additory

v0.1.3a11 suspicious
4.0
Medium Risk

Elegant data operations for DataFrames - add.to(), add.transform(), add.synthetic()

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal direct risks such as network calls or shell execution but has incomplete maintainer information and low community engagement, which raises concerns about its origin and maintenance.

  • Incomplete maintainer information
  • Low community engagement
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires network interactions to function properly.
  • Shell: No shell execution patterns detected, indicating no immediate risk of unauthorized command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer's information is incomplete and the repository lacks community engagement, raising some suspicion.

🔬 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: sekrad.org>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ 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 additory 
Create a Python-based mini-application named 'DataEnhancer' which leverages the 'additory' package to perform sophisticated data manipulation tasks on Pandas DataFrames. This application will serve as a tool for data analysts and scientists who need to quickly enhance their datasets with additional information or synthetic data.

#### Project Overview:
- **Name:** DataEnhancer
- **Purpose:** To provide a user-friendly interface for enhancing DataFrames with new columns based on existing data, transforming data through various functions, and generating synthetic data.
- **Target Audience:** Data Analysts, Data Scientists, and Researchers
- **Key Features:**
  - Add New Columns: Users can define a function or use predefined ones to add new columns to a DataFrame based on existing data.
  - Data Transformation: Transform existing columns using mathematical or statistical functions.
  - Synthetic Data Generation: Generate synthetic data to fill in missing values or expand datasets.

#### Steps to Build the Application:
1. **Setup Environment:** Ensure Python 3.x is installed along with necessary packages like pandas and additory.
2. **Application Structure:** Design the application with a simple command-line interface (CLI) for ease of use.
3. **Core Functionality Implementation:** Implement the main functionalities using the 'additory' package's core methods:
   - `add.to()`: For adding new columns to the DataFrame.
   - `add.transform()`: For applying transformations to existing columns.
   - `add.synthetic()`: For generating synthetic data.
4. **User Input Handling:** Develop mechanisms to accept user inputs such as DataFrame paths, column names, transformation functions, and parameters for synthetic data generation.
5. **Output Management:** Save the enhanced DataFrame to a file or display it directly in the CLI.
6. **Documentation:** Provide clear documentation explaining how to install and use the application, including examples and best practices.
7. **Testing:** Conduct thorough testing to ensure the application works correctly with different types of DataFrames and scenarios.

#### Example Use Cases:
- Enhancing a customer dataset with calculated columns like 'Customer Lifetime Value' based on purchase history.
- Generating synthetic transaction data to augment a dataset for machine learning purposes.
- Applying transformations to normalize or standardize numerical columns in a dataset.

By following these steps and utilizing the 'additory' package effectively, you'll create a powerful yet accessible tool for anyone working with Pandas DataFrames.