add support for new histogram event data by chriddyp · Pull Request #144 · plotly/dash-core-components

chriddyp · 2018-01-11T19:41:12Z

chriddyp · 2018-01-11T20:24:43Z

Usage

importjsonfromtextwrapimportdedentasdimportdashfromdash.dependenciesimportInput, Outputimportdash_core_componentsasdccimportdash_html_componentsashtmlimportdatetimeimportpandasaspddf=pd.read_csv(
('https://raw.githubusercontent.com/plotly/''datasets/master/1962_2006_walmart_store_openings.csv'),
parse_dates=[1, 2],
infer_datetime_format=True
)
future_indices=df['OPENDATE'] >datetime.datetime(year=2050,month=1,day=1)
df.loc[future_indices, 'OPENDATE'] -=datetime.timedelta(days=365.25*100)
app=dash.Dash(__name__)
app.scripts.config.serve_locally=Trueapp.css.config.serve_locally=Truestyles= {
'pre': {
'border': 'thin lightgrey solid',
'overflowX': 'scroll'
}
}
app.layout=html.Div([
dcc.Graph(
id='basic-interactions',
figure={
'data': [
{
'x': df['OPENDATE'],
'text': df['STRCITY'],
'customdata': df['storenum'],
'name': 'Open Date',
'type': 'histogram'
},
# {# 'x': df['date_super'],# 'text': df['STRCITY'],# 'customdata': df['storenum'],# 'name': 'Super Date',# 'type': 'histogram'# }
],
'layout': {}
}
),
html.Div(className='row', children=[
html.Div([
dcc.Markdown(d(""" **Hover Data** Mouse over values in the graph. """)),
html.Pre(id='hover-data', style=styles['pre'])
], className='three columns'),
html.Div([
dcc.Markdown(d(""" **Click Data** Click on points in the graph. """)),
html.Pre(id='click-data', style=styles['pre']),
], className='three columns'),
html.Div([
dcc.Markdown(d(""" **Selection Data** Choose the lasso or rectangle tool in the graph's menu bar and then select points in the graph. """)),
html.Pre(id='selected-data', style=styles['pre']),
], className='three columns'),
html.Div([
dcc.Markdown(d(""" **Zoom and Relayout Data** Click and drag on the graph to zoom or click on the zoom buttons in the graph's menu bar. Clicking on legend items will also fire this event. """)),
html.Pre(id='relayout-data', style=styles['pre']),
], className='three columns')
])
])
@app.callback(Output('hover-data', 'children'), [Input('basic-interactions', 'hoverData')])defdisplay_hover_data(hoverData):
returnjson.dumps(hoverData, indent=2)
@app.callback(Output('click-data', 'children'), [Input('basic-interactions', 'clickData')])defdisplay_click_data(clickData):
returnjson.dumps(clickData, indent=2)
@app.callback(Output('selected-data', 'children'), [Input('basic-interactions', 'selectedData')])defdisplay_selected_data(selectedData):
returnjson.dumps(selectedData, indent=2)
@app.callback(Output('relayout-data', 'children'), [Input('basic-interactions', 'relayoutData')])defdisplay_selected_data(relayoutData):
returnjson.dumps(relayoutData, indent=2)
if__name__=='__main__':
app.run_server(debug=True)

