Applying only newest SelectedPoints in multiple graphs or clearing selection

Looking at Generic Crossfilter Recipe in the documentation, the behaviour is that selecting in one graph and then selecting in another graph selects the intersection of the two graph selections. In order to remove these selections, I have to double-click in both graphs.

My questions is, is there some way to change this behaviour so that either:

  • Selecting in any graph applies only that selection in all graphs regardless of existing selections in the other graphs.
  • I can click a button to clear all current selections.

I’ve tried all I can think of without being able to achieve either of these two. Thanks in advance.

  • I can click a button to clear all current selections.

You could modify the callback to-assign the relevant selection data to None when a button is clicked. I modified the example to illustrate this behaviour. I used a global variable to make sure 1 click = 1 reset and we use the same callback; there’s probably a better way.

import dash
import dash_core_components as dcc
import dash_html_components as html
import numpy as np
import pandas as pd
from dash.dependencies import Input, Output
from dash.exceptions import PreventUpdate

external_stylesheets = ['']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

# make a sample data frame with 6 columns
df = pd.DataFrame({"Col " + str(i+1): np.random.rand(30) for i in range(6)})

total_clicks = 0 # keep track of total clicks

app.layout = html.Div([
    html.Button('Reset selections', id='reset'), # add a button
        dcc.Graph(id='g1', config={'displayModeBar': False}),
        className='four columns'
        dcc.Graph(id='g2', config={'displayModeBar': False}),
        className='four columns'
        dcc.Graph(id='g3', config={'displayModeBar': False}),
        className='four columns'
], className='row')

def get_figure(df, x_col, y_col, selectedpoints, selectedpoints_local):

    if selectedpoints_local and selectedpoints_local['range']:
        ranges = selectedpoints_local['range']
        selection_bounds = {'x0': ranges['x'][0], 'x1': ranges['x'][1],
                            'y0': ranges['y'][0], 'y1': ranges['y'][1]}
        selection_bounds = {'x0': np.min(df[x_col]), 'x1': np.max(df[x_col]),
                            'y0': np.min(df[y_col]), 'y1': np.max(df[y_col])}

    # set which points are selected with the `selectedpoints` property
    # and style those points with the `selected` and `unselected`
    # attribute. see
    # for an explanation
    return {
        'data': [{
            'x': df[x_col],
            'y': df[y_col],
            'text': df.index,
            'textposition': 'top',
            'selectedpoints': selectedpoints,
            'customdata': df.index,
            'type': 'scatter',
            'mode': 'markers+text',
            'marker': { 'color': 'rgba(0, 116, 217, 0.7)', 'size': 12 },
            'unselected': {
                'marker': { 'opacity': 0.3 },
                # make text transparent when not selected
                'textfont': { 'color': 'rgba(0, 0, 0, 0)' }
        'layout': {
            'margin': {'l': 20, 'r': 0, 'b': 15, 't': 5},
            'dragmode': 'select',
            'hovermode': False,
            # Display a rectangle to highlight the previously selected region
            'shapes': [dict({
                'type': 'rect',
                'line': { 'width': 1, 'dash': 'dot', 'color': 'darkgrey' }
            }, **selection_bounds

# this callback defines 3 figures
# as a function of the intersection of their 3 selections
    [Output('g1', 'figure'),
     Output('g2', 'figure'),
     Output('g3', 'figure')],
    [Input('g1', 'selectedData'),
     Input('g2', 'selectedData'),
     Input('g3', 'selectedData'),
     Input('reset', 'n_clicks')] # get the button click data
def callback(selection1, selection2, selection3, n_clicks): # add the click data to the callback
    # reset selectionN and selectedpoints
    global total_clicks
    if n_clicks and n_clicks > total_clicks:
       selectedpoints = selection1 = selection2 = selection3 = None
       total_clicks = n_clicks
        selectedpoints = df.index
        for selected_data in [selection1, selection2, selection3]:
            if selected_data and selected_data['points']:
                selectedpoints = np.intersect1d(selectedpoints,
                    [p['customdata'] for p in selected_data['points']])

    return [get_figure(df, "Col 1", "Col 2", selectedpoints, selection1),
            get_figure(df, "Col 3", "Col 4", selectedpoints, selection2),
            get_figure(df, "Col 5", "Col 6", selectedpoints, selection3)]

if __name__ == '__main__':

Thanks! I suspected it could be done with a button, but I couldn’t figure out how to actually do it. :slight_smile: