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Complete Guide to Pretty-Printing Pandas DataFrames

Learn how to pretty-print entire Pandas DataFrames and Series in terminal with full display, proper alignment, borders, and color-coding options. Complete guide with practical examples.

#color-coding#data-visualization#pandas-dataframe-display#pandas-styling#pretty-printing#python-data-analysis#terminal-formatting

How to pretty-print an entire Pandas Series or DataFrame in the terminal with full display, proper alignment, borders, and color-coding options?

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To pretty-print an entire Pandas Series or DataFrame in the terminal with full display, proper alignment, borders, and color-coding, you can use a combination of Pandas’ built-in display options, styling methods, and terminal-specific formatting techniques. The key approaches include setting display options for full visibility, using the style attribute for advanced formatting, and leveraging terminal color capabilities for enhanced readability.

Contents

Basic Display Configuration

Pandas provides several configuration options to control how objects are displayed in the terminal. These settings ensure your entire dataset is visible with proper formatting.

Displaying Full DataFrames

The pd.set_option() function allows you to control display behavior:

python
import pandas as pd

# Display all rows and columns
pd.set_option('display.max_rows', None)  # Show all rows
pd.set_option('display.max_columns', None)  # Show all columns
pd.set_option('display.width', None)  # No width restriction
pd.set_option('display.max_colwidth', None)  # Show full column content

Display Precision Control

Control the number of decimal places displayed:

python
pd.set_option('display.precision', 2)  # Show 2 decimal places
pd.set_option('display.float_format', '{:.2f}'.format)  # Format all floats

Context Manager for Temporary Settings

For temporary display changes without affecting global settings:

python
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
    print(your_dataframe)

Advanced Display Options

Using to_string() Method

The to_string() method provides extensive customization:

python
df.to_string(
    max_rows=None,           # Show all rows
    max_cols=None,           # Show all columns  
    line_width=None,         # No width limit
    col_space=None,          # Auto column spacing
    header=True,             # Show column headers
    index=True,              # Show row indices
    na_rep='NA',             # Missing value representation
    justify='left',          # Column alignment
    float_format='%.2f',     # Float formatting
    sparsify=True,           # Remove duplicate index names
    show_dimensions=True     # Show dataframe dimensions
)

Formatting Parameters

  • justify: ‘left’, ‘right’, ‘center’, or ‘justify’
  • col_space: Minimum width for each column
  • decimal: Decimal separator
  • thousands: Thousands separator

HTML Display Integration

For enhanced terminal capabilities:

python
from IPython.display import HTML
HTML(df.to_html())

Styling and Formatting

DataFrame Styling with style

Pandas styling provides powerful formatting capabilities:

python
# Basic styling
df.style.format({
    'numeric_column': '{:.2f}',
    'percentage_column': '{:.1%}'
})

# Conditional formatting
def highlight_max(s):
    is_max = s == s.max()
    return ['background-color: yellow' if v else '' for v in is_max]

df.style.apply(highlight_max)

Styling Methods

  • style.format(): Format specific columns
  • style.apply(): Apply custom styling functions
  • style.background_gradient(): Color gradients
  • style.bar(): Bar charts within cells
  • style.set_properties(): Set CSS properties

Combining Multiple Styles

python
(df.style
   .format({'price': '${:,.2f}'})
   .background_gradient(subset=['sales'], cmap='Greens')
   .apply(highlight_max, subset=['profit'])
   .set_table_styles([{
       'selector': 'th',
       'props': [('background-color', '#f7f7f9')]
   }]))

Color Coding and Visual Enhancement

Built-in Color Maps

Pandas supports various color schemes:

python
# Background gradients
df.style.background_gradient(cmap='viridis')

# Heatmap style
df.style.background_gradient(cmap='RdYlBu', subset=['numeric_columns'])

# Bar charts
df.style.bar(color='lightblue', subset=['numeric_columns'])

Custom Color Functions

Create custom color coding:

python
def color_negative_red(val):
    color = 'red' if val < 0 else 'green'
    return f'color: {color}'

df.style.applymap(color_negative_red)

Color Schemes by Data Type

python
# Numeric columns - blue gradient
numeric_cols = df.select_dtypes(include=['number']).columns

# Categorical columns - yellow background
categorical_cols = df.select_dtypes(include=['object']).columns

(df.style
   .background_gradient(cmap='Blues', subset=numeric_cols)
   .applymap(lambda x: 'background-color: yellow' 
             if isinstance(x, str) else '', subset=categorical_cols))

Text Styling

python
def bold_text(val):
    return 'font-weight: bold'

df.style.applymap(bold_text, subset=['important_columns'])

Terminal-Specific Considerations

Terminal Color Support

Check and leverage terminal color capabilities:

python
import sys
print(sys.stdout.isatty())  # Check if terminal supports color

ANSI Color Codes

For direct terminal color formatting:

python
class Colors:
    RED = '\033[91m'
    GREEN = '\033[92m'
    YELLOW = '\033[93m'
    BLUE = '\033[94m'
    MAGENTA = '\033[95m'
    CYAN = '\033[96m'
    WHITE = '\033[97m'
    BOLD = '\033[1m'
    UNDERLINE = '\033[4m'
    END = '\033[0m'

print(f"{Colors.BOLD}{Colors.GREEN}Success!{Colors.END}")

