Data Visualization & Storytelling in Python

Question 1

What Python libraries are commonly used for data visualization?

Answer 1

• Matplotlib
• Seaborn
• Plotly
• ggplot
• Altair

Each library has strengths depending on the use case.

Question 2

What is Matplotlib?

Answer 2

A low-level visualization library for creating static, animated, and interactive plots.

It provides full control over plot appearance.

Question 3

What is Seaborn built on?

Answer 3

Matplotlib

Seaborn simplifies statistical visualizations.

Question 4

What command in Matplotlib is used to create a basic line plot?

Answer 4

plt.plot()

Part of Matplotlib’s pyplot module.

Question 5

Fill in the blank:

plt.show() is used to ____ in Matplotlib.

Answer 5

Display the figure.

Without plt.show(), plots may not render properly in some environments.

Question 6

How do you import Seaborn?

Answer 6

import seaborn as sns

The alias sns is commonly used.

Question 7

How do you load a built-in Seaborn dataset?

Answer 7

sns.load_dataset(“dataset_name”)

Example: sns.load_dataset(“iris”)

Question 8

What is the default style of Seaborn?

Answer 8

darkgrid

Seaborn styles improve readability.

Question 9

What function is used to create a histogram in Seaborn?

Answer 9

sns.histplot()

Histograms help visualize distributions.

Question 10

How do you change the figure size in Matplotlib?

Answer 10

plt.figure(figsize=(width, height))

Helps with readability.

Question 11

Which function is used to create a scatter plot in Matplotlib?

Answer 11

plt.scatter(x, y)

Scatter plots show relationships between two numerical variables.

Question 12

What is the difference between sns.histplot() and sns.kdeplot()?

Answer 12

histplot() shows bar-like frequency, while kdeplot() smooths it into a curve.

KDE plots estimate probability distributions.

Question 13

How do you create a box plot in Seaborn?

Answer 13

sns.boxplot(x=”column”, data=df)

Box plots show distributions and outliers.

Question 14

What does a violin plot add compared to a box plot?

Answer 14

It shows kernel density estimation (KDE) along with quartiles.

sns.violinplot() is used for this.

Question 15

What function is used to create a bar chart in Seaborn?

Answer 15

sns.barplot(x=”category”, y=”value”, data=df)

Used for categorical comparisons.

Question 16

What is the main advantage of using Plotly over Matplotlib?

Answer 16

Plotly supports interactivity.

Good for dashboards and dynamic exploration.

Question 17

Which function in Plotly is used for line plots?

Answer 17

px.line(df, x=”column_x”, y=”column_y”)

px is short for plotly.express.

Question 18

What is the main difference between sns.barplot() and sns.countplot()?

Answer 18

barplot()requires numerical y-values, while countplot() works with categorical counts.

Count plots count occurrences automatically.

Question 19

What is the purpose of ax.set_title(“Title”) in Matplotlib?

Answer 19

Adds a title to the plot.

Helps provide context.

Question 20

How do you change the color palette in Seaborn?

Answer 20

sns.set_palette(“color_palette”)

Example: “pastel”, “muted”, “deep”.

Question 21

What argument in Seaborn adjusts transparency?

Answer 21

alpha

Example: sns.scatterplot(x, y, alpha=0.5).

Question 22

What does plt.savefig(“plot.png”) do?

Answer 22

Saves the plot as an image file.

Supports formats like PNG, JPG, SVG.

Question 23

How do you add labels to the x-axis and y-axis in Matplotlib?

Answer 23

plt.xlabel(“X Label”), plt.ylabel(“Y Label”)

Labels are crucial for clarity.

Question 24

What is the advantage of using tight_layout()?

Answer 24

Adjusts spacing automatically.

Prevents overlapping labels.

Question 25

What does **plt.legend()** do?

Answer 25

Adds a legend to the plot. ## Footnote Legends help differentiate categories.

Question 26

How do you create a **correlation heatmap in Seaborn**?

Answer 26

sns.heatmap(df.corr(), annot=True, cmap="coolwarm") ## Footnote Heatmaps visualize correlations between variables.

Question 27

What does **hue** do in Seaborn plots?

Answer 27

Adds color differentiation based on a categorical variable. ## Footnote Example: sns.scatterplot(x, y, hue="category", data=df).

Question 28

What function is used for **facet grids** in Seaborn?

Answer 28

sns.FacetGrid() ## Footnote Creates multiple subplots for different categories.

Question 29

What is the best visualization for **time series data**?

Answer 29

**Line plot** ## Footnote sns.lineplot() is commonly used.

Question 30

What are ***"small multiples"*** in data visualization?

Answer 30

Repeating the same chart for different subsets of data. ## Footnote Helps in comparative analysis.

Question 31

How do you rotate **x-axis labels** in Matplotlib?

Answer 31

plt.xticks(rotation=45) ## Footnote Useful when dealing with long category names.

Question 32

What does **plt.subplots()** do?

Answer 32

Creates multiple plots in a single figure. ## Footnote Used for dashboards or comparisons.

Question 33

How do you customize **tick labels** in Matplotlib?

Answer 33

plt.xticks(ticks, labels) ## Footnote Helps improve readability.

Question 34

What does **sns.pairplot(df)** do?

Answer 34

Plots pairwise relationships in the dataset. ## Footnote Great for EDA and correlation analysis.

Question 35

What is a **bubble chart**?

Answer 35

**A scatter plot** where point size represents a third variable. ## Footnote Used in business analytics.

Question 36

What does **plt.grid(True)** do?

Answer 36

Adds gridlines to the plot. ## Footnote Improves readability of values.

Question 37

What is the main use of **plt.errorbar()**?

Answer 37

Adds error bars to a plot. ## Footnote Helps show variability or uncertainty.

Question 38

What function is used to create an **animated** plot in Matplotlib?

Answer 38

**FuncAnimation** ## Footnote Part of the matplotlib.animation module.

Question 39

How do you improve performance for large datasets in Seaborn?

Answer 39

Use **sampling **or reduce **marker sizes**. ## Footnote Plotting large datasets can slow performance.

Question 40

What does **sns.set_style("whitegrid")** do?

Answer 40

Changes background style. ## Footnote "darkgrid", "white", and "ticks" are other options.

Question 41

What are the benefits of **interactive visualizations**?

Answer 41

Allows users to **explore data dynamically**. ## Footnote Used in dashboards and web applications.

Question 42

What is the primary use of **dash** in Python?

Answer 42

Creating interactive web-based visualizations. ## Footnote Built on Plotly.

Question 43

How can you create a **dynamic visualization** in Jupyter Notebook?

Answer 43

Use **%matplotlib notebook** or **plotly**. ## Footnote Enables interactive elements.

Question 44

How do you make a visualization publication-ready?

Answer 44

Adjust font sizes, remove unnecessary elements, use appropriate colors. ## Footnote Helps in professional presentations.

Question 45

# True or False: Good data visualization should prioritize aesthetics over accuracy.

Answer 45

**FALSE** ## Footnote Accuracy is always more important than design.