The document outlines the agenda for PyData Meetup #3, focusing on interactive visualizations using tools like Bokeh, Plotly, and Dash, along with automation techniques using Python. It includes hands-on overviews of these visualization libraries, integrating them with Flask applications, and deploying to Azure. Resources and documentation supporting the presentation are also provided.

1. Part 1: Interactive visualisations
Andrey Vykhodtsev anvykhod@Microsoft.com
Cloud Solution Architect, Data & AI
PyDataLjubljanaMeetup#3

2. Agenda
 Welcome to PyData meetup #3!
 This group is sponsored by PyData &
NumFocus
 Snacks and venue are provided by
 Agenda
 Part 1: Interactive visualisations with
Bokeh, Plotly, Dash and Flask (30-40
min)
 Part 2: Automation and measurements
with Python (30-40 min)

3. Agenda(part1)
 Bokeh hands-on overview
 What is bokeh
 Main structures
 Data Sources
 Applications
 Plotly & Dash
 Quick intro to Plotly and Dash
 Examples and demos
 Embedding your visualizations into Flask
applications
 Publishing your Flask apps to Azure App Services

4. WhatisBokeh
 Python library for building rich interactive
visualizations
 Translated to JS (BokehJS)
 Can be embedded into a web app or distributed
standalone

5. BokehBasics
 Installation & getting started
 Glyphs
 Models
 Widgets
 Tools

6. Buildingbasic
plots
 Custom Dataset link1
 Bar chart
 Line charts & Time Series (my Kaggle example)
 Scatterplots
 Multiple plot layout

7. DataSources,
Filtering,
Interactivity
 ColumnDataSource
 Filtering
 Interactivity
 Linked panning
 Linked Brushing
 Filtering
 Tools
 Hover
 Panning
 Zooming
 Selection tools

8. Bokehwidgets
andapplications
 Controls
 Server
 Running apps
 Embedding

9. HighLevel
Charts
 Box Plots
 Histograms
 Sunburst
 Treemap
 Heatmap
 Geo Data & Datashader
 Graphs & NetworkX integration

10. High Level
Charts
 Holoviews
 Bokeh examples

11. PlotlyQuick
intro
 Plotly offline getting started link

12. DashQuick
intro
 Dash getting started

13. Flask
Microframework
 Easy framework for python
 Supported by all major cloud platforms

14. Embedding
BokehintoFlask
app
 More than one way to do it
 Bokeh server
 Flask server talks to bokeh server

15. Publishing to
AzurewithDocker
&AzureApp
Service
 Easiest way is via docker container

16. Linkssupporting
this
presentation
 Bokeh Documentation
 Bokeh Gallery
 Holoviews
 Plotly offline for Python
 Introducing Dash Article
 Dash Documentation and Github
 Python visualization landscape presentation and slides
 Altair main page

17. Questions please