matplotlib
Matplotlib - Data Visualization
The most widely used library for 2D (and basic 3D) plotting. It provides full control over every element of a figure, from line styles to axis spines.
When to Use
- Creating publication-quality 2D plots (Line, Scatter, Bar, Hist)
- Visualizing scientific data (Heatmaps, Contours, Vector fields)
- Generating complex multi-panel figures
- Fine-tuning plots for papers/reports (LaTeX support)
- Building custom visualization tools and dashboards
- Plotting data directly from NumPy arrays or Pandas DataFrames
Reference Documentation
Official docs: https://matplotlib.org/stable/index.html
Gallery: https://matplotlib.org/stable/gallery/index.html (Essential for finding examples)
Search patterns: plt.subplots, ax.set_title, ax.legend, plt.savefig, matplotlib.colors
More from tondevrel/scientific-agent-skills
xgboost-lightgbm
Industry-standard gradient boosting libraries for tabular data and structured datasets. XGBoost and LightGBM excel at classification and regression tasks on tables, CSVs, and databases. Use when working with tabular machine learning, gradient boosting trees, Kaggle competitions, feature importance analysis, hyperparameter tuning, or when you need state-of-the-art performance on structured data.
208opencv
Open Source Computer Vision Library (OpenCV) for real-time image processing, video analysis, object detection, face recognition, and camera calibration. Use when working with images, videos, cameras, edge detection, contours, feature detection, image transformations, object tracking, optical flow, or any computer vision task.
147ortools
Google Optimization Tools. An open-source software suite for optimization, specialized in vehicle routing, flows, integer and linear programming, and constraint programming. Features the world-class CP-SAT solver. Use for vehicle routing problems (VRP), scheduling, bin packing, knapsack problems, linear programming (LP), integer programming (MIP), network flows, constraint programming, combinatorial optimization, resource allocation, shift scheduling, job-shop scheduling, and discrete optimization problems.
76plotly
A high-level interactive graphing library for Python. Ideal for web-based visualizations, 3D plots, and complex interactive dashboards. Built on plotly.js, it allows users to zoom, pan, and hover over data points in a browser-based environment. Use for interactive charts, web applications, Jupyter notebooks, 3D data visualization, geographic maps, financial charts, animations, time-series analysis, and building production-ready dashboards with Dash.
58scipy
Comprehensive guide for SciPy - the fundamental library for scientific and technical computing in Python. Use for integration, optimization, interpolation, linear algebra, signal processing, statistics, ODEs, Fourier transforms, and advanced scientific algorithms. Built on NumPy and essential for research and engineering.
52numpy
Comprehensive guide for NumPy - the fundamental package for scientific computing in Python. Use for array operations, linear algebra, random number generation, Fourier transforms, mathematical functions, and high-performance numerical computing. Foundation for SciPy, pandas, scikit-learn, and all scientific Python.
47