Dash
The jupyter-dash package makes it easy to develop Plotly Dash apps from the Jupyter Notebook and JupyterLab.
Just replace the standard dash.Dash class with the jupyter_dash.JupyterDash subclass.
[1]:
from jupyter_dash import JupyterDash
[2]:
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
When running in JupyterHub or Binder, call the infer_jupyter_config function to detect the proxy configuration.
[ ]:
JupyterDash.infer_jupyter_proxy_config()
Load and preprocess data
[4]:
df = pd.read_csv('https://plotly.github.io/datasets/country_indicators.csv')
available_indicators = df['Indicator Name'].unique()
Construct the app and callbacks
[5]:
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = JupyterDash(__name__, external_stylesheets=external_stylesheets)
# Create server variable with Flask server object for use with gunicorn
server = app.server
app.layout = html.Div([
html.Div([
html.Div([
dcc.Dropdown(
id='crossfilter-xaxis-column',
options=[{'label': i, 'value': i} for i in available_indicators],
value='Fertility rate, total (births per woman)'
),
dcc.RadioItems(
id='crossfilter-xaxis-type',
options=[{'label': i, 'value': i} for i in ['Linear', 'Log']],
value='Linear',
labelStyle={'display': 'inline-block'}
)
],
style={'width': '49%', 'display': 'inline-block'}),
html.Div([
dcc.Dropdown(
id='crossfilter-yaxis-column',
options=[{'label': i, 'value': i} for i in available_indicators],
value='Life expectancy at birth, total (years)'
),
dcc.RadioItems(
id='crossfilter-yaxis-type',
options=[{'label': i, 'value': i} for i in ['Linear', 'Log']],
value='Linear',
labelStyle={'display': 'inline-block'}
)
], style={'width': '49%', 'float': 'right', 'display': 'inline-block'})
], style={
'borderBottom': 'thin lightgrey solid',
'backgroundColor': 'rgb(250, 250, 250)',
'padding': '10px 5px'
}),
html.Div([
dcc.Graph(
id='crossfilter-indicator-scatter',
hoverData={'points': [{'customdata': 'Japan'}]}
)
], style={'width': '49%', 'display': 'inline-block', 'padding': '0 20'}),
html.Div([
dcc.Graph(id='x-time-series'),
dcc.Graph(id='y-time-series'),
], style={'display': 'inline-block', 'width': '49%'}),
html.Div(dcc.Slider(
id='crossfilter-year--slider',
min=df['Year'].min(),
max=df['Year'].max(),
value=df['Year'].max(),
marks={str(year): str(year) for year in df['Year'].unique()},
step=None
), style={'width': '49%', 'padding': '0px 20px 20px 20px'})
])
@app.callback(
dash.dependencies.Output('crossfilter-indicator-scatter', 'figure'),
[dash.dependencies.Input('crossfilter-xaxis-column', 'value'),
dash.dependencies.Input('crossfilter-yaxis-column', 'value'),
dash.dependencies.Input('crossfilter-xaxis-type', 'value'),
dash.dependencies.Input('crossfilter-yaxis-type', 'value'),
dash.dependencies.Input('crossfilter-year--slider', 'value')])
def update_graph(xaxis_column_name, yaxis_column_name,
xaxis_type, yaxis_type,
year_value):
dff = df[df['Year'] == year_value]
return {
'data': [dict(
x=dff[dff['Indicator Name'] == xaxis_column_name]['Value'],
y=dff[dff['Indicator Name'] == yaxis_column_name]['Value'],
text=dff[dff['Indicator Name'] == yaxis_column_name]['Country Name'],
customdata=dff[dff['Indicator Name'] == yaxis_column_name]['Country Name'],
mode='markers',
marker={
'size': 25,
'opacity': 0.7,
'color': 'orange',
'line': {'width': 2, 'color': 'purple'}
}
)],
'layout': dict(
xaxis={
'title': xaxis_column_name,
'type': 'linear' if xaxis_type == 'Linear' else 'log'
},
yaxis={
'title': yaxis_column_name,
'type': 'linear' if yaxis_type == 'Linear' else 'log'
},
margin={'l': 40, 'b': 30, 't': 10, 'r': 0},
height=450,
hovermode='closest'
)
}
def create_time_series(dff, axis_type, title):
return {
'data': [dict(
x=dff['Year'],
y=dff['Value'],
mode='lines+markers'
)],
'layout': {
'height': 225,
'margin': {'l': 20, 'b': 30, 'r': 10, 't': 10},
'annotations': [{
'x': 0, 'y': 0.85, 'xanchor': 'left', 'yanchor': 'bottom',
'xref': 'paper', 'yref': 'paper', 'showarrow': False,
'align': 'left', 'bgcolor': 'rgba(255, 255, 255, 0.5)',
'text': title
}],
'yaxis': {'type': 'linear' if axis_type == 'Linear' else 'log'},
'xaxis': {'showgrid': False}
}
}
@app.callback(
dash.dependencies.Output('x-time-series', 'figure'),
[dash.dependencies.Input('crossfilter-indicator-scatter', 'hoverData'),
dash.dependencies.Input('crossfilter-xaxis-column', 'value'),
dash.dependencies.Input('crossfilter-xaxis-type', 'value')])
def update_y_timeseries(hoverData, xaxis_column_name, axis_type):
country_name = hoverData['points'][0]['customdata']
dff = df[df['Country Name'] == country_name]
dff = dff[dff['Indicator Name'] == xaxis_column_name]
title = '<b>{}</b><br>{}'.format(country_name, xaxis_column_name)
return create_time_series(dff, axis_type, title)
@app.callback(
dash.dependencies.Output('y-time-series', 'figure'),
[dash.dependencies.Input('crossfilter-indicator-scatter', 'hoverData'),
dash.dependencies.Input('crossfilter-yaxis-column', 'value'),
dash.dependencies.Input('crossfilter-yaxis-type', 'value')])
def update_x_timeseries(hoverData, yaxis_column_name, axis_type):
dff = df[df['Country Name'] == hoverData['points'][0]['customdata']]
dff = dff[dff['Indicator Name'] == yaxis_column_name]
return create_time_series(dff, axis_type, yaxis_column_name)
Serve the app using run_server. Unlike the standard Dash.run_server method, the JupyterDash.run_server method doesn’t block execution of the notebook. It serves the app in a background thread, making it possible to run other notebook calculations while the app is running.
This makes it possible to iterativly update the app without rerunning the potentially expensive data processing steps.
[6]:
app.run_server() # run_server() 本身就會產生一個 binder link 了，見 cloud notes
Dash app running on https://hub-binder.mybinder.ovh/user/beginnersc-sandbox-dash-pq7dy8jr/proxy/8050/
By default, run_server displays a URL that you can click on to open the app in a browser tab. The mode argument to run_server can be used to change this behavior. Setting mode="inline" will display the app directly in the notebook output cell.
[7]:
app.run_server(mode="inline")
When running in JupyterLab, with the jupyterlab-dash extension, setting mode="jupyterlab" will open the app in a tab in JupyterLab.
app.run_server(mode="jupyterlab")
[ ]: