data = { 'Year': [2018, 2019, 2020, 2021, 2022], 'Sales': [150, 200, 250, 400, 450], 'Profit': [20, 35, 50, 80, 100] } df = pd.DataFrame(data) print(df)

1. Introduction to Matplotlib
A Python Library for Data
Visualization
Prepared by Muhammad Sarfraz

2. What is Matplotlib?
• - Matplotlib is a Python library for data
visualization.
• - Provides control over plots similar to
MATLAB.
• - Works with NumPy and Pandas for scientific
computing.
• - Allows customization of graphs and charts.

3. Why Use Matplotlib?
• - Versatile: Can create line plots, bar charts,
histograms, etc.
• - Highly customizable.
• - Works well with Pandas and NumPy.
• - Supports interactive plotting.

4. Installation
• Install Matplotlib using pip:
• pip install matplotlib

5. Importing Required Libraries
• import matplotlib.pyplot as plt
• import numpy as np

6. Simple Line Plot
• x = np.linspace(0, 10, 100)
• y = np.sin(x)
• plt.plot(x, y)
• plt.show()

7. Bar Chart Example
• categories = ['A', 'B', 'C', 'D']
• values = [10, 20, 15, 25]
• plt.bar(categories, values)
• plt.show()

8. Histogram Example
• data = np.random.randn(1000)
• plt.hist(data, bins=30, color='blue', alpha=0.7)
• plt.show()

9. Scatter Plot Example
• x = np.random.rand(50)
• y = np.random.rand(50)
• plt.scatter(x, y, color='red')
• plt.show()

10. Pie Chart Example
• labels = ['A', 'B', 'C', 'D']
• sizes = [15, 30, 45, 10]
• plt.pie(sizes, labels=labels, autopct='%1.1f%
%')
• plt.show()

11. Customizing Plots
• - Change line styles and colors.
• - Add titles, labels, and legends.
• - Adjust figure size and save plots.

12. Conclusion
• - Matplotlib is a powerful library for creating
visualizations.
• - Provides extensive customization options.
• - Works well with Pandas and NumPy for data
analysis.
• - Suitable for both simple and complex plots.