Combining Seaborn and Matplotlib - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main reason to combine Seaborn and Matplotlib in a plot?

Combining Seaborn and Matplotlib lets you use Seaborn's easy and beautiful statistical plots with Matplotlib's detailed customization options.

beginner

How do you add a title to a Seaborn plot using Matplotlib?

After creating a Seaborn plot, use Matplotlib's plt.title('Your Title') to add a title.

beginner

Which Matplotlib function is commonly used to adjust the size of a Seaborn plot?

plt.figure(figsize=(width, height)) is used before the Seaborn plot to set the plot size.

intermediate

Can you add Matplotlib annotations to a Seaborn plot? How?

Yes, after creating the Seaborn plot, use plt.annotate() to add text or arrows anywhere on the plot.

beginner

What is the role of plt.show() when combining Seaborn and Matplotlib?

plt.show() displays the final combined plot on the screen. It should be called after all plotting commands.

Which library is mainly used for statistical plotting with simple syntax?

A Seaborn

B Matplotlib

C Pandas

D NumPy

Seaborn is designed for easy and beautiful statistical plots.

How do you set the size of a plot before creating a Seaborn plot?

A sns.set_size()

B sns.figure_size()

C plt.size(width, height)

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

Matplotlib's plt.figure(figsize=(width, height)) sets the plot size.

After creating a Seaborn plot, which function adds a title?

A sns.title()

B plt.set_title()

C plt.title()

D sns.set_title()

Matplotlib's plt.title() adds a title to the plot.

Can you add Matplotlib annotations to a Seaborn plot?

A No, they are incompatible

B Yes, using plt.annotate()

C Only with sns.annotate()

D Only if you convert the plot to Matplotlib

Matplotlib annotations like plt.annotate() work on Seaborn plots.

What does plt.show() do in a combined Seaborn and Matplotlib plot?

A Displays the plot on screen

B Saves the plot to a file

C Clears the plot

D Creates a new plot

plt.show() displays the final plot on the screen.

Explain how you can customize a Seaborn plot using Matplotlib functions.

Think about Matplotlib functions like plt.figure(), plt.title(), plt.annotate(), and plt.show().

Describe the benefits of combining Seaborn and Matplotlib for data visualization.

Consider what each library does best and how they complement each other.

Practice

1. What is the main reason to combine Seaborn and Matplotlib in a plot?

easy

A. Seaborn and Matplotlib cannot be used together

B. Because Seaborn cannot create any plots on its own

C. Matplotlib is only for 3D plots, so Seaborn is needed for 2D

D. To use Seaborn's easy plotting and Matplotlib's customization features

Solution

1. Step 1: Understand Seaborn's strength

   Seaborn creates beautiful and easy statistical plots quickly.

2. Step 2: Understand Matplotlib's strength

   Matplotlib allows detailed customization like adding titles, labels, and lines.

3. Final Answer:

   To use Seaborn's easy plotting and Matplotlib's customization features -> Option D

4. Quick Check:

   Seaborn + Matplotlib = Easy + Customization [OK]

Hint: Seaborn plots, Matplotlib customizes [OK]

Common Mistakes:

• Thinking Seaborn can't plot alone
• Believing Matplotlib is only for 3D
• Assuming they can't be combined

2. Which of the following code snippets correctly adds a title to a Seaborn plot using Matplotlib?

easy

A. import seaborn as sns
import matplotlib.pyplot as plt
sns.histplot(data)
plt.title('Histogram')

B. import seaborn as sns
sns.histplot(data).title('Histogram')

C. import matplotlib.pyplot as plt
plt.histplot(data)
plt.set_title('Histogram')

D. import seaborn as sns
sns.histplot(data)
plt.setTitle('Histogram')

Solution

1. Step 1: Identify correct Seaborn and Matplotlib usage

   Seaborn creates the plot, Matplotlib's plt.title() adds the title.

2. Step 2: Check syntax correctness

   import seaborn as sns
   import matplotlib.pyplot as plt
   sns.histplot(data)
   plt.title('Histogram')

   uses sns.histplot(data) then plt.title('Histogram'), which is correct.

