Heatmap Plot Gallery¶

The heatmap plot is used for visualizing 2D data structures as color-coded matrices with automatic cell value annotations. It's ideal for displaying relationships, patterns, and distributions across two categorical dimensions.

Heatmap plots excel at:

• Correlation Analysis: Visualize relationships between variables or metrics
• Migration Matrices: Track customer movement between segments over time
• Performance Grids: Compare metrics across categories, regions, or time periods
• Confusion Matrices: Display classification results and model performance

In [ ]:

import matplotlib.pyplot as plt
import pandas as pd

from pyretailscience.plots import heatmap

import matplotlib.pyplot as plt import pandas as pd from pyretailscience.plots import heatmap

Basic Heatmap¶

Create a simple heatmap from a DataFrame using default formatting. The DataFrame index becomes the y-axis and columns become the x-axis.

---

In [ ]:

# Create sample data for basic heatmap demonstration
# fmt: off
sample_data = pd.DataFrame(
    {
        "Q1": [125.5, 98.3, 87.2, 134.1],
        "Q2": [142.8, 108.7, 95.4, 156.3],
        "Q3": [138.2, 112.5, 89.6, 148.9],
        "Q4": [165.7, 125.4, 102.3, 178.2],
    },
    index=["North", "South", "West", "East"],
)
# fmt: on

ax = heatmap.plot(
    sample_data,
    cbar_label="Values",
    title="Basic Heatmap with Default Formatting",
    x_label="Columns",
    y_label="Rows",
)
plt.show()

# Create sample data for basic heatmap demonstration # fmt: off sample_data = pd.DataFrame( { "Q1": [125.5, 98.3, 87.2, 134.1], "Q2": [142.8, 108.7, 95.4, 156.3], "Q3": [138.2, 112.5, 89.6, 148.9], "Q4": [165.7, 125.4, 102.3, 178.2], }, index=["North", "South", "West", "East"], ) # fmt: on ax = heatmap.plot( sample_data, cbar_label="Values", title="Basic Heatmap with Default Formatting", x_label="Columns", y_label="Rows", ) plt.show()

Percentage Formatting¶

Customize the colorbar display format using the cbar_format parameter with percentage formatting.

---

In [ ]:

# Create sample data with percentage values
# fmt: off
percentage_data = pd.DataFrame(
    {
        "Week 1": [0.0245, 0.0189, 0.0312, 0.0198],
        "Week 2": [0.0278, 0.0201, 0.0334, 0.0215],
        "Week 3": [0.0256, 0.0195, 0.0298, 0.0187],
        "Week 4": [0.0289, 0.0223, 0.0356, 0.0231],
    },
    index=["Email Campaign", "Social Media", "Google Ads", "Display Ads"],
)
# fmt: on

ax = heatmap.plot(
    percentage_data,
    cbar_label="Rate",
    cbar_format="{x:.1%}",  # Format as percentage with 1 decimal place
    title="Heatmap with Percentage Formatting",
    x_label="Time Period",
    y_label="Categories",
)
plt.show()

# Create sample data with percentage values # fmt: off percentage_data = pd.DataFrame( { "Week 1": [0.0245, 0.0189, 0.0312, 0.0198], "Week 2": [0.0278, 0.0201, 0.0334, 0.0215], "Week 3": [0.0256, 0.0195, 0.0298, 0.0187], "Week 4": [0.0289, 0.0223, 0.0356, 0.0231], }, index=["Email Campaign", "Social Media", "Google Ads", "Display Ads"], ) # fmt: on ax = heatmap.plot( percentage_data, cbar_label="Rate", cbar_format="{x:.1%}", # Format as percentage with 1 decimal place title="Heatmap with Percentage Formatting", x_label="Time Period", y_label="Categories", ) plt.show()