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# Data Visualization with Python Final Assignment Solution – Why Quiz

## Data Visualization with Python Final Assignment Solution

You are required to complete this final assignment.

In this lab also you will utilize an Integrated Development Environment (IDE) based on Theia (an open-source IDE platform similar to VS Code) to develop and run Python code.

To launch the lab, check the box below (next to I agree to use this tool responsibly), and then click on the Open Tool button. This will open a new browser tab with the Cloud IDE (running on Skills Networks Labs environment). Once the lab environment initializes, you will follow the instructions in the left pane of the Cloud IDE and execute them on the right side.

#### Instruction: Data Visualization with Python Final Assignment Solution

In this Assignment, you will demonstrate the data visualization skills you learned by completing this course. Dashboard is based on the concept of demonstrating US Domestic Airline Flights Performance and Yearly average flight delay statistics for a given year ( 2005 to 2020).

### Data Visualization with Python Final Assignment Solution

``````# 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

# Create a dash application
app = dash.Dash(__name__)

# REVIEW1: Clear the layout and do not display exception till callback gets executed
app.config.suppress_callback_exceptions = True

# Read the airline data into pandas dataframe
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)]

"""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
app.layout = html.Div(children=[
# 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([
html.Div([
# Create an division for adding dropdown helper text for report type
html.Div(
[
html.H2('Report Type:', style={'margin-right': '2em'}),
]
),
# 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':'3px', 'font-size': '20px', 'text-align-last' : 'center'}),
# Place them next to each other using the division style
], style={'display':'flex'}),

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'}),
]),

# 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'}),

# TASK3: Add a division with two empty divisions inside. See above disvision for example.
# Enter your code below. Make sure you have correct formatting.
html.Div([
html.Div([ ], id='plot4'),
html.Div([ ], id='plot5')
], style={'display': 'flex'}),
])

# Callback function definition
# Enter your code below. Make sure you have correct formatting.
@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")
])
# 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')

# TASK5: Average flight time by reporting airline
# Enter your code below. Make sure you have correct formatting.
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')

# REVIEW5: 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

# 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:
# REVIEW7: 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)]

# Run the app
if __name__ == '__main__':
app.run_server()``````
##### Conclusion:

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