panel-print 0.1.3

A Python library that provides beautiful console output using Rich panels for enhanced debugging and data visualization.

Panel Print

A Python library that provides beautiful console output using Rich panels for enhanced debugging and data visualization.

Features

• 🎨 Beautiful Output: Pretty print objects in elegant Rich panels
• � DataFrame Display: Interactive pandas DataFrame and Series visualization with show
• 🀽� Easy to Use: Simple API with intuitive functions
• 🔧 Customizable: Configurable max length for container abbreviation
• 🚀 Fast: Built on top of the powerful Rich library and itables
• 🐍 Modern Python: Supports Python 3.10+

Installation

Install using pip:

pip install panel-print

Or using uv:

uv add panel-print

Quick Start

from panel_print import pp, show
import pandas as pd

# Pretty print any Python object
data = {
    "name": "John Doe", 
    "age": 30,
    "skills": ["Python", "JavaScript", "Go"],
    "address": {
        "street": "123 Main St",
        "city": "San Francisco",
        "state": "CA"
    }
}

pp(data)

# Display pandas DataFrame/Series interactively
df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'London', 'Tokyo']
})

show(df)  # Interactive table display

Usage Examples

Basic Usage

from panel_print import pp

# Print simple values
pp("Hello, World!")
pp(42)
pp([1, 2, 3, 4, 5])

Multiple Objects

from panel_print import pp

# Print multiple objects at once
pp("User Info:", {"name": "Alice", "age": 25}, ["admin", "user"])

Custom Max Length

from panel_print import pp

# Control container abbreviation
long_list = list(range(100))
pp(long_list, max_length=10)  # Will abbreviate after 10 items

Complex Data Structures

from panel_print import pp

# Works great with nested data
config = {
    "database": {
        "host": "localhost",
        "port": 5432,
        "credentials": {
            "username": "admin",
            "password": "secret"
        }
    },
    "features": ["auth", "logging", "caching"],
    "debug": True
}

pp(config)

Pandas DataFrame and Series Display

from panel_print import show
import pandas as pd
import numpy as np

# Create sample data
df = pd.DataFrame({
    'Product': ['Laptop', 'Mouse', 'Keyboard', 'Monitor'],
    'Price': [999.99, 25.50, 79.99, 299.00],
    'Stock': [15, 100, 50, 8],
    'Rating': [4.5, 4.2, 4.7, 4.1]
})

# Interactive table display with search, sort, and export features
show(df)

# Works with Series too
series = pd.Series([1, 2, 3, 4, 5], name='Numbers')
show(series)

# Large datasets with automatic pagination
large_df = pd.DataFrame(np.random.randn(1000, 5), 
                       columns=['A', 'B', 'C', 'D', 'E'])
show(large_df)  # Automatically handles large data

Integration with Rich

from panel_print import print, pprint

# Access Rich's print and pprint directly
print("This uses Rich's enhanced print")
pprint({"key": "value"})  # Rich's pretty print without panels

API Reference

pp(*objects, max_length=20)

Pretty print objects in a panel format.

Parameters:

• *objects (Any): One or more objects to pretty print
• max_length (int, optional): Maximum length of containers before abbreviating. Defaults to 20.

Returns:

• None

Example:

pp(data, max_length=50)

show(df, **kwargs)

Display pandas DataFrame or Series in an interactive table format with search, sorting, and export capabilities.

Parameters:

• df (pd.DataFrame | pd.Series): The pandas DataFrame or Series to display
• **kwargs: Additional keyword arguments passed to itables.show()

Features:

• Interactive search and column filtering
• Sortable columns
• Export to CSV, Excel, and HTML
• Automatic pagination for large datasets
• Responsive design
• Index display control

Returns:

• None (displays interactive table in notebook/browser)

Example:

import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
show(df)

Advanced Usage

Debugging Complex Objects

from panel_print import pp
import datetime

class User:
    def __init__(self, name, email):
        self.name = name
        self.email = email
        self.created_at = datetime.datetime.now()
  
    def __repr__(self):
        return f"User(name='{self.name}', email='{self.email}')"

user = User("Alice", "[e-mail]")
pp("Debug User Object:", user, user.__dict__)

Working with APIs

import requests
from panel_print import pp

response = requests.get("https://api.github.com/users/octocat")
pp("GitHub API Response:", response.json())

Data Analysis Workflow

import pandas as pd
from panel_print import pp, show

# Load and explore data
df = pd.read_csv("sales_data.csv")

# Quick overview with pp
pp("Dataset Info:", {
    "Shape": df.shape,
    "Columns": list(df.columns),
    "Memory Usage": f"{df.memory_usage(deep=True).sum() / 1024**2:.2f} MB"
})

# Interactive data exploration with show
show(df.head(20))  # First 20 rows with interactive features
show(df.describe())  # Statistical summary
show(df.groupby('category').sum())  # Grouped analysis

Requirements

• Python 3.10 or higher
• Rich >= 14.1.0
• itables (for pandas DataFrame/Series display)
• pandas (optional, required for DataFrame/Series functionality)

Development

Setting up Development Environment

1. Clone the repository:

git clone https://github.com/yourusername/panel-print.git
cd panel-print

2. Install dependencies using uv:

uv sync

3. Run tests:

uv run pytest

Building the Package

uv build

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

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

Acknowledgments

• Built with Rich - Python library for rich text and beautiful formatting
• Inspired by the need for better debugging output in Python applications

Changelog

v0.1.2

• Added show function for interactive pandas DataFrame and Series display
• Integrated itables for enhanced data visualization
• Added search, sort, and export capabilities for tabular data
• Improved data analysis workflow support

v0.1.0

• Initial release
• Basic panel printing functionality
• Support for multiple objects
• Configurable max length parameter

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Made with ❤️ for the Python community