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Secret Recipes of the Python Ninja
Secret Recipes of the Python Ninja

Secret Recipes of the Python Ninja: Over 70 recipes that uncover powerful programming tactics in Python

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Secret Recipes of the Python Ninja

Utilizing the Python Interpreter

In this chapter, we will talk about the Python interpreter, both as an interactive tool and for launching Python programs. Specifically, we will cover:

  • Launching Python environments
  • Utilizing Python command options
  • Working with environment variables
  • Making scripts executable
  • Modifying interactive interpreter startup
  • Alternative Python implementations
  • Installing Python on Windows
  • Embedding Python with other applications
  • Using alternative Python shells – IPython
  • Using alternative Python shells – bpython
  • Using alternative Python shells – DreamPie

Introduction

One of the benefits of the Python programming language is that it is interpreted, not compiled. This means that Python code is processed when it is called, rather than having to be pre-compiled before use. Because of this, interpreted languages generally have an interactive shell, allowing users to test code and otherwise have immediate feedback without having to create a separate source code file.

Of course, to get the most functionality out of a programming language, having permanent code files is necessary. When using an interactive prompt, the code lives in RAM; once the interactive session is closed, that code is lost. Thus, using an interactive prompt is a great way to quickly test programming ideas, but you wouldn't want to run a full-blown program from it.

This chapter will talk about using the Command Prompt to launch programs, as well as Python&apos...

Launching Python environments

By default, Python is installed on a computer with the Python interpreter included on the system path. This means that the interpreter will monitor the Command Prompt for any call to python

The most common usage for Python is to run a script. However, it may be desirable to launch a specific version of Python for a specific program.

How to do it...

  1. The most basic command to execute a Python program is as follows:
      $ python <script_name>.py
  1. The following examples show how to launch specific versions of Python, as needed:
      $ python2 some_script.py # Use the latest version of Python 2
$ python2.7 ... # Specifically use Python 2.7
$ python3 ... # Use...

Utilizing Python command options

When used non-interactively, the Python interpreter monitors the command line and parses all input before the command is actually executed. The following snippet shows all the possible options available when calling Python from the command line:

python [-bBdEhiIOqsSuvVWx?] [-c command | -m module-name | script | - ] [args] 

When working with a command line interface (CLI), examples of shell commands often show square brackets [] to indicate optional instructions. In this case, there are three groups of optional input that can be provided to the python command: generic options, interface options, and arguments.

How to do it...

  1. A number of options are available for the Python command-line...

Working with environment variables

Environment variables are part of operating systems and affect system operations. Python has Python-specific variables that affect how Python functions, that is, the behavior of the Python interpreter. While they are processed before command-line options, the command-line switches will override environment variables if there is a conflict.

How to do it...

  1. Environment variables are accessed via Python's os.environ.
  2. Because the environ object is a dictionary, you can specify a particular variable to view:
      >>> import os
>>> print(os.environ["PATH"])

/home/cody/anaconda3/bin:/home/cody/bin:/home/cody/
.local/bin:/usr/local/sbin:/usr/local...

Making scripts executable

Normally, executing a Python program requires typing python <program>.py. However, it is possible to make Python programs self-executing so they don't require typing python as the calling command.

How to do it...

  1. On *NIX systems, putting #!/usr/bin/env python as the first line of a program allows the program to be executable by referencing the location of Python on the user's PATH. Of course, this assumes Python is on the PATH; if not, then the program will have to be invoked like normal.
  2. After this has been added to the program, the file itself needs to be modified to make it executable, that is, $ chmod +x <program>.py.
  3. If you are using a terminal program that displays...

Modifying interactive interpreter startup

As mentioned in the Working with environment variables recipe, the PYTHONSTARTUP environment variable can be set to point to a file that contains commands that run prior to the Python interpreter starting up. This functionality is similar to .profile on *NIX shells.

As this startup file is only examined when interactive mode is used, there is no need to worry about trying to set configurations for running scripts (though later on we will show how to include the startup file in a script). The commands in this file are executed within the same namespace as the interactive interpreter, so there is no need to qualify functions or other imports with dot-nomenclature. This file is also responsible for making changes to interactive prompts: >>> (sys.ps1) and ... (sys.ps2).

...

Alternative Python implementations

Python has been ported to a number of other environments, such as Java and .NET. This means Python can be used in these environments like normal, but gains access to the APIs and code underpinnings for these environments.

Jython is used for Java integration, IronPython is used for the .NET framework, Stackless Python is available for enhanced threading performance, and MicroPython is for use with microcontrollers.

How to do it...

  1. To use Jython, a Java .jar file provides the installation executable. Two options are available for installation.
  2. Normal GUI installation is available by using the following:
      java -jar jython_installer-2.7.1.jar
  1. For console-based systems, such as headless...

Introduction


One of the benefits of the Python programming language is that it is interpreted, not compiled. This means that Python code is processed when it is called, rather than having to be pre-compiled before use. Because of this, interpreted languages generally have an interactive shell, allowing users to test code and otherwise have immediate feedback without having to create a separate source code file.

Of course, to get the most functionality out of a programming language, having permanent code files is necessary. When using an interactive prompt, the code lives in RAM; once the interactive session is closed, that code is lost. Thus, using an interactive prompt is a great way to quickly test programming ideas, but you wouldn't want to run a full-blown program from it.

This chapter will talk about using the Command Prompt to launch programs, as well as Python's functionality using the interactive shell. Special functionality with the Windows operating system will be discussed, and...

Launching Python environments


By default, Python is installed on a computer with the Python interpreter included on the system path. This means that the interpreter will monitor the Command Prompt for any call to python

The most common usage for Python is to run a script. However, it may be desirable to launch a specific version of Python for a specific program.

How to do it...

