dash_simple_example_pandas_datareader.py

# See official docs at https://dash.plotly.com
# pip install dash pandas

from dash import Dash, dcc, html, Input, Output
import plotly.express as px

import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gapminderDataFiveYear.csv')

app = Dash(__name__)

app.layout = html.Div([
    dcc.Graph(id='graph-with-slider'),
    dcc.Slider(
        df['year'].min(),
        df['year'].max(),
        step=None,
        value=df['year'].min(),
        marks={str(year): str(year) for year in df['year'].unique()},
        id='year-slider'
    )
])


@app.callback(
    Output('graph-with-slider', 'figure'),
    Input('year-slider', 'value'))
def update_figure(selected_year):
    filtered_df = df[df.year == selected_year]

    fig = px.scatter(filtered_df, x="gdpPercap", y="lifeExp",
                     size="pop", color="continent", hover_name="country",
                     log_x=True, size_max=55)

    return fig


if __name__ == '__main__':
    app.run_server(debug=True)

Hello there,
I just have a quick question. Is there a way to export the resulting report/dashboard as a standalone html file (something with all assets bundled), or does dash require some server-side processing via python? I have seen some efforts to get python running in the browser via webassembly; however, I am not sure if dash already supports this out of the box or not.
I have been using a combination of Rmarkdown/quarto with python and plotly to create dashboard-like reports. The nice thing with Rmarkdown/quarto is that you can export everything as a stand alone html file, which is nice when working with collaborators. Just email them a report, and they can open it with Chrome.

Anyways, please let mw know what you think, and have a great day!

Best Regards,
@skchronicles

Working example:

from dash import Dash, dcc, html, callback
from dash.dependencies import Input, Output
import yfinance as yf

start = '2022-01-01'
end = '2023-01-01'

app = Dash(__name__)

app.layout = html.Div([
dcc.Dropdown(
    id='my-dropdown',
    options=[
        {'label': 'MSFT', 'value': 'MSFT'},
        {'label': 'AAPL', 'value': 'AAPL'},
    ],
    value='MSFT'
),
dcc.Graph(id='my-graph')
], style={'width': '500'})

@callback(Output('my-graph', 'figure'), [Input('my-dropdown', 'value')])
def update_graph(selected_dropdown_value):
    df = yf.download(selected_dropdown_value, start=start, end=end)
    return {
    'data': [{
    'x': df.index,
    'y': df.Close
    }],
    'layout': {'margin': {'l': 40, 'r': 0, 't': 20, 'b': 30}}
    }

app.css.append_css({'external_url': 'https://codepen.io/chriddyp/pen/bWLwgP.css'})

if __name__ == '__main__':
    app.run(debug=True)