DV0101EN-Final-Assign-Part-2-Questions_last.py

#!/usr/bin/env python
# coding: utf-8

# In[ ]:


import dash
import more_itertools
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 Period Statistics'}
]
# 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("Automobile Sales Statistics Dashboard",
    style={
        'color':'#503D36',
        'font-size':'24px'}),
    #TASK 2.2: Add two dropdown menus
    html.Div([
        html.Label("Select Statistics:"),
        dcc.Dropdown(
            id='dropdown-statistics',
            options=dropdown_options,
            value='Select Statistics',
            placeholder='Select a report type',
            style={'width':'80%', 'padding':'3px', 'size':'20px'}
        )
    ]),
    html.Div(dcc.Dropdown(
            id='select-year',
            options=[{'label': i, 'value': i} for i in year_list],
            value='Select-year',
            placeholder='Select-year'
        )),
    html.Div([#TASK 2.3: Add a division for output display
    html.Div(id='output-container', className='chart-grid', 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 Period Statistics':
        # 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
        #Hint:Use Vehicle_Type and Automobile_Sales columns
        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 Sales by Vehicles Types"))
        
# Plot 3 Pie chart for total expenditure share by vehicle type during recessions
        # grouping data for plotting
	# Hint:Use Vehicle_Type and Advertising_Expenditure columns
        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'
            )
        )

# Plot 4 bar chart for the effect of unemployment rate on vehicle type and sales
        #grouping data for plotting
	# Hint:Use unemployment_rate,Vehicle_Type and Automobile_Sales columns
        unemp_data = recession_data.groupby(['unemployment_rate','Vehicle_Type'])['Automobile_Sales'].mean().reset_index()
        R_chart4 = dcc.Graph(
            figure=px.bar(unemp_data,
            x= 'unemployment_rate',
            y= 'Automobile_Sales',
            color= 'Vehicle_Type',
            labels={'unemployment_rate': 'Unemployment Rate', 'Automobile_Sales': 'Average Automobile Sales'},
            title='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_chart2)
                    ],
                ),
            html.Div(
                className='chart-item',
                children=[
                    html.Div(children=R_chart3),
                    html.Div(children=R_chart4)],
                )
            ]

# TASK 2.6: Create and display graphs for Yearly Report Statistics
 # Yearly Statistic Report Plots
    # Check for Yearly Statistics.                             
    elif (input_year and selected_statistics=='Yearly Statistics') :
        yearly_data = data[data['Year'] == input_year]
                              

                              
#plot 1 Yearly Automobile sales using line chart for the whole period.
        # grouping data for plotting.
        # Hint:Use the columns Year and Automobile_Sales.
        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.
        # grouping data for plotting.
	# Hint:Use the columns Month and Automobile_Sales.
        mas=data.groupby('Month')['Automobile_Sales'].sum().reset_index()
        Y_chart2 = dcc.Graph(
            figure=px.line(
                mas,
                x='Month',
                y='Automobile_Sales',
                title='Total Monthly Automobile Sales'))

  # Plot bar chart for average number of vehicles sold during the given year
         # grouping data for plotting.
         # Hint:Use the columns Year and Automobile_Sales
        avr_vdata=yearly_data.groupby('Year')['Automobile_Sales'].mean().reset_index()
        Y_chart3 = dcc.Graph(
             figure=px.bar(
                avr_vdata,
                x='Year',
                y='Automobile_Sales',
                title= 'Average Vehicles Sold by Vehicle Type in the year {}'.format(input_year))
                )

    # Total Advertisement Expenditure for each vehicle using pie chart
         # grouping data for plotting.
         # Hint:Use the columns Vehicle_Type and Advertising_Expenditure
        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='Average Advertising Expenditure by Vehicle Type in the year {}'.format(input_year))
                )

#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_chart2)
                        ]),
                html.Div(
                    className='chart-item',
                    children=[
                        html.Div(children=Y_chart3),
                        html.Div(children=Y_chart4)
                        ]
                        )
        ]
    else:
        return None

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