Summary  
This chapter explains how to use a single high-level interface to create various categorical plots (strip, box, violin, bar, etc.) and easily facet them by one or more categorical variables.

General domain of usage  
Data visualization

The `catplot` (categorical plot) is the general figure-level interface that gives you access to every categorical plot we've learned so far (`stripplot`, `boxplot`, `violinplot`, `barplot`, etc.).



## Why use Catplot?

Just like `displot`, the main superpower of `catplot` is **faceting**. You can easily split your analysis into multiple subplots based on a categorical variable using `row` and `col`.



## Key Parameters

* **`kind`**: the name of the plot type you want to draw. Defaults to `'strip'`, but you can set it to:
  * `'swarm'`, `'box'`, `'violin'`, `'boxen'`, `'point'`, `'bar'`, `'count'`.
* `row` / `col`: variables that define subsets to plot on different facets (subplots);
* **`legend_out`**: if `True` (default), the legend is drawn outside the plot area to prevent it from covering data.


## Live Example

Here we create a **Boxplot** for each time of day (Lunch/Dinner) automatically using `col`.

import seaborn as sns
import matplotlib.pyplot as plt

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

# Create a grid of boxplots
sns.catplot(
    data=df,
    hue='day',
    x='day',
    y='total_bill',
    col='time',       # Split into 2 columns (Lunch/Dinner)
    kind='box',       # Draw boxplots
    palette='Set3'
)

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
        self.mock_df = pd.DataFrame({
            'species': ['Adelie', 'Gentoo', 'Adelie'],
            'body_mass_g': [3750, 5000, 3600],
            'sex': ['Male', 'Female', 'Female'],
            'island': ['Torgersen', 'Biscoe', 'Dream']
        })
        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.catplot'), 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] == 'white'
            
            # Check dictionary configuration
            rc_params = args[1] if len(args) > 1 else kwargs.get('rc', {})
            facecolor_correct = rc_params.get('axes.facecolor') == 'aliceblue'

            _dynamic_test(
                self,
                style_name_correct and facecolor_correct,
                "The style is set to 'white' with 'aliceblue' background.",
                "Expected `sns.set_style('white', {'axes.facecolor': 'aliceblue'})`."
            )

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

            args, kwargs = mock_cat.call_args
            
            # Check data binding
            x_check = kwargs.get('x') == 'species'
            y_check = kwargs.get('y') == 'body_mass_g'
            hue_check = kwargs.get('hue') == 'sex'
            data_check = kwargs.get('data') is user_code.df

            # Check logic parameters
            row_check = kwargs.get('row') == 'island'
            palette_check = kwargs.get('palette') == 'viridis'
            alpha_check = kwargs.get('alpha') == 0.6
            legend_out_check = kwargs.get('legend_out') is False

            _dynamic_test(
                self,
                all([x_check, y_check, hue_check, row_check, palette_check, alpha_check, legend_out_check]),
                "The `catplot` parameters (`row`, `alpha`, `legend_out`, etc.) are configured correctly.",
                f"Expected `row='island'`, `alpha=0.6`, `legend_out=False`, `palette='viridis'`. Got: {kwargs}"
            )

    # Test show
    def test_show_used(self):
        with patch('seaborn.catplot'):
            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.

Mastering the Categorical Interface

Why use Catplot?

Key Parameters

Live Example

解答