arrow-dx

v0.1.2 safe
4.0
Medium Risk

Helpers for the Arrow-backed Python stack: polars, pyarrow, duckdb.

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across multiple checks, with no network calls, shell executions, or obfuscations detected. The metadata risk is slightly elevated due to the maintainer's limited presence, but there's insufficient evidence to suggest a supply-chain attack.

  • No network calls detected
  • Single package maintained by the author
  • No shell execution patterns
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
  • Shell: No shell execution patterns detected, indicating no immediate risk of executing system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious activity related to secrets.
  • Metadata: The maintainer has only one package and lacks a GitHub repository, indicating potential low activity or newness.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 8 test file(s) found

  • 8 test file(s) detected (e.g. test_cli.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (6201 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 32 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "jesse tweedle" 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 arrow-dx 
Create a data analysis and visualization tool using Python that leverages the 'arrow-dx' package to efficiently handle large datasets. This tool will be designed to import data from various sources (CSV, Parquet, SQLite), perform complex data transformations and aggregations, and generate visualizations to help users understand their data better. Here are the key steps and features of the project:

1. **Setup Environment**: Ensure all necessary packages are installed, including 'arrow-dx', 'polars', 'pyarrow', 'duckdb', 'pandas', 'matplotlib', and 'seaborn'.
2. **Data Import Module**: Develop a module that allows users to import data from different file formats (CSV, Parquet, SQLite). Use 'duckdb' for efficient querying and manipulation of imported data.
3. **Data Transformation Engine**: Implement functions to transform data using 'polars' and 'pyarrow'. Include operations like filtering, joining, grouping, and aggregating data. Ensure these operations are optimized for performance.
4. **Visualization Module**: Create a module that generates visualizations based on user-defined parameters. Users should be able to choose from different types of plots (line charts, bar charts, scatter plots) and customize them according to their needs.
5. **User Interface**: Design a simple command-line interface (CLI) where users can interact with the tool, specifying data files, transformation rules, and visualization preferences.
6. **Documentation and Testing**: Provide comprehensive documentation explaining how to use each feature of the tool. Write unit tests to ensure the reliability and correctness of your code.

Throughout the development process, focus on leveraging 'arrow-dx' to enhance the performance and functionality of your data processing and visualization tasks. Demonstrate how the integration of 'arrow-dx' simplifies working with large datasets and accelerates data analysis workflows.

💬 Discussion Feed

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