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
app = dash.Dash(name)
app.config.suppress_callback_exceptions = True
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})
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
app.layout = html.Div(children=[ # TASK1: Add title to the dashboard # Enter your code below. Make sure you have correct formatting. html.H1('US Domestic Airline Flights Performance', style={'textAlign': 'center', 'color': '#503D36', 'font-size': '24'} ),
# REVIEW2: 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'}),
]
),
# TASK2: Add a dropdown
# Enter your code below. Make sure you have correct formatting.
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 ={'width': '80%','padding':3,'font-size': 20,
'textAlign': '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
# REVIEW3: 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')], # REVIEW4: 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") ])
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')
# 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')
# Compute required information for creating graph from the data
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')
# TASK6: Number of flights flying to each state from each reporting airline
# Enter your code below. Make sure you have correct formatting.
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')
# REVIEW6: 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: 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)]
if name == 'main': app.run_server()