  1. The most basic command to execute a Python program is as follows:
      $ python <script_name>.py
  1. The following examples show how to launch specific versions of Python, as needed:
      $ python2 some_script.py # Use the latest version of Python 2
      $ python2.7 ... # Specifically use Python 2.7
      $ python3 ... # Use the latest version of Python 3
      $ python3.5.2 ... # Specifically use Python 3.5.2

How it works...

Calling python2 or python3 opens the latest installed version of the respective branch, whereas the other examples show how to invoke a specific version number. Regardless of whether a newer version...

Utilizing Python command options


When used non-interactively, the Python interpreter monitors the command line and parses all input before the command is actually executed. The following snippet shows all the possible options available when calling Python from the command line:

python [-bBdEhiIOqsSuvVWx?] [-c command | -m module-name | script | - ] [args]

When working with a command line interface (CLI), examples of shell commands often show square brackets [] to indicate optional instructions. In this case, there are three groups of optional input that can be provided to the python command: generic options, interface options, and arguments.

How to do it...

  1. A number of options are available for the Python command-line call. To enter interactive mode, call Python with no additional options:
      $ python
      Python 3.6.3 |Anaconda, Inc.| (default, Oct 13 2017, 12:02:49) 
      [GCC 7.2.0] on linux 
      Type "help", "copyright", "credits" or "license" for
      more information. 
      &gt...

Working with environment variables


Environment variables are part of operating systems and affect system operations. Python has Python-specific variables that affect how Python functions, that is, the behavior of the Python interpreter. While they are processed before command-line options, the command-line switches will override environment variables if there is a conflict.

How to do it...

  1. Environment variables are accessed via Python's os.environ.
  2. Because the environ object is a dictionary, you can specify a particular variable to view:
      >>> import os
      >>> print(os.environ["PATH"])
      /home/cody/anaconda3/bin:/home/cody/bin:/home/cody/
      .local/bin:/usr/local/sbin:/usr/local/bin:/usr
       /sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games
  1. Adding a new variable is as simple as follows:
      >>> os.environ["PYTHONOPTIMIZE"] = "1"

How it works...

There are a large number of Python-specific environment variables available. Some of them are:

  • PYTHONHOME...

Making scripts executable


Normally, executing a Python program requires typing python <program>.py. However, it is possible to make Python programs self-executing so they don't require typing python as the calling command.

How to do it...

  1. On *NIX systems, putting #!/usr/bin/env python as the first line of a program allows the program to be executable by referencing the location of Python on the user's PATH. Of course, this assumes Python is on the PATH; if not, then the program will have to be invoked like normal.
  2. After this has been added to the program, the file itself needs to be modified to make it executable, that is, $ chmod +x <program>.py.
  3. If you are using a terminal program that displays files and directories in different colors depending on their modes, running the command ls on the directory where the file is located should show it with a different color than non-executable files.
  4. To execute the program, simply type ./<program>.py and the program will execute without...

Modifying interactive interpreter startup


As mentioned in the Working with environment variables recipe, the PYTHONSTARTUP environment variable can be set to point to a file that contains commands that run prior to the Python interpreter starting up. This functionality is similar to .profile on *NIX shells.

As this startup file is only examined when interactive mode is used, there is no need to worry about trying to set configurations for running scripts (though later on we will show how to include the startup file in a script). The commands in this file are executed within the same namespace as the interactive interpreter, so there is no need to qualify functions or other imports with dot-nomenclature. This file is also responsible for making changes to interactive prompts: >>> (sys.ps1) and ... (sys.ps2).

How to do it...

  1. To read an additional startup file from the current directory, the following example command shows how to code it in the global startup file (read_startup.py):
 ...
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Key benefits

  • •Access built-in documentation tools and improve your code.
  • •Discover how to make the best use of decorator and generator functions
  • •Enhance speed and improve concurrency by conjuring tricks from the PyPy project

Description

This book covers the unexplored secrets of Python, delve into its depths, and uncover its mysteries. You’ll unearth secrets related to the implementation of the standard library, by looking at how modules actually work. You’ll understand the implementation of collections, decimals, and fraction modules. If you haven’t used decorators, coroutines, and generator functions much before, as you make your way through the recipes, you’ll learn what you’ve been missing out on. We’ll cover internal special methods in detail, so you understand what they are and how they can be used to improve the engineering decisions you make. Next, you’ll explore the CPython interpreter, which is a treasure trove of secret hacks that not many programmers are aware of. We’ll take you through the depths of the PyPy project, where you’ll come across several exciting ways that you can improve speed and concurrency. Finally, we’ll take time to explore the PEPs of the latest versions to discover some interesting hacks.

Who is this book for?

Whether you’ve been working with Python for a few years or you’re a seasoned programmer, you’ll have a lot of new tricks to walk away with.

What you will learn

  • •Know the differences between .py and .pyc files
  • •Explore the different ways to install and upgrade Python packages
  • •Understand the working of the PyPI module that enhances built-in decorators
  • •See how coroutines are different from generators and how they can simulate multithreading
  • •Grasp how the decimal module improves floating point numbers and their operations
  • •Standardize sub interpreters to improve concurrency
  • •Discover Python's built-in docstring analyzer
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Table of Contents

10 Chapters
Working with Python Modules Chevron down icon Chevron up icon
Utilizing the Python Interpreter Chevron down icon Chevron up icon
Working with Decorators Chevron down icon Chevron up icon
Using Python Collections Chevron down icon Chevron up icon
Generators, Coroutines, and Parallel Processing Chevron down icon Chevron up icon
Working with Python&#x27;s Math Module Chevron down icon Chevron up icon
Improving Python Performance with PyPy Chevron down icon Chevron up icon
Python Enhancement Proposals Chevron down icon Chevron up icon
Documenting with LyX Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
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