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app.py
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app.py
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# Airline Performance Dashboard Using Plotly Dash
# by **Young Hun Ji**
# June 4, 2021
# Import required 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
# Creating a dash application
app = dash.Dash(__name__)
server = app.server
# Clearing the layout and do not display exception till callback gets executed
app.config.suppress_callback_exceptions = True
# Reading the airline data into pandas dataframe
airline_data = pd.read_csv('airline_data.csv',
encoding = "ISO-8859-1",
dtype={'Div1Airport': str, 'Div1TailNum': str,
'Div2Airport': str, 'Div2TailNum': str})
# List of years
year_list = [i for i in range(2005, 2021, 1)]
# Computing graph data for creating yearly airline performance report
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
# Computing graph data for creating yearly airline delay report
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
app.layout = html.Div(children=[
# Adding a title to the dashboard
html.H1('US Domestic Airline Flights Performance (created by Young Hun Ji)',
style={'text-align':'center',
'color':'#503D36',
'font-size':24}),
# Creating a dropdown
# Creating an outer division
html.Div([
# Adding a division
html.Div([
# Creating a division for adding dropdown helper text for choosing report
html.Div(
[
html.H2('Report Type:', style={'margin-right': '2em'}),
]
),
# Adding a dropdown
dcc.Dropdown(id='input-type',
options =[
{'label':'Yearly Airline Performance Report', 'value':'OPT1'},
{'label':'Yearly Airline Delay Report','value':'OPT2'}
],
placeholder='Select a report type',
style={'text-align':'center',
'width':'80%',
'padding':'3px',
'font-size':'20px'})
# Placing them next to each other using the division style
], style={'display':'flex'}),
# Adding the next division
html.Div([
# Creating a division for adding dropdown helper text for choosing year
html.Div(
[
html.H2('Choose Year:', style={'margin-right': '2em'})
]
),
dcc.Dropdown(id='input-year',
# Updating 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'}),
# Placing them next to each other using the division style
], style={'display': 'flex'}),
]),
# Adding computed graphs
html.Div([ ], id='plot1'),
html.Div([
html.Div([ ], id='plot2'),
html.Div([ ], id='plot3')
], style={'display': 'flex'}),
# Adding a division with two empty divisions inside
html.Div([
html.Div([], id='plot4'),
html.Div([], id='plot5')
], style={'display':'flex'})
])
# Callback function definition
# Adding 5 ouput components
@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 of the form information (i.e., chart type and year)
[State("plot1", 'children'), State("plot2", "children"),
State("plot3", "children"), State("plot4", "children"),
State("plot5", "children")
])
# Adding computation to callback function and return graph
def get_graph(chart, year, children1, children2, c3, c4, c5):
# Selecting data
df = airline_data[airline_data['Year']==int(year)]
# Report 1: Airline performance
if chart == 'OPT1':
# Computing 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')
# Average flight time by reporting airline
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
# Number of flights flying to each state from each reporting airline
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'
)
# Returning 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:
# Report 2: Airline delays
# Computing required information for creating graph from the data
avg_car, avg_weather, avg_NAS, avg_sec, avg_late = compute_data_choice_2(df)
# Creating graphs
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 the app
if __name__ == '__main__':
app.run_server(debug=False, dev_tools_ui=False, dev_tools_props_check=False)