Home Dataplay Download And Load Merge Data Map Basics Intake An... Nb 2 Html Map Correlation Netw... Timelapse Data Gifs Retrieve Acs Data Pivot Table Sync Data

Don't Look! I'm changing!

URL Copied

Audio Version

BinderBinderBinderOpen Source Love svg3
NPM LicenseActivePython VersionsGitHub last commit

GitHub starsGitHub watchersGitHub forksGitHub followers
TweetTwitter Follow

https://nbdev.fast.ai/tutorials/best_practices.html

https://pandoc.org/MANUAL.html#pandocs-markdown

https://quarto.org/docs/authoring/markdown-basics.html#tables

https://quarto.org/docs/authoring/callouts.html

Table of Contents

Motiviation


Sample Image

I want to create interactive ipynb visualizations that persists when converted to html (without the use of using iframes). Jupyter Notebooks being a web app makes this possible as Ipynb's cell outputs are actually just IFRAMES. Jupyter notebooks run a web server responsible for routing communication from your browser to your local machine which means interactive visualizations are just a javascript script away.

Background

https://bniajfi.org/wp-content/themes/Grido/Theme_v1_3/js/bnia.js https://bniajfi.org/guidepost/communityAdjacents.json

https://bniajfi.org/guidepost/vega_renderer.js https://bniajfi.org/guidepost/custom_plotly_renderers.js

https://bniajfi.org/guidepost/map_handler.js https://bniajfi.org/guidepost/script_vars_events.js https://bniajfi.org/guidepost/script.js

https://github.com/nicolaskruchten/pivottable/wiki/Parameters#options-object-for-pivotui

https://pivottable.js.org/dist/pivot.js

How to Use

pivot_init()
pivot_data(df, rows='Name', cols=['PetalWidth'], output_id='output12', renderName='Table')
  • SepalLength ▾
  • SepalWidth ▾
  • PetalLength ▾
  • ↕↔
  • PetalWidth ▾
  • Name ▾
  • PetalWidth0.10.20.30.40.50.611.11.21.31.41.51.61.71.81.922.12.22.32.42.5Totals
    Name
    Iris-setosa628771150
    Iris-versicolor7351371031150
    Iris-virginica121111566383350
    Totals62877117351381242125663833150

    Perform typical python data science operations

    Methods:

    1. Display the data interactively.

    A new visual is created each time unless you use a referenced id.

    1. Update the referenced table to display a specific column, row, and visualization type

    test

    pivot.pivot_init()
    pivot.pivot_data(df)