Summary  
This chapter demonstrates how to visualize numerical data distributions using histograms and boxplots to identify clusters, density patterns, and outliers.  

General domain of usage  
Financial risk analysis (loan portfolio management)

Understanding the patterns and spread of risk within a loan portfolio is essential for effective portfolio management and meeting regulatory requirements. Visualizing risk distributions helps you quickly spot concentrations of high-risk loans, identify trends, and communicate findings clearly to stakeholders or auditors. By using visual tools, you can make informed decisions about risk mitigation strategies and ensure compliance with oversight bodies.

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

# Sample loan portfolio DataFrame
data = {
    "loan_id": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    "risk_score": [320, 450, 500, 610, 700, 720, 780, 810, 850, 900]
}
df = pd.DataFrame(data)

# Plotting histogram of risk scores
plt.figure(figsize=(8, 5))
sns.histplot(df["risk_score"], bins=7, kde=True, color="skyblue")
plt.title("Distribution of Risk Scores in Loan Portfolio")
plt.xlabel("Risk Score")
plt.ylabel("Number of Loans")
plt.show()

When you interpret **histograms**, you are looking at how `risk scores` are distributed across all loans. A histogram shows whether most loans cluster around a certain risk level or if there are several peaks indicating subgroups with different risk profiles. **Boxplots**, on the other hand, provide a quick summary of the range, median, and spread of data. They also highlight **outliers**—loans whose `interest rates` or `risk scores` are unusually high or low compared to the rest of the portfolio. By examining these visualizations, you can prioritize which loans may need further scrutiny or action.

# Sample DataFrame with interest rates
df["interest_rate"] = [5.2, 6.0, 5.8, 7.1, 6.5, 5.5, 8.2, 5.9, 6.7, 12.0]

# Creating a boxplot to identify outliers in interest rates
plt.figure(figsize=(6, 4))
sns.boxplot(x=df["interest_rate"], color="lightgreen")
plt.title("Boxplot of Interest Rates")
plt.xlabel("Interest Rate (%)")
plt.show()

What does a histogram of risk scores reveal about a loan portfolio?

How can a boxplot help identify risky loans?

Which library is used for advanced statistical visualizations in Python?

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Visualizing Risk Distribution

1. What does a histogram of risk scores reveal about a loan portfolio?

2. How can a boxplot help identify risky loans?

3. Which library is used for advanced statistical visualizations in Python?