Session 21 · Phase 3: Python

Matplotlib & Seaborn Visualization

Turn DataFrames into charts — line, bar, histogram, and scatter with Matplotlib, then statistical plots with Seaborn.

⏱ ~2 hrs 📚 Core content 🎯 High priority

Learning Objectives

Create line, bar, histogram, and scatter plots with Matplotlib.
Customize plots with titles, axis labels, and legends.
Explain the difference between a bar chart and a histogram.
Explain what Seaborn is and how it differs from Matplotlib.
Use Seaborn for countplot, boxplot, and heatmap.

1. What Matplotlib Is

Matplotlib is Python's foundational data-visualization library. It gives you low-level control to build almost any chart, and Seaborn (and Pandas plotting) are built on top of it.

import matplotlib.pyplot as plt

2. The Four Core Plots

Line plot — trend over time

x = [1, 2, 3, 4, 5]
y = [10, 20, 15, 30, 25]
plt.plot(x, y)
plt.show()

Bar chart — compare categories

plt.bar(["Pen", "Notebook", "Eraser"], [25, 120, 10])
plt.show()

Histogram — distribution of a numeric variable

plt.hist(sales_data, bins=10)
plt.show()

Scatter plot — relationship between two numbers

plt.scatter(price, units_sold)
plt.show()

💡

Chart choice recap: trend → line; compare categories → bar; distribution → histogram; relationship between two numbers → scatter.

3. Customizing Plots

plt.plot(x, y)
plt.title("Monthly Sales")       # title
plt.xlabel("Month")              # x-axis label
plt.ylabel("Sales (₹)")          # y-axis label
plt.legend(["Sales"])            # legend
plt.show()

Clear titles, axis labels, and legends turn a bare chart into something a stakeholder can read.

4. Seaborn — Statistical Plots

Seaborn is a library built on top of Matplotlib. It works directly with Pandas DataFrames and produces attractive statistical plots with far less code.

import seaborn as sns

# Countplot — frequency of categories
sns.countplot(x="region", data=df)

# Boxplot — distribution + outliers by category
sns.boxplot(x="region", y="sales", data=df)

# Heatmap — correlation matrix
sns.heatmap(df.corr(), annot=True)

	Matplotlib	Seaborn
Level	low-level, full control	high-level, built on Matplotlib
Data	lists/arrays	DataFrames directly
Look	plain by default	styled, statistical

📋 Stable content — Reviewed: August 2026

5. Interview Questions (with Model Answers)

The visualization questions interviewers ask. Self-test before revealing.

IQ1. What is Matplotlib, and why is it used?

Model answer: "Matplotlib is Python's foundational data-visualization library. It gives low-level control to build almost any chart, and it's the foundation Seaborn and Pandas plotting are built on."

IQ2. How do you create a basic line plot?

Model answer: "With plt.plot(x, y) followed by plt.show(). I can add labels and a title with plt.xlabel(), plt.ylabel(), and plt.title()."

IQ3. How do you customize a plot?

Model answer: "With plt.title() for the title, plt.xlabel() and plt.ylabel() for axis labels, and plt.legend() for the legend. These make a chart readable."

IQ4. What's the difference between a bar chart and a histogram?

Model answer: "A bar chart compares counts/values across distinct categories; a histogram shows the distribution of one numeric variable by binning it into ranges. Categories vs. continuous data."

IQ5. What's the difference between plt.plot and plt.scatter?

Model answer: "plot draws connected points (a line), best for trends over an ordered axis; scatter draws individual unconnected points, best for seeing the relationship between two numeric variables."

IQ6. What is Seaborn, and how does it differ from Matplotlib?

Model answer: "Seaborn is built on top of Matplotlib. It works directly with DataFrames and produces attractive statistical plots with less code, while Matplotlib is lower-level with more control."

IQ7. Which chart would you use to show a distribution of values?

Model answer: "A histogram — it bins a numeric variable into ranges and shows frequency. For comparing distributions across groups, I'd use a Seaborn boxplot."

IQ8. What is a histogram used for?

Model answer: "To visualize the frequency distribution of a numeric variable — how many values fall into each range/bin. It's a quick way to spot skew, outliers, and the overall shape of the data."

IQ9. How do you create a correlation heatmap in Seaborn?

Model answer: "sns.heatmap(df.corr(), annot=True) — it computes the correlation matrix and displays it as a colored grid, making relationships between numeric columns obvious."

Hands-On Project: Visualize a Sales Dataset

Using a sales DataFrame (region, product, sales, units), create the following plots.

Steps

A line plot of monthly sales.
A bar chart of sales by region.
A histogram of the sales values.
A scatter plot of price vs units sold.
A Seaborn countplot of the region column.

View Solution / Walkthrough

import matplotlib.pyplot as plt
import seaborn as sns

# 1. Line plot
plt.plot(months, monthly_sales)
plt.title("Monthly Sales"); plt.show()

# 2. Bar chart by region
plt.bar(regions, sales_by_region)
plt.show()

# 3. Histogram
plt.hist(df["sales"], bins=10)
plt.show()

# 4. Scatter plot
plt.scatter(df["price"], df["units"])
plt.show()

# 5. Seaborn countplot
sns.countplot(x="region", data=df)
plt.show()

Key Takeaways

Matplotlib = low-level control; line/bar/histogram/scatter are the core four.

Always add title, axis labels, and a legend.

Bar chart = categories; histogram = distribution of one numeric variable.

Seaborn builds on Matplotlib, works with DataFrames, gives statistical plots.

countplot/boxplot/heatmap are the workhorse Seaborn charts.

Learning Objectives

1. What Matplotlib Is

2. The Four Core Plots

Line plot — trend over time

Bar chart — compare categories

Histogram — distribution of a numeric variable

Scatter plot — relationship between two numbers

3. Customizing Plots

4. Seaborn — Statistical Plots

5. Interview Questions (with Model Answers)

Hands-On Project: Visualize a Sales Dataset

Steps

Key Takeaways

Objective Questions — Test Your Understanding