Matplotlib Complete Syllabus – Learn Python Data Visualization Step-by-Step

📘 Matplotlib Complete Syllabus

Matplotlib is one of the most powerful Python libraries for creating static, animated, and interactive visualizations. This course helps you go from basics to expert-level data visualization.

✅ 1. Introduction to Data Visualization

• What is Data Visualization?
• Importance and applications in Data Science
• Overview of Python visualization libraries (Matplotlib, Seaborn, Plotly, etc.)
• Installing and setting up Matplotlib

✅ 2. Getting Started with

Matplotlib

• Importing Matplotlib: import matplotlib.pyplot as plt
• Understanding the pyplot interface
• Creating simple line plots
• Displaying plots using plt.show()

✅ 3. Basic Plot Types

• Line Plot
• Bar Chart (plt.bar(), plt.barh())
• Histogram (plt.hist())
• Pie Chart (plt.pie())
• Scatter Plot (plt.scatter())
• Box Plot (plt.boxplot())

✅ 4. Plot Customization

• Titles, labels, and legends
• Colors, markers, and linestyles
• Gridlines and annotations
• Custom axis limits and ticks

✅ 5. Subplots and Figure Management

• Figure and Axes objects
• Creating multiple subplots (plt.subplot())
• Adjusting layout with plt.tight_layout()
• Working with multiple figures

✅ 6. Object-Oriented Interface

• Understanding the OO approach
• Creating fig, ax = plt.subplots()
• Using ax.plot(), ax.bar() etc.

✅ 7. Advanced Customization

• Adding annotations and text
• Axis scaling and tick formatting
• Customizing legends, titles, and labels

✅ 8. Working with Images and Colors

• Displaying images using plt.imshow()
• Using colormaps (cmap)
• Adding colorbars

✅ 9. 3D Plotting

• 3D Axes setup
• 3D Line, Surface, and Scatter plots
• Wireframe and Contour plots

✅ 10. Plotting with Pandas and NumPy

• Using Matplotlib with NumPy arrays
• Plotting from Pandas DataFrames
• df.plot(kind='line' | 'bar' | 'hist')

✅ 11. Saving and Exporting Plots

• plt.savefig('filename.png')
• Supported formats (PNG, JPG, PDF, SVG)
• Adjusting DPI and figure size

✅ 12. Interactive and Live Plots

• Using %matplotlib inline and %matplotlib notebook
• Live updating plots
• Animations using FuncAnimation

✅ 13. Styling and Themes

• Using built-in styles (plt.style.use())
• Customizing rcParams

✅ 14. Final Projects

• Data dashboard visualization
• Animated data visualization
• Time-series plotting