1. Introduction to Matplotlib

  • What is Matplotlib?
  • Why use Matplotlib (vs Seaborn, Plotly, etc.)
  • Installation (pip install matplotlib)
  • Import conventions (import matplotlib.pyplot as plt)
  • Understanding the pyplot interface
  • Overview of Matplotlib architecture: Figure, Axes, Axis, Artist hierarchy

2. Basic Plotting

  • Creating simple line plots
    • plt.plot() basic syntax
    • Plotting multiple lines
    • Customizing line style, color, and width
  • Adding labels and titles:
    • plt.title(), plt.xlabel(), plt.ylabel()
  • Displaying grid: plt.grid()
  • Showing and saving plots:
    • plt.show(), plt.savefig() (format options, dpi, bbox_inches)

3. Figure and Axes

  • Understanding the object-oriented interface
    • fig, ax = plt.subplots()
  • Working with multiple subplots:
    • plt.subplot() vs plt.subplots()
    • Grid layout of plots
    • Adjusting spacing: plt.tight_layout() and fig.subplots_adjust()
  • Controlling figure size and DPI
  • Adding and managing multiple Axes in one Figure
  • Using add_subplot() and add_axes()

4. Customization and Styling

  • Line properties: color (color, c), linewidth, linestyle, marker
  • Marker customization: shape, size, edge color, face color
  • Fonts and text properties:
    • fontdict, fontsize, fontweight, fontstyle
  • Colors:
    • Named colors, RGB, RGBA, HEX
    • Using colormaps (cmap)
  • Background and face color customization
  • Global style settings:
    • plt.style.use()
    • Common styles ('ggplot', 'seaborn', 'dark_background', etc.)
    • Creating custom styles

5. Common Plot Types

a. Line Plot

b. Scatter Plot

  • plt.scatter()
  • Controlling marker size (s), color (c), alpha, and colormap

c. Bar and Barh Plot

  • plt.bar() and plt.barh()
  • Grouped and stacked bars

d. Histogram

  • plt.hist()
  • Bins, density, cumulative, histogram type

e. Pie Chart

  • plt.pie()
  • Labels, explode, shadow, startangle, autopct

f. Box Plot and Violin Plot

  • plt.boxplot(), plt.violinplot()

g. Area Plot (plt.fill_between())

h. Step Plot (plt.step())

i. Stem Plot (plt.stem())

j. Error Bars (plt.errorbar())

6. Axes and Ticks

  • Setting axis limits: plt.xlim(), plt.ylim()
  • Customizing tick marks: plt.xticks(), plt.yticks()
  • Rotating tick labels
  • Logarithmic scales: plt.xscale('log'), plt.yscale('log')
  • Axis labels and formatting
  • Twin axes: ax.twinx(), ax.twiny()
  • Secondary axis: ax.secondary_xaxis()

7. Legends and Annotations

  • Adding legend: plt.legend() or ax.legend()
    • Location, labels, frame options, transparency
  • Adding annotations and text:
    • plt.text(), plt.annotate()
    • Arrows, coordinates, alignment
  • Customizing legend handles and labels

8. Working with Images

  • Displaying images: plt.imshow()
  • Image interpolation and color maps
  • Adjusting color bars: plt.colorbar()
  • Reading and writing images with Matplotlib
  • Rescaling and aspect ratio

9. Layout and Subplots

  • plt.subplot() basics
  • Using plt.subplots() for complex layouts
  • Sharing axes: sharex, sharey
  • GridSpec for flexible layout control
  • Adjusting spacing (wspace, hspace)
  • Adding overall title: fig.suptitle()

10. Saving and Exporting Figures

  • plt.savefig() parameters:
    • Format (.png, .jpg, .pdf, .svg, .eps)
    • DPI, transparent background
    • bbox_inches='tight'
  • Vector vs raster formats
  • Exporting for web or publication

11. Advanced Customization

  • Using custom fonts and LaTeX text rendering (rcParams['text.usetex'] = True)
  • Adding multiple legends
  • Combining different plot types in one figure
  • Polar plots: projection='polar'
  • 3D plots: mpl_toolkits.mplot3d
    • ax.plot3D(), ax.scatter3D(), ax.plot_surface()

12. Matplotlib Configuration (rcParams)

  • Overview of matplotlib.rcParams
  • Temporary style using plt.rc_context()
  • Persistent custom styles using .matplotlibrc
  • Setting figure size, font, line width globally

13. Integration with Other Libraries

  • Plotting Pandas DataFrames (df.plot())
  • Integration with NumPy arrays
  • Using Matplotlib with Seaborn, Plotly, or Scikit-learn
  • Interactive plots in Jupyter Notebook (%matplotlib inline, %matplotlib notebook)

14. Interactivity and Animation

  • Interactive mode (plt.ion())
  • Adding widgets and sliders
  • Animations using FuncAnimation
  • Saving animations as .gif or .mp4

15. Practical Use Cases and Projects

  • Plotting time series data
  • Visualizing statistical distributions
  • Correlation matrix heatmaps
  • Visualizing confusion matrices
  • Real-world examples: stock trends, sensor data, etc.

16. Common Errors and Debugging

  • “Figure size too small” or DPI issues
  • Overlapping labels
  • Missing legend labels
  • RuntimeError: Invalid DISPLAY variable (for headless systems)