chriddyp · 2018-01-11T20:25:40Z

big thanks to @alexcjohnson for this one 🎉

chriddyp · 2018-01-11T19:41:12Z

thanks to plotly/plotly.js#2113

chriddyp · 2018-01-11T20:24:43Z

Usage

importjsonfromtextwrapimportdedentasdimportdashfromdash.dependenciesimportInput, Outputimportdash_core_componentsasdccimportdash_html_componentsashtmlimportdatetimeimportpandasaspddf=pd.read_csv(
('https://raw.githubusercontent.com/plotly/''datasets/master/1962_2006_walmart_store_openings.csv'),
parse_dates=[1, 2],
infer_datetime_format=True
)
future_indices=df['OPENDATE'] >datetime.datetime(year=2050,month=1,day=1)
df.loc[future_indices, 'OPENDATE'] -=datetime.timedelta(days=365.25*100)
app=dash.Dash(__name__)
app.scripts.config.serve_locally=Trueapp.css.config.serve_locally=Truestyles= {
'pre': {
'border': 'thin lightgrey solid',
'overflowX': 'scroll'
}
}
app.layout=html.Div([
dcc.Graph(
id='basic-interactions',
figure={
'data': [
{
'x': df['OPENDATE'],
'text': df['STRCITY'],
'customdata': df['storenum'],
'name': 'Open Date',
'type': 'histogram'
},
# {# 'x': df['date_super'],# 'text': df['STRCITY'],# 'customdata': df['storenum'],# 'name': 'Super Date',# 'type': 'histogram'# }
],
'layout': {}
}
),
html.Div(className='row', children=[
html.Div([
dcc.Markdown(d(""" **Hover Data** Mouse over values in the graph. """)),
html.Pre(id='hover-data', style=styles['pre'])
], className='three columns'),
html.Div([
dcc.Markdown(d(""" **Click Data** Click on points in the graph. """)),
html.Pre(id='click-data', style=styles['pre']),
], className='three columns'),
html.Div([
dcc.Markdown(d(""" **Selection Data** Choose the lasso or rectangle tool in the graph's menu bar and then select points in the graph. """)),
html.Pre(id='selected-data', style=styles['pre']),
], className='three columns'),
html.Div([
dcc.Markdown(d(""" **Zoom and Relayout Data** Click and drag on the graph to zoom or click on the zoom buttons in the graph's menu bar. Clicking on legend items will also fire this event. """)),
html.Pre(id='relayout-data', style=styles['pre']),
], className='three columns')
])
])
@app.callback(Output('hover-data', 'children'), [Input('basic-interactions', 'hoverData')])defdisplay_hover_data(hoverData):
returnjson.dumps(hoverData, indent=2)
@app.callback(Output('click-data', 'children'), [Input('basic-interactions', 'clickData')])defdisplay_click_data(clickData):
returnjson.dumps(clickData, indent=2)
@app.callback(Output('selected-data', 'children'), [Input('basic-interactions', 'selectedData')])defdisplay_selected_data(selectedData):
returnjson.dumps(selectedData, indent=2)
@app.callback(Output('relayout-data', 'children'), [Input('basic-interactions', 'relayoutData')])defdisplay_selected_data(relayoutData):
returnjson.dumps(relayoutData, indent=2)
if__name__=='__main__':
app.run_server(debug=True)

chriddyp · 2018-01-11T20:25:40Z

big thanks to @alexcjohnson for this one 🎉

chriddyp · 2018-01-11T19:41:12Z

thanks to plotly/plotly.js#2113

chriddyp · 2018-01-11T20:24:43Z

Usage

importjsonfromtextwrapimportdedentasdimportdashfromdash.dependenciesimportInput, Outputimportdash_core_componentsasdccimportdash_html_componentsashtmlimportdatetimeimportpandasaspddf=pd.read_csv(
('https://raw.githubusercontent.com/plotly/''datasets/master/1962_2006_walmart_store_openings.csv'),
parse_dates=[1, 2],
infer_datetime_format=True
)
future_indices=df['OPENDATE'] >datetime.datetime(year=2050,month=1,day=1)
df.loc[future_indices, 'OPENDATE'] -=datetime.timedelta(days=365.25*100)
app=dash.Dash(__name__)
app.scripts.config.serve_locally=Trueapp.css.config.serve_locally=Truestyles= {
'pre': {
'border': 'thin lightgrey solid',
'overflowX': 'scroll'
}
}
app.layout=html.Div([
dcc.Graph(
id='basic-interactions',
figure={
'data': [
{
'x': df['OPENDATE'],
'text': df['STRCITY'],
'customdata': df['storenum'],
'name': 'Open Date',
'type': 'histogram'
},
# {# 'x': df['date_super'],# 'text': df['STRCITY'],# 'customdata': df['storenum'],# 'name': 'Super Date',# 'type': 'histogram'# }
],
'layout': {}
}
),
html.Div(className='row', children=[
html.Div([
dcc.Markdown(d(""" **Hover Data** Mouse over values in the graph. """)),
html.Pre(id='hover-data', style=styles['pre'])
], className='three columns'),
html.Div([
dcc.Markdown(d(""" **Click Data** Click on points in the graph. """)),
html.Pre(id='click-data', style=styles['pre']),
], className='three columns'),
html.Div([
dcc.Markdown(d(""" **Selection Data** Choose the lasso or rectangle tool in the graph's menu bar and then select points in the graph. """)),
html.Pre(id='selected-data', style=styles['pre']),
], className='three columns'),
html.Div([
dcc.Markdown(d(""" **Zoom and Relayout Data** Click and drag on the graph to zoom or click on the zoom buttons in the graph's menu bar. Clicking on legend items will also fire this event. """)),
html.Pre(id='relayout-data', style=styles['pre']),
], className='three columns')
])
])
@app.callback(Output('hover-data', 'children'), [Input('basic-interactions', 'hoverData')])defdisplay_hover_data(hoverData):
returnjson.dumps(hoverData, indent=2)
@app.callback(Output('click-data', 'children'), [Input('basic-interactions', 'clickData')])defdisplay_click_data(clickData):
returnjson.dumps(clickData, indent=2)
@app.callback(Output('selected-data', 'children'), [Input('basic-interactions', 'selectedData')])defdisplay_selected_data(selectedData):
returnjson.dumps(selectedData, indent=2)
@app.callback(Output('relayout-data', 'children'), [Input('basic-interactions', 'relayoutData')])defdisplay_selected_data(relayoutData):
returnjson.dumps(relayoutData, indent=2)
if__name__=='__main__':
app.run_server(debug=True)

