Chapter 12 Graphics and Visualization with Jupyter Lab

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Modern Python Cookbook - Third Edition

By : Steven F. Lott

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Modern Python Cookbook

4.9 (17)

By: Steven F. Lott

Buy this Book

Overview of this book

Python is the go-to language for developers, engineers, data scientists, and hobbyists worldwide. Known for its versatility, Python can efficiently power applications, offering remarkable speed, safety, and scalability. This book distills Python into a collection of straightforward recipes, providing insights into specific language features within various contexts, making it an indispensable resource for mastering Python and using it to handle real-world use cases. The third edition of Modern Python Cookbook provides an in-depth look into Python 3.12, offering more than 140 new and updated recipes that cater to both beginners and experienced developers. This edition introduces new chapters on documentation and style, data visualization with Matplotlib and Pyplot, and advanced dependency management techniques using tools like Poetry and Anaconda. With practical examples and detailed explanations, this cookbook helps developers solve real-world problems, optimize their code, and get up to date with the latest Python features.

Preface

What you need for this book

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

Chapter 1 Numbers, Strings, and Tuples

1.1 Choosing between float, decimal, and fraction

1.2 Choosing between true division and floor division

1.3 String parsing with regular expressions

1.4 Building complicated strings with f-strings

1.5 Building complicated strings from lists of strings

1.6 Using the Unicode characters that aren’t on our keyboards

1.7 Encoding strings – creating ASCII and UTF-8 bytes

1.8 Decoding bytes – how to get proper characters from some bytes

1.9 Using tuples of items

1.10 Using NamedTuples to simplify item access in tuples

Chapter 2 Statements and Syntax

2.1 Writing Python script and module files – syntax basics

2.2 Writing long lines of code

2.3 Including descriptions and documentation

2.4 Writing better docstrings with RST markup

2.5 Designing complex if...elif chains

2.6 Saving intermediate results with the := ”walrus” operator

2.7 Avoiding a potential problem with break statements

2.8 Leveraging exception matching rules

2.9 Avoiding a potential problem with an except: clause

2.10 Concealing an exception root cause

2.11 Managing a context using the with statement

Chapter 3 Function Definitions

3.1 Function parameters and type hints

3.2 Designing functions with optional parameters

3.3 Using super flexible keyword parameters

3.4 Forcing keyword-only arguments with the * separator

3.5 Defining position-only parameters with the / separator

3.6 Picking an order for parameters based on partial functions

3.7 Writing clear documentation strings with RST markup

3.8 Designing recursive functions around Python’s stack limits

3.9 Writing testable scripts with the script-library switch

Chapter 4 Built-In Data Structures Part 1: Lists and Sets

4.1 Choosing a data structure

4.2 Building lists – literals, appending, and comprehensions

4.3 Slicing and dicing a list

4.4 Shrinking lists – deleting, removing, and popping

4.5 Writing list-related type hints

4.6 Reversing a copy of a list

4.7 Building sets – literals, adding, comprehensions, and operators

4.8 Shrinking sets – remove(), pop(), and difference

4.9 Writing set-related type hints

Chapter 5 Built-In Data Structures Part 2: Dictionaries

5.1 Creating dictionaries – inserting and updating

5.2 Shrinking dictionaries – the pop() method and the del statement

5.3 Writing dictionary-related type hints

5.4 Understanding variables, references, and assignment

5.5 Making shallow and deep copies of objects

5.6 Avoiding mutable default values for function parameters

Chapter 6 User Inputs and Outputs

6.1 Using the features of the print() function

6.2 Using input() and getpass() for user input

6.3 Debugging with f”{value=}” strings

6.4 Using argparse to get command-line input

6.5 Using invoke to get command-line input

6.6 Using cmd to create command-line applications

6.7 Using the OS environment settings

Chapter 7 Basics of Classes and Objects

7.1 Using a class to encapsulate data and processing

7.2 Essential type hints for class definitions

