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Modern Python Standard Library Cookbook
Modern Python Standard Library Cookbook

Modern Python Standard Library Cookbook: Over 100 recipes to fully leverage the features of the standard library in Python

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Modern Python Standard Library Cookbook

Text Management

In this chapter, we will cover the following recipes:

  • Pattern matching—regular expressions are not the only way to parse patterns; Python provides easier and just as powerful tools to parse patterns
  • Text similarity—detecting how two similar strings in a performing way can be hard but Python has some easy-to-use built-in tools
  • Text suggestionPython looks for the most similar one to suggest to the user the right spelling
  • Templating—when generating text, templating is the easiest way to define the rules
  • Splitting strings preserving spaces—splitting on empty spaces can be easy, but gets harder when you want to preserve some spaces
  • Cleanup textremoves any punctuation or odd character from text
  • Normalizing text—when working with international text, it's often convenient to avoid having to cope with special characters...

Introduction

Python was born for system engineering and a very frequent need when working with shell scripts and shell-based software is to create and parse text. That's why Python has very powerful tools to handle text.

Pattern matching

When looking for patterns in text, regular expressions are frequently the most common way to attach those kind of problems. They are very flexible and powerful, and even though they cannot express all kinds of grammar they frequently can handle most common cases.

The power of regular expressions comes out of the wide set of symbols and expressions they can generate. The problem is that for developers that are not used to regular expressions, they can look just like plain noise, and even people who have experience with them will frequently have to think a bit before understanding an expression like the following one:

"^(*d{3})*( |-)*d{3}( |-)*d{4}$"

This expression actually tries to detect phone numbers.

For most common cases, developers need to look for very simple patterns: for example, file extensions (does it end with .txt?), separated text, and so...

Text similarity

In many cases, when working with text, we might have to recognize text that is similar to other text, even when the two are not equal. This is a very common case in record linkage, finding duplicate entries, or for typing errors correction.

Finding similarity across text is not a straightforward task. If you try to go your own way, you will quickly realize that it gets complex and slow pretty soon.

The Python library provides tools to detect differences between two sequences in the difflib module. Since text itself is a sequence (a sequence of characters), we can apply the provided functions to detect similarities in strings.

How to do it...

Perform the following steps for this recipe:

  1. Given a string, we want...

Text suggestion

In our previous recipe, we saw how difflib can compute the similitude between two strings. This means that we can compute the similitude between two words and suggest corrections to our users.

If the set of correct words is known (which usually is for any language), we can first check if the word is in this set and, if not, we can look for the most similar one to suggest to the user the right spelling.

How to do it...

The steps to follow this recipe are:

  1. First of all we need the set of valid words. To avoid bringing in the whole English dictionary, we will just sample some words:
dictionary = {'ability', 'able', 'about', 'above', 'accept',    
...

Templating

A very frequent need when showing text to users is to generate it dynamically depending on the state of the software.

Typically, this leads to code like this:

name = 'Alessandro'
messages = ['Message 1', 'Message 2']

txt = 'Hello %s, You have %s message' % (name, len(messages))
if len(messages) > 1:
    txt += 's'
txt += ':n'
for msg in messages:
    txt += msg + 'n'
print(txt)

This makes it very hard to foresee the upcoming structure of the message and it's also very hard to maintain in the long term. To generate text, it's usually more convenient to reverse the approach and instead of putting text in code, we shall put code in text. That's exactly what template engines do and, while the standard library has very complete solutions for formatting, it lacks a template engine out...

Splitting strings and preserving spaces

Usually when splitting strings on spaces, developers will tend to rely on str.split, which is able to serve that purpose pretty well. But when the need to split some spaces and preserve others arises, things quickly become harder and implementing a custom solution can require investing time in proper escaping.

How to do it...

Just rely on shlex.split instead of str.split:

>>> import shlex
>>>
>>> text = 'I was sleeping at the "Windsdale Hotel"'
>>> print(shlex.split(text))
['I', 'was', 'sleeping', 'at', 'the', 'Windsdale Hotel']
...

Cleanup text

When analyzing user-provided text, we are frequently interested only in meaningful words; punctuation, spaces, and conjunctions might easily get in our way. Suppose you want to count word frequencies in a book, you don't want to end up with "world" and "world" being counted as two different words.

How to do it...

You have to perform the following steps:

  1. Supply the text you want to clean up:
txt = """And he looked over at the alarm clock,
ticking on the chest of drawers. "God in Heaven!" he thought.
It was half past six and the hands were quietly moving forwards,
it was even later than half past, more like quarter to seven.
Had the alarm clock not rung? He could...

Normalizing text

In many cases, a single word can be written in multiple ways. For example, users who wrote "Über" and "Uber" probably meant the same word. If you were implementing a feature like tagging for a blog, you certainly don't want to end up with two different tags for the two words.

So, before saving your tags, you might want to normalize them to plain ASCII characters so that they end up all being considered as the same tag.

How to do it...