chriddyp · 2018-01-11T20:25:40Z

big thanks to @alexcjohnson for this one 🎉

chriddyp · 2018-01-11T19:41:12Z

thanks to plotly/plotly.js#2113

chriddyp · 2018-01-11T20:24:43Z

Usage

importjsonfromtextwrapimportdedentasdimportdashfromdash.dependenciesimportInput, Outputimportdash_core_componentsasdccimportdash_html_componentsashtmlimportdatetimeimportpandasaspddf=pd.read_csv(
('https://raw.githubusercontent.com/plotly/''datasets/master/1962_2006_walmart_store_openings.csv'),
parse_dates=[1, 2],
infer_datetime_format=True
)
future_indices=df['OPENDATE'] >datetime.datetime(year=2050,month=1,day=1)
df.loc[future_indices, 'OPENDATE'] -=datetime.timedelta(days=365.25*100)
app=dash.Dash(__name__)
app.scripts.config.serve_locally=Trueapp.css.config.serve_locally=Truestyles= {
'pre': {
'border': 'thin lightgrey solid',
'overflowX': 'scroll'
}
}
app.layout=html.Div([
dcc.Graph(
id='basic-interactions',
figure={
'data': [
{
'x': df['OPENDATE'],
'text': df['STRCITY'],
'customdata': df['storenum'],
'name': 'Open Date',
'type': 'histogram'
},
# {# 'x': df['date_super'],# 'text': df['STRCITY'],# 'customdata': df['storenum'],# 'name': 'Super Date',# 'type': 'histogram'# }
],
'layout': {}
}
),
html.Div(className='row', children=[
html.Div([
dcc.Markdown(d(""" **Hover Data** Mouse over values in the graph. """)),
html.Pre(id='hover-data', style=styles['pre'])
], className='three columns'),
html.Div([
dcc.Markdown(d(""" **Click Data** Click on points in the graph. """)),
html.Pre(id='click-data', style=styles['pre']),
], className='three columns'),
html.Div([
dcc.Markdown(d(""" **Selection Data** Choose the lasso or rectangle tool in the graph's menu bar and then select points in the graph. """)),
html.Pre(id='selected-data', style=styles['pre']),
], className='three columns'),
html.Div([
dcc.Markdown(d(""" **Zoom and Relayout Data** Click and drag on the graph to zoom or click on the zoom buttons in the graph's menu bar. Clicking on legend items will also fire this event. """)),
html.Pre(id='relayout-data', style=styles['pre']),
], className='three columns')
])
])
@app.callback(Output('hover-data', 'children'), [Input('basic-interactions', 'hoverData')])defdisplay_hover_data(hoverData):
returnjson.dumps(hoverData, indent=2)
@app.callback(Output('click-data', 'children'), [Input('basic-interactions', 'clickData')])defdisplay_click_data(clickData):
returnjson.dumps(clickData, indent=2)
@app.callback(Output('selected-data', 'children'), [Input('basic-interactions', 'selectedData')])defdisplay_selected_data(selectedData):
returnjson.dumps(selectedData, indent=2)
@app.callback(Output('relayout-data', 'children'), [Input('basic-interactions', 'relayoutData')])defdisplay_selected_data(relayoutData):
returnjson.dumps(relayoutData, indent=2)
if__name__=='__main__':
app.run_server(debug=True)

