Matplotlib Basics (Data Visualization)

Lesson 26/35 | Study Time: 60 Min

Course: Python Essentials Course Online | Start Learning Today

Numbers alone rarely tell the full story. Visualizing data helps you spot patterns, understand distributions, identify outliers, and communicate findings clearly — all critical steps in AI and data science.

Matplotlib is Python's most widely used data visualization library. It gives you full control over charts and plots, from simple line graphs to complex multi-panel figures.

Installing and Importing Matplotlib

Matplotlib comes pre-installed with Anaconda. To install manually:

Import the plotting module using the standard alias:

The Basic Plot Structure

Every Matplotlib chart follows the same simple pattern — create data, plot it, add labels, and display it.

Line Chart

A line chart is used to show trends over time or a continuous sequence of values. It is one of the most common charts in data analysis.

1. marker="o" adds a dot at each data point.

2. grid(True) adds a background grid for readability.

3. linewidth controls the thickness of the line.

Bar Chart

A bar chart compares values across categories. It is ideal for comparing groups, scores, or frequencies side by side.

For a horizontal bar chart, replace plt.bar() with plt.barh().

Histogram

A histogram shows the distribution of a dataset, how frequently values fall within certain ranges. It is widely used in AI to understand data spread and detect outliers.

1. bins controls how many bars appear.

2. edgecolor adds borders between bars for clarity.

Scatter Plot

A scatter plot displays the relationship between two numerical variables. It is heavily used in AI to visualize correlations and spot clusters in data.

Pie Chart

A pie chart shows proportions of a whole. Use it when you want to display percentage breakdowns of categories.

1. autopct="%1.1f%%" displays percentage values inside each slice.

2. startangle=90 rotates the chart so it starts from the top.

Customizing Charts

Matplotlib gives you full control over the appearance of your plots.

python

x = [1, 2, 3, 4, 5]

y = [10, 20, 15, 30, 25]

plt.plot(x, y,

color="red",

linestyle="--", # Dashed line

linewidth=2,

marker="s", # Square markers

markersize=8,

label="Trend Line")

plt.title("Customized Chart", fontsize=14)

plt.xlabel("X Axis", fontsize=11)

plt.ylabel("Y Axis", fontsize=11)

plt.legend() # Shows the label

plt.grid(True, linestyle=":")

plt.show()

Multiple Plots — Subplots

When you need to display several charts together, use plt.subplot() to arrange them in a grid layout.

python

x = [1, 2, 3, 4, 5]

y = [10, 20, 15, 30, 25]

plt.figure(figsize=(10, 4)) # Set overall figure size

# First chart

plt.subplot(1, 2, 1) # 1 row, 2 columns, position 1

plt.plot(x, y, color="blue")

plt.title("Line Chart")

# Second chart

plt.subplot(1, 2, 2) # 1 row, 2 columns, position 2

plt.bar(x, y, color="orange")

plt.title("Bar Chart")

plt.tight_layout() # Prevents overlapping

plt.show()

Saving a Chart

Instead of just displaying, you can save charts as image files for reports or presentations.

1. dpi controls image quality (150–300 for print quality).

2. bbox_inches="tight" removes excess whitespace around the chart.

Chart Types — When to Use Which

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1- What is Python and Why It Is Used in AI 2- Overview of Artificial Intelligence and Its Applications 3- Setting Up Your Python Environment 4- Writing and Running Your First Python Program 5- Variables and Data Types 6- Type Casting and Basic Input/Output 7- Operators (Arithmetic, Comparison, Logical) 8- Writing Clean and Readable Code 9- Conditional Statements (if, elif, else) 10- Loops (for, while) 11- Loop Control Statements (break, continue, pass) 12- Basic Problem-Solving Using Control Flow 13- Lists and List Operations 14- Tuples and Their Usage 15- Dictionaries (Key-Value Pairs) 16- Sets and Basic Operations 17- Choosing the Right Data Structure 18- Defining and Calling Functions 19- Parameters and Return Values 20- Lambda (Anonymous) Functions 21- Scope of Variables (Local vs Global) 22- Writing Modular Code 23- Introduction to Python Libraries 24- NumPy Basics (Arrays, Operations) 25- Pandas Basics (DataFrames, Data Handling) 26- Matplotlib Basics (Data Visualization) 27- Installing and Managing Packages (pip / conda) 28- Reading and Writing Files (Text, CSV) 29- Working with Datasets 30- Basic Data Cleaning Techniques 31- Error Handling (try-except) 32- What is Machine Learning (ML) 33- Types of ML (Supervised, Unsupervised) 34- Using Scikit-learn (Basic Example) 35- Simple AI Project Walkthrough (Prediction Model)