wikiwand.com

Matplotlib - Wikiwand

Matplotlib

Matplotlib (portmanteau of MATLAB, plot, and library[3]) is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK. There is also a procedural "pylab" interface based on a state machine (like OpenGL), designed to closely resemble that of MATLAB, though its use is discouraged.[4] SciPy makes use of Matplotlib.

Quick Facts Original author(s), Developer(s) ...

Close

Matplotlib was originally written by John D. Hunter. Since then it has had an active development community[5] and is distributed under a BSD-style license. Michael Droettboom was nominated as matplotlib's lead developer shortly before John Hunter's death in August 2012[6] and was further joined by Thomas Caswell.[7][8] Matplotlib is a NumFOCUS fiscally sponsored project.[9]

Usage in Scientific Research and Education

Matplotlib is widely used in scientific research as a tool for data visualization. Researchers across disciplines such as physics, astronomy, engineering, and biology use Matplotlib to create publication-quality graphs and plots for their analyses and papers. The library has been used in well-known scientific projects; for example, the Event Horizon Telescope collaboration used Matplotlib to produce visualizations during the effort to create the first image of a black hole.[10] Matplotlib also underpins the plotting functionality of many scientific Python libraries (for instance, pandas uses Matplotlib as its default backend for plotting). Its importance to the scientific community has been acknowledged by institutions such as NASA, which in 2024 awarded a grant to support Matplotlib’s continued development as part of an initiative to fund widely used open-source scientific software.[11]

Thumb
A scatter plot created in Jupyter Notebook using Matplotlib

In education and data science, Matplotlib is frequently used to teach programming and data visualization. It integrates with Jupyter Notebook, allowing students and instructors to generate inline plots and interactively explore data within a notebook environment.[12] Many educational institutions incorporate Matplotlib into their curricula for teaching STEM concepts,[13] and it is widely featured in tutorials, workshops, and open online courses as a primary plotting library. This broad adoption across both academia and industry has helped establish Matplotlib as a standard component of scientific and educational visualization workflows.

Pyplot is a Matplotlib module that provides a MATLAB-like interface.[14] Matplotlib is designed to be as usable as MATLAB, with the ability to use Python, and the advantage of being free and open-source.

Matplotlib supports various types of 2 dimensional and 3 dimensional plots. The support for two dimensional plots is robust including: line plots, histogram, scatter plots, polar plots, box plots, pie charts, bar graphs, and heat maps. The support for three dimensional plots was added later and while it is good, it is not as robust as 2 dimensional plots. You can have 3 dimensional line plots, scatter plots, and surface plots. You can determine what plot type you need by considering a few factors:
Comparing a relationship between different variables: line plot, heat map, contour plot, scatter plot
Looking for distribution of your dataset: box plot, histogram
Comparing different categories: box plot, pie chart, bar graph

Examples

  • Thumb

    Line plot (2D or 3D)
    Displays data in line format showing trends over time

  • Thumb

    Histogram
    Used to display the frequency of subsets in a dataset

  • Thumb

    Scatter plot (2D or 3D)
    Displays each component of the dataset as a single plotted point; shows relationships between points

  • Thumb

    3D surface plot
    Represents the dataset in the X, Y, and Z plane

  • Thumb

    Contour plot
    Graphs data connecting points of equal values giving a 3D effect

  • Thumb

    Polar plot
    Visualizing data in a circular plane

  • Thumb

    Image plot
    Visualizes 2D arrays as an image

  • Thumb

    Box Plot
    Used to show the distribution of a data set. The median and outliers are highlighted

  • Thumb

    Pie Chart
    Useful for showing the proportions of each category involved

  • Thumb

    Bar Graph
    Useful for comparing different groups side by side

  • Thumb

    Heat map
    Displays 2D array of dataset in a colored image to visualize the frequency or density in a specific area

Matplotlib-animation[15] capabilities are intended for visualizing how certain data changes. However, one can use the functionality in any way required.

These animations are defined as a function of frame number (or time). In other words, one defines a function that takes a frame number as input and defines/updates the matplotlib-figure based on it.

The time at the beginning of a frame-number since the start of animation can be calculated as - {\displaystyle {\text{time}}={\frac {{\text{frame-number}}-1}{\text{FPS}}}}

Several toolkits are available which extend Matplotlib functionality. Some are separate downloads, others ship with the Matplotlib source code but have external dependencies.[16]

  • Basemap: map plotting with various map projections, coastlines, and political boundaries[17]
  • Cartopy: a mapping library featuring object-oriented map projection definitions, and arbitrary point, line, polygon and image transformation capabilities.[18] (Matplotlib v1.2 and above)
  • Excel tools: utilities for exchanging data with Microsoft Excel
  • GTK tools: interface to the GTK library
  • Qt interface
  • Mplot3d: 3-D plots
  • Natgrid: interface to the natgrid library for gridding irregularly spaced data.
  • tikzplotlib: export to Pgfplots for smooth integration into LaTeX documents (formerly known as matplotlib2tikz)[19]
  • Seaborn: provides an API on top of Matplotlib that offers sane choices for plot style and color defaults, defines simple high-level functions for common statistical plot types, and integrates with the functionality provided by Pandas
  • GeoPandas:[20] simplifies geospatial work in Python without needing a spatial database like PostGIS[21]
  • Cartopy: streamlines map creation in matplotlib by enabling users to specify a projection and add coastlines with a single line of code[22]
  • Biggles[23]
  • Chaco[24]
  • DISLIN
  • GNU Octave
  • gnuplotlib – plotting for numpy with a gnuplot backend
  • Gnuplot-py[25]
  • PLplot – Python bindings available
  • SageMath – uses Matplotlib to draw plots
  • SciPy (modules plt and gplt)
  • Plotly – for interactive, online Matplotlib and Python graphs
  • Bokeh[26] – Python interactive visualization library that targets modern web browsers for presentation
Loading related searches...