chriddyp · 2018-01-11T20:25:40Z

big thanks to @alexcjohnson for this one 🎉

chriddyp · 2018-01-11T19:41:12Z

thanks to plotly/plotly.js#2113

chriddyp · 2018-01-11T20:24:43Z

Usage

importjsonfromtextwrapimportdedentasdimportdashfromdash.dependenciesimportInput, Outputimportdash_core_componentsasdccimportdash_html_componentsashtmlimportdatetimeimportpandasaspddf=pd.read_csv(
('https://raw.githubusercontent.com/plotly/''datasets/master/1962_2006_walmart_store_openings.csv'),
parse_dates=[1, 2],
infer_datetime_format=True
)
future_indices=df['OPENDATE'] >datetime.datetime(year=2050,month=1,day=1)
df.loc[future_indices, 'OPENDATE'] -=datetime.timedelta(days=365.25*100)
app=dash.Dash(__name__)
app.scripts.config.serve_locally=Trueapp.css.config.serve_locally=Truestyles= {
'pre': {
'border': 'thin lightgrey solid',
'overflowX': 'scroll'
}
}
app.layout=html.Div([
dcc.Graph(
id='basic-interactions',
figure={
'data': [
{
'x': df['OPENDATE'],
'text': df['STRCITY'],
'customdata': df['storenum'],
'name': 'Open Date',
'type': 'histogram'
},
# {# 'x': df['date_super'],# 'text': df['STRCITY'],# 'customdata': df['storenum'],# 'name': 'Super Date',# 'type': 'histogram'# }
],
'layout': {}
}
),
html.Div(className='row', children=[
html.Div([
dcc.Markdown(d(""" **Hover Data** Mouse over values in the graph. """)),
html.Pre(id='hover-data', style=styles['pre'])
], className='three columns'),
html.Div([
dcc.Markdown(d(""" **Click Data** Click on points in the graph. """)),
html.Pre(id='click-data', style=styles['pre']),
], className='three columns'),
html.Div([
dcc.Markdown(d(""" **Selection Data** Choose the lasso or rectangle tool in the graph's menu bar and then select points in the graph. """)),
html.Pre(id='selected-data', style=styles['pre']),
], className='three columns'),
html.Div([
dcc.Markdown(d(""" **Zoom and Relayout Data** Click and drag on the graph to zoom or click on the zoom buttons in the graph's menu bar. Clicking on legend items will also fire this event. """)),
html.Pre(id='relayout-data', style=styles['pre']),
], className='three columns')
])
])
@app.callback(Output('hover-data', 'children'), [Input('basic-interactions', 'hoverData')])defdisplay_hover_data(hoverData):
returnjson.dumps(hoverData, indent=2)
@app.callback(Output('click-data', 'children'), [Input('basic-interactions', 'clickData')])defdisplay_click_data(clickData):
returnjson.dumps(clickData, indent=2)
@app.callback(Output('selected-data', 'children'), [Input('basic-interactions', 'selectedData')])defdisplay_selected_data(selectedData):
returnjson.dumps(selectedData, indent=2)
@app.callback(Output('relayout-data', 'children'), [Input('basic-interactions', 'relayoutData')])defdisplay_selected_data(relayoutData):
returnjson.dumps(relayoutData, indent=2)
if__name__=='__main__':
app.run_server(debug=True)

