python-page-rank 0.1.3

Static import graph + PageRank for Python projects

Python Page Rank

Static dependency analysis and PageRank-style importance scoring for Python modules.

This tool scans a Python codebase, builds a directed import graph between modules, and computes a PageRank-like score indicating which modules are most structurally important based on how much other code depends on them.

No code execution and no imports.

Quickstart

Install from PyPI:

pip install python-page-rank

• Run on a project directory: python-page-rank .
• Show the top 5 ranked modules: python-page-rank . --n 5
• Emit JSON instead of a table: python-page-rank . --json

Run from src:

python cli.py /path/to/project

Example

python-page-rank /path/to/project

output:

Module                                     LOC  Importers   PageRank
----------------------------------------------------------------------
MyApp\authentication\models.py              29         12   0.114471
MyApp\common\utils.py                       66          6   0.094118
MyApp\payments\models.py                   168         15   0.053272
...

CLI usage

The tool is intended to be run from the command line.

Syntax

python-page-rank [path] [--n N] [--alpha A] [--iters I] [--json] [--include-init] 

Arguments

path
Project root directory to scan.
Optional. Defaults to the current directory (.).

--n N
Number of top-ranked files to display.
Optional. Default is 10.

--alpha A
PageRank damping factor.
Optional. Default is 0.85.

--iters ITERS
Number of PageRank iterations to run.
Optional. Default is 50.

--json
Prints results as JSON instead of a text table.

--include-init
Include __init__.py files in the output.
By default, package initializer files are hidden to reduce noise.

Examples

Analyze the current directory with default settings: python-page-rank
Analyze a specific project directory: python-page-rank MyApp
Show only the top 10 ranked files: python-page-rank --n 10
Use a custom damping factor and iteration count: python-page-rank --alpha 0.9 --iters 100
Analyze a directory and output results as JSON: python-page-rank MyApp --json
Combine options: python-page-rank MyApp --n 20 --alpha 0.8 --iters 75 --json

What it does

• Recursively scans a project directory for .py files
• Parses files using AST (no code execution)
• Builds a module-level dependency graph from import / from X import Y
• Computes PageRank over that graph
• Outputs per-file:
  • lines of code (LOC)
  • number of importers
  • PageRank score

Typical uses:

• identify central / risky modules
• guide refactors
• estimate blast radius of changes
• understand large or unfamiliar codebases

What it does not do (by design)

• Execute or import any project code
• Modify files
• Resolve runtime-dependent imports
• Guess ambiguous dependencies

This is a conservative static analyzer.

How imports are handled

Supported

• import package.module
• import package.module as alias
• from package import module
• from package.subpackage import thing

All resolved to absolute module paths and mapped to files when possible.

Intentionally ignored

• from . import models
• from .. import utils

These produce no edge in the graph.
Relative imports with an explicit module (e.g. from .models import X) are handled.

Why:
The AST provides no absolute module path (node.module is None). Resolving these requires guessing project roots and can be wrong in multi-root or namespace-package layouts. This behavior is deliberate and covered by tests.

Directory handling

The scanner:

• automatically detects multiple import roots
• prefers the shortest valid module path when duplicates exist
• skips common junk directories:
  • .git, .venv, venv, node_modules, __pycache__, etc.

This allows it to work on:

• monorepos
• Django / FastAPI projects
• repos with multiple top-level packages

PageRank details

• Standard iterative PageRank
• Damping factor: 0.85
• Teleportation distributes rank uniformly
• Ranks are normalized to sum to 1.0

Interpretation:
A module is "important" if many important modules depend on it.

This measures structural importance, not runtime usage frequency.

Notes

• The analysis is fully static
• No code is executed
• Imports are resolved via AST parsing only
• PYTHONPATH and runtime environment are ignored

Tests

Tests cover:

• absolute import resolution
• multi-root handling
• preference of shortest module paths
• ignored relative dot-imports
• stability of PageRank output

Run tests with:

python -m pytest

Limitations (read before filing issues)

• Relative imports without explicit module paths are ignored
• Namespace packages may require manual root layout
• Large repos may take time due to full AST parsing
• No attempt is made to infer dynamic imports

These are tradeoffs, not bugs.

License

MIT License

Disclaimer / Caveat

If you need aggressive inference, this tool is intentionally not that.