memory_graph
A Python package that visualizes data structures, references, and mutability to aid debugging and teaching.
What is memory_graph?
Memory Graph is a Python package that creates visual graphs of data structures to illustrate references, mutability, and copying behavior. It helps developers debug complex data and understand Python's memory model by showing how variables share or copy values.
Python developers and educators who need to debug data structures, teach Python's data model, or visualize references and mutability in their code.
It provides an intuitive visual representation of Python's memory model that plain text output cannot, with deep integration into popular debuggers and support for a wide range of data types and custom configurations.
Overview
Teaching tool and debugging aid in context of references, mutable data types, and shallow and deep copy.
Use Cases
Best For
- Debugging reference-related bugs in Python data structures
- Teaching Python's mutability and copying concepts to students
- Visualizing recursive function calls and call stack state
- Understanding shallow vs deep copy behavior in nested structures
- Integrating data visualization into VS Code or PyCharm debugging sessions
- Exploring complex data structures like linked lists, trees, and hash sets
Not Ideal For
- Production environments where graph generation overhead could impact performance
- Projects requiring zero external dependencies beyond standard Python libraries
- Developers needing static code analysis or visualization of non-Python languages
- Teams that prefer textual debuggers without visual output integration
Pros & Cons
Pros
Supports a wide range of Python types from basic lists to numpy arrays and PyTorch tensors, with clear graphs showing references and mutability. Evidence includes the 'Key Features' section and examples like binary trees and hash sets.
Seamlessly works with popular debuggers like VS Code and PyCharm via watch expressions, and offers specific functions for different environments. The README shows gifs and details for setup in various IDEs.
Clearly illustrates mutability, shallow vs deep copies, and call stack behavior with interactive examples. Sections like 'Python Data Model' include step-by-step visualizations for teaching concepts.
Allows rendering to multiple formats (PDF, SVG, etc.) and extensive customization of graph appearance via a config object. The Configuration section lists over a dozen adjustable parameters for depth, colors, and node types.
Cons
Requires separate installation of Graphviz, adding complexity to setup and potential compatibility issues across platforms, as noted in the Installation section.
The extensive configuration options, while powerful, can be overwhelming for casual users or beginners, requiring time to learn for advanced customization beyond defaults.
Exclusively targets Python data structures, making it unsuitable for projects involving other programming languages or needing cross-language debugging tools.
Graph generation for very large or deep data structures may be slow or memory-intensive, hinted at by the need for graph depth limits and slicing in the 'Graph Depth' section.
Open Source Alternative To
memory_graph is an open-source alternative to the following products:
Frequently Asked Questions
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