Plotly Dash heatmap, linked to 2 dropdowns

Question

how is it possible to create an application in plotly dash, in which i want to create a heatmap linked to 2 dropdowns? The dropdowns are reading from a Pandas DF, ‘my_dropdown1’ and ‘my_dropdown2’.

Answer 1

I think a good example of this case is in the Dash documentation: basic callbacks, Dash App With Multiple Inputs. https://dash.plotly.com/basic-callbacks

This example has 2 dropdowns and 2 radio buttons. And the graph created is a scatter plot. You could reformat this example to create a heat map and use two drop downs.

import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import plotly.express as px

import pandas as pd

app = dash.Dash(__name__)

df = pd.read_csv('https://plotly.github.io/datasets/country_indicators.csv')

available_indicators = df['Indicator Name'].unique()

app.layout = html.Div([
    html.Div([

        html.Div([
            dcc.Dropdown(
                id='xaxis-column',
                options=[{'label': i, 'value': i} for i in available_indicators],
                value='Fertility rate, total (births per woman)'
            ),
            dcc.RadioItems(
                id='xaxis-type',
                options=[{'label': i, 'value': i} for i in ['Linear', 'Log']],
                value='Linear',
                labelStyle={'display': 'inline-block'}
            )
        ], style={'width': '48%', 'display': 'inline-block'}),

        html.Div([
            dcc.Dropdown(
                id='yaxis-column',
                options=[{'label': i, 'value': i} for i in available_indicators],
                value='Life expectancy at birth, total (years)'
            ),
            dcc.RadioItems(
                id='yaxis-type',
                options=[{'label': i, 'value': i} for i in ['Linear', 'Log']],
                value='Linear',
                labelStyle={'display': 'inline-block'}
            )
        ], style={'width': '48%', 'float': 'right', 'display': 'inline-block'})
    ]),

    dcc.Graph(id='indicator-graphic'),

    dcc.Slider(
        id='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
    )
])


@app.callback(
    Output('indicator-graphic', 'figure'),
    Input('xaxis-column', 'value'),
    Input('yaxis-column', 'value'),
    Input('xaxis-type', 'value'),
    Input('yaxis-type', 'value'),
    Input('year--slider', 'value'))
def update_graph(xaxis_column_name, yaxis_column_name,
                 xaxis_type, yaxis_type,
                 year_value):
    dff = df[df['Year'] == year_value]

    fig = px.scatter(x=dff[dff['Indicator Name'] == xaxis_column_name]['Value'],
                     y=dff[dff['Indicator Name'] == yaxis_column_name]['Value'],
                     hover_name=dff[dff['Indicator Name'] == yaxis_column_name]['Country Name'])

    fig.update_layout(margin={'l': 40, 'b': 40, 't': 10, 'r': 0}, hovermode='closest')

    fig.update_xaxes(title=xaxis_column_name,
                     type='linear' if xaxis_type == 'Linear' else 'log')

    fig.update_yaxes(title=yaxis_column_name,
                     type='linear' if yaxis_type == 'Linear' else 'log')

    return fig


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