Summary  
This chapter covers how to create and customize point plots to display central tendency estimates with uncertainty, including adjusting markers, line styles, and dodging points to highlight changes between categories.

General domain of usage  
Statistical data visualization

A `pointplot` represents an estimate of the central tendency (mean) by the position of scatter plot points and provides an indication of uncertainty using error bars.

## Pointplot vs. Barplot

Technically, they show the exact same data. However, a `pointplot` connects the estimates with a line. This visualizes the **slope of change**, making it easier to see how a variable evolves from one category to another.

## Key Parameters

To make the plot easier to read (especially in black-and-white), you can customize the markers and lines for different groups:

* **`markers`**: a list of symbols to use for points (e.g., `['o', 'x']`);
* **`linestyles`**: a list of line styles (e.g., `['-']` for solid, `['--']` for dashed);
* **`dodge=True`**: slightly shifts the points along the axis so they don't overlap, making error bars distinct.



## Example

Here is a pointplot showing how the average bill changes throughout the week. Notice how the dashed line helps distinguish "Lunch" from "Dinner" even without color.


import seaborn as sns
import matplotlib.pyplot as plt

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

# Create a styled pointplot
sns.pointplot(
    data=df,
    x='day',
    y='total_bill',
    hue='time',
    markers=['o', '^'],      # Circle and Triangle markers
    linestyles=['-', '--'],  # Solid and Dashed lines
    dodge=True               # Avoid overlap
)

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({
            'day': ['Sun', 'Sat', 'Sun', 'Fri'],
            'tip': [3.5, 5.0, 2.0, 4.0],
            'sex': ['Male', 'Female', 'Male', 'Female']
        })
        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.pointplot') as mock_point, patch('matplotlib.pyplot.show'):
            # Mock return value to prevent attribute errors on .set_title
            mock_point.return_value = MagicMock()
            
            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', {})
            facecolor_correct = rc_params.get('axes.facecolor') == 'azure'

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

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

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

            # Check styling parameters
            markers_check = kwargs.get('markers') == ['v', 'o']
            linestyles_check = kwargs.get('linestyles') == ['-', '--']
            dodge_check = kwargs.get('dodge') is True
            capsize_check = kwargs.get('capsize') == 0.2
            palette_check = kwargs.get('palette') == 'rocket'

            _dynamic_test(
                self,
                all([x_check, y_check, hue_check, markers_check, linestyles_check, dodge_check, capsize_check, palette_check]),
                "The `pointplot` parameters (`markers`, `linestyles`, `dodge`, etc.) are configured correctly.",
                f"Expected `markers=['v', 'o']`, `linestyles=['-', '--']`, `dodge=True`. Got: {kwargs}"
            )

    # Test if title was set
    def test_title_set(self):
        with patch('seaborn.pointplot') as mock_point:
            mock_axes = MagicMock()
            mock_point.return_value = mock_axes
            
            with patch('matplotlib.pyplot.show'):
                if 'user_code' in sys.modules:
                    importlib.reload(sys.modules['user_code'])
                import user_code
            
            title_called = False
            if mock_axes.set_title.called:
                args, _ = mock_axes.set_title.call_args
                if args and args[0] == 'Tips pointplot':
                    title_called = True
            
            _dynamic_test(
                self,
                title_called,
                "The plot title is set to 'Tips pointplot'.",
                "Expected `g.set_title('Tips pointplot')` to be called."
            )

    # Test show
    def test_show_used(self):
        with patch('seaborn.pointplot') as mock_point:
            mock_point.return_value = MagicMock() # Prevent crash
            
            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.

Visualizing Point Estimates

Pointplot vs. Barplot

Key Parameters

Example

解答