🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network calls, shell executions, or obfuscations. The metadata risk is slightly elevated due to incomplete author information and a single package from the maintainer.

No network calls detected
Incomplete author information

Per-check LLM notes

Network: No network calls detected, which is normal for a utility package.
Shell: No shell execution patterns detected, indicating no direct system command risks.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The author information is incomplete and the maintainer has only one package, raising some suspicion but not conclusive evidence of malice.

🔬 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: datalier.nl>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ 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 adls-pandas-utils

Develop a data analysis tool using the 'adls-pandas-utils' Python package that allows users to interact with Parquet files stored in Azure Data Lake Storage Gen2. This tool will provide a simple yet powerful interface for querying, filtering, and visualizing data from these files. Here’s a step-by-step guide on how to build this application:

1. **Setup Environment**: Begin by setting up your Python environment. Install the necessary packages including 'adls-pandas-utils', 'pandas', 'matplotlib', and 'seaborn'. Ensure you have access credentials to your Azure Data Lake Storage Gen2 account.

2. **Connecting to ADLS Gen2**: Use 'adls-pandas-utils' to connect to your Azure Data Lake Storage Gen2 account. Implement functions to list all Parquet files within a specified directory.

3. **Loading Data**: Create a function to load a selected Parquet file into a pandas DataFrame. Utilize 'adls-pandas-utils' for efficient loading of large datasets.

4. **Data Exploration**: Develop features to explore the loaded data. Include functionalities like displaying basic statistics, checking for missing values, and summarizing data types.

5. **Querying and Filtering**: Allow users to query and filter the data based on specific columns and conditions. Implement advanced filtering options such as date range filters and categorical filters.

6. **Visualization**: Integrate visualization capabilities using 'matplotlib' and 'seaborn'. Enable users to create various plots such as line charts, bar charts, histograms, and scatter plots based on the filtered data.

7. **Export Options**: Provide options for exporting the filtered or queried data back to Parquet format or other formats like CSV or Excel. Ensure that the export process leverages 'adls-pandas-utils' for seamless integration with Azure Data Lake Storage.

8. **User Interface**: While the primary focus is on backend functionality, consider building a simple command-line interface (CLI) or a graphical user interface (GUI) using libraries like 'tkinter' or 'streamlit' to enhance usability.

9. **Documentation and Testing**: Write comprehensive documentation for your application, explaining each feature and how it can be used. Conduct thorough testing to ensure reliability and performance.

This project aims to streamline the process of working with big data stored in Azure Data Lake Storage Gen2, making it accessible and easy to analyze for users without requiring deep knowledge of cloud storage systems or complex data handling techniques.