Summary  
This chapter covers creating and customizing box plots in code to visualize and summarize the distribution of numerical data using the five-number summary and additional styling parameters.  

General domain of usage  
Exploratory data analysis

A `boxplot` is a standardized way of displaying the distribution of data based on a **five-number summary**:

1. **Minimum** (lowest value excluding outliers);
2. **First quartile (Q1)** (25th percentile);
3. **Median** (50th percentile);
4. **Third quartile (Q3)** (75th percentile);
5. **Maximum** (highest value excluding outliers).

## Why use a Boxplot?

It is the best tool for **comparing distributions** between groups. It immediately tells you:

* **Central tendency:** where is the median line?;
* **Spread:** how tall is the box? (the interquartile range);
* **Symmetry:** is the median in the center of the box?;
* **Outliers:** are there dots outside the whiskers?



## Key Parameters

* **`saturation`**: controls the intensity of the colors (0 to 1). Lower values make the colors more muted;
* **`linewidth`**: controls the thickness of the box outlines and whiskers;
* **`width`**: controls the width of the box itself.



## Example

Here is a boxplot analyzing the "Tips" dataset. Notice how the dots representing outliers appear above the whiskers.

import seaborn as sns
import matplotlib.pyplot as plt

# Load dataset
df = sns.load_dataset('tips')

# Create a boxplot
sns.boxplot(
    data=df,
    hue='day',
    x='day',
    y='total_bill',
    palette='coolwarm',
    linewidth=2,     # Thicker lines
    saturation=0.7   # Slightly muted colors
)

plt.show()

import unittest
import importlib
import sys
import pandas as pd
from unittest.mock import patch, MagicMock

# Helper function to dynamically generate test names and assertions
def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True, success_message)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)

class TestUserCode(unittest.TestCase):

    def setUp(self):
        self.patcher_csv = patch('pandas.read_csv')
        self.mock_read_csv = self.patcher_csv.start()
        
        # Provide real data structure for planets dataset
        self.mock_df = pd.DataFrame({
            'method': ['Radial Velocity', 'Imaging', 'Eclipse Timing', 'Transit'],
            'distance': [77.4, 15.0, 100.2, 500.0]
        })
        self.mock_read_csv.return_value = self.mock_df

    def tearDown(self):
        self.patcher_csv.stop()

    # Test custom style configuration
    def test_style_configuration(self):
        with patch('seaborn.boxplot'), patch('matplotlib.pyplot.show'):
            with patch('seaborn.set_style') as mock_style:
                if 'user_code' in sys.modules:
                    importlib.reload(sys.modules['user_code'])
                import user_code
            
            if not mock_style.called:
                 _dynamic_test(self, False, "", "Expected `sns.set_style()` to be called.")
                 return

            args, kwargs = mock_style.call_args
            
            style_name_correct = args[0] == 'ticks'
            
            # Check dictionary configuration
            rc_params = args[1] if len(args) > 1 else kwargs.get('rc', {})
            xtick_correct = rc_params.get('xtick.color') == 'white'
            ytick_correct = rc_params.get('ytick.color') == 'white'
            facecolor_correct = rc_params.get('figure.facecolor') == 'grey'

            _dynamic_test(
                self,
                style_name_correct and xtick_correct and ytick_correct and facecolor_correct,
                "The style is set to 'ticks' with white ticks and grey background.",
                "Expected `sns.set_style('ticks', {'xtick.color': 'white', 'ytick.color': 'white', 'figure.facecolor': 'grey'})`."
            )

    # Test boxplot implementation
    def test_boxplot_params(self):
        with patch('seaborn.boxplot') as mock_box:
            with patch('matplotlib.pyplot.show'):
                if 'user_code' in sys.modules:
                    importlib.reload(sys.modules['user_code'])
                import user_code
            
            if not mock_box.called:
                _dynamic_test(self, False, "", "Expected `sns.boxplot()` to be used.")
                return

            args, kwargs = mock_box.call_args
            
            # Check data binding
            x_check = kwargs.get('x') == 'distance'
            y_check = kwargs.get('y') == 'method'
            data_check = kwargs.get('data') is user_code.df

            # Check styling parameters
            width_check = kwargs.get('width') == 0.6
            linewidth_check = kwargs.get('linewidth') == 2
            saturation_check = kwargs.get('saturation') == 0.4
            palette_check = kwargs.get('palette') == 'vlag'

            _dynamic_test(
                self,
                all([x_check, y_check, width_check, linewidth_check, saturation_check, palette_check]),
                "The `boxplot` parameters (`width`, `linewidth`, `saturation`, etc.) are configured correctly.",
                f"Expected `width=0.6`, `linewidth=2`, `saturation=0.4`, `palette='vlag'`. Got: {kwargs}"
            )

    # Test show
    def test_show_used(self):
        with patch('seaborn.boxplot'):
            with patch('matplotlib.pyplot.show') as mock_show:
                if 'user_code' in sys.modules:
                    importlib.reload(sys.modules['user_code'])
                import user_code

                _dynamic_test(
                    self,
                    mock_show.called,
                    "The `plt.show()` function is used.",
                    "Expected `plt.show()` to be used to display the plot."
                )

if __name__ == '__main__':
    unittest.main()

test_code.py

Explore the power of statistical data visualization designed to reveal patterns and relationships. Use Seaborn to create informative distribution plots, heatmaps, and categorical charts with minimal code. Learn to apply aesthetic themes and color palettes that make complex statistical data accessible and easy to interpret.

Summarizing Data with Box Plots

Why use a Boxplot?

Key Parameters

Example

解答