When to use Seaborn vs Matplotlib - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the Matplotlib plotting library.

Matplotlib

import [1] as plt

Drag options to blanks, or click blank then click option'

Apandas

Bseaborn

Cmatplotlib.pyplot

Dnumpy

Attempts:

3 left

💡 Hint

Common Mistakes

Importing seaborn instead of matplotlib.pyplot

Importing numpy or pandas which are not plotting libraries

Matplotlib's plotting functions are in the pyplot module, so we import matplotlib.pyplot as plt.

2fill in blank

medium

Complete the code to create a simple line plot using Matplotlib.

Matplotlib

plt.[1]([1, 2, 3], [4, 5, 6])

Drag options to blanks, or click blank then click option'

Aplot

Bhist

Cscatter

Dbar

Attempts:

3 left

💡 Hint

Common Mistakes

Using scatter which creates scatter plots, not line plots

Using hist which creates histograms

Using bar which creates bar charts

The plot function creates line plots in Matplotlib.

3fill in blank

hard

Fix the error in the code to create a Seaborn scatter plot.

Matplotlib

sns.[1](data=df, x='age', y='income')

Drag options to blanks, or click blank then click option'

Ascatterplot

Blineplot

Chistplot

Dbarplot

Attempts:

3 left

💡 Hint

Common Mistakes

Using lineplot which creates line charts

Using histplot which creates histograms

Using barplot which creates bar charts

Seaborn's function for scatter plots is scatterplot.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that filters words longer than 3 letters.

Matplotlib

{word: [1] for word in words if len(word) [2] 3}

Drag options to blanks, or click blank then click option'

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as value instead of its length

Using less than symbol which filters shorter words

The dictionary stores the length of each word. We filter words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase keys and values filtered by positive values.

Matplotlib

result = [1]: [2] for k, v in data.items() if v [3] 0}

Drag options to blanks, or click blank then click option'

Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using original keys instead of uppercase

Filtering values less than zero

Swapping keys and values

Keys are converted to uppercase with k.upper(). Values are v. We keep only positive values using >.

Practice

1. Which library is best when you want quick and beautiful statistical charts with minimal code?

easy

A. Seaborn

B. Matplotlib

C. Pandas

D. NumPy

Solution

1. Step 1: Understand the purpose of Seaborn

   Seaborn is designed to create attractive statistical plots quickly with simple commands.

2. Step 2: Compare with Matplotlib

   Matplotlib offers more control but requires more code and customization for beautiful charts.

3. Final Answer:

   Seaborn -> Option A

4. Quick Check:

   Quick, beautiful stats charts = Seaborn [OK]

Hint: Seaborn = quick & pretty stats plots [OK]

Common Mistakes:

• Confusing Matplotlib as the quickest for beautiful charts
• Thinking Pandas or NumPy create statistical plots directly
• Assuming Seaborn requires complex code

2. Which of the following is the correct way to import Matplotlib's pyplot module?

easy

A. import matplotlib.pyplot as plt

B. import seaborn as plt

C. from matplotlib import seaborn

D. import matplotlib as sns

Solution

1. Step 1: Recall standard import syntax for Matplotlib pyplot

   The common and correct way is to import pyplot as plt using: import matplotlib.pyplot as plt.

2. Step 2: Check other options for errors

   import seaborn as plt imports seaborn as plt (wrong library and alias). from matplotlib import seaborn tries to import seaborn from matplotlib (incorrect). import matplotlib as sns imports matplotlib as sns (wrong alias).

3. Final Answer:

   import matplotlib.pyplot as plt -> Option A

4. Quick Check:

   Matplotlib pyplot import = import matplotlib.pyplot as plt [OK]

Hint: Matplotlib pyplot is always imported as plt [OK]

Common Mistakes:

• Mixing up seaborn and matplotlib imports
• Using wrong aliases like sns for matplotlib
• Trying to import seaborn from matplotlib

3. What will the following code output?

import seaborn as sns
import matplotlib.pyplot as plt

sns.histplot([1, 2, 2, 3, 3, 3, 4])
plt.show()

medium

A. A scatter plot of the numbers

B. A line plot of the numbers

C. A histogram showing counts of each number

D. An error because histplot is not in seaborn

Solution

1. Step 1: Understand sns.histplot function

   Seaborn's histplot creates a histogram showing frequency counts of values in the list.

2. Step 2: Analyze the input data

   The list has repeated numbers: 1 once, 2 twice, 3 thrice, 4 once. The histogram will show bars with heights matching these counts.

3. Final Answer:

   A histogram showing counts of each number -> Option C

4. Quick Check:

   sns.histplot = histogram plot [OK]

Hint: sns.histplot makes histograms from data lists [OK]

Common Mistakes:

• Thinking histplot creates line or scatter plots
• Assuming histplot is not a seaborn function
• Expecting no plot or error

4. Identify the error in this code snippet:

import matplotlib.pyplot as plt
import seaborn as sns

sns.lineplot(x=[1,2,3], y=[4,5])
plt.show()

medium

A. Incorrect import of seaborn

B. x and y lists have different lengths causing an error

C. sns.lineplot does not exist

D. Missing plt.show() call

Solution

1. Step 1: Check the lengths of x and y lists

   x has 3 elements, y has 2 elements. Plotting requires equal lengths for x and y.

2. Step 2: Understand consequence of length mismatch

   This mismatch causes a ValueError when seaborn tries to plot the data.

3. Final Answer:

   x and y lists have different lengths causing an error -> Option B

4. Quick Check:

   Equal x,y lengths needed for lineplot [OK]

Hint: Check x and y lengths match for plots [OK]

Common Mistakes:

• Ignoring length mismatch of x and y
• Thinking plt.show() is missing
• Assuming sns.lineplot is invalid

5. You want to create a customized scatter plot with specific colors, sizes, and labels for each point. Which approach is best?

hard

A. Use Seaborn only because it automatically styles everything

B. Use Seaborn with no Matplotlib because Matplotlib cannot customize points

C. Use Pandas plot function for advanced customization

D. Use Matplotlib for full control and customize each element manually

Solution

1. Step 1: Understand customization needs

   Custom colors, sizes, and labels for each point require detailed control over plot elements.

2. Step 2: Compare Matplotlib and Seaborn capabilities

   Matplotlib allows manual control of every plot element, while Seaborn simplifies styling but limits fine-tuning.

3. Step 3: Evaluate other options

   Pandas plotting is simpler and less flexible. Seaborn alone cannot handle detailed per-point customization without Matplotlib.

4. Final Answer:

   Use Matplotlib for full control and customize each element manually -> Option D

5. Quick Check:

   Full control for custom plots = Matplotlib [OK]

Hint: For full custom plots, choose Matplotlib [OK]

Common Mistakes:

• Assuming Seaborn alone can customize every plot detail
• Using Pandas plot for advanced styling
• Believing Matplotlib cannot customize points