pip install pandas dash
"""
# Loading all the libraries
"""
import pandas as pd
import dash
import dash_html_components as html
import dash_core_components as dcc
from dash.dependencies import Input, Output, State
import plotly.graph_objects as go
import plotly.express as px
from dash import no_update
"""
# Bringing Data
"""
airline_data =  pd.read_csv("../input/us-airline-data/airline_data.csv", 
                            encoding = "ISO-8859-1",
                            dtype={'Div1Airport': str, 'Div1TailNum': str, 
                                   'Div2Airport': str, 'Div2TailNum': str})
"""
### Reviewing Dataframe
"""
airline_data.head()
"""
### Checking statistical overview of data ,with columns including descriptive data also, will be evaluated by [ include='all' ] parameter
"""
airline_data.describe(include='all')
"""
# Creating an Instance of web application we are going to create.
"""
app = dash.Dash(__name__)
"""
### Let's begin.  
**I am going to code all the program in one cell, because if I tried to execute in seprate cell, it will throw an error saying function is incomplete**
"""
# List of years 
year_list = [i for i in range(2005, 2021, 1)]

"""Compute graph data for creating yearly airline performance report 

Function that takes airline data as input and create 5 dataframes based on the grouping condition to be used for plottling charts and grphs.

Argument:
     
    df: Filtered dataframe
    
Returns:
   Dataframes to create graph. 
"""

def compute_data_choice_1(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

"""Compute graph data for creating yearly airline delay report

This function takes in airline data and selected year as an input and performs computation for creating charts and plots.

Arguments:
    df: Input airline data.
    
Returns:
    Computed average dataframes for carrier delay, weather delay, NAS delay, security delay, and late aircraft delay.
"""

def compute_data_choice_2(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

# Application layout
# This layout is created by dash/dash_components_html/dash_core_components and we don't need to understand html or xml to build layout
# just try to visualize the layout in tree like structure and it will be easy for use to create any layout.
app.layout = html.Div(children=[ 
                               html.H1('US Domestic Airline Flights Performance', style={'color':'#503D36','textAlign':'center','font-size':24}),
                                # Dropdown creation
                                # Create an outer division 
                                html.Div([
                                    # Add an division
                                    html.Div([
                                        # Create an division for adding dropdown helper text for report type
                                        html.Div(
                                            [
                                            html.H2('Report Type:', style={'margin-right': '2em'}),
                                            ]
                                        ),
                                        # Add a dropdown
                                        dcc.Dropdown(id='input-type',
                                        options=[
                                            {'label':'early Airline Performance Report','value':'OPT1'},
                                            {'label':'Yearly Airline Delay Report', 'value':'OPT2'}
                                        ],
                                        placeholder='Select a report type',
                                        style={'width':'80%','padding':'3px','font-size':'20px','text-align-last':'center'})
                                    # Place them next to each other using the division style
                                    ], style={'display':'flex'}),
                                    
                                   # Add next division 
                                   html.Div([
                                       # Create an division for adding dropdown helper text for choosing year
                                        html.Div(
                                            [
                                            html.H2('Choose Year:', style={'margin-right': '2em'})
                                            ]
                                        ),
                                        dcc.Dropdown(id='input-year', 
                                                     # Update dropdown values using list comphrehension
                                                     options=[{'label': i, 'value': i} for i in year_list],
                                                     placeholder="Select a year",
                                                     style={'width':'80%', 'padding':'3px', 'font-size': '20px', 'text-align-last' : 'center'}),
                                            # Place them next to each other using the division style
                                            ], style={'display': 'flex'}),  
                                          ]),
                                
                                # Add Computed graphs
                                # Observe how we add an empty division and providing an id that will be updated during callback
                                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'})
                                ])


# Callback function definition
@app.callback( [Output(component_id='plot1', component_property='children'),
                Output(component_id='plot2', component_property='children'),
                Output(component_id='plot3', component_property='children'),
                Output(component_id='plot4', component_property='children'),
                Output(component_id='plot5', component_property='children')],

               [Input(component_id='input-type', component_property='value'),
                Input(component_id='input-year', component_property='value')],
               # Holding output state till user enters all the form information. In this case, it will be chart type and year
               [State("plot1", 'children'), State("plot2", "children"),
                State("plot3", "children"), State("plot4", "children"),
                State("plot5", "children")
               ])

# Add computation to callback function and return graph
def get_graph(chart, year, children1, children2, c3, c4, c5):
      
        # Select data
        df =  airline_data[airline_data['Year']==int(year)]
       
        if chart == 'OPT1':
            # Compute required information for creating graph from the data
            bar_data, line_data, div_data, map_data, tree_data = compute_data_choice_1(df)
            
            # Number of flights under different cancellation categories
            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 monthly flight time (minutes) by airline')
            
            # Percentage of diverted airport landings per reporting airline
            pie_fig = px.pie(div_data, values='Flights', names='Reporting_Airline', title='% of flights by reporting airline')
            
            # Number of flights flying from each state using choropleth
            map_fig = px.choropleth(map_data,  # Input data
                    locations='OriginState', 
                    color='Flights',  
                    hover_data=['OriginState', 'Flights'], 
                    locationmode = 'USA-states', # Set to plot as US States
                    color_continuous_scale='GnBu',
                    range_color=[0, map_data['Flights'].max()]) 
            map_fig.update_layout(
                    title_text = 'Number of flights from origin state', 
                    geo_scope='usa') # Plot only the USA instead of globe
            
            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 component to the empty division
            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)
                   ]
        else:
            # This covers chart type 2 and we have completed this exercise under Flight Delay Time Statistics Dashboard section
            # Compute required information for creating graph from the data
            avg_car, avg_weather, avg_NAS, avg_sec, avg_late = compute_data_choice_2(df)
            
            # Create graph
            carrier_fig = px.line(avg_car, x='Month', y='CarrierDelay', color='Reporting_Airline', title='Average carrrier 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)]
        

"""
# Running our application
"""
"""
# *Important Note*  
1. You can't run this file on kaggle or jupyter or any other online platform.
1. To run this program you need to copy whole prorgam and run it on local IDE say VS-Code. 
1. You need to uncomment last cell of {Running  our application} and then run in local IDE. I have commented this beacuse if I didn't , it would not upload on kaggle
"""
# if __name__ == '__main__':
#     app.run_server(host='0.0.0.0', port= 8050,debug = False)
"""
## All we have to do now  
*Run the whole program and* **-->** *The last O/P  probabily will be an IP adress* **-->** *go to that ip adress in browser* **-->** *Use interactive Dashboard*
"""