Make 3D analysis and visualization feel like the rest of scientific Python, so you can focus on the research instead of wrangling spatial data structures and 3D rendering.
pip install 'pyvista[all]' PyVista brings 3D data, mesh analysis, and plotting into the same idiom as NumPy, pandas, and xarray. Array-native datasets, direct memory sharing where possible, and an interface that reads the way your research code already does.
Affiliated Scientific Python interop
PyVista's dataset classes expose points, cells, and attached fields as NumPy arrays, often sharing memory with the underlying object. Hand them to pandas, xarray, scikit-image, or SciPy and keep the same objects through analysis, transformation, and plotting.
More than plotting
Threshold, slice, warp, contour, clip, decimate, interpolate, and run boolean operations with one-line methods on the dataset. Chain them together, inspect intermediate results, and drop the output back into NumPy or pandas when you are done.
From notebooks to applications
The same code runs in Jupyter for exploration, on the server behind a web application, inside a PyQt desktop tool, and in CI as an image regression test. No rewrites when the use case grows.
Built for production
PyVista is image-regression tested on every commit across the supported matrix of Python and VTK releases. Public APIs follow a deliberate deprecation lifecycle, and rendering behavior is locked under visual regression baselines. Code written today still produces the same picture two years from now.
Example
Describe what you want to see and PyVista builds the scene, manages GPU resources, and cleans up when the window closes.
import pyvista as pv
from pyvista import examples
# or pv.read('path/to/mesh.obj')
mesh = examples.download_aero_bracket()
mesh.plot()
import pyvista as pv
from pyvista import examples
# Kitchen CFD simulation: grid with a velocity field
# and furniture meshes as a separate dataset
mesh = examples.download_kitchen()
furniture = examples.download_kitchen(split=True)
# Trace streamlines from above the stove
source = pv.Line((0.08, 2.5, 0.71), (0.08, 4.5, 0.71), resolution=15)
streamlines = mesh.streamlines_from_source(source, max_length=200)
# Compose the scene
pl = pv.Plotter()
pl.add_mesh(furniture, color='slategray')
pl.add_mesh(streamlines.tube(radius=0.02))
pl.show()
Reading and plotting an STL file in low-level Python graphics code versus PyVista. Same result, a fraction of the work.
import vtk
reader = vtk.vtkSTLReader()
reader.SetFileName('bunny.stl')
mapper = vtk.vtkDataSetMapper()
mapper.SetInputConnection(reader.GetOutputPort())
actor = vtk.vtkActor()
actor.SetMapper(mapper)
ren = vtk.vtkRenderer()
ren.AddActor(actor)
renWin = vtk.vtkRenderWindow()
renWin.AddRenderer(ren)
iren = vtk.vtkRenderWindowInteractor()
iren.SetRenderWindow(renWin)
iren.SetInteractorStyle(vtk.vtkInteractorStyleTrackballCamera())
iren.Initialize()
renWin.Render()
iren.Start()import pyvista as pv
mesh = pv.read('bunny.stl')
mesh.plot()
Explore PyVista
Whether you're installing PyVista for the first time, browsing the gallery, or reaching for an API reference, the documentation has what you need.
A guided introduction that walks through PyVista's core workflows.
Start the tutorialInstallation, how-to guides, the API reference, and the full project documentation.
Read the docsRunnable examples across plotting, filters, widgets, volume rendering, and meshing.
Browse examplesThe full plotting, filtering, dataset, and utility API.
Open the APIEcosystem
Official projects extend PyVista with mesh repair, meshing, testing, and compression. Community projects build on it for neuroscience, geosciences, engineering analysis, and more.
Repair damaged or non-watertight surface meshes before handing them off to PyVista for analysis and visualization.
A pytest plugin for image-based regression testing of PyVista plots in visualization-heavy projects.
Zstandard compression for PyVista datasets that keeps large geometries light to store and move.
Mesh generation with Gmsh that hands off directly to PyVista for inspection and visualization.
3D structural geological modeling, with PyVista for interactive visualization.
A neuroscience toolkit for MEG and EEG, with PyVista for 3D visualization of brain activity.
Finite-element solver for partial differential equations, with PyVista for mesh and solution plotting.
Aircraft design and aerodynamic optimization, with PyVista for 3D geometry views.
Pythonic interface to Ansys MAPDL, with PyVista for 3D plotting of finite-element results.
Contributors
PyVista is developed and maintained by contributors from around the world. Thanks to everyone who has helped shape the project.
Cite & publications
PyVista shows up in published research across aerospace, geoscience, neuroscience, electrical engineering, and more. If it helps your work, please cite the JOSS paper so the project stays visible in the scientific record.
Sebastian Müller, Lennart Schüler, Alraune Zech, et al. · Geoscientific model development · 2022
Lorenz Lamm, Simon Zufferey, Hanyi Zhang, et al. · bioRxiv (Cold Spring Harbor Laboratory) · 2024
Sobhit Singh, Logan Lang, Viviana Dovale-Farelo, et al. · Computer Physics Communications · 2021
Shahram Yalameha, Zahra Nourbakhsh, Daryoosh Vashaee · Computer Physics Communications · 2021
Anna Bieber, Cristina Capitanio, Philipp S. Erdmann, et al. · Proceedings of the National Academy of Sciences · 2022
Sullivan, B., & Kaszynski, A. (2019). PyVista: 3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK). Journal of Open Source Software, 4(37), 1450. https://doi.org/10.21105/joss.01450