When to use Seaborn vs Matplotlib - Hands-On Comparison

When to use Seaborn vs Matplotlib

📖 Scenario: You are a data analyst working with sales data. You want to create charts to understand your data better. You have two popular tools: Matplotlib and Seaborn. Each tool is good for different tasks.

🎯 Goal: Learn when to use Matplotlib and when to use Seaborn by creating simple charts with both libraries using the same data.

📋 What You'll Learn

Create a simple sales data dictionary

Set a threshold value for sales

Use Matplotlib to plot a basic bar chart

Use Seaborn to plot a styled bar chart

Print a message explaining which library is better for which task

💡 Why This Matters

🌍 Real World

Data analysts often need to choose the right plotting tool to communicate insights clearly and attractively.

💼 Career

Knowing when to use Matplotlib or Seaborn helps create effective visual reports and dashboards in data science roles.

1

Create sales data dictionary

Create a dictionary called sales_data with these exact entries: 'January': 150, 'February': 200, 'March': 170, 'April': 220, 'May': 180.

Matplotlib

# Create the sales_data dictionary with the given months and sales
# Your code here

Hint

Use curly braces {} to create a dictionary with month names as keys and sales numbers as values.

2

Set sales threshold

Create a variable called threshold and set it to 180.

Matplotlib

sales_data = {'January': 150, 'February': 200, 'March': 170, 'April': 220, 'May': 180}
# Set the threshold variable to 180
# Your code here

Hint

Just assign the number 180 to a variable named threshold.

3

Plot bar chart with Matplotlib

Import matplotlib.pyplot as plt. Use plt.bar() with sales_data.keys() and sales_data.values() to create a bar chart. Then call plt.show() to display it.

Matplotlib

sales_data = {'January': 150, 'February': 200, 'March': 170, 'April': 220, 'May': 180}
threshold = 180
# Import matplotlib.pyplot as plt
# Create a bar chart using plt.bar with sales_data keys and values
# Show the plot
# Your code here

Hint

Use plt.bar() to create the bar chart and plt.show() to display it.

4

Plot styled bar chart with Seaborn and explain usage

Import seaborn as sns and pandas as pd. Convert sales_data to a DataFrame with columns 'Month' and 'Sales'. Use sns.barplot() to plot the sales data. Call plt.show() to display the plot. Then print the exact message: "Use Matplotlib for simple plots and Seaborn for styled, statistical plots."

Matplotlib

import matplotlib.pyplot as plt

sales_data = {'January': 150, 'February': 200, 'March': 170, 'April': 220, 'May': 180}
threshold = 180

plt.bar(sales_data.keys(), sales_data.values())
plt.title('Monthly Sales - Matplotlib')
plt.xlabel('Month')
plt.ylabel('Sales')
plt.show()

# Import seaborn as sns and pandas as pd
# Convert sales_data to a DataFrame with columns 'Month' and 'Sales'
# Use sns.barplot to plot the data
# Show the plot
# Print the message about when to use Matplotlib vs Seaborn
# Your code here

Hint

Use pd.DataFrame() to convert the dictionary to a table. Use sns.barplot() to create a nicer bar chart. Finally, print the message exactly as given.

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