AutoStatsDash.py

#!/usr/bin/env python
# coding: utf-8
# In[ ]:
import dash
from dash import dcc, 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"

#---------------------------------------------------------------------------------
#The dropdown menu options
dropdown_options = [
    {'label': 'Yearly Statistics', 'value': 'Yearly Statistics'},
    {'label': 'Recession Period Statistics', 'value': 'Recession Statistics'}
]
# List of years 
year_list = [i for i in range(1980, 2024, 1)]
#---------------------------------------------------------------------------------------
# Create the layout of the app
app.layout = html.Div([
    html.H1(app.title,
            style={'textAlign': 'center', 'color': '#503D36', 'fontSize': 24}
            ),
    html.Div([
        html.Label("Select Statistics:"),
        dcc.Dropdown(
            id='dropdown-statistics',
            options=dropdown_options,
            placeholder='Select a report type',
            value='Select Statistics',
            style={'width': '80%', 'padding': '3px', 'fontSize': '20px', 'textAlignLast': 'center'}
        )
    ]),
    html.Div([
        dcc.Dropdown(
            id='select-year',
            options=[{'label': i, 'value': i} for i in year_list],
            placeholder='Select a year',
            style={'width': '80%', 'padding': '3px', 'fontSize': '20px', 'textAlignLast': 'center'}
        )
    ]),
    html.Div([
    html.Div(id='output-container', className='chart-grid', style={'display': 'flex'}),
    ])
])
#Creating Callbacks
#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
#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='select-year', component_property='value'), 
     Input(component_id='dropdown-statistics', component_property='value')])


def update_output_container(input_year, selected_statistics):
    if selected_statistics  == 'Recession Statistics':
        # Filter the data for recession periods
        recession_data = data[data['Recession'] == 1]
#Create and display graphs for Recession Report Statistics
#Plot 1 Automobile sales fluctuate over Recession Period (year wise) using line chart
        yearly_rec = recession_data.groupby('Year')['Automobile_Sales'].mean().reset_index()
        # Plotting the line graph
        R_chart1 = dcc.Graph(
            figure=px.line(yearly_rec, 
                x='Year',
                y='Automobile_Sales',
                title="Yearly Average Automobile Sales During Recession"))

# Plot 2 The average number of vehicles sold by vehicle type and represent as a Bar chart
        average_sales = recession_data.groupby('Vehicle_Type')['Automobile_Sales'].mean().reset_index()
        R_chart2 = dcc.Graph(
            figure=px.bar(average_sales,
                x='Vehicle_Type',
                y='Automobile_Sales',
                title="Average Vehicle Type Sales During Recession"))
        
# Plot 3  Pie chart for total expenditure share by vehicle type during recessions
        exp_rec = recession_data.groupby('Vehicle_Type')['Advertising_Expenditure'].sum().reset_index()
        R_chart3 = dcc.Graph(
                    figure=px.pie(exp_rec,
                    values='Advertising_Expenditure',
                    names='Vehicle_Type',
                    title="Advertisement Expenditure Share by Vehicle Type During Recession"
                )
        )

# Plot 4 Bar chart for the effect of unemployment rate on vehicle type and sales
        unemployment_effect = recession_data.groupby('Vehicle_Type')[['unemployment_rate', 'Automobile_Sales']].mean().reset_index()
        R_chart4 = dcc.Graph(
            figure=px.bar(unemployment_effect, 
                x='Vehicle_Type',
                y='Automobile_Sales', 
                color_discrete_sequence=['#50C878'],
                title="Effect of Unemployment Rate on Vehicle Type and Sales During Recession"))

        return [
                html.Div(className='chart-item', children=[R_chart1, R_chart2]),
                html.Div(className='chart-item', children=[R_chart3, R_chart4])
            ]

# 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'] == int(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 Average Automobile Sales"))
            
# Plot 2 Total Monthly Automobile sales using line chart.
        monthly_sales = yearly_data.groupby('Month')['Automobile_Sales'].sum().reset_index()
        Y_chart2 = dcc.Graph(
                    figure=px.line(monthly_sales, 
                    x='Month', 
                    y='Automobile_Sales', 
                    title='Total Monthly Automobile Sales'))

        # Plot bar chart for average number of vehicles sold during the given year
        avr_vdata = yearly_data.groupby('Vehicle_Type')['Automobile_Sales'].mean().reset_index()
        Y_chart3 = dcc.Graph(
                    figure=px.bar(avr_vdata, 
                    x='Vehicle_Type', 
                    y='Automobile_Sales', 
                    title='Average Monthly Automobile Sales by Vehicle Type'))

        # Total Advertisement Expenditure for each vehicle using pie chart
        exp_data = yearly_data.groupby('Vehicle_Type')['Advertising_Expenditure'].sum().reset_index()
        Y_chart4 = dcc.Graph(
                    figure=px.pie(exp_data, 
                    values='Advertising_Expenditure', 
                    names='Vehicle_Type', 
                    title='Total Advertisement Expenditure for Each Vehicle'))


# Returning the graphs for displaying Yearly data
        return [
                html.Div(className='chart-item', children=[Y_chart1, Y_chart2]),
                html.Div(className='chart-item', children=[Y_chart3, Y_chart4])
            ]
            
    else:
        return None

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