Introduction to Matplotlib for Beginners

Learn Matplotlib step by step with outputs

🧠 What is Matplotlib?

Matplotlibis a popular Python library used for data visualization. It helps you create static, animated, and interactive plots easily. It’s especially useful for exploring data, reporting insights, and building dashboards.

🎯 Key Features of Matplotlib

• Wide range of plotting options: Matplotlib supports a vast array of plotting types, from simple line plots to complex 3D visualizations.
• Customization: Matplotlib allows for extensive customization of plots, including labels, titles, colors, and styles.
• Line, bar, scatter, histogram, and many other plots
• Customizable plots (labels, legends, colors, styles)
• Supports multiple backends (notebook, GUI, PNG, etc.)
• Works well with NumPy and Pandas

1. Installing Matplotlib

You can install Matplotlib using pip:

pip install matplotlib

Example

import matplotlib.pyplot as plt

# Sample data for the plot
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

# Creating the line plot
plt.plot(x, y)

# Adding a title and labels
plt.title("Simple Line Plot")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")

# Displaying the plot
plt.show()

Output:

Explanation

• pyplot is a module in Matplotlib that provides a MATLAB-like interface.
• plot() draws a line graph.
• title(), xlabel(), and ylabel() add labels to the plot.
• show() displays the plot in a new window or notebook output.

2. Customizing Line Styles and Colors

You can make your plots more informative and visually appealing by customizing the line style, color, and marker.

Example 1: Change Line Color

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

plt.plot(x, y, color='green')  # Line color
plt.title("Line Color Example")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.show()

Output:

Try This Code Yourself & See the Result

Change Line Style :

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [1, 4, 9, 16, 25]

plt.plot(x, y, linestyle='--')  # Dashed line
plt.title("Dashed Line Example")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.show()

Add Markers to Points

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [5, 7, 6, 8, 7]

# Circle marker on points
plt.plot(x, y, marker='o')  
plt.title("Markers Example") 
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.show()

3. Customize Multiple Properties

You can combine style, color, and marker in one line using short format:

import matplotlib.pyplot as plt

x = [0, 1, 2, 3, 4]
y = [0, 1, 4, 9, 16]

plt.plot(x, y, 'r--o')  # red dashed line with circle markers
plt.title("Combined Style Example")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.show()

4. Creating a Histogram

A histogram shows the distribution of a dataset. It divides the data into bins (ranges) and counts how many values fall into each bin.

🧠 What is a Histogram?

• Imagine you have a list of students' scores: data = [45, 50, 50, 55, 55, 55, 60, 60, 65, 65, 65, 65, 70, 70, 75]
• A histogram will group similar scores together into ranges (called bins), like:
• 40-49
• 50-59
• 60-69
• 70-79
• Then it will count how many scores fall in each range and display that count as a bar.

Example: Basic Histogram

    
import matplotlib.pyplot as plt

# Sample data
data = [1, 2, 2, 3, 3, 3, 4, 4, 5, 5, 5, 5, 6, 6, 7]

# Create a histogram with 5 bins
plt.hist(data, bins=5)

# Add title and axis labels
plt.title("Histogram")
plt.xlabel("Value Range")
plt.ylabel("Frequency")

# Show the plot
plt.show()
    

• 🔍 What does this code do?
  • plt.hist(data, bins=5) → Tells Python to group the data into 5 ranges (bins) and count how many values fall in each.
  • plt.title("Histogram") → Adds a title to your chart.
  • plt.xlabel("Value Range") → Labels the X-axis (the horizontal line) with the value ranges.
  • plt.ylabel("Frequency") → Labels the Y-axis (the vertical line) with how many times the values occur.

• 🖼️ What will the graph look like?
  • The x-axis shows value ranges like 1–2, 3–4, etc.
  • The y-axis shows how many values were in each range.
  • Each bar height tells you how common values in that range are.

📌 Notes:

• data is a list of numbers.
• bins=5 divides the data into 5 value ranges (or "bins").
• The y-axis shows how many data points fall into each bin.

