Filtering options for plotly dashboard in dropdown format

Below is a dataset along with code that generates a chart:

Column A Tools Category role figure Occurences After idx
0 PostgreSQL DatabaseHaveWorkedWith Developer, front-end 38.044286 4 False
1 MySQL DatabaseHaveWorkedWith Developer, front-end 45.883489 2 False
0 MongoDB DatabaseWantToWorkWith Developer, front-end 39.018110 1 True
1 PostgreSQL DatabaseWantToWorkWith Developer, front-end 48.236203 3 False
0 MySQL DatabaseHaveWorkedWith Developer, back-end 26.096002 1 True
1 PostgreSQL DatabaseHaveWorkedWith Developer, back-end 33.771734 2 False
0 Redis DatabaseWantToWorkWith Developer, back-end 28.495408 1 True
1 PostgreSQL DatabaseWantToWorkWith Developer, back-end 40.314136 1 True 
from itertools import cycle
import plotly.express as px
fig = go.Figure()

palette = cycle(px.colors.qualitative.Alphabet)
colors = {c:next(palette) for c in daata['Tools'].values}
#Build dropdown Labels
labels = daata['role'].unique()

for i, row in daata.iterrows():
    fig.add_trace(
        go.Bar(x=[[row['role']],[row["Category"]]],
            y=[row["figure"]],
            name=row["Tools"],
            text=str(round(row["figure"],2))+'%', showlegend=row['idx'],marker_color=colors[row["Tools"]],
            legendgroup=row["Tools"]# Fix legend
            ))

fig.update_layout({
   'barmode': 'group',
    'xaxis': {
        'tickangle': -45,
    },
    'yaxis': {
        'title_text': "figures",
    },
})
fig.show()

The provided code results in the chart displayed here

Adding a dropdown filter for roles is desired (e.g., filtering by 'Developer, back-end', 'Developer, front-end' and 'All')

Attempts to add button labels have not been successful in correctly filtering the chart.

pythonplotlyplotly-dash

Answer №1

Utilize the functionality of dash to implement a Dropdown menu and retrieve relevant data. Refer to the code snippet below for guidance:

from dash import Dash, dcc, html, dash_table, Input, Output, callback
import plotly.express as px
import dash_bootstrap_components as dbc
import pandas as pd
from itertools import cycle

palette = cycle(px.colors.qualitative.Alphabet)
colors = {c:next(palette) for c in daata['Tools'].values}
#Build dropdown Labels
labels = daata['role'].unique()
app = Dash(__name__, external_stylesheets=[dbc.themes.LUX])
app.layout = html.Div([
     dcc.Dropdown(id='dropdown',
                  options=[
                      {'label': x, 'value': x} for x in df['role'].unique()],
                         value=[],
                         multi=True,
                         disabled=False,
                         clearable=True,
                         searchable=True),
    dcc.Graph(id='graph',figure={})
])

@app.callback(Output('graph','figure'),
              Input('dropdown','value'))
def update_graph(role):
    if role == []:
        dff= daata.copy()
        fig = go.Figure()
        for i, row in dff.iterrows():
            fig.add_trace(
            go.Bar(x=[[row['role']],[row["Category"]]],
            y=[row["figure"]],
            name=row["Tools"],
            text=str(round(row["figure"],2))+'%', showlegend=row['idx'],marker_color=colors[row["Tools"]],
            legendgroup=row["Tools"]# Fix legend
            ))

            fig.update_layout({
                'barmode': 'group',
                'xaxis': {'tickangle': -45},
                'yaxis': {'title_text': "figures",},
            })
    else:
        dff= daata[daata['role'].isin(role)]
        fig = go.Figure()
        for i, row in dff.iterrows():
            fig.add_trace(
            go.Bar(x=[[row['role']],[row["Category"]]],
            y=[row["figure"]],
            name=row["Tools"],
            text=str(round(row["figure"],2))+'%', showlegend=row['idx'],marker_color=colors[row["Tools"]],
            legendgroup=row["Tools"]# Fix legend
            ))

            fig.update_layout({
                'barmode': 'group',
                'xaxis': {'tickangle': -45},
                'yaxis': {'title_text': "figures",},
            })
    return fig

if __name__ == "__main__":
    app.run_server(debug=False)

For visuals on the implementation, you can refer https://i.stack.imgur.com/rbRhS.png

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