10 Python Best Practices

Python files are often used to define a consistent look and feel for a package, to initialize the package level logger, to register entry points, and more. Here are 10 best practices for using files in Python.

Python files are used to initialize Python packages. They are executed when the package is first imported. files can be used to set up global variables, define functions and classes, and run code.

While files are not required, they are often used to customize the behavior of a package.

In this article, we will discuss 10 best practices for working with Python files.

1. Use to control what is imported by from module import *

When you use from module import *, Python will import everything that is in the module. This includes variables, functions, and classes. However, it can also include things that you don’t want to be imported, like private variables and functions.

If you have a lot of code in your module, it can be hard to keep track of what is being imported and what isn’t. By using, you can control exactly what is being imported, so you can be sure that only the public API is being used.

This is especially important when you are distributing your module to other people, because you don’t want them to accidentally use private API.

2. Use __all__ to control what is imported by from package import *

When you use from package import *, Python will import everything in the file. This can be a problem if your file contains things that you don’t want to be imported this way. For example, you might have some utility functions in your file that you only want to be imported explicitly.

To solve this problem, you can use the __all__ variable. __all__ is a list of strings, and each string is the name of something that should be imported by from package import *. For example:

__all__ = [‘foo’, ‘bar’]

With this in place, from package import * will only import foo and bar, and not anything else in the file.

3. Use __version__ for version numbering

__version__ is a special variable that is automatically created by Python when the interpreter starts up. It contains information about the current version of the interpreter, including the release number, build number, and platform.

This information can be useful for a number of purposes, such as debugging and compatibility checking. For example, if you are using a third-party library that is not compatible with the current version of Python, you can use __version__ to check whether or not you need to upgrade your interpreter.

Additionally, __version__ can be used to enforce backward compatibility. For example, if your code relies on features that were added in a particular version of Python, you can use __version__ to ensure that those features are available.

Finally, __version__ can be used as a simple way to provide version information to users of your code. If you include __version__ in your file, it will be available as a global variable that can be imported by other modules.

4. Use logging in

When your Python code is executed, the interpreter will first execute the code in This makes the ideal place to put code that you want to run when your module is first imported.

However, if you have a lot of code in, it can be difficult to debug because all of the code is executed at once. This is where logging comes in.

Logging allows you to see what code is being executed and when. It’s also useful for tracking errors. By default, Python’s logging module will log messages to stdout, which means you can see them in your terminal.

To use logging in, simply add the following code at the top of the file:

import logging

Now, any code you add below this will be logged. For example, let’s say you have the following code in

def foo():

def bar():

If you import your module, you’ll see the following output in your terminal:


As you can see, logging is a great way to debug your code.

5. Use to define a consistent look and feel for your packages

When you import a package, Python will automatically execute the code in This means that is the perfect place to put code that you want to run every time a package is imported.

For example, let’s say you have a package with several subpackages, and each subpackage has its own file. You can use the files to ensure that all of the subpackages have the same look and feel. For example, you could use to set the logging level for all of the subpackages, or to configure the database connection for all of the subpackages.

This consistent look and feel makes it much easier for users of your packages to understand how they work, and makes it more likely that they will be able to use them correctly.

6. Use to initialize the package level logger

When you have a package with multiple modules, it can be helpful to have a single point of entry for logging. This way, you can ensure that all messages are going to the same place, and you can configure the logger once instead of in each module.

Additionally, if you’re using third-party libraries that also use logging, initializing the logger in ensures that your messages don’t get lost in the shuffle.

7. Use to register entry points

If you have a project with multiple modules, it can be difficult to keep track of what’s where. By using to register entry points, you can make it easier for yourself and others to find the code they’re looking for.

For example, say you have a project with two modules, foo and bar. If you register an entry point for each module in, then someone can go to the project’s root directory and run foo or bar directly.

This is especially useful if you have command-line scripts in your project. By registering their entry points in, you can make it so that people don’t have to remember which directory the script is in.

Of course, you can also use for other purposes, such as setting up logging or defining constants. But if you’re looking for a way to make your project more user-friendly, then registering entry points is a great place to start.

8. Use to configure matplotlib

Matplotlib is a powerful plotting library that can produce publication quality figures. However, the default configuration is often not ideal for scientific plotting. For example, the default font size is too small, the default figure size is too small, and the default colors are not aesthetically pleasing.

Fortunately, matplotlib can be configured to look much better. The easiest way to do this is to put the following code in your file:

import matplotlib as mpl
mpl.rcParams[‘font.size’] = 18
mpl.rcParams[‘figure.figsize’] = (8,6)
mpl.rcParams[‘lines.linewidth’] = 2
mpl.rcParams[‘xtick.major.width’] = 2
mpl.rcParams[‘ytick.major.width’] = 2
mpl.rcParams[‘axes.facecolor’] = ‘white’
mpl.rcParams[‘savefig.facecolor’] = ‘white’
This will change the font size, figure size, line width, tick width, and background color of all matplotlib plots. This makes them much easier to read and more aesthetically pleasing.

9. Use to load data files

When you have data files that are needed by your package, it’s best to load them into memory when the package is imported. That way, the data is always available and there’s no need to worry about file paths or loading the data every time it’s needed.

This also has the added benefit of making your package more self-contained. If someone wants to use your package, they don’t need to worry about where to get the data files from or how to load them. Everything is taken care of by

Of course, this only works for small data files. If you have large data files, you’ll need to find another way to load them into memory.

10. Use to set default values for other modules

When you have a lot of code in your project, it can be difficult to keep track of what values are being used by other modules. This is especially true if those values are constantly changing. By setting default values in, you can make sure that all of the modules in your project are using the same values.

This also makes it easier to change those values in the future. If you need to update the value of a variable in one module, you can simply change the value in and all of the other modules will use the new value. is also a good place to put code that should be run when the module is first imported. For example, you could use it to initialize variables or connect to a database.


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