7.3 Designing classes with lots of processing

7.4 Using typing.NamedTuple for immutable objects

7.5 Using dataclasses for mutable objects

7.6 Using frozen dataclasses for immutable objects

7.7 Optimizing small objects with __slots__

7.8 Using more sophisticated collections

7.9 Extending a built-in collection – a list that does statistics

7.10 Using properties for lazy attributes

7.11 Creating contexts and context managers

7.12 Managing multiple contexts with multiple resources

Chapter 8 More Advanced Class Design

8.1 Choosing between inheritance and composition – the ”is-a” question

8.2 Separating concerns via multiple inheritance

8.3 Leveraging Python’s duck typing

8.4 Managing global and singleton objects

8.5 Using more complex structures – maps of lists

8.6 Creating a class that has orderable objects

8.7 Deleting from a list of complicated objects

Chapter 9 Functional Programming Features

9.1 Writing generator functions with the yield statement

9.2 Applying transformations to a collection

9.3 Using stacked generator expressions

9.4 Picking a subset – three ways to filter

9.5 Summarizing a collection – how to reduce

9.6 Combining the map and reduce transformations

9.7 Implementing “there exists” processing

9.8 Creating a partial function

9.9 Writing recursive generator functions with the yield from statement

Chapter 10 Working with Type Matching and Annotations

10.1 Designing with type hints

10.2 Using the built-in type matching functions

10.3 Using the match statement

10.4 Handling type conversions

10.5 Implementing more strict type checks with Pydantic

10.6 Including run-time valid value checks

Chapter 11 Input/Output, Physical Format, and Logical Layout

11.1 Using pathlib to work with filenames

11.2 Replacing a file while preserving the previous version

11.3 Reading delimited files with the CSV module

11.4 Using dataclasses to simplify working with CSV files

11.5 Reading complex formats using regular expressions

11.6 Reading JSON and YAML documents

11.7 Reading XML documents

11.8 Reading HTML documents

Chapter 12 Graphics and Visualization with Jupyter Lab

12.1 Starting a Notebook and creating cells with Python code

12.2 Ingesting data into a notebook

12.3 Using pyplot to create a scatter plot

12.4 Using axes directly to create a scatter plot

12.5 Adding details to markdown cells

12.6 Including Unit Test Cases in a Notebook

Chapter 13 Application Integration: Configuration

13.1 Finding configuration files

13.2 Using TOML for configuration files

13.3 Using Python for configuration files

13.4 Using a class as a namespace for configuration

13.5 Designing scripts for composition

13.6 Using logging for control and audit output

Chapter 14 Application Integration: Combination

14.1 Combining two applications into one

14.2 Combining many applications using the Command design pattern

14.3 Managing arguments and configuration in composite applications

14.4 Wrapping and combining CLI applications

14.5 Wrapping a program and checking the output

Chapter 15 Testing

15.1 Using docstrings for testing

15.2 Testing functions that raise exceptions

15.3 Handling common doctest issues

15.4 Unit testing with the unittest module

15.5 Combining unittest and doctest tests

15.6 Unit testing with the pytest module

15.7 Combining pytest and doctest tests

15.8 Testing things that involve dates or times

15.9 Testing things that involve randomness

15.10 Mocking external resources

Chapter 16 Dependencies and Virtual Environments

16.1 Creating environments using the built-in venv

16.2 Installing packages with a requirements.txt file

16.3 Creating a pyproject.toml file

16.4 Using pip-tools to manage the requirements.txt file

16.5 Using Anaconda and the conda tool

16.6 Using the poetry tool

16.7 Coping with changes in dependencies

Chapter 17 Documentation and Style

17.1 The bare minimum: a README.rst file

17.2 Installing Sphinx and creating documentation

17.3 Using Sphinx autodoc to create the API reference

17.4 Identifying other CI/CD tools in pyproject.toml

17.5 Using tox to run comprehensive quality checks

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Modern Python Cookbook - Third Edition

By : Steven F. Lott

Modern Python Cookbook

By: Steven F. Lott

Overview of this book

12.1 Starting a Notebook and creating cells with Python code

12.1.1 Getting ready

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