chriddyp · 2018-01-11T20:25:40Z

big thanks to @alexcjohnson for this one 🎉

chriddyp · 2018-01-11T19:41:12Z

thanks to plotly/plotly.js#2113

chriddyp · 2018-01-11T20:24:43Z

Usage

importjsonfromtextwrapimportdedentasdimportdashfromdash.dependenciesimportInput, Outputimportdash_core_componentsasdccimportdash_html_componentsashtmlimportdatetimeimportpandasaspddf=pd.read_csv(
('https://raw.githubusercontent.com/plotly/''datasets/master/1962_2006_walmart_store_openings.csv'),
parse_dates=[1, 2],
infer_datetime_format=True
)
future_indices=df['OPENDATE'] >datetime.datetime(year=2050,month=1,day=1)
df.loc[future_indices, 'OPENDATE'] -=datetime.timedelta(days=365.25*100)
app=dash.Dash(__name__)
app.scripts.config.serve_locally=Trueapp.css.config.serve_locally=Truestyles= {
'pre': {
'border': 'thin lightgrey solid',
'overflowX': 'scroll'
}
}
app.layout=html.Div([
dcc.Graph(
id='basic-interactions',
figure={
'data': [
{
'x': df['OPENDATE'],
'text': df['STRCITY'],
'customdata': df['storenum'],
'name': 'Open Date',
'type': 'histogram'
},
# {# 'x': df['date_super'],# 'text': df['STRCITY'],# 'customdata': df['storenum'],# 'name': 'Super Date',# 'type': 'histogram'# }
],
'layout': {}
}
),
html.Div(className='row', children=[
html.Div([
dcc.Markdown(d(""" **Hover Data** Mouse over values in the graph. """)),
html.Pre(id='hover-data', style=styles['pre'])
], className='three columns'),
html.Div([
dcc.Markdown(d(""" **Click Data** Click on points in the graph. """)),
html.Pre(id='click-data', style=styles['pre']),
], className='three columns'),
html.Div([
dcc.Markdown(d(""" **Selection Data** Choose the lasso or rectangle tool in the graph's menu bar and then select points in the graph. """)),
html.Pre(id='selected-data', style=styles['pre']),
], className='three columns'),
html.Div([
dcc.Markdown(d(""" **Zoom and Relayout Data** Click and drag on the graph to zoom or click on the zoom buttons in the graph's menu bar. Clicking on legend items will also fire this event. """)),
html.Pre(id='relayout-data', style=styles['pre']),
], className='three columns')
])
])
@app.callback(Output('hover-data', 'children'), [Input('basic-interactions', 'hoverData')])defdisplay_hover_data(hoverData):
returnjson.dumps(hoverData, indent=2)
@app.callback(Output('click-data', 'children'), [Input('basic-interactions', 'clickData')])defdisplay_click_data(clickData):
returnjson.dumps(clickData, indent=2)
@app.callback(Output('selected-data', 'children'), [Input('basic-interactions', 'selectedData')])defdisplay_selected_data(selectedData):
returnjson.dumps(selectedData, indent=2)
@app.callback(Output('relayout-data', 'children'), [Input('basic-interactions', 'relayoutData')])defdisplay_selected_data(relayoutData):
returnjson.dumps(relayoutData, indent=2)
if__name__=='__main__':
app.run_server(debug=True)

