🔥📊📣 Streamlit Plotly Events 📣📊🔥

Ever wanted to harness those awesome events from Plotly charts inside of Streamlit? So did I, so now you can!

Overview, TL;DR

Installation

Install via Pip!

pip install streamlit-plotly-events

Usage

Import the component, and use it like any other Streamlit custom component!

import streamlit as st
from streamlit_plotly_events import plotly_events

# Writes a component similar to st.write()
fig = px.line(x=[1], y=[1])
selected_points = plotly_events(fig)

# Can write inside of things using with!
with st.beta_expander('Plot'):
    fig = px.line(x=[1], y=[1])
    selected_points = plotly_events(fig)

# Select other Plotly events by specifying kwargs
fig = px.line(x=[1], y=[1])
selected_points = plotly_events(fig, click_event=False, hover_event=True)

What the component returns:

Returns
-------
list of dict
    List of dictionaries containing point details (in case multiple overlapping points have been clicked).

    Details can be found here: 
        https://plotly.com/javascript/plotlyjs-events/#event-data

    Format of dict:
        {
            x: int (x value of point),
            y: int (y value of point),
            curveNumber: (index of curve),
            pointNumber: (index of selected point),
            pointIndex: (index of selected point)
        }

Events

Currently, a number of plotly events can be enabled. They can be enabled/disabled using kwargs on the plotly_event() function.

• Click click_event (defaults to True): Triggers event on mouse click of point
• Select select_event: Triggers event when points have been lasso
• Hover hover_event: Triggers event on mouse hover of point (WARNING: VERY RESOURCE INTENSIVE)

Contributing

Please! I'm hardly a frontend developer! I think there's a bunch of amazing functionality we can add into streamlit/plotly!!

This repo follows black formatting standards for the Python parts of the project.

Follow the instructions on the streamlit_components example repository to get up and running, or follow along below!

Quickstart

• Ensure you have Python 3.6+, Node.js, and npm installed.
• Clone this repo.
• Create a new Python virtual environment for the template:

$ cd template
$ python3 -m venv venv  # create venv
$ . venv/bin/activate   # activate venv
$ pip install streamlit # install streamlit
$ pip install plotly # install plotly

• Initialize and run the component template frontend:

$ cd src/streamlit_plotly_events/frontend
$ npm install    # Install npm dependencies
$ npm run start  # Start the Webpack dev server

• From a separate terminal, run the template's Streamlit app:

$ cd src/streamlit_plotly_events
$ . venv/bin/activate  # activate the venv you created earlier
$ streamlit run __init__.py  # run the example server