DataComparerLibrary

v0.860 suspicious
4.0
Medium Risk

For comparing csv-files, 2d-array with a csv-file or 2d-arrays. For comparing text-files, text variable with a text-file or text variables. Including a sorting module.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risk in terms of network activity, shell execution, obfuscation, and credential handling. However, the incomplete author details and apparent inactivity of the maintainer raise concerns about its provenance and long-term support.

  • Incomplete author details
  • Apparent inactivity of the maintainer
Per-check LLM notes
  • Network: No network calls detected, which is normal for a data comparison library that does not require external services.
  • Shell: No shell execution patterns detected, consistent with a benign package focused on data processing.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author's details are incomplete and the maintainer seems new or inactive, raising some concern but not definitive evidence of malicious intent.

🔬 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

Email domain looks legitimate: outlook.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository ReneFromHolland/DataComparerLibrary appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 DataComparerLibrary 
Develop a comprehensive data comparison utility called 'DataSyncMaster' using Python's 'DataComparerLibrary'. This utility will serve as a powerful tool for data analysts and software developers who need to compare datasets across various formats such as CSV files, 2D arrays, and plain text files. The goal is to create a user-friendly interface where users can upload two sets of data and receive detailed reports on discrepancies between them.

**Steps to Develop 'DataSyncMaster':**
1. **Setup Environment:** Begin by setting up your Python environment and installing the necessary packages including 'DataComparerLibrary'. Ensure all dependencies are managed via a requirements.txt file.
2. **Design User Interface:** Design a simple yet effective command-line interface (CLI) for the utility. The CLI should allow users to select the type of data they wish to compare (CSV, 2D array, Text).
3. **Implement Data Comparison Functions:** Use 'DataComparerLibrary' to implement functions that can compare:
   - Two CSV files
   - A 2D array against a CSV file
   - Two 2D arrays
   - Two text files
   - A text variable against a text file
4. **Sorting Module Integration:** Utilize the sorting module provided by 'DataComparerLibrary' to sort the data before comparison, ensuring accurate and meaningful results.
5. **Generate Detailed Reports:** After comparisons, generate detailed reports highlighting differences. These reports should include:
   - Number of differences found
   - Types of differences (e.g., missing rows/columns, different values)
   - Specific row and column details for each difference
6. **Error Handling and Logging:** Implement robust error handling to manage issues like file not found errors, invalid input types, etc. Log these errors for debugging purposes.
7. **Testing and Documentation:** Thoroughly test all functionalities and write comprehensive documentation explaining how to use 'DataSyncMaster', its features, and troubleshooting tips.

**Suggested Features:**
- Support for large datasets by implementing efficient memory management techniques.
- Option to filter out specific columns/rows before comparison.
- Ability to save comparison results into a new CSV file for further analysis.
- User-friendly error messages guiding users on how to correct issues.

By completing this project, you will have developed a versatile and valuable tool for anyone dealing with data comparison tasks.