What we need is a translation map that converts all accented characters to their plain representation:

import unicodedata, sys

class unaccented_map(dict):
    def __missing__(self, key):
        ch = self.get(key)
        if ch is not None:
 ...

Introduction


Python was born for system engineering and a very frequent need when working with shell scripts and shell-based software is to create and parse text. That's why Python has very powerful tools to handle text.

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Key benefits

  • Strategic recipes for effective application development in Python
  • Techniques to create GUIs and implement security through cryptography
  • Best practices for developing readily scalable, production-ready applications

Description

The Python 3 Standard Library is a vast array of modules that you can use for developing various kinds of applications. It contains an exhaustive list of libraries, and this book will help you choose the best one to address specific programming problems in Python. The Modern Python Standard Library Cookbook begins with recipes on containers and data structures and guides you in performing effective text management in Python. You will find Python recipes for command-line operations, networking, filesystems and directories, and concurrent execution. You will learn about Python security essentials in Python and get to grips with various development tools for debugging, benchmarking, inspection, error reporting, and tracing. The book includes recipes to help you create graphical user interfaces for your application. You will learn to work with multimedia components and perform mathematical operations on date and time. The recipes will also show you how to deploy different searching and sorting algorithms on your data. By the end of the book, you will have acquired the skills needed to write clean code in Python and develop applications that meet your needs.

Who is this book for?

If you are a developer who wants to write highly responsive, manageable, scalable, and resilient code in Python, this book is for you. Prior programming knowledge in Python will help you make the most out of the book.

What you will learn

  • Store multiple values per key in associative containers
  • Create interactive character-based user interfaces
  • Work with native time and display data for your time zone
  • Read/write SGML family languages, both as a SAX and DOM parser to meet file sizes and other requirements
  • Group equivalent items using itertools and sorted features together
  • Use partials to create unary functions out of multi-argument functions
  • Implement hashing algorithms to store passwords in a safe way
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Length: 366 pages
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Language : English
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Table of Contents

15 Chapters
Containers and Data Structures Chevron down icon Chevron up icon
Text Management Chevron down icon Chevron up icon
Command Line Chevron down icon Chevron up icon
Filesystem and Directories Chevron down icon Chevron up icon
Date and Time Chevron down icon Chevron up icon
Read/Write Data Chevron down icon Chevron up icon
Algorithms Chevron down icon Chevron up icon
Cryptography Chevron down icon Chevron up icon
Concurrency Chevron down icon Chevron up icon
Networking Chevron down icon Chevron up icon
Web Development Chevron down icon Chevron up icon
Multimedia Chevron down icon Chevron up icon
Graphical User Interfaces Chevron down icon Chevron up icon
Development Tools Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4
(7 Ratings)
5 star 85.7%
4 star 0%
3 star 0%
2 star 0%
1 star 14.3%
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gsamn Aug 29, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very knowledgeable writer. This book should be twice size or a book set of 3 with more break down. It's a good format
Amazon Verified review Amazon
Jason Crowe Jul 07, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great book for realizing the power of the python standard library. Probably not a great choice for beginners.
Amazon Verified review Amazon
Vlad Bezden Apr 17, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
It's a great book. I can only compare this book to "Effective Python: 90 Specific Ways to Write Better Python". I thought I know Python well. Well, after reading this book, I realized that I was mistaken. This book covers the hidden gems of Python standard library. In many cases, I was using third party libraries instead of using just regular standard libraries.This book also follows and teaches the proper Pythonic way of writing code.This book covers Python 3.6, and I wish Alessandro Molina updated this book with new Python libraries.
Amazon Verified review Amazon
Tom Bolt Mar 30, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
There's so much power available in the standard library I had no idea, I really like the idea of using the tools that are delivered before jumping right into another outside module. I enjoy the "recipes" because it conveys the idea and discussion in a somewhat small space so you can pace out the lessons and learn a lot in a small period of time. Not a beginners book however, you should have a good foundation prior to starting this. It presents really well the iPhone and iPad, not so much on Kindle's Web Reader. I think the price is too high for an ebook IMO.
Amazon Verified review Amazon
Jay Nov 29, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is a good book by Alessandro Molina. He have used his 15+ years of experience to design and layout all the chapters of the book carefully.The book covers recipes and wide aspects of the language from simple day to day stuffs like containers, data structures, file system, date & time, read &write to some advanced topics like cryptography, concurrency, networking etc.The 300+ pages of the book, is relevant to intermediate to advanced python developers and covers over 100 recipes to fully leverage the features of the standard library in Python.It have lot of code snippets and all the recipes are explained well. The chapter layouts are designed in a way that make finding a topic easy for the readers.Each recipe have 'How it works' section that gives an insight of how that particular recipe works. Most of the recipes have 'There is more' section as well that dives deep in the topic for curious readers.It enables you to use the powerful features of python's standard library in most effective way.I keep this book on my rack of Effective python (by Brett Slatkin) and Python Cookbook shelf. :)Over all this is a good read.
Amazon Verified review Amazon
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