Summary  
Introduces a unified distribution-plotting interface that can generate histograms, KDE plots, or ECDFs and automatically facet them into subplots by category.

General domain of usage  
Data visualization

The `displot` (distribution plot) is the "parent" function for all the plots we have learned so far. It provides a single interface to create histograms, KDE plots, and ECDFs.

## Why Use Displot?

While `histplot` and `kdeplot` are great for single plots, `displot` has a superpower: **faceting**.

Using the `col` (column) or `row` parameter, `displot` can automatically split your dataset into multiple side-by-side subplots based on a category.

### Key Parameters

* **`kind`**: determines the type of plot;
  * `'hist'` (default);
  * `'kde'`;
  * `'ecdf'`.
* `col` / `row`: splits the data into separate subplots arranged in columns or rows.

### Example

Here is how you can instantly create 3 separate histograms for different species using just one line of code.

import seaborn as sns
import matplotlib.pyplot as plt

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

# Create a grid of plots
sns.displot(
    data=df,
    x='bill_length_mm',
    col='species',     # Creates 3 subplots (one per species)
    kind='hist',       # Draw histograms
    element='step'
)

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({
            'carat': [0.23, 0.21, 0.25, 0.20, 0.31],
            'cut': ['Ideal', 'Premium', 'Good', 'Premium', 'Good'],
            'color': ['E', 'E', 'J', 'I', 'J']
        })
        self.mock_read_csv.return_value = self.mock_df

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

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

                args, kwargs = mock_style.call_args
                passed_style = args[0] if args else kwargs.get('style')
                
                _dynamic_test(
                    self,
                    passed_style == 'darkgrid',
                    "The style is set to 'darkgrid'.",
                    f"Expected style to be `'darkgrid'`, but got `{passed_style}`."
                )

    # Test displot call
    def test_displot_called(self):
        with patch('seaborn.displot') as mock_displot:
            with patch('matplotlib.pyplot.show'):
                if 'user_code' in sys.modules:
                    importlib.reload(sys.modules['user_code'])
                else:
                    import user_code
            
            _dynamic_test(
                self,
                mock_displot.called,
                "The `sns.displot()` function is used.",
                "Expected `sns.displot()` to be used."
            )

    # Test logic parameters (kind, col, multiple)
    def test_displot_params(self):
        with patch('seaborn.displot') as mock_displot:
            with patch('matplotlib.pyplot.show'):
                if 'user_code' in sys.modules:
                    importlib.reload(sys.modules['user_code'])
                else:
                    import user_code
            
            if not mock_displot.called:
                return

            args, kwargs = mock_displot.call_args
            
            # Check faceting and kind
            kind_check = kwargs.get('kind') == 'kde'
            col_check = kwargs.get('col') == 'color'
            multiple_check = kwargs.get('multiple') == 'fill'
            palette_check = kwargs.get('palette') == 'viridis'

            _dynamic_test(
                self,
                kind_check and col_check and multiple_check and palette_check,
                "The `displot` is configured correctly (`kind='kde'`, `col='color'`, `multiple='fill'`).",
                f"Expected `kind='kde'`, `col='color'`, `multiple='fill'`, `palette='viridis'`. Got: {kwargs}"
            )

    # Test basic data parameters
    def test_data_params(self):
        with patch('seaborn.displot') as mock_displot:
            with patch('matplotlib.pyplot.show'):
                if 'user_code' in sys.modules:
                    importlib.reload(sys.modules['user_code'])
                else:
                    import user_code
            
            if not mock_displot.called:
                return

            args, kwargs = mock_displot.call_args
            
            x_check = kwargs.get('x') == 'carat'
            hue_check = kwargs.get('hue') == 'cut'
            
            # We mock the dataframe, so we check if the passed object is the same instance
            data_check = kwargs.get('data') is user_code.df

            _dynamic_test(
                self,
                x_check and hue_check and data_check,
                "The `x`, `hue`, and `data` parameters are set correctly.",
                "Expected `x='carat'`, `hue='cut'`, and `data=df`."
            )

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

Using the Distribution Interface

Why Use Displot?

Key Parameters

Example

解答