How to Make Histograms with Density Plots with Seaborn histplot?

Creating Histograms with Density Plots Using Seaborn’s histplot() in Python

Visualizing the underlying distribution of numeric data is essential for exploratory analysis. Using Seaborn's histplot() with the kde option allows you to combine histograms (for frequency) with smooth density curves (for probability estimations) in a single, informative plot.

Understanding Histogram and Density Plots

Histograms group data into bins and show how often data points fall into each bin. Density plots (or KDE curves) estimate the probability density function, giving a smooth continuous representation of the data. By overlaying both, we gain a richer understanding of the distribution’s shape and variation.

Creating a Basic Histogram with Density

import seaborn as sns
import matplotlib.pyplot as plt

# Load sample dataset
tips = sns.load_dataset("tips")

# Plot histogram with KDE
sns.histplot(data=tips, x="total_bill", kde=True)
plt.title("Histogram with Density Curve for Total Bill")
plt.xlabel("Total Bill")
plt.ylabel("Frequency")
plt.show()

This generates a histogram showing how often bills fall into specific ranges, overlaid with a KDE curve that highlights the smooth distribution.

Customizing Bins and Density Smoothing

Fine-tune your plot using:

• bins: sets the number of histogram bins (e.g., bins=20)
• binwidth: sets the width of each bin
• kde_kws: controls KDE appearance (e.g., bw_adjust)

sns.histplot(data=tips, x="total_bill", bins=20, kde=True,
             kde_kws={"bw_adjust":0.5}, color="skyblue", edgecolor="black")
plt.title("Refined Histogram with Density Smoothing")
plt.show()

This plot uses 20 bins, tighter KDE smoothing for detail, and a black edge for visual contrast.

Styling and Layout Options

Enhance readability by applying:

• palette or color: customize fill color
• alpha: adjust transparency
• stat: set to "density" to plot proportion instead of counts

sns.histplot(data=tips, x="tip", stat="density", kde=True,
             color="coral", alpha=0.6)
plt.title("Tip Distribution as Density Plot")
plt.show()

This version shows tip density rather than counts, with transparency for overlapping visuals.

Overlaying Multiple Distributions

Compare distributions across categorical groups using hue:

sns.histplot(data=tips, x="total_bill", hue="time", kde=True,
             element="step", common_norm=False, palette="Set1")
plt.title("Total Bill Distribution by Meal Time")
plt.show()

Here, dinner and lunch distributions are shown separately, each with its own KDE curve. The element="step" style and common_norm=False prevent normalization across both groups.

When to Use Histplot with KDE

This combined plot is ideal when you want to:

• Understand frequency distribution and shape
• Visualize skewness and multimodal patterns
• Compare spreads across groups

Use this approach early in your analysis to guide further modeling or hypothesis exploration.

Conclusion

Seaborn’s histplot() with kde=True offers an efficient way to explore data distributions in Python. By tuning binning, smoothing, styling, and grouping, you can build clear, insightful plots that convey both frequency and density—ideal for thorough exploratory data analysis.