atoti-client-parquet

v0.9.15 safe
3.0
Low Risk

Code to load Apache Parquet files

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity and poses minimal risk. The maintainer's single package might be worth monitoring, but it does not currently indicate any malicious intent.

  • No network, shell, obfuscation, or credential risks detected.
  • Maintainer has only one package, warranting future observation.
Per-check LLM notes
  • Network: No network calls detected, which is normal for a data processing library like atoti-client-parquet.
  • Shell: No shell execution patterns detected, aligning with the expected behavior for a data processing library.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of credential theft.
  • Metadata: The maintainer has only one package, suggesting a new or less active account which may warrant further investigation but does not strongly indicate malicious intent.

📦 Package Quality Overall: Low (4.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.activeviam.com/products/atoti/python-sdk/0.9.15
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in atoti/atoti
  • Two distinct contributors found

🔬 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: activeviam.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository atoti/atoti appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "ActiveViam" 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 atoti-client-parquet 
Develop a Python-based data analysis mini-application named 'ParquetInsight' that leverages the 'atoti-client-parquet' package to load, manipulate, and visualize Apache Parquet files. This application will serve as a user-friendly tool for data scientists and analysts to quickly explore and understand large datasets stored in Parquet format. Here are the key steps and features for your project:

1. **Setup**: Install necessary packages including 'atoti-client-parquet', 'pandas', 'matplotlib', and 'seaborn'. Ensure you have a local environment or a cloud-based Jupyter notebook setup.
2. **File Upload Interface**: Implement a simple file upload interface where users can select their Parquet file(s). Use a library like 'tkinter' for a basic GUI or 'streamlit' for a web-based interface.
3. **Data Loading**: Utilize the 'atoti-client-parquet' package to efficiently load the selected Parquet file into memory. Showcase the loading process and highlight any optimizations that 'atoti-client-parquet' offers over standard methods.
4. **Data Exploration**: Provide tools for basic exploratory data analysis such as summary statistics, missing value checks, and data type inspections. Allow users to filter and sort data based on specific columns.
5. **Visualization**: Integrate visualization capabilities using 'matplotlib' and 'seaborn' to create interactive plots and charts. Include options for bar charts, line graphs, scatter plots, and histograms to help users visualize data distributions and relationships.
6. **Advanced Analytics**: Offer advanced analytical functions like correlation matrices, time series analysis (if applicable), and clustering algorithms. Explain how these analyses are conducted and what insights they provide.
7. **Export Options**: Enable users to export their analyzed data back into Parquet or other common formats (CSV, Excel) for further use or sharing.
8. **Documentation & User Guide**: Create comprehensive documentation detailing how to use each feature of 'ParquetInsight'. Include tutorials, FAQs, and examples to assist new users.

Throughout the development process, focus on making 'ParquetInsight' intuitive and efficient, highlighting the benefits of using 'atoti-client-parquet' for handling large-scale Parquet files.

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