Final Assignment 2 Code Review

Final_Assign_Part_2.py

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

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 Sales 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={'textAlign': 'center', 'color': '#503D36', 'fontSize': 24}), #May include style for title
    html.Div([#TASK 2.2: Add two dropdown menus
        html.Label("Select Statistics:"),
        dcc.Dropdown(
            id='dropdown-statistics',
            options=[{'label': 'Yearly Statistics', 'value': 'Yearly Statistics'},
                    {'label': 'Recession Period Statistics', 'value': 'Recession Period Statistics'}],
            value='Select Statistics',
            placeholder='Select a report type',
            style={'width':'80%', 'padding':'3px', 'fontSize':'20px', 'textAlign':'center'}
        )
    ]),
    html.Div(dcc.Dropdown(
            id='select-year',
            options=[{'label': i, 'value': i} for i in year_list],
            value='Select-year' ,
            # placeholder="Select year",
            # style={'width':'80%', 'padding':'3px', 'fontSize':'20px', 'textAlign':'center'},
        )),
    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):
    print("Selected Statistics:", 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(input_year, selected_statistics):
    print("Selected Statistics:", selected_statistics)
    print("Selected Year:", input_year)

    if selected_statistics == 'Recession Period Statistics':
        # Filter the data for recession periods
        recession_data = data[data['Recession'] == 1]
        recession_data = recession_data[recession_data['Year'] == input_year]
        

#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()

        # Plotting the line graph
        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('Year')['Automobile_Sales'].mean().reset_index()                           
        R_chart2  = dcc.Graph(figure=px.line(average_sales, x='Year', y='Automobile_Sales'))
        
# 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')['Advertisement_Expenditure'].sum().reset_index()
        R_chart3 = dcc.Graph(
            figure=px.pie(exp_rec,
                          values='Advertisement_Expenditure',
                          names='Vehicle',
                          title='Expenditure Share by Vehicle Type during Recessions')
        )

# Plot 4 bar chart for the effect of unemployment rate on vehicle type and sales
        # use groupby to create relevant data for plotting
        unemp_rec = recession_data.groupby('Vehicle')['Unemployment_Rate'].sum().reset_index()
        R_chart4 = dcc.Graph(
            figure=px.bar(unemp_rec,
                          x='Vehicle',
                          y='Unemployment_Rate',
                          color='Vehicle',
                          title='Effect of Unemployment Rate on Vehicle Type and Sales'),
                          labels={'unemployment_rate': 'Unemployment Rate', 'Automobile_Sales': 'Average Automobile 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                             
    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 = yearly_data.groupby('Year')['Automobile_Sales'].mean().reset_index()
        Y_chart1 = dcc.Graph(figure=px.line(yas, x='Year', y='Automobile_Sales'))
            
# Plot 2 Total Monthly Automobile sales using line chart.
        mas = yearly_data.groupby('Month')
        Y_chart2 = dcc.Graph(figure=px.line(mas, x='Year', y='Automobile_Sales'))

        # Plot bar chart for average number of vehicles sold during the given year
        avr_vdata = yearly_data.groupby('Vehicle')['Automobile_Sales'].mean().reset_index()
        Y_chart3 = dcc.Graph(
            figure=px.bar(unemp_rec,
                          x='Vehicle',
                          y='Automobile_Sales',
                          color='Vehicle',
                          title='Effect of Unemployment Rate on Vehicle Type and Sales'))

        # Total Advertisement Expenditure for each vehicle using pie chart
        exp_data = yearly_data.groupby('Vehicle')['Advertisement_Expenditure'].sum().reset_index()
        Y_chart4 = dcc.Graph(
            figure=px.pie(exp_data,
                          values='Advertisement_Expenditure',
                          names='Vehicle',
                          title='Expenditure Share by Vehicle Type during Recessions'))

#TASK 2.6: 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 the Dash app
if __name__ == '__main__':
    app.run_server(debug=True)