chriddyp · 2018-01-11T20:25:40Z

big thanks to @alexcjohnson for this one 🎉

chriddyp · 2018-01-11T19:41:12Z

thanks to plotly/plotly.js#2113

chriddyp · 2018-01-11T20:24:43Z

Usage

importjsonfromtextwrapimportdedentasdimportdashfromdash.dependenciesimportInput, Outputimportdash_core_componentsasdccimportdash_html_componentsashtmlimportdatetimeimportpandasaspddf=pd.read_csv(
('https://raw.githubusercontent.com/plotly/''datasets/master/1962_2006_walmart_store_openings.csv'),
parse_dates=[1, 2],
infer_datetime_format=True
)
future_indices=df['OPENDATE'] >datetime.datetime(year=2050,month=1,day=1)
df.loc[future_indices, 'OPENDATE'] -=datetime.timedelta(days=365.25*100)
app=dash.Dash(__name__)
app.scripts.config.serve_locally=Trueapp.css.config.serve_locally=Truestyles= {
'pre': {
'border': 'thin lightgrey solid',
'overflowX': 'scroll'
}
}
app.layout=html.Div([
dcc.Graph(
id='basic-interactions',
figure={
'data': [
{
'x': df['OPENDATE'],
'text': df['STRCITY'],
'customdata': df['storenum'],
'name': 'Open Date',
'type': 'histogram'
},
# {# 'x': df['date_super'],# 'text': df['STRCITY'],# 'customdata': df['storenum'],# 'name': 'Super Date',# 'type': 'histogram'# }
],
'layout': {}
}
),
html.Div(className='row', children=[
html.Div([
dcc.Markdown(d(""" **Hover Data** Mouse over values in the graph. """)),
html.Pre(id='hover-data', style=styles['pre'])
], className='three columns'),
html.Div([
dcc.Markdown(d(""" **Click Data** Click on points in the graph. """)),
html.Pre(id='click-data', style=styles['pre']),
], className='three columns'),
html.Div([
dcc.Markdown(d(""" **Selection Data** Choose the lasso or rectangle tool in the graph's menu bar and then select points in the graph. """)),
html.Pre(id='selected-data', style=styles['pre']),
], className='three columns'),
html.Div([
dcc.Markdown(d(""" **Zoom and Relayout Data** Click and drag on the graph to zoom or click on the zoom buttons in the graph's menu bar. Clicking on legend items will also fire this event. """)),
html.Pre(id='relayout-data', style=styles['pre']),
], className='three columns')
])
])
@app.callback(Output('hover-data', 'children'), [Input('basic-interactions', 'hoverData')])defdisplay_hover_data(hoverData):
returnjson.dumps(hoverData, indent=2)
@app.callback(Output('click-data', 'children'), [Input('basic-interactions', 'clickData')])defdisplay_click_data(clickData):
returnjson.dumps(clickData, indent=2)
@app.callback(Output('selected-data', 'children'), [Input('basic-interactions', 'selectedData')])defdisplay_selected_data(selectedData):
returnjson.dumps(selectedData, indent=2)
@app.callback(Output('relayout-data', 'children'), [Input('basic-interactions', 'relayoutData')])defdisplay_selected_data(relayoutData):
returnjson.dumps(relayoutData, indent=2)
if__name__=='__main__':
app.run_server(debug=True)

chriddyp · 2018-01-11T20:25:40Z

big thanks to @alexcjohnson for this one 🎉

chriddyp · 2018-01-11T19:41:12Z

thanks to plotly/plotly.js#2113

chriddyp · 2018-01-11T20:24:43Z

Usage

importjsonfromtextwrapimportdedentasdimportdashfromdash.dependenciesimportInput, Outputimportdash_core_componentsasdccimportdash_html_componentsashtmlimportdatetimeimportpandasaspddf=pd.read_csv(
('https://raw.githubusercontent.com/plotly/''datasets/master/1962_2006_walmart_store_openings.csv'),
parse_dates=[1, 2],
infer_datetime_format=True
)
future_indices=df['OPENDATE'] >datetime.datetime(year=2050,month=1,day=1)
df.loc[future_indices, 'OPENDATE'] -=datetime.timedelta(days=365.25*100)
app=dash.Dash(__name__)
app.scripts.config.serve_locally=Trueapp.css.config.serve_locally=Truestyles= {
'pre': {
'border': 'thin lightgrey solid',
'overflowX': 'scroll'
}
}
app.layout=html.Div([
dcc.Graph(
id='basic-interactions',
figure={
'data': [
{
'x': df['OPENDATE'],
'text': df['STRCITY'],
'customdata': df['storenum'],
'name': 'Open Date',
'type': 'histogram'
},
# {# 'x': df['date_super'],# 'text': df['STRCITY'],# 'customdata': df['storenum'],# 'name': 'Super Date',# 'type': 'histogram'# }
],
'layout': {}
}
),
html.Div(className='row', children=[
html.Div([
dcc.Markdown(d(""" **Hover Data** Mouse over values in the graph. """)),
html.Pre(id='hover-data', style=styles['pre'])
], className='three columns'),
html.Div([
dcc.Markdown(d(""" **Click Data** Click on points in the graph. """)),
html.Pre(id='click-data', style=styles['pre']),
], className='three columns'),
html.Div([
dcc.Markdown(d(""" **Selection Data** Choose the lasso or rectangle tool in the graph's menu bar and then select points in the graph. """)),
html.Pre(id='selected-data', style=styles['pre']),
], className='three columns'),
html.Div([
dcc.Markdown(d(""" **Zoom and Relayout Data** Click and drag on the graph to zoom or click on the zoom buttons in the graph's menu bar. Clicking on legend items will also fire this event. """)),
html.Pre(id='relayout-data', style=styles['pre']),
], className='three columns')
])
])
@app.callback(Output('hover-data', 'children'), [Input('basic-interactions', 'hoverData')])defdisplay_hover_data(hoverData):
returnjson.dumps(hoverData, indent=2)
@app.callback(Output('click-data', 'children'), [Input('basic-interactions', 'clickData')])defdisplay_click_data(clickData):
returnjson.dumps(clickData, indent=2)
@app.callback(Output('selected-data', 'children'), [Input('basic-interactions', 'selectedData')])defdisplay_selected_data(selectedData):
returnjson.dumps(selectedData, indent=2)
@app.callback(Output('relayout-data', 'children'), [Input('basic-interactions', 'relayoutData')])defdisplay_selected_data(relayoutData):
returnjson.dumps(relayoutData, indent=2)
if__name__=='__main__':
app.run_server(debug=True)

