MATPLOTLIB

matt
Why Matplotlib is Important
in Data Science and AI

Matplotlib is the foundation of Python data visualization, forming the base for libraries like Seaborn and Pandas plotting.

It enables quick visualization of patterns, trends, and outliers, crucial for exploratory data analysis (EDA). Understanding data visually helps in selecting appropriate features for machine learning models.

  • It offers full control over plots, making it ideal for publication-quality and customized visualizations.

  • Matplotlib supports a wide range of plots: line, bar, scatter, histogram, pie, 3D, heatmaps, etc.

  • It integrates seamlessly with NumPy and Pandas, making it easy to visualize structured data.

  • Visualizations improve communication of results to non-technical stakeholders and decision makers.

  • Diagnostic plots like residuals, correlation matrices, and learning curves are vital in AI model tuning.

  • It supports interactive plotting in Jupyter notebooks, ideal for teaching, demos, and prototyping.

  • Mastery of Matplotlib is a stepping stone to more advanced tools like Seaborn, Plotly, and Dash.

mat
01

Module 1: Introduction to Data Visualization

  1. Why visualize data?

  2. Data visualization in Data Science & AI

  3. Overview of Python plotting libraries

  4. Installing and importing Matplotlib

  5. pyplot vs object-oriented API

02

Module 2: Basic Plotting with pyplot

  1. Creating a simple line plot

  2. Plotting multiple lines

  3. Adding title, labels (xlabel, ylabel, title)

  4. Legends, grid, and axis limits

  5. Saving plots (savefig())

03

Module 3: Plot Types in Matplotlib

  1. Line plots

  2. Bar charts

    1. Vertical and horizontal bars

    2. Grouped and stacked bar charts

  3. Histograms

    1. Bins, frequency distribution

    2. Normalization

  4. Scatter plots

    1. Customizing markers, colors

    2. Annotating points

  5. Pie charts

    1. Labels, explode, percentages

04

Module 4: Customization and Styling

  1. Line styles, markers, and colors

  2. Figure size and DPI

  3. Fonts and label customization

  4. Using styles (plt.style.use)

  5. Themes and rcParams

05

Module 5: Subplots and Layouts

  1. subplot() vs subplots()

  2. GridSpec for advanced layout control

  3. Adjusting spacing: tight_layout() and subplots_adjust()

  4. Sharing axes across plots

06

Module 6: Working with Axes Object (OOP Approach)

  1. Creating figure and axes with plt.subplots()

  2. Accessing and modifying Axes methods

  3. Adding multiple plots on same Axes

  4. Titles, labels, legends using OOP API

07

Module 7: Annotations and Text

  1. Adding text with text(), annotate()

  2. Arrows and highlights

  3. Customizing annotation styles

  4. Highlighting important data points

08

Module 8: Plotting with Pandas and NumPy

  1. Plotting Pandas Series and DataFrames

  2. Using NumPy arrays for visualizations

  3. Comparing with pure matplotlib.pyplot

  4. Time series plotting with Pandas

09

Module 9: Advanced Plot Types

  1. Box plots

  2. Violin plots (via extension)

  3. Heatmaps using imshow() or pcolormesh()

  4. Error bars (errorbar())

  5. 3D plots using mpl_toolkits.mplot3d

    1. Line, scatter, surface plots

10

Module 10: Interactive Plots (Jupyter + Widgets)

  1. %matplotlib inline, %matplotlib notebook

  2. Enabling zoom, pan

  3. Integrating with ipywidgets

  4. Simple dashboard with interactive controls

11

Module 11: Plot Styling for Presentation and Publication

  1. Plot aesthetics for reports

  2. Exporting high-resolution images (SVG, PDF, PNG)

  3. Consistent color palettes

  4. Branding and watermarking plots

12

Module 12: Case Studies and Projects

  1. EDA of Titanic Dataset

  2. Visualizing COVID-19 trends

  3. Visualizing ML results (confusion matrix, ROC curve)

  4. Comparing models visually

  5. Creating a visual story from a dataset

Need Help?