Why Seaborn complements Matplotlib - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import Seaborn with its common alias.

Matplotlib

import [1] as sns

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

Apandas

Bmatplotlib

Cnumpy

Dseaborn

Attempts:

3 left

💡 Hint

Common Mistakes

Importing matplotlib as sns instead of seaborn

Using numpy or pandas as sns

Seaborn is commonly imported as sns to make plotting easier and more readable.

2fill in blank

medium

Complete the code to create a simple scatter plot using Seaborn.

Matplotlib

sns.scatterplot(x='total_bill', y='tip', data=[1])

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

Aplt

Btips

Cnp

Ddf

Attempts:

3 left

💡 Hint

Common Mistakes

Passing plt or np instead of a DataFrame

Using an undefined variable like df without loading data

The tips dataset is a common example dataset used with Seaborn for plotting.

3fill in blank

hard

Fix the error in the code to set a Seaborn style for Matplotlib plots.

Matplotlib

sns.set_style([1])

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

Ascatter

Bblue

Cdarkgrid

Dplot

Attempts:

3 left

💡 Hint

Common Mistakes

Using color names instead of style names

Passing plot types instead of style names

Seaborn styles include 'darkgrid', 'whitegrid', 'dark', 'white', and 'ticks'. 'darkgrid' adds a grid background.

4fill in blank

hard

Fill both blanks to create a Seaborn histogram with kernel density estimate.

Matplotlib

sns.histplot(data=[1], x='total_bill', kde=[2])

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

Atips

BTrue

CFalse

Dplt

Attempts:

3 left

💡 Hint

Common Mistakes

Passing plt instead of a DataFrame

Setting kde to False or a string

Using tips dataset and setting kde=True adds a smooth density curve over the histogram.

5fill in blank

hard

Fill all three blanks to create a Seaborn boxplot grouped by day and colored by smoker status.

Matplotlib

sns.boxplot(x=[1], y='total_bill', hue=[2], data=[3])

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

A'day'

B'smoker'

Ctips

D'total_bill'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'total_bill' as x or hue instead of 'day' or 'smoker'

Passing plt or other variables instead of the dataset

The boxplot groups data by day on the x-axis, colors by smoker status, and uses the tips dataset.

Practice

1. Why do many data scientists use Seaborn along with Matplotlib?

easy

A. Seaborn replaces Matplotlib completely for all plots.

B. Seaborn simplifies creating attractive statistical plots with less code.

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

D. Seaborn is used only for data cleaning, not visualization.

Solution

1. Step 1: Understand Seaborn's purpose

   Seaborn is designed to make statistical plots easier and prettier with fewer lines of code.

2. Step 2: Compare with Matplotlib

   Matplotlib is powerful but requires more code for styling; Seaborn complements it by simplifying common plot types.

3. Final Answer:

   Seaborn simplifies creating attractive statistical plots with less code. -> Option B

4. Quick Check:

   Seaborn simplifies plots = B [OK]

Hint: Seaborn = easier, prettier plots with less code [OK]

Common Mistakes:

• Thinking Seaborn replaces Matplotlib entirely
• Confusing Seaborn with data cleaning tools
• Believing Matplotlib is only for 3D plots

2. Which of the following is the correct way to import Seaborn and Matplotlib for plotting?

easy

A. import seaborn as sns
import matplotlib.pyplot as plt

B. import seaborn as plt
import matplotlib as sns

C. from seaborn import plt
import matplotlib.pyplot as sns

D. import seaborn.pyplot as sns
import matplotlib as plt

Solution

1. Step 1: Recall standard import conventions

   Seaborn is commonly imported as 'sns' and Matplotlib's pyplot as 'plt'.

2. Step 2: Check each option

   import seaborn as sns import matplotlib.pyplot as plt matches the standard and correct import syntax; others mix names or use invalid imports.

3. Final Answer:

   import seaborn as sns import matplotlib.pyplot as plt -> Option A

4. Quick Check:

   Standard imports = A [OK]

Hint: Seaborn as sns, Matplotlib.pyplot as plt [OK]

Common Mistakes:

• Swapping aliases between seaborn and matplotlib
• Using incorrect module names like seaborn.pyplot
• Importing seaborn or matplotlib incorrectly

3. What will the following code output?

import seaborn as sns
import matplotlib.pyplot as plt

sns.set_style('darkgrid')
data = [1, 2, 3, 4, 5]
plt.plot(data)
plt.show()

medium

A. A line plot with a dark grid background

B. A scatter plot with no grid

C. An error because sns.set_style is invalid

D. A bar chart with default style

Solution

1. Step 1: Understand sns.set_style('darkgrid')

   This sets the plot background to a dark grid style, affecting Matplotlib plots.

2. Step 2: Analyze plt.plot(data) and plt.show()

   plt.plot creates a line plot of the data list, and plt.show displays it with the dark grid style applied.

3. Final Answer:

   A line plot with a dark grid background -> Option A

4. Quick Check:

   sns.set_style('darkgrid') + plt.plot = line plot with grid [OK]

Hint: sns.set_style changes background; plt.plot draws line [OK]

Common Mistakes:

• Confusing plot types (line vs scatter vs bar)
• Thinking sns.set_style causes errors
• Ignoring style effects on Matplotlib plots

4. Identify the error in this code snippet:

import seaborn as sns
import matplotlib.pyplot as plt

sns.set_style('whitegrid')
plt.bar([1, 2, 3], [4, 5])
plt.show()

medium

A. plt.show() is missing parentheses.

B. sns.set_style('whitegrid') is not a valid style.

C. The lengths of x and y data lists do not match.

D. plt.bar cannot be used with seaborn styles.

Solution

1. Step 1: Check sns.set_style usage

   'whitegrid' is a valid style in Seaborn, so no error here.

2. Step 2: Check plt.bar arguments

   plt.bar requires x and y lists of the same length; here x has 3 items, y has 2, causing an error.

3. Final Answer:

   The lengths of x and y data lists do not match. -> Option C

4. Quick Check:

   Mismatch in bar plot data lengths = D [OK]

Hint: Bar plot x and y must have same length [OK]

Common Mistakes:

• Assuming sns.set_style causes error
• Thinking plt.show needs no parentheses
• Believing seaborn styles restrict Matplotlib functions

5. You want to create a quick, attractive boxplot of a dataset with minimal code and good default styling. Which approach best uses Seaborn and Matplotlib together?

hard

A. Use Matplotlib's plt.plot for boxplots and Seaborn for scatterplots.

B. Use Matplotlib's boxplot function only, then customize colors manually.

C. Use Seaborn only for data cleaning, then Matplotlib for plotting.

D. Use Seaborn's boxplot function for the plot and Matplotlib's plt.show() to display it.

Solution

1. Step 1: Identify best tool for quick, styled boxplots

   Seaborn provides simple functions like boxplot with attractive default styles and minimal code.

2. Step 2: Understand display method

   Matplotlib's plt.show() is used to display any plot, including those created by Seaborn.

3. Final Answer:

   Use Seaborn's boxplot function for the plot and Matplotlib's plt.show() to display it. -> Option D

4. Quick Check:

   Seaborn plots + plt.show() = quick, pretty boxplot [OK]

Hint: Seaborn plots + plt.show() = easy, styled visuals [OK]

Common Mistakes:

• Using Matplotlib only for complex styling
• Confusing Seaborn's role in data cleaning
• Trying to use plt.plot for boxplots