chriddyp · 2018-01-11T20:25:40Z

big thanks to @alexcjohnson for this one 🎉

chriddyp added 3 commits January 11, 2018 14:40

add support for new histogram event data
4a46a5d

binNumber is included as part of the previous logic bloc
7294dbd

v0.17.0
df034fa

chriddyp requested review from alishobeiri and removed request for alishobeiri January 11, 2018 20:25

chriddyp merged commit 21556aa into masterJan 11, 2018

chriddyp deleted the chriddyp-patch-2 branch January 11, 2018 20:42

chriddyp added 3 commits January 11, 2018 14:40

add support for new histogram event data
4a46a5d

binNumber is included as part of the previous logic bloc
7294dbd

v0.17.0
df034fa

chriddyp requested review from alishobeiri and removed request for alishobeiri January 11, 2018 20:25

chriddyp merged commit 21556aa into masterJan 11, 2018

chriddyp deleted the chriddyp-patch-2 branch January 11, 2018 20:42

chriddyp added 3 commits January 11, 2018 14:40

add support for new histogram event data
4a46a5d

binNumber is included as part of the previous logic bloc
7294dbd

v0.17.0
df034fa

chriddyp requested review from alishobeiri and removed request for alishobeiri January 11, 2018 20:25

chriddyp merged commit 21556aa into masterJan 11, 2018

chriddyp deleted the chriddyp-patch-2 branch January 11, 2018 20:42

chriddyp added 3 commits January 11, 2018 14:40

add support for new histogram event data
4a46a5d

binNumber is included as part of the previous logic bloc
7294dbd

v0.17.0
df034fa

chriddyp requested review from alishobeiri and removed request for alishobeiri January 11, 2018 20:25

chriddyp merged commit 21556aa into masterJan 11, 2018

chriddyp deleted the chriddyp-patch-2 branch January 11, 2018 20:42

chriddyp added 3 commits January 11, 2018 14:40

add support for new histogram event data
4a46a5d

binNumber is included as part of the previous logic bloc
7294dbd

v0.17.0
df034fa

chriddyp requested review from alishobeiri and removed request for alishobeiri January 11, 2018 20:25

chriddyp merged commit 21556aa into masterJan 11, 2018

chriddyp deleted the chriddyp-patch-2 branch January 11, 2018 20:42

chriddyp added 3 commits January 11, 2018 14:40

add support for new histogram event data
4a46a5d

binNumber is included as part of the previous logic bloc
7294dbd

v0.17.0
df034fa

chriddyp requested review from alishobeiri and removed request for alishobeiri January 11, 2018 20:25

chriddyp merged commit 21556aa into masterJan 11, 2018

chriddyp deleted the chriddyp-patch-2 branch January 11, 2018 20:42

chriddyp added 3 commits January 11, 2018 14:40

add support for new histogram event data
4a46a5d

binNumber is included as part of the previous logic bloc
7294dbd

v0.17.0
df034fa

chriddyp requested review from alishobeiri and removed request for alishobeiri January 11, 2018 20:25

chriddyp merged commit 21556aa into masterJan 11, 2018

chriddyp deleted the chriddyp-patch-2 branch January 11, 2018 20:42

chriddyp added 3 commits January 11, 2018 14:40

add support for new histogram event data
4a46a5d

binNumber is included as part of the previous logic bloc
7294dbd

v0.17.0
df034fa

chriddyp requested review from alishobeiri and removed request for alishobeiri January 11, 2018 20:25

chriddyp merged commit 21556aa into masterJan 11, 2018

chriddyp deleted the chriddyp-patch-2 branch January 11, 2018 20:42

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