Student Activity

Student Activity Guide:

1) Introduction to Matplotlib and Seaborn

• Learn the basics of matplotlib and seaborn for data visualization.

• Understand how to create basic plots.

Example Code:

import matplotlib.pyplot as plt
import seaborn as sns

# Simple line plot
x = [1, 2, 3, 4, 5]
y = [10, 15, 7, 20, 25]
plt.plot(x, y)
plt.show()

Student Exercise: Create a simple plot using matplotlib with different datasets.

2) Creating Line, Bar, and Scatter Plots

• Use matplotlib to create different types of plots.

• Understand the differences between line, bar, and scatter plots.

Example Code:

# Line Plot
plt.plot(x, y, marker='o', linestyle='-', color='b', label='Line Plot')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.title('Line Plot Example')
plt.legend()
plt.show()

# Bar Plot
categories = ['A', 'B', 'C', 'D']
values = [10, 20, 15, 25]
plt.bar(categories, values, color='green')
plt.title('Bar Chart Example')
plt.show()

# Scatter Plot
plt.scatter(x, y, color='red', label='Scatter Data')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.title('Scatter Plot Example')
plt.legend()
plt.show()

Student Exercise: Create a bar plot for a dataset with different categories and values. Also, create a scatter plot comparing two sets of values.

3) Customizing Plots with Labels, Legends, and Colors

• Learn how to modify charts with labels, legends, and colors.

• Customize grid styles and tick marks.

Example Code:

plt.plot(x, y, marker='o', linestyle='--', color='purple', label='Customized Line')
plt.xlabel('X Values', fontsize=12, color='blue')
plt.ylabel('Y Values', fontsize=12, color='red')
plt.title('Customized Plot')
plt.legend()
plt.grid(True)
plt.show()

Student Exercise: Customize an existing plot by changing the colors, line styles, and grid appearance.

4) Interactive Visualizations with Seaborn

• Use seaborn to enhance visualization aesthetics.

• Create interactive and stylish plots using seaborn.

Example Code:

# Sample dataset
import seaborn as sns
import pandas as pd

data = {'Category': ['A', 'B', 'C', 'D', 'E'], 'Values': [10, 20, 15, 30, 25]}
df = pd.DataFrame(data)

# Seaborn bar plot
sns.barplot(x='Category', y='Values', data=df, palette='viridis')
plt.title('Seaborn Bar Chart')
plt.show()

# Seaborn scatter plot
sns.scatterplot(x=x, y=y, hue=y, size=y, palette='coolwarm', sizes=(20, 200))
plt.title('Seaborn Scatter Plot')
plt.show()

Student Exercise: Use seaborn to create a heatmap or violin plot using a sample dataset.