py-data-viewer 0.0.2

A python library to display data structure values for easier navigation and exploration.

Data Viewer

Data Viewer is a Python library designed to help mushroom people (like me) explore and navigate complex data structures with ease. It provides a clear view of nested dictionaries, lists, objects, and more, making debugging and data analysis more efficient.

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Features

• Tree View: Visualize data structures as a tree for better clarity.
• Colorized Output: Easily distinguish different parts of the data structure.
• Supports Multiple Data Types: Works with dictionaries, lists, objects, namedtuples, and mixed structures.
• API Response Exploration: Simplifies debugging and understanding of API responses, especially for complex outputs.
• Programmatic Usage: Integrate directly into your Python scripts with the vprint function.

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Installation

To install the package, use pip:

pip install py-data-viewer

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Usage

Programmatic Usage with vprint

The vprint function is the easiest way to explore data structures in your Python scripts. It provides a tree-like visualization of your data, making it ideal for debugging API responses, especially from frameworks!

Example: Exploring an API Response

from py_data_viewer import vprint

# Simulated API response
response = {
    "chat_message": {
        "source": "Assistant",
        "content": "This is a response from an LLM.",
        "metadata": {},
    },
    "inner_messages": [
        {
            "type": "ToolCallRequestEvent",
            "content": [{"name": "search", "arguments": '{"query":"example"}'}],
        },
        {
            "type": "ToolCallExecutionEvent",
            "content": [{"name": "search", "content": "Search result here."}],
        },
    ],
}

vprint(response, var_name="response")

Output:

response
├── response.chat_message = dict with 3 items
│   ├── response.chat_message.source = 'Assistant'
│   ├── response.chat_message.content = 'This is a response from an LLM.'
│   └── response.chat_message.metadata = dict with 0 items
└── response.inner_messages = list with 2 items
    ├── response.inner_messages[0] = dict with 2 items
    │   ├── response.inner_messages[0].type = 'ToolCallRequestEvent'
    │   └── response.inner_messages[0].content = list with 1 items
    │       └── response.inner_messages[0].content[0] = dict with 2 items
    │           ├── response.inner_messages[0].content[0].name = 'search'
    │           └── response.inner_messages[0].content[0].arguments = '{"query":"example"}'
    └── response.inner_messages[1] = dict with 2 items
        ├── response.inner_messages[1].type = 'ToolCallExecutionEvent'
        └── response.inner_messages[1].content = list with 1 items
            └── response.inner_messages[1].content[0] = dict with 2 items
                ├── response.inner_messages[1].content[0].name = 'search'
                └── response.inner_messages[1].content[0].content = 'Search result here.'

Example: Exploring a Complex Data Structure

from data_viewer import vprint

data = {
    "user": {"id": 1, "name": "Alice"},
    "actions": [
        {"type": "login", "timestamp": "2023-01-01T12:00:00Z"},
        {"type": "purchase", "details": {"item": "book", "price": 12.99}},
    ],
}

vprint(data, var_name="data", tree_view=True)

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Advanced Options

The vprint function supports several options to customize the output:

• var_name: Specify the variable name to display in the output.
• indent_size: Set the number of spaces for indentation (default: 2).
• colorize: Enable or disable colorized output (default: True).

Example:

vprint(data, var_name="custom_data", tree_view=False, indent_size=4, colorize=False)

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Contributing

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a new branch for your feature or bugfix.
3. Commit your changes and push the branch.
4. Open a pull request.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Links

• Homepage: GitHub Repository
• Issues: Report Issues