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Create line, bar, scatter, and histogram plots with full customization.
Support for pie charts, error bars, stacked plots, and subplots.
Save plots as PNG, SVG, PDF, and control fonts, colors, and layout.
Use interactive mode in Jupyter, Qt, Tkinter, or web dashboards.
Use `pip install matplotlib` and `import matplotlib.pyplot as plt`.
Use NumPy or Pandas to structure your data for plotting.
Use `plt.plot()`, `plt.bar()`, or `plt.scatter()` to generate visuals.
Add titles, labels, legends, and tweak styles with `plt` functions.
Use `plt.savefig()` or `plt.show()` to export or render the chart.
# Matplotlib Line Plot Example
import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]
y = [10, 20, 25, 30, 40]
plt.plot(x, y, marker='o', color='teal')
plt.title("Sales Over Time")
plt.xlabel("Month")
plt.ylabel("Revenue")
plt.grid(True)
plt.show()Visualize distributions, trends, and relationships in datasets.
Generate publication-quality plots for research papers and presentations.
Plot confusion matrices, ROC curves, and training metrics.
Render charts in web apps or notebooks for interactive analysis.
Explore Matplotlib’s ecosystem and find the tools, platforms, and docs to accelerate your workflow.
Common questions about Matplotlib’s capabilities, usage, and ecosystem.