colab-print

Colab Print

Colab Print is a Python library that enhances the display capabilities of Jupyter and Google Colab notebooks, providing beautiful, customizable HTML outputs for text, lists, dictionaries, tables, pandas DataFrames, and progress bars.

Features

• 🎨 Rich Text Styling - Display text with predefined styles or custom CSS
• 📊 Beautiful DataFrame Display - Present pandas DataFrames with extensive styling options
• 📑 Customizable Tables - Create HTML tables with headers, rows, and custom styling
• 📜 Formatted Lists - Display Python lists and tuples as ordered or unordered HTML lists
• 📖 Readable Dictionaries - Render dictionaries as structured definition lists
• 🎭 Extensible Themes - Use built-in themes or create your own custom styles
• 📏 Smart Row/Column Limiting - Automatically display large DataFrames with sensible limits
• 🔍 Cell Highlighting - Highlight specific rows, columns, or individual cells in tables and DataFrames
• 📊 Progress Tracking - Display elegant progress bars with tqdm compatibility
• 🔄 Graceful Fallbacks - Works even outside Jupyter/IPython environments
• 🧩 Structured Data Detection - Automatic handling of nested structures, matrices, and array-like objects

Installation

pip install colab-print

Quick Start

from colab_print import Printer, header, success, progress
import pandas as pd
import time

# Create a printer with default styles
printer = Printer()

# Use pre-configured styling functions
header("Colab Print Demo")
success("Library loaded successfully!")

# Display styled text
printer.display("Hello, World!", style="highlight")

# Display a list with nested elements (automatically detected and styled)
my_list = ['apple', 'banana', ['nested', 'item'], 'cherry', {'key': 'value'}]
printer.display_list(my_list, ordered=True, style="info")

# Show a progress bar
for i in progress(range(10), desc="Processing"):
    time.sleep(0.2)  # Simulate work

# Display a dictionary
my_dict = {
    'name': 'Alice', 
    'age': 30, 
    'address': {'street': '123 Main St', 'city': 'Anytown'}
}
printer.display_dict(my_dict, style="success")

# Display a simple table
headers = ["Name", "Age", "City"]
rows = [
    ["Alice", 28, "New York"],
    ["Bob", 34, "London"],
    ["Charlie", 22, "Paris"]
]
printer.display_table(headers, rows, style="default")

# Display a pandas DataFrame with styling
df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [28, 34, 22],
    'City': ['New York', 'London', 'Paris']
})
printer.display_df(df, 
                  highlight_cols=['Name'],
                  highlight_cells={(0, 'Age'): "background-color: #FFEB3B;"},
                  caption="Sample DataFrame")

Styling & Shortcut Functions

Predefined Style Shortcuts

Colab Print provides convenient shortcut functions with pre-configured styling:

from colab_print import (
    header, title, subtitle, section_divider, subheader,
    code, card, quote, badge, data_highlight, footer,
    highlight, info, success, warning, error, muted, primary, secondary,
    dfd, table, list_, dict_, progress
)

# Display styled text with a single function call
header("Main Section")
title("Document Title")
subtitle("Supporting information")
success("Operation completed!")
warning("Proceed with caution")
error("An error occurred")
code("print('Hello World')")

# Display different content types with shortcuts
table(headers, rows)
list_(my_list, ordered=True)
dict_(my_dict)
dfd(df, highlight_cols=["Name"])

Predefined Styles

• default - Clean, professional styling
• highlight - Stand-out text with emphasis
• info - Informational blue text
• success - Positive green message
• warning - Attention-grabbing yellow alert
• error - Critical red message
• muted - Subtle gray text
• primary - Primary blue-themed text
• secondary - Secondary purple-themed text
• code - Code-like display with monospace font
• card - Card-like container with shadow
• quote - Styled blockquote
• notice - Attention-drawing notice
• badge - Compact badge-style display

Custom Styling

You can add your own styles:

printer = Printer()
printer.add_style("custom", "color: purple; font-size: 20px; font-weight: bold;")
printer.display("Custom styled text", style="custom")

Display Methods

• printer.display(text, style="default", **inline_styles): Displays styled text.
• printer.display_list(items, ordered=False, style="default", item_style=None, **inline_styles): Displays lists/tuples.
• printer.display_dict(data, style="default", key_style=None, value_style=None, **inline_styles): Displays dictionaries.
• printer.display_table(headers, rows, style="default", **table_options): Displays basic tables.
• printer.display_df(df, style="default", **df_options): Displays pandas DataFrames with many options.
• printer.display_progress(total, desc="", style="default", **progress_options): Displays a progress bar.

