Data Visualization with Python

Python has a lot of libraries for data visualization and I recently stumbled over an awesome talk from PyCon 2017 by Jake VanderPlas titled "The Python Visualization Landscape" which gives an overview over them:

• Matplotlib
  • seaborn: statistical data visualization
  • Pandas: Dataframes
  • networkx: Graphs
  • ggpy: Python implementation of the grammar of graphics
  • ggplot: Also based on the Grammar of Graphics
  • Yellow Brick
  • scikit-plot
• Datashader: Turns even the largest data into images
• Vaex: visualize and explore large (~billion rows/objects) tabular datasets interactively
• Holoviews
• Javascript
  • plotly
  • bokeh
  • cufflinks
  • bqplot: Plotting library for IPython/Jupyter Notebooks
  • pythreejs: A Jupyter - ThreeJS bridge
  • ipyleaflet: IPython Widget for Leaflet Maps
  • ipyvolume
• OpenGL
  • Vispy: interactive scientific visualization
  • Glumpy: scientific visualization
• Specification languages:
  • Vega
  • Vincent: A Python to Vega Translator
  • Vega Lite
  • Altair
  • d3po: Denoising, Deconvolving, and Decomposing Photon Observations

Maps ¶

Visualizing maps is super hard, as the tools which exist don't have good installers.

Here is what I've tried/seen so far:

• gmt-python: issue 215, video
• geoplotlib

3D ¶

Mayavi ¶

MayaVi is a scientific data visualizer written in Python, which uses VTK and provides a GUI via Tkinter.

• Docs

Example:

# 3rd party modules
from mayavi import mlab
import numpy

x, y = numpy.mgrid[-3:3:100j, -3:3:100j]
z = numpy.sin(x ** 2 + y ** 2)
mlab.surf(x, y, z)

gives