Combining Seaborn and Matplotlib - Mini Project: Build & Apply

Combining Seaborn and Matplotlib

📖 Scenario: You are analyzing sales data for a small store. You want to visualize the sales amounts for different products using a bar chart. You will use Seaborn to create the bar chart and Matplotlib to add a title and labels.

🎯 Goal: Create a bar chart using Seaborn to show sales amounts for products, then use Matplotlib to add a title and axis labels.

📋 What You'll Learn

Create a dictionary called sales_data with product names as keys and sales amounts as values.

Create a list called products containing the product names from sales_data.

Create a list called sales containing the sales amounts from sales_data.

Use Seaborn's barplot function with products and sales to create the bar chart.

Use Matplotlib's plt.title, plt.xlabel, and plt.ylabel to add a title and axis labels.

Use plt.show() to display the final plot.

💡 Why This Matters

🌍 Real World

Combining Seaborn and Matplotlib is common when you want to create attractive plots with Seaborn and customize them further with Matplotlib's features.

💼 Career

Data scientists and analysts often need to create clear visualizations for reports and presentations, using multiple libraries together to get the best results.

1

Create the sales data dictionary

Create a dictionary called sales_data with these exact entries: 'Apples': 50, 'Bananas': 30, 'Cherries': 20, 'Dates': 15, 'Elderberries': 10.

Matplotlib

# Create the sales_data dictionary with product names and sales amounts
# Your code here

Hint

Use curly braces {} to create a dictionary with the given keys and values.

2

Create lists for products and sales

Create a list called products containing the keys from sales_data. Create a list called sales containing the values from sales_data.

Matplotlib

sales_data = {'Apples': 50, 'Bananas': 30, 'Cherries': 20, 'Dates': 15, 'Elderberries': 10}
# Create the products list from sales_data keys
# Create the sales list from sales_data values
# Your code here

Hint

Use list() with sales_data.keys() and sales_data.values() to create the lists.

3

Create the bar chart with Seaborn

Import seaborn as sns and matplotlib.pyplot as plt. Use sns.barplot with x=products and y=sales to create the bar chart.

Matplotlib

sales_data = {'Apples': 50, 'Bananas': 30, 'Cherries': 20, 'Dates': 15, 'Elderberries': 10}
products = list(sales_data.keys())
sales = list(sales_data.values())

# Import seaborn as sns and matplotlib.pyplot as plt
# Create a barplot with x=products and y=sales
# Your code here

Hint

Use import seaborn as sns and import matplotlib.pyplot as plt. Then call sns.barplot(x=products, y=sales).

4

Add title and labels with Matplotlib and show plot

Use plt.title to add the title 'Sales Amounts by Product'. Use plt.xlabel with 'Product' and plt.ylabel with 'Sales'. Finally, use plt.show() to display the plot.

Matplotlib

import seaborn as sns
import matplotlib.pyplot as plt

sales_data = {'Apples': 50, 'Bananas': 30, 'Cherries': 20, 'Dates': 15, 'Elderberries': 10}
products = list(sales_data.keys())
sales = list(sales_data.values())

sns.barplot(x=products, y=sales)

# Add title, x-label, y-label, and show the plot
# Your code here

Hint

Use plt.title('Sales Amounts by Product'), plt.xlabel('Product'), plt.ylabel('Sales'), and plt.show().

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