Q&A 25 How do you visualize smooth distributions using density plots?

25.1 Explanation

A density plot estimates the probability distribution of a numeric variable using a smoothed curve. Unlike histograms, it doesn’t rely on fixed bins and provides a clearer sense of:

• Skewness or symmetry
• Peaks (modes) and valleys
• Overlapping distributions (when grouped)

Density plots are especially useful for comparing multiple groups with minimal visual clutter.

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25.2 Python Code

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Load dataset
df = pd.read_csv("data/iris.csv")

# Plot density of sepal length
plt.figure(figsize=(6, 4))
sns.kdeplot(df["sepal_length"], fill=True, color="purple", alpha=0.5)
plt.title("Density Plot of Sepal Length")
plt.xlabel("Sepal Length (cm)")
plt.ylabel("Density")
plt.tight_layout()
plt.show()

25.3 R Code

library(ggplot2)
library(readr)

# Load dataset
df <- read_csv("data/iris.csv", show_col_types = FALSE)

# Plot density
ggplot(df, aes(x = sepal_length)) +
  geom_density(fill = "purple", alpha = 0.5) +
  labs(title = "Density Plot of Sepal Length",
       x = "Sepal Length (cm)", y = "Density") +
  theme_minimal()