Comparing
The go-to plotting library for Python — create static, animated, and interactive visualizations with a MATLAB-like API.
The standard machine learning library for Python — clean API for classification, regression, clustering, and preprocessing.
| Metric | matplotlib | scikit-learn |
|---|---|---|
| Ecosystem | Python | Python |
| Difficulty | Intermediate | Intermediate |
| Weekly downloads | 25.0M↑ | 12.0M |
| Latest version | v3.9.2 | v1.5.2 |
| Recipes | 3↑ | 2 |
matplotlib only
Shared
scikit-learn only
Scientific plots, publication figures, embedding charts in reports, quick data exploration, custom visualizations that seaborn can't express, or when you need pixel-level control over a chart.
Classical ML (not deep learning): tabular data, structured features, interpretable models, baseline modeling, feature engineering, cross-validation, and hyperparameter tuning.
Interactive web dashboards (use Plotly or Bokeh), real-time streaming charts, or when you just want something beautiful fast (try seaborn first).
Deep learning / neural networks (use PyTorch or TensorFlow), unstructured data like images or text at scale, real-time inference at very high throughput.
matplotlib
scikit-learn