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Thursday, August 13, 2026

matplotlib

Matplotlib for Data Science

A Practical Python Tutorial with Examples and Output

Introduction to Matplotlib

Matplotlib is a Python library used to create visualizations such as line charts, bar charts, scatter plots, histograms, pie charts and many other types of graphs.

It is widely used in Data Science, Machine Learning, Scientific Computing, Statistics and data analysis. The matplotlib.pyplot interface provides convenient plotting functions, while Matplotlib also supports an object-oriented approach using Figure and Axes objects.

Student Tip: Learn Matplotlib after NumPy because NumPy arrays are commonly used as the numerical input for plotting.

1. Installing Matplotlib

Matplotlib can be installed using pip.

pip install matplotlib

After installation, import the pyplot module.

import matplotlib.pyplot as plt

Output

Matplotlib imported successfully.

2. Creating a Simple Line Plot

The plot() function is one of the most commonly used Matplotlib functions. 

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 20, 30, 40, 50]

plt.plot(x, y)
plt.show()

Output

A line graph is displayed with the values of x on the horizontal axis and y on the vertical axis.

3. Adding a Title

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 20, 30, 40, 50]

plt.plot(x, y)
plt.title("Student Performance")
plt.show()

Output

Student Performance

The graph appears with Student Performance as its title.

4. Adding X-Axis and Y-Axis Labels

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 20, 30, 40, 50]

plt.plot(x, y)

plt.title("Student Performance")
plt.xlabel("Study Hours")
plt.ylabel("Marks")

plt.show()

Output

The graph contains:

  • Title: Student Performance
  • X-axis: Study Hours
  • Y-axis: Marks

5. Adding Markers

Markers can be used to show individual data points. 

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [20, 35, 30, 50, 45]

plt.plot(x, y, marker='o')

plt.title("Marks")
plt.xlabel("Student")
plt.ylabel("Marks")

plt.show()

Output

A line graph is displayed with circular markers at every data point.

6. Changing Line Style

Matplotlib allows different line styles such as solid, dashed and dotted lines. 

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 25, 20, 40, 35]

plt.plot(x, y, linestyle='--')

plt.title("Dashed Line")
plt.show()

Output

A dashed line graph is displayed.

7. Multiple Lines in One Graph

import matplotlib.pyplot as plt

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

sales = [100, 120, 150, 170, 200]
profit = [20, 30, 35, 45, 55]

plt.plot(months, sales)
plt.plot(months, profit)

plt.title("Sales and Profit")
plt.xlabel("Month")
plt.ylabel("Amount")

plt.show()

Output

Two lines are displayed on the same graph representing Sales and Profit.

8. Adding a Legend

import matplotlib.pyplot as plt

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

sales = [100, 120, 150, 170, 200]
profit = [20, 30, 35, 45, 55]

plt.plot(months, sales, label="Sales")
plt.plot(months, profit, label="Profit")

plt.title("Sales and Profit")
plt.xlabel("Month")
plt.ylabel("Amount")

plt.legend()

plt.show()

Output

The graph displays a legend identifying the two lines as Sales and Profit.

9. Adding Grid Lines

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 20, 15, 30, 25]

plt.plot(x, y, marker='o')
plt.grid()

plt.title("Line Graph with Grid")
plt.show()

Output

Grid lines are displayed behind the graph to make the values easier to read.

10. Creating a Bar Chart

The bar() function is useful for comparing categorical values. 

import matplotlib.pyplot as plt

subjects = ["C", "Python", "Java", "DBMS"]
marks = [75, 85, 80, 70]

plt.bar(subjects, marks)

plt.title("Subject-wise Marks")
plt.xlabel("Subjects")
plt.ylabel("Marks")

plt.show()

Output

A bar chart displays the marks obtained in C, Python, Java and DBMS.

11. Horizontal Bar Chart

import matplotlib.pyplot as plt

subjects = ["C", "Python", "Java", "DBMS"]
marks = [75, 85, 80, 70]

plt.barh(subjects, marks)

plt.title("Subject-wise Marks")
plt.xlabel("Marks")
plt.ylabel("Subjects")

plt.show()

Output

A horizontal bar chart is displayed.

