Python plotting libraries

• How to choose a chart
  • Individual charts
  • Regression
• Preliminaries
• Images
• Plotting
  • Seaborn
  • Bokeh interactive app (standalone)
  • Plotly
  • Altair
  • Useful code snippets
  • Dashboards
    • Streamlit

How to choose a chart

image source: experception.net

good source

source

Individual charts

overview of charts explained: https://datavizcatalogue.com/index.html

boxplot: https://towardsdatascience.com/understanding-boxplots-5e2df7bcbd51

Regression

https://seaborn.pydata.org/tutorial/regression.html

Preliminaries

import matplotlib.pyplot as plt
import pandas as pd

Images

Image from Numpy array

from PIL import Image
j = Image.fromarray(img, mode='RGB')
#print('saving file ',outfile)
j.save(outfile)

Plotting

List of links to plotting resources

Seaborn

Seaborn cheat sheet: https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Seaborn_Cheat_Sheet.pdf Interactive testing of plots: https://www.datacamp.com/community/tutorials/seaborn-python-tutorial

Bokeh interactive app (standalone)

blog post, nbviewer plotly (dash) example: kigadataset Graz (nbviewer)

Plotly

express

main page,walkthrough.ipynb

Great tutorial using plot.ly: link and plotly editor for jupyter lab here

Altair

gallery use cases: https://covid19dashboards.com/,

vega: examples, interactive_seattle_weather, jupyter plugin (not needed for jupyterlab)

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Useful code snippets

to be added

Dashboards

Streamlit

main page, https://www.streamlit.io/gallery

Visualize any Data Easily, from Notebooks to Dashboards

Scipy 2019 Tutorial

James Bednar: link

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