It looks quite same with this post https://community.plotly.com/t/help-with-displaying-graphs-with-dash-plotly/78306/4
I think have some problem in your code as below:
- First: I think
selected_statistics =='Yearly Statistics'disabled option should be False
@app.callback(
Output(component_id='select-year', component_property='disabled'),
Input(component_id='dropdown-statistics',component_property='value'))
def update_input_container(selected_statistics):
if selected_statistics =='Yearly Statistics':
return False
else:
return True
- Second: You should change from
selected_statistics=='Yearly'toselected_statistics=='Yearly Statistics'
# TASK 2.6: Create and display graphs for Yearly Report Statistics
# Yearly Statistic Report Plots
elif (input_year and selected_statistics=='Yearly Statistics') :
yearly_data = data[data['Year'] == input_year]
Full code:
import dash
from dash import dcc
from dash import html
from dash.dependencies import Input, Output
import pandas as pd
import plotly.graph_objs as go
import plotly.express as px
# Load the data using pandas
data = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/historical_automobile_sales.csv')
# Initialize the Dash app
app = dash.Dash(__name__)
# Set the title of the dashboard
app.title = "Automobile Statistics Dashboard"
#---------------------------------------------------------------------------------
# Create the dropdown menu options
dropdown_options = [
{'label': 'Yearly Statistics', 'value': 'Yearly Statistics'},
{'label': 'Recession Period Statistics', 'value': 'Recession'}
]
# List of years
year_list = [i for i in range(1980, 2024, 1)]
#---------------------------------------------------------------------------------------
# Create the layout of the app
app.layout = html.Div([
#TASK 2.1 Add title to the dashboard
html.H1("Yearly Statistics"),#May include style for title
html.Div([#TASK 2.2: Add two dropdown menus
html.Label("Select Statistics:"),
dcc.Dropdown(
id='dropdown-statistics',
options=dropdown_options,
value='Yearly',
placeholder='Select a report type'
)
]),
html.Div(dcc.Dropdown(
id='select-year',
options=[{'label': i, 'value': i} for i in year_list],
value=1980
)),
html.Div([#TASK 2.3: Add a division for output display
html.Div(id='output-container', className='division', style={'display': 'flex'}),])
])
#TASK 2.4: Creating Callbacks
# Define the callback function to update the input container based on the selected statistics
@app.callback(
Output(component_id='select-year', component_property='disabled'),
Input(component_id='dropdown-statistics',component_property='value'))
def update_input_container(selected_statistics):
if selected_statistics =='Yearly Statistics':
return False
else:
return True
#Callback for plotting
# Define the callback function to update the input container based on the selected statistics
@app.callback(
Output(component_id='output-container', component_property='children'),
Input(component_id='dropdown-statistics', component_property='value'),
Input(component_id='select-year', component_property='value'),
)
def update_output_container(selected_statistics, input_year):
if selected_statistics == 'Recession':
# Filter the data for recession periods
recession_data = data[data['Recession'] == 1]
#TASK 2.5: Create and display graphs for Recession Report Statistics
#Plot 1 Automobile sales fluctuate over Recession Period (year wise)
# use groupby to create relevant data for plotting
yearly_rec=recession_data.groupby('Year')['Automobile_Sales'].mean().reset_index()
R_chart1 = dcc.Graph(
figure=px.line(yearly_rec,
x='Year',
y='Automobile_Sales',
title="Average Automobile Sales fluctuation over Recession Period"))
#Plot 2 Calculate the average number of vehicles sold by vehicle type
# use groupby to create relevant data for plotting
average_sales = recession_data.groupby('Vehicle_Type')['Automobile_Sales'].mean().reset_index()
R_chart2 = dcc.Graph(figure=px.line(average_sales, x='Automobile_Sales', y='Vehicle_Type', title='Average number of vehicles sold by vehicle type'))
# Plot 3 Pie chart for total expenditure share by vehicle type during recessions
# use groupby to create relevant data for plotting
exp_rec = recession_data.groupby('Vehicle_Type')['Advertising_Expenditure'].mean().reset_index()
R_chart3 = dcc.Graph(figure=px.pie(exp_rec, values='Advertising_Expenditure', names='Vehicle_Type', title='Total expenditure share by vehicle type during recessions',
)
)
# Plot 4 bar chart for the effect of unemployment rate on vehicle type and sales
unemployment_sales= recession_data.groupby(['Vehicle_Type', 'unemployment_rate'])['Automobile_Sales'].mean().reset_index()
R_chart4 = dcc.Graph(
figure=px.bar(unemployment_sales, x='unemployment_rate', y='Automobile_Sales', color='Vehicle_Type', title='The effect of unemployment rate on vehicle type and sales')
)
return [
html.Div(className='chart-item', children=[html.Div(children=R_chart1),html.Div(children=R_chart1)],style={'display': 'flex'}),
html.Div(className='chart-item', children=[html.Div(children=R_chart2),html.Div(children=R_chart2)],style={'display': 'flex'}),
html.Div(className='chart-item', children=[html.Div(children=R_chart3),html.Div(children=R_chart3)],style={'display': 'flex'}),
html.Div(className='chart-item', children=[html.Div(children=R_chart4),html.Div(children=R_chart4)],style={'display': 'flex'})
]
# TASK 2.6: Create and display graphs for Yearly Report Statistics
# Yearly Statistic Report Plots
elif (input_year and selected_statistics=='Yearly Statistics') :
yearly_data = data[data['Year'] == input_year]
#TASK 2.5: Creating Graphs Yearly data
#plot 1 Yearly Automobile sales using line chart for the whole period.
yas= data.groupby('Year')['Automobile_Sales'].mean().reset_index()
Y_chart1 = dcc.Graph(figure=px.line(yas, x='Year', y='Automobile_Sales', title ='Yearly Automobile sales'))
# Plot 2 Total Monthly Automobile sales using line chart.
mas= data.groupby('Month')['Automobile_Sales'].mean().reset_index()
Y_chart2 = dcc.Graph(figure=px.line(mas, x='Month', y='Automobile_Sales', title ='Yearly Automobile sales'))
# Plot bar chart for average number of vehicles sold during the given year
avr_data=yearly_data.groupby('Year')['Automobile_Sales'].mean().reset_index()
Y_chart3 = dcc.Graph(figure=px.bar(avr_data, x='Year', y='Automobile_Sales',title='Average number of vehicles sold during the given year {}'.format(input_year)))
# Total Advertisement Expenditure for each vehicle using pie chart
exp_data=yearly_data.groupby('Vehicle_Type')['Advertising_Expenditure'].mean().reset_index()
Y_chart4 = dcc.Graph(figure=px.pie(exp_data,
values='Advertising_Expenditure',
names='Vehicle_Type',
title='Total Advertisement Expenditure for each vehicle'))
#TASK 2.6: Returning the graphs for displaying Yearly data
return [
html.Div(className='chart-item', children=[html.Div(children=Y_chart1),html.Div(children=Y_chart1)],style={'display': 'flex'}),
html.Div(className='chart-item', children=[html.Div(children=Y_chart2),html.Div(children=Y_chart2)],style={'display': 'flex'}),
html.Div(className='chart-item', children=[html.Div(children=Y_chart3),html.Div(children=Y_chart3)],style={'display': 'flex'}),
html.Div(className='chart-item', children=[html.Div(children=Y_chart4),html.Div(children=Y_chart4)],style={'display': 'flex'})
]
else:
return None
# Run the Dash app
if __name__ == '__main__':
app.run_server(debug=False)