12. Creating a Scatter Plot

A scatter plot displays individual observations as points and is useful for studying relationships between two numerical variables. 

import matplotlib.pyplot as plt

study = [1, 2, 3, 4, 5, 6]
marks = [40, 45, 55, 65, 75, 85]

plt.scatter(study, marks)

plt.title("Study Hours vs Marks")
plt.xlabel("Study Hours")
plt.ylabel("Marks")

plt.show()

Output

A scatter plot is displayed showing the relationship between study hours and marks.

13. Creating a Histogram

A histogram shows the distribution of numerical data by grouping values into bins. 

import matplotlib.pyplot as plt

marks = [45, 50, 52, 55, 60, 62, 65, 68,
         70, 72, 75, 78, 80, 85, 90]

plt.hist(marks, bins=5)

plt.title("Distribution of Marks")
plt.xlabel("Marks")
plt.ylabel("Frequency")

plt.show()

Output

A histogram displays how frequently marks occur within different ranges.

14. Creating a Pie Chart

import matplotlib.pyplot as plt

subjects = ["C", "Python", "Java", "DBMS"]
marks = [75, 85, 80, 70]

plt.pie(marks, labels=subjects, autopct='%1.1f%%')

plt.title("Subject-wise Marks Distribution")

plt.show()

Output

A pie chart is displayed showing the percentage contribution of each subject.

15. Exploding a Pie Chart Slice

import matplotlib.pyplot as plt

subjects = ["C", "Python", "Java", "DBMS"]
marks = [75, 85, 80, 70]

explode = [0, 0.1, 0, 0]

plt.pie(
    marks,
    labels=subjects,
    explode=explode,
    autopct='%1.1f%%'
)

plt.title("Subject-wise Marks")

plt.show()

Output

The Python portion is separated slightly from the center of the pie chart.

16. Creating Subplots

Subplots allow multiple graphs to be displayed within the same figure. 

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 20, 15, 30, 25]

plt.subplot(1, 2, 1)
plt.plot(x, y)
plt.title("Line Plot")

plt.subplot(1, 2, 2)
plt.bar(x, y)
plt.title("Bar Plot")

plt.show()

Output

Two plots are displayed side by side:

  • Line plot
  • Bar plot

17. Using Figure and Axes

For more structured and reusable visualizations, Matplotlib supports the object-oriented approach using a Figure and one or more Axes. 

import matplotlib.pyplot as plt

x = [1, 2, 3, 4]
y = [10, 20, 15, 30]

fig, ax = plt.subplots()

ax.plot(x, y)
ax.set_title("Simple Plot")
ax.set_xlabel("X Values")
ax.set_ylabel("Y Values")

plt.show()

Output

A line plot is displayed with a title and axis labels.

18. Changing Figure Size

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 20, 30, 40, 50]

plt.figure(figsize=(8, 5))

plt.plot(x, y)
plt.title("Large Figure")

plt.show()

Output

The graph is displayed using a figure size of 8 × 5 inches.

19. Saving a Graph

A Matplotlib figure can be saved to an image file using savefig(). 

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 20, 30, 40, 50]

plt.plot(x, y)
plt.title("My Graph")

plt.savefig("my_graph.png")

plt.show()

Output

my_graph.png

The graph is saved as my_graph.png.

20. Practical Example — Student Performance

The following example combines several Matplotlib features to visualize student performance. 

import matplotlib.pyplot as plt

students = ["A", "B", "C", "D", "E"]

marks = [65, 72, 88, 55, 92]

plt.bar(students, marks)

plt.title("Student Performance")
plt.xlabel("Students")
plt.ylabel("Marks")

plt.ylim(0, 100)
plt.grid(axis='y')

plt.show()

Output

A bar chart is displayed showing the marks of five students. The Y-axis ranges from 0 to 100.

