Customizing Seaborn plots with Matplotlib - Mini Project: Build & Apply

Customizing Seaborn plots with Matplotlib

📖 Scenario: You are analyzing sales data for a small store. You want to visualize the monthly sales using a bar plot. You will use Seaborn to create the plot and then customize it using Matplotlib to make it clearer and more attractive.

🎯 Goal: Create a Seaborn bar plot of monthly sales and customize it using Matplotlib by adding a title, axis labels, and changing the color of the bars.

📋 What You'll Learn

Create a dictionary called sales_data with months as keys and sales numbers as values

Create a list called highlight_months containing months to highlight

Use Seaborn to create a bar plot of the sales data

Use Matplotlib to add a title and axis labels

Change the color of bars for the months in highlight_months to a different color

💡 Why This Matters

🌍 Real World

Visualizing sales data helps businesses understand trends and make decisions. Customizing plots makes the information clearer and more appealing to stakeholders.

💼 Career

Data analysts and scientists often use Seaborn and Matplotlib to create and customize visualizations for reports and presentations.

1

DATA SETUP: Create the sales data dictionary

Create a dictionary called sales_data with these exact entries: 'January': 150, 'February': 200, 'March': 170, 'April': 220, 'May': 190.

Matplotlib

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

Hint

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

2

CONFIGURATION: Define months to highlight

Create a list called highlight_months containing the months 'February' and 'April' to highlight in the plot.

Matplotlib

sales_data = {'January': 150, 'February': 200, 'March': 170, 'April': 220, 'May': 190}
# Create the highlight_months list with 'February' and 'April'
# Your code here

Hint

Use square brackets [] to create a list with the two month names as strings.

3

CORE LOGIC: Create and customize the Seaborn bar plot

Import seaborn as sns and matplotlib.pyplot as plt. Create a bar plot using sns.barplot with months on the x-axis and sales on the y-axis from sales_data. Use a list comprehension to set the color of bars to 'orange' if the month is in highlight_months, otherwise 'skyblue'. Then add a title 'Monthly Sales' and axis labels 'Month' and 'Sales' using Matplotlib.

Matplotlib

sales_data = {'January': 150, 'February': 200, 'March': 170, 'April': 220, 'May': 190}
highlight_months = ['February', 'April']
# Import seaborn and matplotlib.pyplot
# Create the bar plot with custom colors
# Add title and axis labels
# Your code here

Hint

Use list() to get keys and values from the dictionary. Use a list comprehension to create the colors list. Pass palette=colors to sns.barplot. Use plt.title, plt.xlabel, and plt.ylabel to add text.

4

OUTPUT: Display the customized plot

Use plt.show() to display the plot with the custom colors, title, and axis labels.

Matplotlib

import seaborn as sns
import matplotlib.pyplot as plt

sales_data = {'January': 150, 'February': 200, 'March': 170, 'April': 220, 'May': 190}
highlight_months = ['February', 'April']

months = list(sales_data.keys())
sales = list(sales_data.values())

colors = ['orange' if month in highlight_months else 'skyblue' for month in months]

sns.barplot(x=months, y=sales, palette=colors)

plt.title('Monthly Sales')
plt.xlabel('Month')
plt.ylabel('Sales')
# Display the plot
# Your code here

Hint

Call plt.show() to display the plot window.

Practice

1. What is the main reason to use Matplotlib functions when working with Seaborn plots?

easy

A. To convert Seaborn plots into interactive web charts

B. To create Seaborn plots from scratch without Seaborn

C. To customize titles, labels, and figure size for better clarity

D. To replace Seaborn's default color palette

Solution

1. Step 1: Understand Seaborn's default features

   Seaborn provides nice default plots but limited direct customization options.

2. Step 2: Role of Matplotlib in customization

   Matplotlib functions let you add titles, labels, grids, and adjust figure size to improve clarity.

3. Final Answer:

   To customize titles, labels, and figure size for better clarity -> Option C

4. Quick Check:

   Matplotlib customizes Seaborn plots [OK]

Hint: Matplotlib adds polish to Seaborn plots [OK]

Common Mistakes:

• Thinking Matplotlib replaces Seaborn plotting
• Believing Matplotlib changes Seaborn colors only
• Confusing customization with interactivity

2. Which of the following is the correct way to set a title on a Seaborn plot using Matplotlib?

easy

A. sns_plot.title('My Title')

B. >edoc/<)'eltiT yM'(eltit.tolp_sns>edoc<

C. sns.set_title('My Title')

D. plt.title('My Title')

Solution

1. Step 1: Identify how Seaborn plots integrate with Matplotlib

   Seaborn plots are Matplotlib objects, so Matplotlib functions like plt.title() work.

2. Step 2: Check the syntax for setting titles

   Matplotlib's plt.title() sets the title for the current plot.

