How to fix 'Dropdown Menu Read' Error in P

I have tried to re-create the following example Towards Data Science Example shown on the web

I have written the following code which I modified to this:

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

import pandas as pd
import plotly.graph_objs as go

# Step 1. Launch the application
app = dash.Dash()

# Step 2. Import the dataset
filepath = 'https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv'
st = pd.read_csv(filepath)


# range slider options
st['Date'] = pd.to_datetime(st.Date)
dates = ['2015-02-17', '2015-05-17', '2015-08-17', '2015-11-17',
         '2016-02-17', '2016-05-17', '2016-08-17', '2016-11-17', '2017-02-17']

features = st.columns[1:-1]
opts = [{'label' : i, 'value' : i} for i in features]

# Step 3. Create a plotly figure
trace_1 = go.Scatter(x = st.Date, y = st['AAPL.High'],
                    name = 'AAPL HIGH',
                    line = dict(width = 2,
                                color = 'rgb(229, 151, 50)'))
layout = go.Layout(title = 'Time Series Plot',
                   hovermode = 'closest')
fig = go.Figure(data = [trace_1], layout = layout)


# Step 4. Create a Dash layout
app.layout = html.Div([
                # a header and a paragraph
                html.Div([
                    html.H1("This is my first dashboard"),
                    html.P("Dash is so interesting!!")
                         ],
                     style = {'padding' : '50px' ,
                              'backgroundColor' : '#3aaab2'}),
                # adding a plot
                dcc.Graph(id = 'plot', figure = fig),
                # dropdown
                html.P([
                    html.Label("Choose a feature"),
                        dcc.Dropdown(
                                id='opt',                              
                                options=opts,
                                value=features[0],
                                multi=True

                                ),
                # range slider
                html.P([
                    html.Label("Time Period"),
                    dcc.RangeSlider(id = 'slider',
                                    marks = {i : dates[i] for i in range(0, 9)},
                                    min = 0,
                                    max = 8,
                                    value = [1, 7])
                        ], style = {'width' : '80%',
                                    'fontSize' : '20px',
                                    'padding-left' : '100px',
                                    'display': 'inline-block'})
                      ])
                        ])


# Step 5. Add callback functions
@app.callback(Output('plot', 'figure'),
             [Input('opt', 'value'),
             Input('slider', 'value')])
def update_figure(input1, input2):
    # filtering the data
    st2 = st[(st.Date > dates[input2[0]]) & (st.Date < dates[input2[1]])]
    # updating the plot
    trace_1 = go.Scatter(x = st2.Date, y = st2['AAPL.High'],
                        name = 'AAPL HIGH',
                        line = dict(width = 2,
                                    color = 'rgb(229, 151, 50)'))
    trace_2 = go.Scatter(x = st2.Date, y = st2[input1],
                        name = str(input1),
                        line = dict(width = 2,
                                    color = 'rgb(106, 181, 135)'))
    fig = go.Figure(data = [trace_1, trace_2], layout = layout)
    return fig

# Step 6. Add the server clause
if __name__ == '__main__':
    app.run_server(debug = True)

When I change the feature input, it does not update the plot correctly and does not show the selected features in the plot.

Either there is something wrong with the callback function or the initialization of the graph with the second trace. But I cant figure out where the issue is.

As you are only providing two scatter traces within your callback. From both, one is static for 'AAPL.High'. So you need to limit the dropdown values to Multi=False.

Valid plots are only generated for choosing options like 'AAPL.LOW' and others like dic won't display a second trace. The callback wouldn't terminate if you would keepmulti=True the callback would stil work, if always only one option is selected. The moment you select two or more options the script will fail as it would try to find faulty data for the data return block here:

trace_2 = go.Scatter(x = st2.Date, y = st2[**MULTIINPUT**],
                        name = str(input1),
                        line = dict(width = 2,
                                    color = 'rgb(106, 181, 135)'))

Only one column id is allowed to be passed at MULTIINPUT. If you want to introduce more traces please use a for loop.

Change the code to the following:

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

import pandas as pd
import plotly.graph_objs as go

# Step 1. Launch the application
app = dash.Dash()

# Step 2. Import the dataset
filepath = 'https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv'
st = pd.read_csv(filepath)


# range slider options
st['Date'] = pd.to_datetime(st.Date)
dates = ['2015-02-17', '2015-05-17', '2015-08-17', '2015-11-17',
         '2016-02-17', '2016-05-17', '2016-08-17', '2016-11-17', '2017-02-17']

features = st.columns

opts = [{'label' : i, 'value' : i} for i in features]

# Step 3. Create a plotly figure
trace_1 = go.Scatter(x = st.Date, y = st['AAPL.High'],
                    name = 'AAPL HIGH',
                    line = dict(width = 2,
                                color = 'rgb(229, 151, 50)'))
layout = go.Layout(title = 'Time Series Plot',
                   hovermode = 'closest')
fig = go.Figure(data = [trace_1], layout = layout)


# Step 4. Create a Dash layout
app.layout = html.Div([
                # a header and a paragraph
                html.Div([
                    html.H1("This is a Test Dashboard"),
                    html.P("Dash is great!!")
                         ],
                     style = {'padding' : '50px' ,
                              'backgroundColor' : '#3aaab2'}),
                # adding a plot
                dcc.Graph(id = 'plot', figure = fig),
                # dropdown
                html.P([
                    html.Label("Choose a feature"),
                        dcc.Dropdown(
                                id='opt',
                                options=opts,
                                value=features[0],
                                multi=False

                                ),
                # range slider
                html.P([
                    html.Label("Time Period"),
                    dcc.RangeSlider(id = 'slider',
                                    marks = {i : dates[i] for i in range(0, 9)},
                                    min = 0,
                                    max = 8,
                                    value = [1, 7])
                        ], style = {'width' : '80%',
                                    'fontSize' : '20px',
                                    'padding-left' : '100px',
                                    'display': 'inline-block'})
                      ])
                        ])


# Step 5. Add callback functions
@app.callback(Output('plot', 'figure'),
             [Input('opt', 'value'),
             Input('slider', 'value')])
def update_figure(input1, input2):
    # filtering the data
    st2 = st#[(st.Date > dates[input2[0]]) & (st.Date < dates[input2[1]])]
    # updating the plot
    trace_1 = go.Scatter(x = st2.Date, y = st2['AAPL.High'],
                        name = 'AAPL HIGH',
                        line = dict(width = 2,
                                    color = 'rgb(229, 151, 50)'))
    trace_2 = go.Scatter(x = st2.Date, y = st2[input1],
                        name = str(input1),
                        line = dict(width = 2,
                                    color = 'rgb(106, 181, 135)'))
    fig = go.Figure(data = [trace_1, trace_2], layout = layout)
    return fig

# Step 6. Add the server clause
if __name__ == '__main__':
    app.run_server(debug = True)

I hope this cleared things up and solved your issues. :)