21. Practical Example — Sales Analysis

import matplotlib.pyplot as plt

months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun"]

sales = [120, 150, 135, 180, 210, 240]

plt.plot(
    months,
    sales,
    marker='o'
)

plt.title("Monthly Sales")
plt.xlabel("Month")
plt.ylabel("Sales")

plt.grid()
plt.show()

Output

A line chart displays the monthly sales trend from January to June.

22. Practical Example — Study Hours and Marks

import matplotlib.pyplot as plt

study_hours = [1, 2, 3, 4, 5, 6, 7]
marks = [35, 42, 50, 61, 70, 82, 90]

plt.scatter(study_hours, marks)

plt.title("Study Hours vs Marks")
plt.xlabel("Study Hours")
plt.ylabel("Marks")

plt.grid()
plt.show()

Output

A scatter plot shows the relationship between study hours and marks.

23. Important Matplotlib Plotting Functions

Function Purpose
plt.plot() Line plot
plt.bar() Vertical bar chart
plt.barh() Horizontal bar chart
plt.scatter() Scatter plot
plt.hist() Histogram
plt.pie() Pie chart
plt.subplot() Create subplot
plt.show() Display figure
plt.savefig() Save figure

24. Important Matplotlib Commands

Command Purpose
plt.title() Set graph title
plt.xlabel() Label X-axis
plt.ylabel() Label Y-axis
plt.legend() Display legend
plt.grid() Display grid
plt.xlim() Set X-axis limits
plt.ylim() Set Y-axis limits
plt.figure() Create/configure a figure
plt.show() Display the graph

25. Matplotlib with NumPy

NumPy and Matplotlib are frequently used together for numerical data analysis and visualization. 

import numpy as np
import matplotlib.pyplot as plt

x = np.arange(0, 10, 0.5)

y = x ** 2

plt.plot(x, y)

plt.title("y = x²")
plt.xlabel("x")
plt.ylabel("y")

plt.grid()
plt.show()

Output

A smooth line graph representing the mathematical function y = x² is displayed.

26. Matplotlib in Data Science

Matplotlib is useful when data scientists need to understand data visually. Graphs can reveal trends, relationships, distributions and unusual observations that may not be obvious from raw numerical values.

  • Line Plot: Useful for trends over time.
  • Bar Chart: Useful for comparing categories.
  • Scatter Plot: Useful for relationships between numerical variables.
  • Histogram: Useful for understanding distributions.
  • Pie Chart: Useful for showing proportions.

27. Figure and Axes — Basic Concept

Figure: The complete visualization window or canvas.

Axes: The area inside a figure where data is actually plotted.

Axis: The numerical or categorical scale shown along the X-axis or Y-axis.

A figure can contain one or more Axes. This is particularly useful when creating dashboards or multiple related visualizations.

28. Complete Example

import numpy as np
import matplotlib.pyplot as plt

students = np.array([
    "A", "B", "C", "D", "E"
])

marks = np.array([
    65, 72, 88, 55, 92
])

fig, ax = plt.subplots(figsize=(8, 5))

ax.bar(students, marks)

ax.set_title("Student Performance")
ax.set_xlabel("Students")
ax.set_ylabel("Marks")

ax.set_ylim(0, 100)

ax.grid(axis='y')

plt.show()

Output

A professional student-performance bar chart is displayed with:

  • Student names on the X-axis
  • Marks on the Y-axis
  • Title
  • Grid lines
  • Y-axis range from 0 to 100

Summary

In this tutorial, we learned the fundamentals of Matplotlib and how it can be used to visualize numerical and categorical data.

  • Installing Matplotlib
  • Importing matplotlib.pyplot
  • Creating line plots
  • Adding titles and labels
  • Using markers and line styles
  • Creating multiple lines
  • Adding legends and grids
  • Creating bar charts
  • Creating horizontal bar charts
  • Creating scatter plots
  • Creating histograms
  • Creating pie charts
  • Creating subplots
  • Using Figure and Axes
  • Changing figure size
  • Saving graphs
  • Using Matplotlib with NumPy
  • Visualizing student and sales data

Created by Bijan Krishna Paul

Computer Science • Python • Data Science

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