Distribution Plots (displot, histplot)

Why distribution plots matter

Distribution plots answer:

• What values are most common?
• Is the data skewed?
• Are there outliers?
• Do groups behave differently?

diagram How histplot builds a histogram mermaid

  flowchart LR
  A["Raw values"] --> B["Split into bins
(equal-width ranges)"]
  B --> C["Count values
per bin"]
  C --> D["Draw bars
(histplot)"]
  D --> E["Optional: overlay
a smooth KDE curve"]

Values are sorted into equal-width bins, counted, then drawn as bars — with an optional KDE curve laid on top.

From the book

McKinney describes a histogram as “a kind of bar plot that gives a discretized display of value frequency” — the data is split into evenly spaced bins and each bar shows how many points fall in that bin. seaborn’s histplot can draw the histogram and a smooth density curve in the same call, which is handy for spotting bimodal (two-peak) data.

histplot (single plot)

histplot

import seaborn as sns
import matplotlib.pyplot as plt
 
values = [55, 60, 62, 63, 65, 67, 70, 72, 76, 80, 81, 85, 90, 92, 95]
 
plt.figure(figsize=(7, 4))
sns.histplot(values, bins=8, kde=False)
plt.title("Histogram")
plt.xlabel("Value")
plt.ylabel("Count")
plt.tight_layout()
plt.show()

Add KDE on top

histplot + kde

import seaborn as sns
import matplotlib.pyplot as plt
 
plt.figure(figsize=(7, 4))
sns.histplot(values, bins=8, kde=True)
plt.title("Histogram + KDE")
plt.tight_layout()
plt.show()

Visualize it

Bars count how many points land in each bin. The smooth curve is a KDE — an estimate of the underlying probability density that doesn’t depend on where the bin edges happen to fall:

sketch Histogram bars with a KDE curve p5.js Pause Restart

Amber bars are the binned counts; the blue curve is the smooth density estimate drawn over them.

displot (figure-level)

displot can create more complex plots and handles facets.

displot

import seaborn as sns
 
# Seaborn includes sample datasets
 
tips = sns.load_dataset("tips")
 
sns.displot(data=tips, x="total_bill", bins=20, kde=True)

Compare distributions by group

Compare groups

import seaborn as sns
import matplotlib.pyplot as plt
 
tips = sns.load_dataset("tips")
 
plt.figure(figsize=(7, 4))
sns.histplot(data=tips, x="total_bill", hue="sex", bins=20, kde=True, element="step")
plt.title("Total bill distribution by sex")
plt.tight_layout()
plt.show()

Tips

• Use the same bin settings when comparing groups.
• KDE is a smooth estimate; it can hide small details.
• For heavy outliers, consider log scaling.

Next

Continue to Kernel Density Estimation (KDE) to look at the smooth curve on its own, and learn how bandwidth controls its shape.

🧪 Try It Yourself

Exercise 1 – A Basic Histogram

pch.exerciseTag pch.exerciseTitle

Exercise 2 – Add a KDE Curve

pch.exerciseTag pch.exerciseTitle

Exercise 3 – Compare Groups by Column

pch.exerciseTag pch.exerciseTitle