Summary  
This chapter explains how to generate and customize heatmaps by converting 2D data into a color‐coded matrix, with options for annotating cell values, selecting color maps, and formatting numeric output.  

General domain of usage  
Correlation matrix visualization

A `heatmap` is a plot where data values are represented as colors in a matrix.

This is the standard way to visualize **correlation matrices** (how variables relate to each other) or **time series grids** (e.g., months vs. years).

**Important:** unlike `scatterplot` or `barplot` which take long lists of data, `heatmap` typically requires your data to be in a **matrix (2D)** format. This is often achieved using `df.pivot_table()` before plotting.

## Key Parameters

* **`annot=True`**: writes the data value in each cell;
* **`cmap`**: the color map (gradient) to use. Common choices: `'viridis'`, `'coolwarm'`, `'magma'`;
* **`fmt`**: string formatting code to control how numbers look;
* `'d'`: integers (no decimals);
* `'.2f'`: floats with 2 decimal places;
* `'g'`: general format (compact);
* `linewidths` / `linecolor`: adds distinct borders between cells.



## Example

Here is a heatmap showing the correlation between numerical variables in the `tips` dataset.

import seaborn as sns
import matplotlib.pyplot as plt

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

# 1. Calculate Correlation Matrix (creates the grid)
corr_matrix = df.corr(numeric_only=True)

# 2. Plot Heatmap
sns.heatmap(
    data=corr_matrix,
    annot=True,     # Show numbers
    fmt='.2f',      # 2 decimal places
    cmap='coolwarm',# Red-Blue gradient
    linewidths=1    # Separation lines
)

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()
        
        # Create a real DataFrame structure that can be pivoted
        self.mock_df = pd.DataFrame({
            'year': [1949, 1949, 1950, 1950],
            'month': ['Jan', 'Feb', 'Jan', 'Feb'],
            'passengers': [112, 118, 115, 126]
        })
        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.heatmap'), 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] == 'ticks'
            
            rc_params = args[1] if len(args) > 1 else kwargs.get('rc', {})
            facecolor_correct = rc_params.get('figure.facecolor') == 'seagreen'

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

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

            args, kwargs = mock_heat.call_args
            
            # 1. Check if the first argument is indeed the pivoted dataframe
            # Since user_code pivots the mock_df, we check if the passed object equals the pivoted mock_df
            expected_df = user_code.upd_df
            
            # The data is usually passed as the first positional argument
            passed_data = args[0] if args else kwargs.get('data')
            
            # Compare shapes or instance identity
            data_check = passed_data is expected_df

            # 2. Check styling parameters
            cmap_check = kwargs.get('cmap') == 'viridis'
            annot_check = kwargs.get('annot') is True
            fmt_check = kwargs.get('fmt') == '0.99g'
            linecolor_check = kwargs.get('linecolor') == 'plum'

            _dynamic_test(
                self,
                all([data_check, cmap_check, annot_check, fmt_check, linecolor_check]),
                "The `heatmap` parameters (`cmap`, `annot`, `fmt`, `linecolor`) are configured correctly.",
                f"Expected `cmap='viridis'`, `annot=True`, `fmt='0.99g'`, `linecolor='plum'`. Got: {kwargs}"
            )

    # Test show
    def test_show_used(self):
        with patch('seaborn.heatmap'):
            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 Matrix Data

Key Parameters

Example

解答