[100% Off] Python Data Visualization - Practice Questions 2026
Python Data Visualization (Matplotlib, Seaborn) 120 unique high-quality test questions with detailed explanations!
What you’ll learn
- Master Matplotlib and Seaborn to create professional-quality data visualizations.
- Learn to choose the right charts for different datasets and business scenarios.
- Build strong interview-ready knowledge of data visualization concepts and techniques.
- Apply advanced plotting
- customization
- and real-world visualization strategies confidently.
Requirements
- Basic understanding of Python programming (variables
- functions
- and data types).
- Familiarity with simple data concepts like lists
- dictionaries
- and datasets.
- A computer with internet access and the ability to install Python and libraries.
- No prior data visualization experience required — beginners are welcome.
Description
Mastering data visualization is a critical skill for any data scientist or analyst. Whether you are aiming to ace a technical interview or looking to build professional-grade dashboards, these practice exams are designed to bridge the gap between theoretical knowledge and practical execution.
Welcome to the most comprehensive practice exams to help you prepare for your Python Data Visualization journey. These exams are meticulously crafted to test your proficiency across the most popular libraries, including Matplotlib, Seaborn, and Plotly.
Why Serious Learners Choose These Practice Exams
Serious learners understand that watching tutorials is not enough; you must be able to solve problems and debug code under pressure. This course offers:
Original Question Bank: A massive collection of unique questions that you won’t find anywhere else.
Instructor Support: Gain access to expert guidance if you find a concept challenging.
Detailed Explanations: Every question includes a deep dive into why an answer is correct and why others fail.
Unlimited Retakes: Practice until you achieve a perfect score and feel confident.
Flexibility: Fully mobile-compatible via the Udemy app, allowing you to learn on the go.
Risk-Free Learning: A 30-day money-back guarantee ensures you can invest in your skills with total peace of mind.
Course Structure
The course is organized into a progressive learning path to ensure you master every layer of data visualization.
Basics / Foundations: Focuses on the core logic of plotting. You will be tested on figure creation, basic axes manipulation, and simple line and scatter plots using Matplotlib.
Core Concepts: Covers essential styling and labeling. This includes customizing colors, markers, legends, and titles to make charts readable and professional.
Intermediate Concepts: Introduces statistical visualizations. You will face questions on Seaborn’s high-level interface, including distribution plots, box plots, and heatmaps.
Advanced Concepts: Dives into complex layouts. This section tests your ability to work with subplots, twin axes, 3D plotting, and interactive elements using Plotly.
Real-world Scenarios: Challenges you with messy, real-life data situations. You must choose the right chart type to communicate specific insights effectively.
Mixed Revision / Final Test: A comprehensive simulation of a professional environment, pulling questions from all previous levels to ensure long-term retention.
Sample Practice Questions
QUESTION 1
Which Seaborn function is specifically designed to visualize the relationship between two numerical variables while also showing the marginal distributions of each variable?
OPTION 1: sns. relplot()
OPTION 2: sns. jointplot()
OPTION 3: sns. pairplot()
OPTION 4: sns. catplot()
OPTION 5: sns. heatmap()
CORRECT ANSWER: OPTION 2
CORRECT ANSWER EXPLANATION:
The sns. jointplot() function is the standard tool for displaying a bivariate relationship (like a scatter plot) along with the univariate distribution of each variable on the top and right axes.
WRONG ANSWERS EXPLANATION:
OPTION 1: relplot() is a figure-level function for relational plots but does not include marginal distributions by default.
OPTION 3: pairplot() visualizes pairwise relationships across an entire dataset, not just two specific variables with detailed marginals.
OPTION 4: catplot() is used for categorical data, not primarily for the relationship between two numerical variables.
OPTION 5: heatmap() displays data in a matrix format and does not show individual data points or marginal distributions.
QUESTION 2
In Matplotlib, if you want to ensure that the proportions of your plot remain equal (e. g. , a circle looks like a circle rather than an ellipse), which command should you use?
OPTION 1: plt. tight_layout()
OPTION 2: plt. show()
OPTION 3: plt. axis(‘equal’)
OPTION 4: plt. figure(figsize=(10,10))
OPTION 5: plt. grid(True)
CORRECT ANSWER: OPTION 3
CORRECT ANSWER EXPLANATION:
The plt. axis(‘equal’) command sets the limits of the x and y axes to be the same, ensuring that the aspect ratio is 1:1. This is vital for geometric accuracy.
WRONG ANSWERS EXPLANATION:
OPTION 1: tight_layout() is used to automatically adjust subplot parameters so that coordinates fit into the figure area without overlapping.
OPTION 2: show() simply displays the plot and has no effect on the scaling or aspect ratio.
OPTION 4: figsize sets the total size of the figure in inches but does not force the data axes to scale equally relative to each other.
OPTION 5: grid() adds a background grid to the plot for readability but does not change the geometry of the plot.
We hope that by now you’re convinced! And there are a lot more questions inside the course.
