Summary  
Demonstrates how to use a figure-level function that combines regression plotting with facet grids to compare linear relationships across different data subsets by grouping with color and splitting into rows or columns.

General domain of usage  
Data visualization

The `lmplot` (linear model plot) is a figure-level function that combines `regplot` and `FacetGrid`.

While `regplot` is great for a single relationship, `lmplot` allows you to **compare** linear relationships across different categories. You can separate data by color (`hue`) or by splitting it into different subplots (`col`/`row`), making it powerful for answering questions like "Does the relationship between bill and tip change if the customer is a smoker?" .



## Key Parameters

* **`hue`**: separating the data by color and drawing a separate regression line for each group;
* `col` / `row`: separating the data into distinct subplots;
* **`markers`**: a list of symbols to distinguish groups visually (e.g., `['o', 'x']`), which is helpful for accessibility.



## Example

Here we compare the tips given during Lunch vs. Dinner. Notice how `col` splits the view, while `hue` compares smokers within each view.

import seaborn as sns
import matplotlib.pyplot as plt

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

# Create a grid of regression plots
sns.lmplot(
    data=df,
    x='total_bill',
    y='tip',
    col='time',       # Split: Lunch vs Dinner
    hue='smoker',     # Color: Yes vs No
    palette='Set1'
)

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({
            'total_bill': [16.99, 10.34, 21.01],
            'tip': [1.01, 1.66, 3.50],
            'smoker': ['Yes', 'No', 'Yes'],
            'sex': ['Female', 'Male', 'Male']
        })
        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.lmplot'), 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] == 'darkgrid'
            
            # Check dictionary configuration
            rc_params = args[1] if len(args) > 1 else kwargs.get('rc', {})
            facecolor_correct = rc_params.get('figure.facecolor') == 'lightpink'

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

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

            args, kwargs = mock_lm.call_args
            
            # Check data binding
            x_check = kwargs.get('x') == 'total_bill'
            y_check = kwargs.get('y') == 'tip'
            data_check = kwargs.get('data') is user_code.df

            # Check logic parameters
            hue_check = kwargs.get('hue') == 'smoker'
            col_check = kwargs.get('col') == 'sex'
            markers_check = kwargs.get('markers') == ['o', 'x']
            palette_check = kwargs.get('palette') == 'crest'

            _dynamic_test(
                self,
                all([x_check, y_check, hue_check, col_check, markers_check, palette_check]),
                "The `lmplot` parameters (`hue`, `col`, `markers`) are configured correctly.",
                f"Expected `col='sex'`, `hue='smoker'`, `markers=['o', 'x']`. Got: {kwargs}"
            )

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

Combining Regression and Grids

Key Parameters

Example

解答