Try This Code Yourself & See the Result

import matplotlib.pyplot as plt

# Example: Test scores of students
scores = [55, 60, 61, 65, 65, 66, 68, 70, 72, 72, 73, 75, 78, 80, 82, 85, 85, 87, 90, 92]

# Create histogram with 6 bins
plt.hist(scores, bins=6, color='skyblue', edgecolor='black')

# Add title and labels
plt.title("Distribution of Student Scores")
plt.xlabel("Score Range")
plt.ylabel("Number of Students")

# Show the plot
plt.show()

5. Creating a Bar Plot

A bar plot is used to compare different categories or groups by showing rectangular bars with lengths proportional to the values they represent.

• 🧠 What is a Bar Plot?
  • Imagine you want to compare how many fruits of each type you have:
  • Apple: 5
  • Banana: 7
  • Cherry: 3
  • Date: 8
  • Elderberry: 4

A bar plot will create one bar for each fruit, and the height of the bar shows how many you have.

Example: Basic Bar Plot

import matplotlib.pyplot as plt

# Categories and their quantities
categories = ['Apple', 'Banana', 'Cherry', 'Date', 'Elderberry']
values = [5, 7, 3, 8, 4]

# Create a bar plot
plt.bar(categories, values)

# Add title and axis labels
plt.title("Simple Bar Plot")
plt.xlabel("Fruits")
plt.ylabel("Quantity")

# Show the plot
plt.show()

• 🔍 What does this code do?
  • plt.bar(categories, values) → Draws vertical bars for each fruit with heights based on their quantities.
  • plt.title("Simple Bar Plot") → Adds a title to the chart.
  • plt.xlabel("Fruits") → Labels the horizontal axis with category names.
  • plt.ylabel("Quantity") → Labels the vertical axis with numbers.
• 🖼️ What will the graph look like?
  • The x-axis shows the names of the fruits.
  • The y-axis shows how many of each fruit you have.
  • Each bar’s height visually represents the quantity for that fruit.
• 📌 Notes:
  • Bar plots are great for comparing different groups or categories side by side.
  • The categories can be anything — fruits, countries, product types, etc.
  • You can change the color or orientation to customize the plot further (we’ll see that later).

Try This Code Yourself & See the Result

import matplotlib.pyplot as plt

# Example: Number of students in different classes
classes = ['Class A', 'Class B', 'Class C', 'Class D']
students = [30, 45, 20, 35]

plt.bar(classes, students, color='orange')
plt.title("Number of Students per Class")
plt.xlabel("Classes")
plt.ylabel("Number of Students")
plt.show()

Quiz Time

Matplotlib Practice Questions

• 🎨 Matplotlib Practice Questions (Beginner Level)
  • Simple Line Plot
    Plot x = [0, 1, 2, 3, 4] and y = [0, 1, 4, 9, 16]. Add labels and a title.
    📌 Try changing the line color to red.
  • Multiple Lines in One Plot
    Plot two lines on the same chart:
    Line 1: x = [1, 2, 3], y = [1, 4, 9]
    Line 2: x = [1, 2, 3], y = [2, 3, 5]
    Add a legend for both lines.
  • Histogram of Random Data
    Use the following data: [5, 7, 7, 8, 9, 10, 10, 10, 11, 12]
    Create a histogram with 4 bins and a light blue color.
  • Bar Plot of Favorite Fruits
    Categories = ["Apple", "Banana", "Mango", "Orange"]
Values = [15, 25, 10, 20]
    Use different colors and add title/axis labels.
  • Custom Line Styles
    Plot y = x² for x in range(1, 6)
    Change the line style to dashed and add markers to each point.
  • Grid Lines and Styling
    Plot any line plot and add gridlines using plt.grid(True)
    Try different line colors and widths.
  • Vertical vs Horizontal Bar Chart
    Create a vertical bar chart with student names and marks.
    Then, create the same chart as a horizontal bar chart using plt.barh().
  • Pie Chart of Activities
    Create a pie chart showing your daily routine: Sleep, Study, Play, Eat
    Add percentage labels and use the explode parameter for fun effect.
  • Bar Plot with Edge Color
    Reuse a bar plot and make the bars green with black edges using edgecolor='black'.
  • Line Plot with Annotations
    Create a simple line plot and add a text label on one of the points using plt.text().

💡 Need help? See the GitHub Page For AnswersGitHub.

Tip: Try running these codes in your Python editor or Google Colab.