3. Final Answer:

   import seaborn as sns import matplotlib.pyplot as plt sns.histplot(data) plt.title('Histogram') -> Option A

4. Quick Check:

   Seaborn plot + plt.title() = Correct [OK]

Hint: Use plt.title() after Seaborn plot [OK]

Common Mistakes:

• Calling title() directly on Seaborn plot object
• Using plt.set_title() instead of plt.title()
• Misspelling method names like setTitle

3. What will be the output of this code?

import seaborn as sns
import matplotlib.pyplot as plt

sns.scatterplot(x=[1,2,3], y=[4,5,6])
plt.xlabel('X axis')
plt.ylabel('Y axis')
plt.title('Scatter Plot')
plt.show()

medium

A. A scatter plot with labeled X axis, Y axis, and title

B. A scatter plot without any labels or title

C. An error because plt.xlabel() cannot be used with Seaborn

D. A line plot instead of scatter plot

Solution

1. Step 1: Understand the plot creation

   sns.scatterplot creates a scatter plot with given x and y points.

2. Step 2: Check Matplotlib label and title additions

   plt.xlabel, plt.ylabel, and plt.title add labels and title correctly.

3. Final Answer:

   A scatter plot with labeled X axis, Y axis, and title -> Option A

4. Quick Check:

   Seaborn plot + plt labels = Labeled plot [OK]

Hint: Matplotlib labels work after Seaborn plot [OK]

Common Mistakes:

• Expecting errors using plt.xlabel with Seaborn
• Confusing scatterplot with line plot
• Forgetting plt.show() to display plot

4. Identify the error in this code that tries to combine Seaborn and Matplotlib:

import seaborn as sns
import matplotlib.pyplot as plt

sns.boxplot(data=[1,2,3,4,5])
plt.xlabel('Values')
plt.title('Boxplot')
plt.show()

medium

A. plt.xlabel() causes error because boxplot has no x-axis

B. No error; code runs and shows labeled boxplot

C. Missing plt.figure() before plotting causes error

D. sns.boxplot requires x and y parameters, so data alone causes error

Solution

1. Step 1: Check sns.boxplot usage

   Passing data as a list is valid for sns.boxplot; it plots distribution.

2. Step 2: Check Matplotlib label usage

   plt.xlabel('Values') adds label to x-axis; plt.title adds title; no error occurs.

3. Final Answer:

   No error; code runs and shows labeled boxplot -> Option B

4. Quick Check:

   Seaborn boxplot + plt labels = Works fine [OK]

Hint: Boxplot accepts data list; plt.xlabel works [OK]

Common Mistakes:

• Thinking plt.xlabel errors without x parameter
• Assuming plt.figure() is mandatory before plot
• Believing sns.boxplot needs x and y always

5. You want to create a Seaborn barplot and add a horizontal line at y=5 using Matplotlib. Which code correctly does this?

hard

A. import seaborn as sns
import matplotlib.pyplot as plt
sns.barplot(x=['A','B'], y=[3,7])
plt.hline(y=5, color='red')
plt.show()

B. import seaborn as sns
import matplotlib.pyplot as plt
sns.barplot(x=['A','B'], y=[3,7])
plt.lineh(y=5, color='red')
plt.show()

C. import seaborn as sns
import matplotlib.pyplot as plt
sns.barplot(x=['A','B'], y=[3,7])
plt.axhline(y=5, color='red')
plt.show()

D. import seaborn as sns
import matplotlib.pyplot as plt
sns.barplot(x=['A','B'], y=[3,7])
plt.axline(y=5, color='red')
plt.show()

Solution

1. Step 1: Create barplot with Seaborn

   sns.barplot with x and y lists creates the bar chart correctly.

2. Step 2: Add horizontal line with Matplotlib

   plt.axhline(y=5, color='red') adds a horizontal line at y=5; other options are invalid methods.

3. Final Answer:

   import seaborn as sns import matplotlib.pyplot as plt sns.barplot(x=['A','B'], y=[3,7]) plt.axhline(y=5, color='red') plt.show() -> Option C

4. Quick Check:

   Use plt.axhline() for horizontal line [OK]

Hint: Use plt.axhline() for horizontal lines [OK]

Common Mistakes:

• Using plt.lineh or plt.hline which don't exist
• Confusing plt.axline with plt.axhline
• Forgetting plt.show() to display plot