hawk is a modern data analysis tool designed to streamline the exploration of structured data formats such as JSON, YAML, and CSV. By uniting the simplicity of awk with the analytical power of pandas, hawk provides a user-friendly terminal-based solution for data analysis without the complexities typically associated with heavyweight data science frameworks.

Key Features

• Multi-format Support: Process and analyze JSON, YAML, and CSV data with automatic format detection.
• Pandas-like Operations: Execute complex queries involving filtering, grouping, and aggregating data, all while avoiding the limitations of line-based processing found in traditional tools like awk.
• Smart Output Formatting: Get results in various formats (tables, lists, JSON), intuitively tailored to the structure of the input data.
• High Performance: Built with Rust, hawk is optimized for speed, ensuring lightweight and efficient data analysis.
• Developer-friendly Syntax: Use a unified query syntax for all supported formats, making the transition to hawk seamless for users familiar with query languages.
• Type Awareness: Understands and handles data types such as numbers, strings, and booleans natively, unlike simpler text-processing tools that treat everything as strings.

Ideal for Users:

• Data Scientists seeking an efficient method for quick CSV or JSON analysis without extensive setup.
• DevOps Engineers who require succinct analysis of Kubernetes YAML files, Docker Compose files, and Terraform configurations.
• API Developers looking to explore and validate REST API responses.
• Business Analysts who need rapid insights into structured datasets for data-driven decisions.

Examples of Usage

To access the name of the first user from a JSON file:

hawk '.users[0].name' users.json

To filter users older than 30 from a YAML file and count them:

hawk '.users[] | select(.age > 30) | count' users.yaml

Users can leverage hawk in various real-world scenarios, such as analyzing API responses, performing infrastructure analysis, and conducting data-driven decision-making in business environments.

Supported Data Formats

hawk supports the same powerful query syntax across all major structured data formats, allowing for seamless analysis whether the data is in JSON, YAML, or CSV format.

For those exploring the capabilities of hawk, join the movement towards efficient, powerful, and user-friendly data analysis in terminal applications.