Pirrtools to_rich Tutorial#

This notebook demonstrates all features of the to_rich method for creating beautiful Rich tables from pandas DataFrames and Series.

Setup#

First, let’s import the required libraries and create sample data:

[ ]:

import pandas as pd
import pirrtools
from rich.console import Console
from rich import box
import numpy as np

# Create console for output
console = Console()

# Create sample DataFrame
df = pd.DataFrame({
    'Q1': [100.123, 150.456, 200.789],
    'Q2': [120.50, 180.75, 220.25],
    'Q3': [140.0, 200.0, 180.0],
    'Q4': [160.555, 170.333, 240.111]
}, index=['Product A', 'Product B', 'Product C'])

print("Sample DataFrame:")
df

Basic conversion#

table = df.pirr.to_rich(title=”Basic Rich Table”) console.print(table)

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# Basic conversion using to_rich_docs for better documentation display
table = df.pirr.to_rich_docs(title="Basic Rich Table", theme="dark")
console.print(table)

Background gradient with viridis colormap#

table = df.pirr.to_rich( bg=”viridis”, title=”📊 Background Gradient (Viridis)”, column_header_style=”bold white on blue” ) console.print(table)

[ ]:

# Different colormap with axis control
table = df.pirr.to_rich(
    bg="plasma",
    bg_kwargs={"axis": 0},  # Column-wise gradient
    title="🌈 Plasma Gradient (Column-wise)"
)
console.print(table)

Text Gradients#

Apply gradients to the text itself:

[ ]:

# Text gradient
table = df.pirr.to_rich(
    tg="coolwarm",
    title="🎨 Text Gradient (Coolwarm)",
    index_style="bold cyan"
)
console.print(table)

String Formatting#

Format numbers and strings using the enhanced format parameter:

[ ]:

# Different formats for different columns
table = df.pirr.to_rich(
    format={
        'Q1': '${:.0f}',      # Currency format
        'Q2': '{:.1f}M',      # Millions format
        'Q3': '{:.0f}',       # Integer format
        'Q4': '{:.2f}%'       # Percentage format
    },
    title="💰 Custom Number Formatting"
)
console.print(table)

Pandas Styler Integration#

Use existing pandas Styler objects with enhanced to_rich features:

[ ]:

# Create pandas styler with custom formatting
styler = df.style.format({
    'Q1': 'Product: {:.1f}',
    'Q2': 'Sales: ${:.0f}K',
    'Q3': '{:.0f} units'
})

# Use styler with to_rich
table = df.pirr.to_rich(
    styler=styler,
    title="🏭 Pandas Styler Integration"
)
console.print(table)

Format Override#

Override styler formats with the format parameter:

[ ]:

# Original styler format
original_styler = df.style.format({'Q1': 'OLD: {:.1f}', 'Q2': 'KEEP: {:.0f}'})

# Override some formats
table = df.pirr.to_rich(
    styler=original_styler,
    format={'Q1': 'NEW: ${:.0f}', 'Q3': 'ADDED: {:.1f}'},  # Overrides Q1, adds Q3
    title="🔄 Format Override Demo"
)
console.print(table)

Alternating Rows#

Improve readability with alternating row colors:

[ ]:

# Alternating row colors
table = df.pirr.to_rich(
    alternating_rows=True,
    alternating_row_colors=("", "on grey11"),
    title="📋 Alternating Row Colors"
)
console.print(table)

Manual Table Control#

Override automatic table optimization for custom styling:

[ ]:

# Manual table settings
table = df.pirr.to_rich(
    auto_optimize=False,
    box=box.DOUBLE,
    padding=(1, 2),
    show_edge=True,
    title="⚙️ Manual Table Control"
)
console.print(table)

Series Support#

to_rich also works with pandas Series:

[ ]:

# Create a Series
series = pd.Series([0.123, 0.456, 0.789],
                  name='Performance Metrics',
                  index=['Accuracy', 'Precision', 'Recall'])

# Format the Series
table = series.pirr.to_rich(
    format='{:.1%}',
    title="📈 Series with Formatting",
    index_style="bold blue"
)
console.print(table)

Professional Report Style#

Combine multiple features for professional-looking reports:

[ ]:

# Professional styling combining multiple features
table = df.pirr.to_rich(
    bg="viridis",
    format={
        'Q1': '${:.0f}K',
        'Q2': '${:.0f}K',
        'Q3': '${:.0f}K',
        'Q4': '${:.0f}K'
    },
    column_header_style="bold white on dark_blue",
    index_header_style="bold yellow on dark_red",
    index_style="italic cyan",
    alternating_rows=True,
    alternating_row_colors=("", "on grey11"),
    title="📊 Quarterly Sales Report - 2024"
)
console.print(table)

Large Dataset Example#

Demonstrate with a larger dataset:

[ ]:

# Create larger dataset
np.random.seed(42)
large_df = pd.DataFrame({
    'Revenue': np.random.uniform(50000, 200000, 8),
    'Profit': np.random.uniform(5000, 50000, 8),
    'Growth': np.random.uniform(-0.1, 0.3, 8),
    'Market_Share': np.random.uniform(0.05, 0.25, 8)
}, index=[f'Region_{i}' for i in range(8)])

# Display with comprehensive formatting
table = large_df.pirr.to_rich(
    bg="plasma",
    format={
        'Revenue': '${:,.0f}',
        'Profit': '${:,.0f}',
        'Growth': '{:+.1%}',
        'Market_Share': '{:.1%}'
    },
    title="🌍 Regional Performance Dashboard",
    column_header_style="bold white on purple",
    show_index=True
)
console.print(table)

CLI Tutorial Command#

After installing pirrtools, you can also run an interactive tutorial from the command line:

pirrtools-tutorial

This launches a comprehensive interactive tutorial covering all features.

Summary#

The to_rich method provides powerful formatting capabilities:

✅ Background & text gradients for data visualization
✅ String formatting with pandas Styler integration
✅ Manual table control to override automatic optimization
✅ Professional styling options for reports
✅ Series support with all formatting features
✅ Easy console output with Rich integration

For more details, see the documentation and API reference.

Pirrtools to_rich Tutorial

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