association-measures

v0.3.2 safe
4.0
Medium Risk

Statistical association measures for Python pandas

🤖 AI Analysis

Final verdict: SAFE

The package is deemed safe with low to moderate risk, primarily due to typical shell execution during build processes and minor concerns over metadata.

  • No network calls detected
  • Shell executions are common in build processes
  • Minor concerns over maintainer history and non-HTTPS links
Per-check LLM notes
  • Network: No network calls detected, which is normal for a package not involving direct network operations.
  • Shell: Shell executions observed are typical for package building and deployment processes, but could indicate potential risks if the commands are misused.
  • Metadata: The package has no typosquatting or email domain flags, but the maintainer history and non-HTTPS links raise minor concerns.

📦 Package Quality Overall: Medium (5.0/10)

✦ High Test Suite 9.0

Test suite present — 7 test file(s) found

  • 7 test file(s) detected (e.g. test_binomial.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (14283 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 100 commits in fau-klue/pandas-association-measures
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

  • rsal) distribution…') os.system('{0} setup.py sdist bdist_wheel --universal'.format(sys.exec
  • to PyPI via Twine…') os.system('twine upload dist/*') self.status('Pushing git tag
  • ('Pushing git tags…') os.system('git tag v{0}'.format(version['__version__'])) os.sy
  • sion['__version__'])) os.system('git push --tags') sys.exit() setup( name=NAM
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: fau.de

⚠ Suspicious Page Links score 4.0

Found 2 suspicious link(s) on the package page

  • Non-HTTPS external link: http://www.collocations.de/AM/index.html
  • Non-HTTPS external link: http://cass.lancs.ac.uk/log-ratio-an-informal-introduction/
✓ Git Repository History

Repository fau-klue/pandas-association-measures appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Philipp Heinrich & Markus Opolka" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with association-measures 
Create a data analysis tool that helps users understand the relationships between different variables in their dataset using the 'association-measures' Python package. This tool should be able to take in a CSV file as input, load it into a pandas DataFrame, and then calculate various statistical association measures between all pairs of columns. The output should include a heatmap visualization of these associations, allowing users to quickly identify which pairs of variables have strong relationships.

Steps:
1. Develop a user-friendly interface where users can upload their CSV files.
2. Load the uploaded CSV file into a pandas DataFrame.
3. Use the 'association-measures' package to compute association measures such as Pearson correlation, Spearman rank correlation, and mutual information between all numerical and categorical columns.
4. Display the results in a heatmap format, color-coded based on the strength and type of association (positive/negative).
5. Allow users to click on specific cells in the heatmap to get more detailed information about the selected pair of variables, including the exact value of the association measure and a scatter plot (for numerical variables) or a contingency table (for categorical variables).
6. Implement a feature to save the heatmap as an image file.

Features:
- Support for both numerical and categorical data types.
- Calculation of multiple types of association measures.
- Interactive heatmap display with hover-over details.
- Option to download the heatmap as an image.
- Error handling for invalid file formats or missing data.

The 'association-measures' package will be crucial in this project as it provides efficient and accurate computation of various association measures, which are essential for understanding the relationships within the dataset.

💬 Discussion Feed

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