heatmap

Plot a heatmap of any pandas DataFrame with optional axis labels, annotations, and custom color-scaling.

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📥 Arguments

Name	Type	Required	Description
data	pd.DataFrame	✅	Pandas DataFrame containing the matrix to visualize; index and columns are used as y- and x-labels by default.
x_labels	List[str]	❌	Labels for columns (overrides DataFrame columns).
y_labels	List[str]	❌	Labels for rows (overrides DataFrame index).
x_category	str	❌	Category for x-axis labels (overrides DataFrame columns).
y_category	str	❌	Category for y-axis labels (overrides DataFrame index).
figsize	tuple	❌	Figure size (width, height), e.g., (8, 6).
cmap	str	❌	Name of matplotlib colormap, e.g., ‘viridis’.
vmin	float	❌	Minimum data value for color scaling.
vmax	float	❌	Maximum data value for color scaling.
annot	bool	❌	If True, overlay numeric annotations on each cell.
fmt	str	❌	Format string for annotations, e.g. ‘.2f’ or ‘d’.
cbar	bool	❌	Whether to display the colorbar.
title	str	❌	Title of the heatmap.
save	str	❌	Base filename to save PNG and PDF (without extension).
cbar_title	str	❌	Title for the colorbar.
ax	matplotlib.axes.Axes	❌	Matplotlib Axes object to plot on. If None, a new figure is created.

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📦 Example Output

Click to show example code

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from swizz import plot

# 1) Create a toy DataFrame (5 rows × 4 columns)
rows = [f"Sample {i}" for i in range(1, 6)]
cols = [f"Feature {j}" for j in range(1, 5)]
data = pd.DataFrame(
    np.random.rand(5, 4),
    index=rows,
    columns=cols
)

# 2) Plot with annotations, custom colormap, and save output
fig, ax = plot(
    "heatmap",
    data=data,
    figsize=(7, 5),
    cmap="Oranges",
    vmin=0.0,
    vmax=1.0,
    annot=False,
    fmt=".2f",
    cbar=True,
    title="Toy DataFrame Heatmap",
    save="dataframe_heatmap_example"
)

# 3) Show the figure
plt.show()