3. Final Answer:

   plt.title('My Title') -> Option D

4. Quick Check:

   Use plt.title() to set titles [OK]

Hint: Use plt.title() to add titles on Seaborn plots [OK]

Common Mistakes:

• Trying to call title() directly on sns object
• Using sns.set_title which does not exist
• Confusing plot object methods with Matplotlib functions

3. What will be the effect of the following code on a Seaborn plot?

import seaborn as sns
import matplotlib.pyplot as plt

sns_plot = sns.scatterplot(x=[1,2,3], y=[4,5,6])
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.grid(True)
plt.show()

medium

A. Scatter plot with labeled X and Y axes and grid lines visible

B. Scatter plot without axis labels and no grid lines

C. Scatter plot with grid lines but no axis labels

D. Scatter plot with axis labels but grid lines hidden

Solution

1. Step 1: Analyze axis labeling commands

   plt.xlabel('X Axis') and plt.ylabel('Y Axis') add labels to X and Y axes respectively.

2. Step 2: Analyze grid command

   plt.grid(True) enables grid lines on the plot.

3. Final Answer:

   Scatter plot with labeled X and Y axes and grid lines visible -> Option A

4. Quick Check:

   Labels and grid enabled by plt commands [OK]

Hint: plt.xlabel/ylabel add labels; plt.grid(True) shows grid [OK]

Common Mistakes:

• Assuming grid is off by default
• Forgetting plt.show() to display plot
• Confusing sns and plt labeling functions

4. Identify the error in the code below that tries to change the figure size of a Seaborn plot:

import seaborn as sns
import matplotlib.pyplot as plt

sns_plot = sns.barplot(x=[1,2,3], y=[4,5,6])
sns_plot.figure(figsize=(10,5))
plt.show()

medium

A. The correct method is sns_plot.set_figsize(10,5)

B. The figure size should be set using plt.figure(figsize=(10,5)) before plotting

C. sns.barplot does not support figure size changes

D. Figure size cannot be changed after plotting

Solution

1. Step 1: Understand how to set figure size in Matplotlib

   Figure size is set by creating a figure with plt.figure(figsize=(width,height)) before plotting.

2. Step 2: Identify the mistake in the code

   Calling sns_plot.figure(figsize=(10,5)) is incorrect because 'figure' is not a method of the plot object.

3. Final Answer:

   The figure size should be set using plt.figure(figsize=(10,5)) before plotting -> Option B

4. Quick Check:

   Set figure size with plt.figure() before plotting [OK]

Hint: Use plt.figure(figsize=...) before plotting [OK]

Common Mistakes:

• Calling figure() on plot object
• Trying to set figure size after plot creation
• Using non-existent set_figsize method

5. You want to create a Seaborn line plot with a custom figure size of 12x6 inches, a title 'Sales Over Time', X-axis label 'Month', Y-axis label 'Sales', and grid lines visible. Which code snippet correctly achieves this?

hard

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

plt.figure(figsize=(12,6))
sns.lineplot(x=[1,2,3], y=[100,200,300])
plt.title('Sales Over Time')
plt.xlabel('Month')
plt.ylabel('Sales')
plt.grid(True)
plt.show()

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

sns.lineplot(x=[1,2,3], y=[100,200,300], figsize=(12,6))
plt.title('Sales Over Time')
plt.xlabel('Month')
plt.ylabel('Sales')
plt.grid(True)
plt.show()

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

sns.set_figsize(12,6)
sns.lineplot(x=[1,2,3], y=[100,200,300])
plt.title('Sales Over Time')
plt.xlabel('Month')
plt.ylabel('Sales')
plt.grid(True)
plt.show()

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

plt.figure(figsize=(12,6))
sns.lineplot(x=[1,2,3], y=[100,200,300])
sns.title('Sales Over Time')
sns.xlabel('Month')
sns.ylabel('Sales')
sns.grid(True)
plt.show()

Solution

1. Step 1: Set figure size before plotting

   Use plt.figure(figsize=(12,6)) to set the plot size before creating the plot.

2. Step 2: Use Matplotlib functions for title, labels, and grid

   Matplotlib functions plt.title(), plt.xlabel(), plt.ylabel(), and plt.grid(True) customize the plot after creation.

3. Step 3: Verify code correctness

   import seaborn as sns
   import matplotlib.pyplot as plt
   
   plt.figure(figsize=(12,6))
   sns.lineplot(x=[1,2,3], y=[100,200,300])
   plt.title('Sales Over Time')
   plt.xlabel('Month')
   plt.ylabel('Sales')
   plt.grid(True)
   plt.show()

   correctly uses plt.figure and Matplotlib functions; other options misuse parameters or functions.

4. Final Answer:

   plt.figure(figsize=(12,6)); sns.lineplot(); plt.title('Sales Over Time'); plt.xlabel('Month'); plt.ylabel('Sales'); plt.grid(True) -> Option A

5. Quick Check:

   Set figure size with plt.figure, customize with plt functions [OK]

Hint: Set size with plt.figure, customize with plt.title/labels/grid [OK]

Common Mistakes:

• Passing figsize to sns.lineplot (not supported)
• Using sns.set_figsize (does not exist)
• Calling sns.title or sns.xlabel (wrong library)