Python Seaborn Interview & Data Science Practice Exams

Master Seaborn: 500+ Realistic Python Data Viz Questions

Python Seaborn is the industry-standard library for creating beautiful, statistically-informed visualizations, and mastering it is essential for any data scientist or analyst aiming to communicate complex insights effectively. This comprehensive practice test suite is meticulously designed to mirror real-world technical interviews and production-level challenges, moving far beyond basic syntax to test your deep understanding of the Seaborn architecture. You will gain hands-on experience navigating the nuances of the "Object-Oriented" vs. "Functional" interfaces, optimizing multi-plot grids for high-dimensional data, and integrating Seaborn perfectly with Matplotlib for production-ready reports. Whether you are preparing for a senior data science role or a technical certification, these questions will sharpen your ability to choose the most truthful visualizations, manage large-scale datasets without performance lags, and customize aesthetics to meet professional business standards.

Exam Domains & Sample Topics

Fundamentals and Statistical Relationships: Relational plots (relplot), mapping aesthetics (hue, size, style), and statistical transformations.
Categorical Data and Distribution Analysis: Mastery of catplot, violin plots, swarm plots, and visualizing uncertainty/density.
Multi-Plot Grids and Advanced Faceting: Customizing FacetGrid, PairGrid, and JointGrid for high-dimensional analysis.
Aesthetics and Matplotlib Integration: Theme control (set_theme), color palettes, and manipulating Axes objects.
Real-World Scenarios & Best Practices: Performance optimization, data anonymization in visuals, and choosing KPIs.

Sample Practice Questions

1. When using sns.relplot(), which parameter is specifically designed to create subplots across different columns based on a categorical variable, effectively leveraging the FacetGrid figure-level interface?

A) hue B) style C) col D) split E) dodge F) kind

Correct Answer: C
Overall Explanation: In Seaborn's figure-level functions (like relplot, displot, and catplot), the col and row parameters are used to facet the data into multiple subplots (small multiples) based on categorical variables.
Option A Incorrect: hue maps variables to the color of the plot elements within the same axes.
Option B Incorrect: style changes the marker or line style within a single plot.
Option C Correct: col assigns a categorical variable to the columns of a grid, creating a multi-plot layout.
Option D Incorrect: split is used in violin plots to merge two halves of a distribution.
Option E Incorrect: dodge is used in categorical plots to prevent elements from overlapping.
Option F Incorrect: kind determines the type of plot (e.g., 'scatter' or 'line') but does not handle faceting.

2. Which Seaborn function is most appropriate for visualizing the relationship between two variables while simultaneously showing the marginal distributions of each variable on the sides?

A) sns.heatmap() B) sns.jointplot() C) sns.kdeplot() D) sns.stripplot() E) sns.rugplot() F) sns.boxplot()

Correct Answer: B
Overall Explanation: jointplot() is a specialized function that creates a multi-panel figure showing both the bivariate relationship (center) and the univariate distributions (top and right margins).
Option A Incorrect: heatmap() displays data in a 2D matrix format using color, not marginal distributions.
Option B Correct: jointplot() is the standard tool for combined bivariate and marginal analysis.
Option C Incorrect: kdeplot() visualizes a kernel density estimate but usually for one or two variables in a single pane.
Option D Incorrect: stripplot() is a categorical scatter plot and does not show marginal distributions.
Option E Incorrect: rugplot() draws small vertical ticks for a single variable but doesn't handle the central bivariate relationship.
Option F Incorrect: boxplot() shows the quartiles of a dataset but is not a joint-distribution tool.

3. You are working with a massive dataset and want to change the global aesthetic style to a dark grid background with specific scaling for a "talk" presentation. Which command achieves this?

A) sns.set_palette("dark") B) sns.despine() C) sns.set_theme(style="darkgrid", context="talk") D) sns.plotting_context("paper") E) sns.set_color_codes("muted") F) plt.style.use("ggplot")

Correct Answer: C
Overall Explanation: sns.set_theme() is the modern, preferred function to set multiple parameters (style, context, palette) simultaneously to control the look and feel of your plots.
Option A Incorrect: set_palette only affects the colors of the data elements, not the grid or background.
Option B Incorrect: despine() is used to remove the top and right spines from a plot, not set global styles.
Option C Correct: This correctly sets both the background grid style and the scaling context for a presentation.
Option D Incorrect: plotting_context() returns a dictionary of parameters; it doesn't apply the "darkgrid" style.
Option E Incorrect: This only modifies how colors are interpreted in subsequent calls.
Option F Incorrect: This is a Matplotlib function; while it works, it does not utilize Seaborn's specific talk context or native theme engine.

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