming, data structures and teaches you how to manipulate them. It then moves on to controlling the flow of a program and writing reusable and error proof code. You will then explore different programming paradigms that will allow you to find the best approach to any situation, and also learn how to perform performance optimization as well as effective debugging. Throughout, the book steers you through the various types of applications, and it concludes with a complete mini website built upon all the concepts that you learned.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Introduction and First Steps – Take a Deep Breath

1. Introduction and First Steps – Take a Deep Breath

A proper introduction

Enter the Python

About Python

What are the drawbacks?

Who is using Python today?

Setting up the environment

Installing Python

How you can run a Python program

How is Python code organized

Python's execution model

Guidelines on how to write good code

The Python culture

A note on the IDEs

Summary

2. Built-in Data Types

2. Built-in Data Types

Everything is an object

Mutable or immutable? That is the question

Numbers

Immutable sequences

Mutable sequences

Set types

Mapping types – dictionaries

The collections module

Final considerations

Summary

3. Iterating and Making Decisions

3. Iterating and Making Decisions

Conditional programming

Looping

Putting this all together

A quick peek at the itertools module

Summary

4. Functions, the Building Blocks of Code

4. Functions, the Building Blocks of Code

Why use functions?

Scopes and name resolution

Input parameters

Return values

A few useful tips

Recursive functions

Anonymous functions

Function attributes

Built-in functions

One final example

Documenting your code

Importing objects

Summary

5. Saving Time and Memory

5. Saving Time and Memory

map, zip, and filter

Comprehensions

Generators

Some performance considerations

Don't overdo comprehensions and generators

Name localization

Generation behavior in built-ins

One last example

Summary

6. Advanced Concepts – OOP, Decorators, and Iterators

6. Advanced Concepts – OOP, Decorators, and Iterators

Decorators

Object-oriented programming

Writing a custom iterator

Summary

7. Testing, Profiling, and Dealing with Exceptions

7. Testing, Profiling, and Dealing with Exceptions

Testing your application

Test-driven development

Exceptions

Profiling Python

Summary

8. The Edges – GUIs and Scripts

8. The Edges – GUIs and Scripts

First approach – scripting

Second approach – a GUI application

Where do we go from here?

Summary

9. Data Science

9. Data Science

IPython and Jupyter notebook

Dealing with data

Where do we go from here?

Summary

10. Web Development Done Right

10. Web Development Done Right

What is the Web?

How does the Web work?

The Django web framework

A regex website

The future of web development

Summary

11. Debugging and Troubleshooting

11. Debugging and Troubleshooting

Debugging techniques

Troubleshooting guidelines

Summary

12. Summing Up – A Complete Example

12. Summing Up – A Complete Example

The challenge

Our implementation

Implementing the Django interface

Implementing the Falcon API

Where do you go from here?

Summary

A word of farewell

Index

Index

Customer Reviews

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Generation behavior in built-ins

Amongst the built-in types, the generation behavior is now quite common. This is a major difference between Python 2 and Python 3. A lot of functions such as map, zip, and filter have been transformed so that they return objects that behave like iterables. The idea behind this change is that if you need to make a list of those results you can always wrap the call in a list() class, and you're done. On the other hand, if you just need to iterate and want to keep the impact on memory as light as possible, you can use those functions safely.

Another notable example is the range function. In Python 2 it returns a list, and there is another function called xrange that returns an object that you can iterate on, which generates the numbers on the fly. In Python 3 this function has gone, and range now behaves like it.

But this concept in general is now quite widespread. You can find it in the open() function, which is used to operate on file objects (we'll see it in...

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