Progress Tracking

Colab Print offers powerful progress tracking with tqdm compatibility:

from colab_print import progress, Printer
import time

# Simple progress bar using iterable
for i in progress(range(100), desc="Processing"):
    time.sleep(0.01)  # Do some work

# Manual progress
printer = Printer()
progress_id = printer.display_progress(total=50, desc="Manual progress")
for i in range(50):
    time.sleep(0.05)  # Do some work
    printer.update_progress(progress_id, i+1)

# Progress with customization
for i in progress(range(100), 
                 desc="Custom progress", 
                 color="#9C27B0", 
                 height="25px",
                 style="card"):
    time.sleep(0.01)

# Undetermined progress (loading indicator)
progress_id = printer.display_progress(total=None, desc="Loading...", animated=True)
time.sleep(3)  # Do some work with unknown completion time

DataFrame Display Options

The display_df method supports numerous customization options:

printer.display_df(df,
                  style='default',           # Base style
                  max_rows=20,               # Max rows to display
                  max_cols=10,               # Max columns to display
                  precision=2,               # Decimal precision for floats
                  header_style="...",        # Custom header styling
                  odd_row_style="...",       # Custom odd row styling
                  even_row_style="...",      # Custom even row styling
                  index=True,                # Show index
                  width="100%",              # Table width
                  caption="My DataFrame",    # Table caption
                  highlight_cols=["col1"],   # Highlight columns
                  highlight_rows=[0, 2],     # Highlight rows
                  highlight_cells={(0,0): "..."}, # Highlight specific cells
                  font_size="14px",          # Custom font size for all cells
                  text_align="center")       # Text alignment for all cells

Advanced List Display

Colab Print automatically detects and optimally displays complex data structures:

from colab_print import list_
import numpy as np
import pandas as pd

# Nested lists are visualized with hierarchical styling
nested_list = [1, 2, [3, 4, [5, 6]], 7]
list_(nested_list)

# Matrices are displayed as tables automatically
matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
list_(matrix)

# NumPy arrays are handled seamlessly
np_array = np.array([[1, 2, 3], [4, 5, 6]])
list_(np_array)

# Pandas data structures work too
series = pd.Series([1, 2, 3, 4])
list_(series)

# Control display format manually if needed
list_(matrix, matrix_mode=False)  # Force list display for a matrix

Advanced Usage

Creating Custom Themes

custom_themes = {
    'dark': 'color: white; background-color: #333; font-size: 16px;',
    'fancy': 'color: #8A2BE2; font-family: "Brush Script MT", cursive; font-size: 20px;'
}

printer = Printer(additional_styles=custom_themes)
printer.display("Dark theme", style="dark")
printer.display("Fancy theme", style="fancy")

Creating Reusable Display Functions

# Create a function with predefined styling
my_header = printer.create_styled_display("header", color="#FF5722", font_size="24px")

# Use it multiple times with consistent styling
my_header("First Section")
my_header("Second Section")

# Still allows overrides at call time
my_header("Special Section", color="#9C27B0")

Handling Non-Notebook Environments

The library gracefully handles non-IPython environments by printing fallback text representations:

# This will work in regular Python scripts
printer.display_list([1, 2, 3])
printer.display_dict({'a': 1})
printer.display_df(df)

Exception Handling

Colab Print includes a comprehensive exception hierarchy for robust error handling:

from colab_print import (
    ColabPrintError,        # Base exception
    StyleNotFoundError,     # When a style isn't found
    DataFrameError,         # DataFrame-related issues
    InvalidParameterError,  # Parameter validation failures
    HTMLRenderingError      # HTML rendering problems
)

try:
    printer.display("Some text", style="non_existent_style")
except StyleNotFoundError as e:
    print(f"Style error: {e}")

Full Examples

For a comprehensive demonstration of all features, please see the example script:

example.py

This script covers:

• Text, List, Dictionary, Table, and DataFrame display
• Using built-in styles and inline styles
• Adding custom styles
• Creating a Printer instance with custom themes
• Highlighting options for DataFrames
• Progress bar usage and customization
• Advanced list and nested structure display
• Exception handling
• Fallback behavior notes

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

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