Q&A 22 How do you visualize the frequency of categorical variables?

22.1 Explanation

A bar plot displays the frequency (or proportion) of each category. It’s the most straightforward way to understand how data is distributed across different groups or labels.

Use bar plots to: - Explore class imbalance - Profile survey responses or demographic data - Prepare categorical variables for modeling or encoding

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22.2 Python Code

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

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

# Fix: Assign 'species' to both x and hue, set legend=False
plt.figure(figsize=(5, 4))
sns.countplot(data=df, x="species", hue="species", palette="viridis", legend=False)
plt.title("Count of Each Iris Species")
plt.xlabel("Species")
plt.ylabel("Count")
plt.tight_layout()
plt.show()

22.3 R Code

library(ggplot2)
library(readr)

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

# Plot species count
ggplot(df, aes(x = species)) +
  geom_bar(fill = "skyblue") +
  labs(title = "Count of Each Iris Species",
       x = "Species", y = "Count") +
  theme_minimal()