Rich Library Integration

The rich library provides advanced terminal formatting:

python
from rich.console import Console
from rich.table import Table

console = Console()

table = Table(show_header=True, header_style="bold magenta")
table.add_column("Column 1", style="dim")
table.add_column("Column 2", justify="right")

for row in df.itertuples():
    table.add_row(str(row[1]), str(row[2]))

console.print(table)

Terminal Size Detection

python
import shutil
terminal_width = shutil.get_terminal_size().columns
terminal_height = shutil.get_terminal_size().lines

print(f"Terminal size: {terminal_width}x{terminal_height}")

Practical Examples

Complete Pretty-Print Function

python
def pretty_print_dataframe(df, max_rows=None, max_cols=None, 
                          highlight_numeric=True, color_scheme='viridis'):
    """
    Enhanced pretty-print function for DataFrames with full display options.
    """
    # Set display options
    with pd.option_context('display.max_rows', max_rows,
                          'display.max_columns', max_cols,
                          'display.width', None,
                          'display.precision', 2):
        
        # Apply styling if requested
        if highlight_numeric:
            numeric_cols = df.select_dtypes(include=['number']).columns
            styled = df.style.background_gradient(cmap=color_scheme, subset=numeric_cols)
            return styled
        else:
            return df

# Usage
pretty_print_dataframe(your_dataframe, max_rows=50, color_scheme='plasma')

Financial Data Example

python
import numpy as np

# Create sample financial data
np.random.seed(42)
dates = pd.date_range('2024-01-01', periods=10)
financial_data = pd.DataFrame({
    'Date': dates,
    'Revenue': np.random.uniform(1000, 5000, 10),
    'Expenses': np.random.uniform(500, 2000, 10),
    'Profit': np.random.uniform(200, 3000, 10),
    'Growth_Rate': np.random.uniform(-0.1, 0.3, 10)
})

# Format and display
financial_formatted = (financial_data.style
    .format({
        'Revenue': '${:,.2f}',
        'Expenses': '${:,.2f}',
        'Profit': '${:,.2f}',
        'Growth_Rate': '{:.1%}'
    })
    .background_gradient(cmap='RdYlGn', subset=['Profit'])
    .applymap(lambda x: 'color: red' if x < 0 else 'color: green', 
              subset=['Growth_Rate'])
    .set_table_styles([{
        'selector': 'th',
        'props': [('background-color', '#f8f9fa'), 
                 ('color', '#495057'),
                 ('font-weight', 'bold')]
    }])
    .set_properties(**{'text-align': 'right'}, subset=['Revenue', 'Expenses', 'Profit'])
    .set_properties(**{'text-align': 'center'}))

financial_formatted

Statistical Summary with Color Coding

python
# Create statistical summary
stats_summary = df.describe().T

# Enhanced statistical summary display
(stats_summary.style
    .format({'count': '{:.0f}',
             'mean': '{:.2f}',
             'std': '{:.2f}',
             'min': '{:.2f}',
             '25%': '{:.2f}',
             '50%': '{:.2f}',
             '75%': '{:.2f}',
             'max': '{:.2f}'})
    .background_gradient(cmap='coolwarm', subset=['mean', 'std'])
    .applymap(lambda x: 'font-weight: bold' if x > stats_summary['mean'].mean() else '',
              subset=['mean'])
    .set_caption('Statistical Summary with Color Coding')
    .set_table_styles([
        {'selector': 'caption',
         'props': [('caption-side', 'top'),
                  ('font-size', '16px'),
                  ('font-weight', 'bold'),
                  ('color', '#2c3e50')]}
    ]))

Conclusion

To effectively pretty-print Pandas DataFrames and Series in your terminal, combine Pandas’ built-in display options with advanced styling techniques. Start with basic display configuration using pd.set_option() to ensure full visibility, then leverage the style attribute for sophisticated formatting including color gradients, conditional formatting, and custom CSS styling. For enhanced terminal capabilities, consider integrating libraries like rich or using ANSI color codes directly. Remember to balance visual appeal with readability, especially for large datasets, and use context managers for temporary display changes without affecting your global settings. Experiment with different color schemes and formatting options to find the presentation that best suits your data and analytical needs.

Sources

  1. Pandas Documentation - Display Options
  2. Pandas Documentation - Styling
  3. Pandas Documentation - DataFrame Display
  4. Rich Library Documentation - Tables
  5. Pandas Styling Examples
How to customize pandas DataFrame display options for different terminal sizes?What are the best color schemes for pandas DataFrame visualization in terminal?How to export styled pandas DataFrames to HTML or PDF while preserving formatting?How to handle large DataFrames when pretty-printing in terminal without performance issues?How to create custom pandas formatters for specific data types in terminal display?How to integrate pandas DataFrame display with Jupyter notebooks for enhanced visualization?
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