How To Get A List Of Variables In Specific Python Module

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Connect and share knowledge within a single location that is structured and easy to search. When working with Python modules, it is often helpful to obtain a list of all the variables defined within a module. This information can be useful for various purposes, such as debugging, documentation generation, or dynamically accessing and manipulating variables in your code. In this article, we will explore different methods to obtain a list of variables in a Python module. Python provides a built-in function called dir() that returns a list of names in the current local scope or a specified object. When used with a module as an argument, dir() returns a list of all the names defined in that module.

These names include variables, functions, classes, and other objects. The above code imports the math module and uses the dir() function to obtain a list of all the names defined in the module. The resulting list, stored in the variables variable, is then printed to the console. Running this code will output a list of variables and other names defined in the math module, such as pi, sqrt, sin, and many others. In addition to dir(), Python provides two other built-in functions called globals() and locals(). These functions return dictionaries containing the names defined in the global and local scopes, respectively.

In this article, we are going to discuss how to view all defined variables in Python. Viewing all defined variables plays a major role while debugging the code. dir() is a built-in function to store all the variables inside a program along with the built-in variable functions and methods. It creates a list of all declared and built-in variables. There are two different ways to view all defined variables using dir( ). They are discussed below.

When no user-defined variable starts with '__' : var2 is <class 'str'> and is equal to Welcome to geeksforgeeks var3 is <class 'dict'> and is equal to {'1': 'a', '2': 'b'} Let's assume I have the following file structure: I need to get all the variables defined in data.py file. How can I achieve that?

I could use dir(), but it returns all the attributes of the module including __name__ and so on. We can list down all the functions present in a Python module by simply using the dir() method in the Python shell or in the command prompt shell. Since a list can contain any Python variables, it can even contain other lists. dir() is a built-in function to store all the variables inside a program along with the built-in variable functions and methods. It creates a list of all declared and built-in variables. Python modules are fundamental building blocks for organizing code, but retrieving all objects (not just their names) within a module is a common task for developers working on documentation generation, dynamic analysis, testing, or...

While tools like dir() return object names, they don’t provide direct access to the objects themselves. This guide will walk you through step-by-step methods to extract all objects from a Python module, filter them, and classify them (e.g., functions, classes, variables). By the end, you’ll be able to programmatically inspect modules and work with their underlying objects. A module is a file containing Python definitions and statements (e.g., my_module.py). When imported, it becomes an object of type module, with attributes representing the code elements defined within it. Our goal is to extract these objects programmatically, not just their names.

Before diving into solutions, let’s clarify limitations of common approaches: dir(module) returns a list of string names of the module’s attributes (e.g., ['greet', 'Person', 'MY_VAR']). This is useful for exploration but doesn’t give access to the actual objects (e.g., the greet function itself or Person class). I’m currently working with Python in a shell environment, and I find myself often needing to view all defined variables, similar to how one can in Matlab. This allows me to keep track of the variables I’ve created and their corresponding values. In this post, we’ll explore several effective methods for displaying user-defined variables in Python.

For a more enhanced shell experience, consider using IPython . IPython offers a magic command %who that lists all user-defined variables. For more detailed information, you can use %whos, which provides the type and value of each variable: Explore all the available magic commands in the IPython Documentation and discover more tips in this Dataquest article on Jupyter Notebooks . Python has something called wildcard imports, which look like from module import *. This type of import allows you to quickly get all the objects from a module into your namespace.

However, using this import on a package can be confusing because it’s not clear what you want to import: subpackages, modules, objects? Python has the __all__ variable to work around this issue. The __all__ variable is a list of strings where each string represents the name of a variable, function, class, or module that you want to expose to wildcard imports. To get the most out of this tutorial, you should be familiar with a few Python concepts, including modules and packages, and the import system. Get Your Code: Click here to download the free sample code that shows you how to use Python’s __all__ attribute. When creating a Python project or application, you’ll need a way to access code from the standard library or third-party libraries.

You’ll also need to access your own code from the multiple files that may make up your project. Python’s import system is the mechanism that allows you to do this. Have you ever needed to analyze a Python script to list all variables, functions, or methods? Whether you’re documenting code, refactoring, building static analysis tools, or simply trying to understand a unfamiliar script, extracting variable and method names is a common task. However, doing this manually is error-prone, and naive approaches like regular expressions often fail to handle Python’s complex syntax (e.g., nested functions, comments, or string literals that mimic code). In this guide, we’ll explore reliable, automated methods to extract variable and method names from Python scripts.

We’ll start with the limitations of manual approaches, then dive into the gold standard: using Python’s built-in ast module (Abstract Syntax Tree) for precise parsing. We’ll also cover edge cases, tools, and best practices to ensure accuracy. Before diving in, let’s clarify definitions: A common first thought is to use regular expressions (regex) to “scrape” variable and method names. For example, you might use a pattern like r"\bdef\s+(\w+)\(" to find function names. However, regex struggles with Python’s complexity:

A regex might extract x, welcome, x, y—but x (comment) and welcome (string) are not actual variables/methods.

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