Styling and themes in Data Analysis Python - Mini Project: Build & Apply

Styling and themes

📖 Scenario: You work as a data analyst for a small company. You have sales data for different products and want to create a clear and attractive table to share with your team. You will learn how to style a pandas DataFrame to make it easier to read and visually appealing.

🎯 Goal: Create a pandas DataFrame with sales data, set a style theme, and highlight the highest sales values to make the table easy to understand.

📋 What You'll Learn

Create a pandas DataFrame with given sales data

Define a style theme using pandas styling options

Highlight the highest sales values in the DataFrame

Display the styled DataFrame as output

💡 Why This Matters

🌍 Real World

Styling tables helps make reports easier to read and understand for business teams.

💼 Career

Data analysts often need to present data clearly using styles and themes in tools like pandas.

1

Create the sales data DataFrame

Create a pandas DataFrame called sales_data with these exact values: columns 'Product', 'Region', and 'Sales'. The rows should be: 'Apples', 'North', 250, 'Bananas', 'South', 150, 'Cherries', 'East', 300, 'Dates', 'West', 200.

Data Analysis Python

import pandas as pd
# Create the sales_data DataFrame with the exact values
# Your code here

Hint

Use pd.DataFrame with a dictionary where keys are column names and values are lists of column data.

2

Set a style theme for the DataFrame

Create a variable called styled_sales that applies the background_gradient style with the coolwarm colormap to the sales_data DataFrame.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apples', 'Bananas', 'Cherries', 'Dates'],
    'Region': ['North', 'South', 'East', 'West'],
    'Sales': [250, 150, 300, 200]
})
# Apply background_gradient style with 'coolwarm' colormap to sales_data
# Your code here

Hint

Use sales_data.style.background_gradient(cmap='coolwarm') to add color shading based on values.

3

Highlight the highest sales value

Update styled_sales to highlight the maximum value in the 'Sales' column using the highlight_max method with color='yellow'.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apples', 'Bananas', 'Cherries', 'Dates'],
    'Region': ['North', 'South', 'East', 'West'],
    'Sales': [250, 150, 300, 200]
})
styled_sales = sales_data.style.background_gradient(cmap='coolwarm')
# Highlight the max value in 'Sales' column with yellow color
# Your code here

Hint

Chain highlight_max(subset=['Sales'], color='yellow') after background_gradient.

4

Display the styled DataFrame

Print the styled_sales variable to display the styled DataFrame.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apples', 'Bananas', 'Cherries', 'Dates'],
    'Region': ['North', 'South', 'East', 'West'],
    'Sales': [250, 150, 300, 200]
})
styled_sales = sales_data.style.background_gradient(cmap='coolwarm').highlight_max(subset=['Sales'], color='yellow')
# Print styled_sales to show the styled DataFrame
# Your code here

Hint

Use print(styled_sales) to display the styled DataFrame in the console.

Practice

1. What is the main purpose of using styling and themes in data visualization?

easy

A. To change the data values automatically

B. To increase the size of the dataset

C. To speed up data processing

D. To make charts look better and easier to understand

Solution

1. Step 1: Understand the role of styling in charts

   Styling and themes improve the visual appeal and clarity of charts.

2. Step 2: Identify the correct purpose

   They help viewers understand data better by making charts clearer and more attractive.

3. Final Answer:

   To make charts look better and easier to understand -> Option D

4. Quick Check:

   Styling improves chart clarity [OK]

Hint: Styling improves look and clarity of charts [OK]

Common Mistakes:

• Confusing styling with data processing
• Thinking styling changes data values
• Assuming styling increases dataset size

2. Which of the following is the correct way to apply a style named 'ggplot' using matplotlib in Python?

easy

A. plt.style.use('ggplot')

B. plt.style('ggplot')

C. style.use('ggplot')

D. plt.use.style('ggplot')

Solution

1. Step 1: Recall matplotlib style syntax

   The correct syntax to apply a style is plt.style.use('style_name').

2. Step 2: Match the correct option

   plt.style.use('ggplot') matches this syntax exactly for 'ggplot'.

3. Final Answer:

   plt.style.use('ggplot') -> Option A

4. Quick Check:

   Use plt.style.use('style') to apply style [OK]

Hint: Use plt.style.use('style_name') to apply styles [OK]

Common Mistakes:

• Using plt.style('style') without .use
• Calling style.use without plt prefix
• Incorrect method order like plt.use.style

3. What will be the effect of running this code snippet?

import matplotlib.pyplot as plt
plt.style.use('seaborn-dark')
plt.plot([1, 2, 3], [4, 5, 6])
plt.show()

medium

A. A line plot with seaborn-dark style applied

B. A line plot with default matplotlib style

C. Syntax error due to wrong style name

D. No plot will be shown

Solution

1. Step 1: Understand plt.style.use('seaborn-dark')

   This line sets the plot style to 'seaborn-dark', changing colors and grid style.

2. Step 2: Analyze the plot commands

   plt.plot creates a line plot, plt.show displays it with the applied style.

3. Final Answer:

   A line plot with seaborn-dark style applied -> Option A

4. Quick Check:

   Style changes plot look [OK]

Hint: plt.style.use sets style before plotting [OK]

Common Mistakes:

• Assuming default style is used after plt.style.use
• Thinking style name causes syntax error
• Expecting no plot to show without plt.show

4. Identify the error in this code that tries to apply a style and plot data:

import matplotlib.pyplot as plt
plt.style.use('dark_background')
plt.plot([1, 2, 3], [3, 2, 1])
plt.show

medium

A. plt.plot arguments are reversed

B. Wrong style name 'dark_background'

C. Missing parentheses in plt.show call

D. plt.style.use should be called after plt.plot

Solution

1. Step 1: Check plt.show usage

   plt.show is a function and needs parentheses to execute.

2. Step 2: Verify style name and plot arguments

   'dark_background' is a valid style, and plot arguments are correctly ordered.

3. Final Answer:

   Missing parentheses in plt.show call -> Option C

4. Quick Check:

   plt.show() needs parentheses to display plot [OK]

Hint: Always call plt.show() with parentheses [OK]

Common Mistakes:

• Forgetting parentheses on plt.show
• Assuming style name is invalid without checking
• Thinking plot arguments order causes error

5. You want to create a bar chart with a style that uses a dark background and grid lines. Which code snippet correctly applies the style and plots the data?

import matplotlib.pyplot as plt
data = [5, 7, 3]
labels = ['A', 'B', 'C']
# Choose the correct code to apply style and plot

hard

A. plt.bar(labels, data)
plt.style.use('dark_background')
plt.show()

B. plt.style.use('dark_background')
plt.bar(labels, data)
plt.show()

C. plt.style.use('ggplot')
plt.bar(labels, data)
plt.show()

D. plt.style('dark_background')
plt.bar(labels, data)
plt.show()

Solution

1. Step 1: Apply style before plotting

   Style must be set before plotting to affect the chart appearance.

2. Step 2: Check style name and plotting method

   'dark_background' is the correct style for dark theme with grid lines; plt.bar plots bar chart correctly.

3. Step 3: Verify plt.show() usage

   plt.show() is called with parentheses to display the plot.

4. Final Answer:

   plt.style.use('dark_background') plt.bar(labels, data) plt.show() -> Option B

5. Quick Check:

   Set style before plot, use plt.show() [OK]

Hint: Set style before plotting, call plt.show() with () [OK]

Common Mistakes:

• Setting style after plotting
• Using plt.style instead of plt.style.use
• Forgetting parentheses on plt.show