Matplotlib Practice Online: Free Exercises

📝 Check out a comprehensive set of Matplotlib exercises and practice with our Online Matplotlib Compiler. This library is mainly used for data visualization in Python. From this tutorial, you will get some idea about – how to analyze trends, build machine learning models, and explore datasets.

What is Matplotlib?

Matplotlib is famous for its data visualizing features. It comes as a package for Python. It includes several plot types such as bar charts, scatter plots, histograms, pie, and various other. This is how, it helps us in analyzing data, in machine learning, and helping us understand trends and patterns easily.

We have included many practical exercises in this tutorial to demonstrate the usage of Matplotlib. To compile and run them, you will need to install Python 3 and Matplotlib package.

However, with an online Matplotlib compiler, you can write, test, and visualize data in seconds. No setup. No delays. Just pure coding. Let’s dive in!

Understanding How Matplotlib Plots Work

Every Matplotlib visualization consists of these core components. It is important to know these before start to dive into the exercises.

Types of Plots in Matplotlib

The following few are a basic and commonly used plot type:

• Bar Charts (Great for comparisons)
• Scatter Plots (For correlations)
• Histograms (Data distribution)
• Pie Charts (Proportions & percentages)

Why Use an Online Matplotlib Compiler?

Matplotlib is the #1 Python library for data visualization, used in analytics, machine learning, and research. But setting it up locally can be tedious.

With our free online Matplotlib compiler, you can:
✔ Plot instantly – No installations or setup.
✔ Practice anywhere – Works on all devices.
✔ Access key libraries – Matplotlib, pandas, and NumPy pre-installed.
✔ Learn faster – Experiment without breaking your local environment.

Ideal for: Data scientists, students, and developers who need quick, hassle-free plotting.

Practical Matplotlib Exercises (Try Them Live!)

Let’s now learn how to use Matplotlib in Python. Please ensure either you have opened our online matplotlib compiler or press the “run code” button in the top left of the coding snippets.

Exercise 1: Your First Plot – Website Traffic Trend

The purpose of this example is to make you aware of the Matplotlib core components. We’ll create a simple line plot showing monthly website visitors. Here’s what each element does:

import matplotlib.pyplot as plt  # The visualization engine

# Sample data
months = ["Jan", "Feb", "Mar", "Apr"]  # X-axis values
visitors = [1200, 1800, 2100, 1600]  # Y-axis values

# Creating the plot
plt.plot(months, visitors, 
        color="blue",  # Line color
        marker="o",    # Data point markers
        linestyle="--") # Dashed line

# Adding labels and title
plt.title("Monthly Website Visitors")  # Chart title
plt.xlabel("Month")                   # X-axis label
plt.ylabel("Visitors")                # Y-axis label

# Enhancing readability
plt.grid(True)  # Show grid lines
plt.show()      # Display the plot

👉 Key Learning Points

The code created the following line plot.

1. plt.plot() – Creates the basic line chart
2. Customization options (color, marker, linestyle)
3. Essential labels for clarity
4. Grid lines for better data interpretation

Try modifying:

• Remove linestyle to get a solid line
• Change color="green" to see immediate effect
• Try different markers: "s" (square), "^" (triangle)

Exercise 2: Product Comparison – Bar Chart

With this exercise, you’ll learn when to use the bar chart. It is perfect for comparing discrete categories. We’ll visualize quarterly product sales:

Example: Bar Chart

import matplotlib.pyplot as plt 
products = ["Laptops", "Phones", "Tablets"]  # Categories
sales = [200, 350, 150]                      # Values

plt.bar(products, sales, 
       color=["#4CAF50", "#2196F3", "#FF5722"],  # Custom colors
       width=0.6)                                # Bar width

plt.title("Q1 Product Sales")
plt.ylabel("Units Sold (Thousands)")
plt.ylim(0, 400)  # Setting Y-axis range
plt.show()

👉 What’s different here?

You can see that three bars are formed in our bar chart, each reflecting a product.

• plt.bar() instead of plot() for categorical data
• Custom color palette using hex codes
• ylim() to control axis range
• width parameter adjusting bar thickness

Pro Tip: Add this line before show() to display exact values on bars:

for i, v in enumerate(sales):
    plt.text(i, v+10, str(v), ha='center')

Exercise 3: Data Relationships – Scatter Plot

This exercise show cases a basic correlation between two variables. Let’s examine ad spend vs. revenue:

import matplotlib.pyplot as plt 
ad_spend = [100, 200, 300, 400]  # X-axis
revenue = [150, 350, 420, 500]    # Y-axis

plt.scatter(ad_spend, revenue,
           color="red",
           s=100)  # Marker size

plt.title("Ad Spend vs. Revenue")
plt.xlabel("Advertising Budget ($)")
plt.ylabel("Revenue Generated ($)")
plt.show()

👉 Analysis Techniques

1. Positive correlation? Points moving upwards right
2. Outliers? Points far from the general cluster
3. No correlation? Randomly scattered points

Enhancement: Add a trendline with:

import numpy as np
z = np.polyfit(ad_spend, revenue, 1)
p = np.poly1d(z)
plt.plot(ad_spend, p(ad_spend), "b--")

Exercise 4: Data Distribution – Histogram

They show how numerical data is distributed – crucial for statistics and machine learning pre-processing.

import matplotlib.pyplot as plt 
ages = [22, 45, 30, 34, 28, 40, 35, 29, 33, 27, 31, 38]

plt.hist(ages, 
        bins=5,            # Number of bars
        color="purple",
        edgecolor="black", # Bar borders
        alpha=0.7)         # Transparency

plt.title("Customer Age Distribution")
plt.xlabel("Age Groups")
plt.ylabel("Number of Customers")
plt.show()

👉 Interpreting OUR Histogram Results

i. Skewed Left/Right? Our data shows:

• A short “tail” on the right (ages 44.4-50 has just 1 customer)
• Most data clusters on the left side (ages 22-38.8)

This means:

• Our customer base is younger-skewed
• The 45-year-old is an outlier compared to others

ii. Normal Distribution? Our plot is NOT perfectly normal because:

• No clear bell curve shape
• Peaks at 27.6-33.2 range (5 customers)
• Missing middle-aged customers (38.8-44.4 group is empty)

iii. Bins Matter!

With bins=5:
✅ Good: Clearly shows:

• The 27.6-33.2 age group dominates
• The 44.4-50 group is a clear outlier

❌ If you changed bins:

• bins=2: Would hide the empty 38.8-44.4 group
• bins=10: Might show empty bins between ages

🎓 Take It Further: Advanced Practice Ideas

✔ Add annotations (plt.annotate()) to highlight key data points.
✔ Use subplots (plt.subplots()) for side-by-side comparisons.
✔ Try themes (plt.style.use('ggplot')) for professional styling.

🎯 Ready to Try It Yourself?

Click here to open our Online Matplotlib Compiler and run these examples live!

💬 Which Matplotlib plot will you try first? Drop a comment below! 👇

Final Thoughts

An online Matplotlib compiler removes barriers, letting you focus on what matters—mastering data visualization. Whether you’re a student, data analyst, or Python enthusiast, practicing online accelerates learning.

🚀 Happy Plotting!

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