Key Features
2D Plotting
Create line, bar, scatter, and histogram plots with full customization.
Chart Variety
Support for pie charts, error bars, stacked plots, and subplots.
Export & Styling
Save plots as PNG, SVG, PDF, and control fonts, colors, and layout.
Interactive Backends
Use interactive mode in Jupyter, Qt, Tkinter, or web dashboards.
How It Works
Install & Import
Use `pip install matplotlib` and `import matplotlib.pyplot as plt`.
Prepare Data
Use NumPy or Pandas to structure your data for plotting.
Create Plot
Use `plt.plot()`, `plt.bar()`, or `plt.scatter()` to generate visuals.
Customize Layout
Add titles, labels, legends, and tweak styles with `plt` functions.
Save or Display
Use `plt.savefig()` or `plt.show()` to export or render the chart.
Code Example
# 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()Use Cases
Exploratory Data Analysis
Visualize distributions, trends, and relationships in datasets.
Scientific Reporting
Generate publication-quality plots for research papers and presentations.
ML Model Evaluation
Plot confusion matrices, ROC curves, and training metrics.
Dashboard Embedding
Render charts in web apps or notebooks for interactive analysis.
Integrations & Resources
Explore Matplotlib’s ecosystem and find the tools, platforms, and docs to accelerate your workflow.
Popular Integrations
- NumPy, Pandas, Seaborn
- Jupyter, Colab, VS Code
- Tkinter, PyQt, Dash
- SciPy, Scikit-learn, TensorFlow
- SVG, PNG, PDF, EPS
Helpful Resources
FAQ
Common questions about Matplotlib’s capabilities, usage, and ecosystem.