Matplotlib
Matplotlib is a comprehensive 2D plotting library for Python that allows users to create a wide variety of publication-quality graphs, charts, and visualizations. It integrates well with NumPy and Pandas data structures.
plotting
graphs
charts
visualization
python
Matplotlib: Comprehensive 2D Plotting Library
A Python library for creating publication-quality graphs, charts, and visualizations, integrating well with NumPy and Pandas data structures.
What is Matplotlib?
Matplotlib is a comprehensive 2D plotting library for Python that enables users to create a wide variety of publication-quality graphs, charts, and visualizations. It supports many basic and advanced plotting functionalities and integrates well with NumPy and Pandas data structures.
Matplotlib can be used to visualize data in a variety of 2D plots including line plots, bar charts, histograms, scatter plots, area plots, pie charts, etc. It provides a MATLAB-style interface, state-based interface, and an object-oriented interface for building plots. Some key features include support for NumPy arrays, automatic plot generation, customization of all plot elements, LaTeX integration for mathematical expressions, built-in animations, and easy saving of plots in various file formats.
It is an extremely flexible and highly customizable tool that works well for prototyping visualizations, creating graphs for reports/papers, data analysis, and machine learning model visualization. Matplotlib can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc., with just a few lines of code.
Matplotlib Features
Features
- 2D plotting
- Publication quality output
- Support for many plot types (line, bar, scatter, histogram etc)
- Extensive customization options
- IPython/Jupyter notebook integration
- Animations and interactivity
- LaTeX support for mathematical typesetting
Pricing
- Open Source
Pros
Mature and feature-rich
Large user community and extensive documentation
Highly customizable
Integrates well with NumPy, Pandas and SciPy
Output can be saved to many file formats
Cons
Steep learning curve
Plotting code can be verbose
3D plotting support is limited
Cannot do web visualization (unlike Bokeh or Plotly)
Official Links
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