Final Assignment.py

from re import T
import pandas as pd
import plotly.express as px
import dash
from dash import html, dcc, no_update
from dash.dependencies import Input, Output, State


def compute_performance_data(df):
    # Cancellation Category Count
    bar_data = df.groupby(['Month','CancellationCode'])['Flights'].sum().reset_index()
    # Average flight time by reporting airline
    line_data = df.groupby(['Month','Reporting_Airline'])['AirTime'].mean().reset_index()
    # Diverted Airport Landings
    div_data = df[df['DivAirportLandings'] != 0.0]
    # Source state count
    map_data = df.groupby(['OriginState'])['Flights'].sum().reset_index()
    # Destination state count
    tree_data = df.groupby(['DestState', 'Reporting_Airline'])['Flights'].sum().reset_index()
    return bar_data, line_data, div_data, map_data, tree_data


def compute_delay_data(df):
    # Compute delay averages
    avg_car = df.groupby(['Month','Reporting_Airline'])['CarrierDelay'].mean().reset_index()
    avg_weather = df.groupby(['Month','Reporting_Airline'])['WeatherDelay'].mean().reset_index()
    avg_NAS = df.groupby(['Month','Reporting_Airline'])['NASDelay'].mean().reset_index()
    avg_sec = df.groupby(['Month','Reporting_Airline'])['SecurityDelay'].mean().reset_index()
    avg_late = df.groupby(['Month','Reporting_Airline'])['LateAircraftDelay'].mean().reset_index()
    return avg_car, avg_weather, avg_NAS, avg_sec, avg_late


airline_data =  pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/airline_data.csv',
                            encoding = "ISO-8859-1",
                            dtype={'Div1Airport': str, 'Div1TailNum': str,
                                   'Div2Airport': str, 'Div2TailNum': str})
app = dash.Dash(__name__, suppress_callback_exceptions=True)

# Layout section for Dash
app.layout = html.Div([
    # Title
    html.H1('US Domestic Airline Flights Performance',
            style={'textAlign': 'center', 'color': '#503D36', 'font-size': 24}),

    # Dropdown menu 1
    html.Div([
        html.Div([
            html.H2('Report Type:', style={'margin-right': '2em'})
        ]),
        dcc.Dropdown(id='report-type',
                     options=[
                         {'label': 'Yearly Airline Performance Report', 'value': 'performance'},
                         {'label': 'Yearly Airline Delay Report', 'value': 'delay'}
                     ],
                     placeholder='Select a report type',
                     style={
                         'width': '80%',
                         'padding': '3px',
                         'font-size': '20px',
                         'textAlign': 'center'
                     }
        ),
    ], style={'display': 'flex'}),

    # Dropdown menu 2
    html.Div([
        html.Div([
            html.H2('Choose Year:', style={'margin-right': '2em'})
        ]),
        dcc.Dropdown(id='input-year',
                     options=[dict(label=i, value=i) for i in range(2005, 2021)],
                     placeholder='Select a year',
                     style={
                         'width': '80%',
                         'padding': '3px',
                         'font-size': '20px',
                         'textAlign': 'center'
                     }
        )
    ], style={'display': 'flex'}),

    # Plotly graphs
    html.Div([], id='plot1'),
    html.Div([
        html.Div([], id='plot2'),
        html.Div([], id='plot3'),
    ], style={'display': 'flex'}),
    html.Div([
        html.Div([], id='plot4'),
        html.Div([], id='plot5')
    ], style={'display': 'flex'}),
])

@app.callback([Output('plot1', 'children'),
               Output('plot2', 'children'),
               Output('plot3', 'children'),
               Output('plot4', 'children'),
               Output('plot5', 'children')],
              [Input('report-type', 'value'),
               Input('input-year', 'value')],
              [State('plot1', 'children'),
               State('plot2', 'children'),
               State('plot3', 'children'),
               State('plot4', 'children'),
               State('plot5', 'children')])
def get_graph(report_type, year, plot1, plot2, plot3, plot4, plot5):
    df = airline_data[airline_data['Year'] == year]
    if report_type == 'performance':
        bar_data, line_data, div_data, map_data, tree_data = compute_performance_data(df)

        # Create graphs
        bar_fig = px.bar(bar_data, x='Month', y='Flights', color='CancellationCode',
                         title='Monthly Flight Cancellation')
        line_fig = px.line(line_data, x='Month', y='AirTime', color='Reporting_Airline',
                           title='Average flight time (minites) by airline')
        pie_fig = px.pie(div_data, values='Flights', names='Reporting_Airline',
                         title=r'% of flights by reporting airline')
        map_fig = px.choropleth(map_data,
                                locations='OriginState',
                                color='Flights',
                                hover_data=['OriginState', 'Flights'],
                                locationmode='USA-states',
                                color_continuous_scale='GnBu',
                                range_color=[0, map_data['Flights'].max()]
                               )
        map_fig.update_layout(title_text='Number of flights from origin stat', geo_scope='usa')
        tree_fig = px.treemap(tree_data,
                              path=['DestState', 'Reporting_Airline'],
                              values='Flights',
                              color='Flights',
                              color_continuous_scale='RdBu',
                              title='Flight count by airline to destination state')
        return [dcc.Graph(figure=tree_fig),
                dcc.Graph(figure=pie_fig),
                dcc.Graph(figure=map_fig),
                dcc.Graph(figure=bar_fig),
                dcc.Graph(figure=line_fig)]

    elif report_type == 'delay':
        avg_car, avg_weather, avg_NAS, avg_sec, avg_late = compute_delay_data(df)

        # Create line graphs
        carrier_fig = px.line(avg_car, x='Month', y='CarrierDelay', color='Reporting_Airline',
                              title='Average carrier delay time (minutes) by airline')
        weather_fig = px.line(avg_weather, x='Month', y='WeatherDelay', color='Reporting_Airline',
                              title='Average weather delay time (minutes) by airline')
        nas_fig = px.line(avg_NAS, x='Month', y='NASDelay', color='Reporting_Airline',
                          title='Average NAS delay time (minutes) by airline')
        sec_fig = px.line(avg_sec, x='Month', y='SecurityDelay', color='Reporting_Airline',
                          title='Average security delay time (minutes) by airline')
        late_fig = px.line(avg_late, x='Month', y='LateAircraftDelay', color='Reporting_Airline',
                           title='Average late aircraft delay time (minutes) by airline')
        return [dcc.Graph(figure=carrier_fig),
                dcc.Graph(figure=weather_fig),
                dcc.Graph(figure=nas_fig),
                dcc.Graph(figure=sec_fig),
                dcc.Graph(figure=late_fig)]
    return [plot1, plot2, plot3, plot4, plot5]

if __name__ == '__main__':
    app.run_server()