Program: Data Visualization using Matplotlib

This program demonstrates how to create multiple types of plots using Matplotlib for visualizing a dataset. It includes line plots, bar charts, scatter plots, and pie charts.

Scenario: Sales Data Analysis  

We have sales data for different regions over a year. The goal is to visualize trends, distributions, and comparisons.

Code Implementation

import matplotlib.pyplot as plt

import numpy as np

# Sample Data

months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']

region_a_sales = [300, 400, 350, 500, 600, 700, 750, 800, 650, 550, 450, 400]

region_b_sales = [200, 300, 250, 450, 500, 650, 700, 750, 600, 500, 400, 300]

region_c_sales = [100, 150, 200, 300, 400, 500, 550, 600, 500, 400, 300, 200]

regions = ['Region A', 'Region B', 'Region C']

# --- 1. Line Plot: Monthly Sales Trends ---

plt.figure(figsize=(10, 6))

plt.plot(months, region_a_sales, label='Region A', marker='o')

plt.plot(months, region_b_sales, label='Region B', marker='s')

plt.plot(months, region_c_sales, label='Region C', marker='^')

plt.title('Monthly Sales Trends')

plt.xlabel('Months')

plt.ylabel('Sales ($)')

plt.legend()

plt.grid(True)

plt.show()

# --- 2. Bar Chart: Total Sales Comparison ---

total_sales = [sum(region_a_sales), sum(region_b_sales), sum(region_c_sales)]

plt.figure(figsize=(8, 5))

plt.bar(regions, total_sales, color=['blue', 'orange', 'green'])

plt.title('Total Sales by Region')

plt.xlabel('Regions')

plt.ylabel('Total Sales ($)')

plt.show()

# --- 3. Scatter Plot: Region A vs. Region B Sales ---

plt.figure(figsize=(8, 5))

plt.scatter(region_a_sales, region_b_sales, color='purple')

plt.title('Region A vs. Region B Sales')

plt.xlabel('Region A Sales ($)')

plt.ylabel('Region B Sales ($)')

plt.grid(True)

plt.show()

# --- 4. Pie Chart: Sales Distribution ---

plt.figure(figsize=(8, 8))

plt.pie(total_sales, labels=regions, autopct='%1.1f%%', startangle=140, colors=['skyblue', 'orange', 'lightgreen'])

plt.title('Sales Distribution by Region')

plt.show()

# --- 5. Combined Plot: Highlighting Trends and Totals ---

plt.figure(figsize=(10, 6))

width = 0.25  # Bar width

x = np.arange(len(months))  # X positions for bars

# Bar plots for monthly sales

plt.bar(x - width, region_a_sales, width, label='Region A', alpha=0.8, color='blue')

plt.bar(x, region_b_sales, width, label='Region B', alpha=0.8, color='orange')

plt.bar(x + width, region_c_sales, width, label='Region C', alpha=0.8, color='green')

# Line plot overlay for totals

plt.plot(months, np.array(region_a_sales) + np.array(region_b_sales) + np.array(region_c_sales), 

         label='Total Sales', color='red', marker='o', linewidth=2)

plt.title('Monthly Sales by Region with Total Trend')

plt.xlabel('Months')

plt.ylabel('Sales ($)')

plt.xticks(x, months)

plt.legend()

plt.show()

Explanation of Code

1. Line Plot:

   - Visualizes monthly sales trends for all regions with distinct markers and labels.

   - Helps identify peaks and troughs in sales.

2. Bar Chart:

   - Compares total sales across regions.

   - Useful for highlighting differences in overall performance.

3. Scatter Plot:

   - Examines the relationship between sales of Region A and Region B.

   - Highlights potential correlations.

4. Pie Chart:

   - Shows the percentage distribution of total sales across regions.

   - Useful for understanding regional contributions.

5. Combined Plot:

   - Combines bar charts for monthly regional sales with a line plot for total sales.

   - Provides a comprehensive view of sales trends and totals.

Key Features of Matplotlib Used

Customization:

  - Titles, axis labels, legends, and gridlines.

Styling:

  - Different colors, markers, and line styles.

Multiple Plot Types:

  - Line, bar, scatter, pie, and combined plots.

Annotations:

  - Labels like `autopct` in pie charts and legends in all plots.

Sample Visuals

1. Line Plot:

   Shows how sales fluctuate month by month for each region.

2. Bar Chart:

   Highlights which region performed the best in total sales.

3. Scatter Plot:

   Helps in identifying relationships between two regions' sales.

4. Pie Chart:

   Demonstrates the proportion of sales from each region.

5. Combined Plot:

   Offers a consolidated view for detailed analysis.

Program Extension

- Add interactivity with Plotly for dynamic visualizations.

- Use real-world sales datasets from CSV or databases.

- Save plots as images using `plt.savefig('filename.png')`.

This program serves as a foundational tool for analyzing and presenting data visually using Matplotlib.