Summary  
This chapter covers how to customize histogram plots by adjusting statistical transformations (e.g., density vs. count), visual elements (e.g., step outlines vs. bars), and binning parameters (e.g., bin width).  

General domain of usage  
Data visualization

The `histplot` (histogram plot) is a classic tool that represents the distribution of one or more variables by counting the number of observations that fall within discrete bins. It helps to answer questions like: "What is the most common value?", "Is the data symmetrical?", or "Are there outliers?".



## Customizing the Histogram

By default, `histplot` draws bars and counts the number of occurrences. However, you can customize it to reveal more insights.

### 1. Changing the Statistics (`stat`)

Instead of a simple count, you can calculate the **density**. This is useful when comparing groups of different sizes, as it normalizes the area under the curve to 1.

```python
stat='density'
```

### 2. Visual Style (`element`)

When plotting multiple groups using `hue`, standard bars can become cluttered. Using a step plot creates an outline, making it easier to see overlaps.

```python
element='step'
```

### 3. Bin Width (`binwidth`)

The size of the bins determines how much detail you see.

```python
binwidth=1
```

**Example:**
here is how you combine these parameters to create a step-filled density plot:


import seaborn as sns
import matplotlib.pyplot as plt

# Loading dataset
data = sns.load_dataset('penguins')

# Creating a customized histplot
sns.histplot(
    data=data,
    x='bill_length_mm',
    hue='species',      # Color by species
    element='step',     # Use step lines instead of bars
    stat='density',     # Show density instead of count
    common_norm=False   # Normalize each group separately
)

plt.show()

import unittest
import importlib
import sys
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):
        # We mock pandas read_csv to avoid network requests during testing
        self.patcher_csv = patch('pandas.read_csv')
        self.mock_read_csv = self.patcher_csv.start()
        # Create a dummy DataFrame to return
        self.mock_df = MagicMock()
        self.mock_read_csv.return_value = self.mock_df

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

    # Test if libraries are imported correctly
    def test_imports(self):
        import user_code
        _dynamic_test(
            self,
            hasattr(user_code, 'sns') and hasattr(user_code, 'plt'),
            "Libraries `seaborn` and `matplotlib.pyplot` are imported.",
            "Expected `seaborn` as `sns` and `matplotlib.pyplot` as `plt`."
        )

    # Test if histplot is called
    def test_histplot_called(self):
        with patch('seaborn.histplot') as mock_histplot:
            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_histplot.called,
                "The `sns.histplot()` function is used.",
                "Expected `sns.histplot()` to be called."
            )

    # Test if basic parameters (data, x, hue) are correct
    def test_basic_params(self):
        with patch('seaborn.histplot') as mock_histplot:
            with patch('matplotlib.pyplot.show'):
                if 'user_code' in sys.modules:
                    importlib.reload(sys.modules['user_code'])
                else:
                    import user_code
            
            if not mock_histplot.called:
                _dynamic_test(self, False, "", "Expected `sns.histplot()` to be called.")
                return

            args, kwargs = mock_histplot.call_args
            
            # Check data (we compare with the mocked df returned by read_csv)
            data_check = kwargs.get('data') == user_code.df
            x_check = kwargs.get('x') == 'bill_length_mm'
            hue_check = kwargs.get('hue') == 'island'

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

    # Test stylistic parameters (element, stat, binwidth, palette)
    def test_style_params(self):
        with patch('seaborn.histplot') as mock_histplot:
            with patch('matplotlib.pyplot.show'):
                if 'user_code' in sys.modules:
                    importlib.reload(sys.modules['user_code'])
                else:
                    import user_code
            
            if not mock_histplot.called:
                return

            args, kwargs = mock_histplot.call_args
            
            element_check = kwargs.get('element') == 'step'
            stat_check = kwargs.get('stat') == 'density'
            binwidth_check = kwargs.get('binwidth') == 1
            palette_check = kwargs.get('palette') == 'flare'

            _dynamic_test(
                self,
                element_check and stat_check and binwidth_check and palette_check,
                "The visualization style parameters (`element`, `stat`, `binwidth`, `palette`) are correct.",
                f"Expected `element='step'`, `stat='density'`, `binwidth=1`, `palette='flare'`. Got: {kwargs}"
            )

    # Test if plt.show() is used
    def test_show_used(self):
        with patch('seaborn.histplot'):
            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.

Visualizing Histograms

Customizing the Histogram

1. Changing the Statistics (`stat`)

2. Visual Style (`element`)

3. Bin Width (`binwidth`)

解答

Visualizing Histograms

Customizing the Histogram

1. Changing the Statistics (stat)

2. Visual Style (element)

3. Bin Width (binwidth)

解答

1. Changing the Statistics (`stat`)

2. Visual Style (`element`)

3. Bin Width (`binwidth`)