8.5.Final Project & Exam - sj50179/IBM-Data-Science-Professional-Certificate GitHub Wiki
Final Assignment
Code
# Import required libraries
import pandas as pd
import dash
#import dash_html_components as html
#import dash_core_components as dcc
from dash import html
from dash import 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
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})
# 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=[
# 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={'text-align-last': 'center', 'font-size': '20px', 'width': '80%', 'padding': '3px'})
# 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'}),
# 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([ ], id='plot1'),
html.Div([
html.Div([ ], id='plot4'),
html.Div([ ], id='plot5')
], style={'display': 'flex'})
])
# Callback function definition
# TASK4: Add 5 ouput components
# 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()
Screenshots
Final Exam
LATEST SUBMISSION GRADE 100%
Question 1
According to the author in the video, what does Dark Horse Analytics state are the 3 best practices for creating a visual?
Less is not effective; Less is not attractive; Less is not impactive.- Less is more effective; Less is more attractive; Less is more impactive.
Less is more effective; Less is not attractive; Less is more impactive.None of the above.
Correct
Question 1-1
Which of the following is not true regarding data visualizations?
Supports recommendations to different stakeholders.Shares unbiased representation of data.Explores a given dataset.- Trains and tests a machine learning algorithm.
Correct
Question 2
What are the layers that make up the Matplotlib architecture?
FigureCanvas Layer, Renderer Layer, and Artist Layer.Figure Layer, Artist Layer, and Scripting Layer.Backend Layer, FigureCanvas Layer, Renderer Layer, Artist Layer, and Scripting Layer.- Backend Layer, Artist Layer, and Scripting Layer.
Backend_Bases Layer, Artist Layer, Scripting Layer.
Correct
Question 3
The following code uses what layer to create a stacked area plot of the data in the pandas dataframe, area_df?
ax = area_df.plot(kind='area', figsize=(20, 10))
ax.set_title('Plot Title')
ax.set_ylabel('Vertical Axis Label')
ax.set_xlabel('Horizontal Axis Label')
- Artist layer
Scripting layerBackend LayerNone of the above
Correct
Question 3-1
Which of the following codes uses the scripting layer to create a stacked area plot of the data in the pandas dataframe, area_df?
**import matplotlib.pyplot as plt
area_df.plot(kind='area', figsize=(20, 10))
plt.title('Plot Title')
plt.ylabel('Vertical Axis Label')
plt.xlabel('Horizontal Axis Label')
plt.show()**
~~ax = area_df.plot(kind='area', figsize=(20, 10)) ax.title('Plot Title') ax.ylabel('Vertical Axis Label') ax.xlabel('Horizontal Axis Label')~~~~ax = area_df.plot(type='area', figsize=(20, 10)) ax.set_title('Plot Title') ax.set_ylabel('Vertical Axis Label') ax.set_xlabel('Horizontal Axis Label')~~None of the above
Correct
Question 4
The following code will create a stacked area plot of the data in the pandas dataframe, area_df, with a transparency value of 0.35?
import matplotlib.pyplot as plt
transparency = 0.35
area_df.plot(kind='area', alpha=transparency, figsize=(20, 10))
plt.title('Plot Title')
plt.ylabel('Vertical Axis Label')
plt.xlabel('Horizontal Axis Label')
plt.show()
- True
False
Correct
Question 4-1
Which of the following codes will create an unstacked area plot of the data in the pandas dataframe, area_df, with a transparency value of 0.55?
**transparency = 0.55
ax = area_df.plot(kind='area', alpha=transparency, stacked=False, figsize=(20, 10))
ax.set_title('Plot Title')
ax.set_ylabel('Vertical Axis Label')
ax.set_xlabel('Horizontal Axis Label')**
import matplotlib.pyplot as plt transparency = 1 - 0.55 area_df.plot(kind='area', alpha=transparency, stacked=False, figsize=(20, 10)) plt.title('Plot Title') plt.ylabel('Vertical Axis Label') plt.xlabel('Horizontal Axis Label') plt.show()import matplotlib.pyplot as plt area_df.plot(kind='area', stacked=False, figsize=(20, 10)) plt.title('Plot Title') plt.ylabel('Vertical Axis Label') plt.xlabel('Horizontal Axis Label') plt.show()transparency = 0.35 ax = area_df.plot(kind='area', alpha=transparency, stacked=False, figsize=(20, 10)) ax.title('Plot Title') ax.ylabel('Vertical Axis Label') ax.xlabel('Horizontal Axis Label')
Correct
Question 5
What is a circular graphic that displays numeric proportions by dividing a circle into proportional slices?
Bar chartRadial column chart- Pie chart
Table chart
Correct
Question 6
A ___________ is a variation of the scatter plot that displays three dimensions of data.
Heatmap- Bubble plot
Scatter mapBar chart
Correct
Question 7
A waffle chart is great way to visualize data in relation to a whole, or to do what?
Identify variables that have an impact on a topic of interest.- Highlight progress against a given threshold.
Show frequency or importance.Summarize a set of data measured on an interval scale.
Correct
Question 8
What is a depiction of the meaningful words in some textual data, where the more a specific word appears in the text, the bigger and bolder it appears?
A Regression PlotA Waffle ChartA Box Plot- A Word Cloud
Correct
Question 8-1
Which of the followings are TRUE regarding a word cloud?
- A Word Cloud can be generated in Python using the word_cloud package that was developed by Andreas Mueller.
- A Word Cloud is a depiction of the frequency of different words in some textual data.
A Word Cloud is a depiction of the frequency of the stopwords, such as a, the, and, in some textual data.
Correct
Question 9
Which of the following are tile styles of Folium maps?
- Mapbox Control Room
- Stamen Watercolor
- Stamen Terrain
- OpenStreetMap
- All of the above
Correct
Question 9-1
Which of the followings are correct statements regarding Folium?
- Folium builds on the data wrangling strengths of the Python ecosystem and the mapping strengths of the Leaflet.js library.
- Folium is a powerful Python library that helps you create several type of Leaflet maps.
- The Folium results are interactive, which makes this library very useful for dashboard building.
Folium is available by default and does not need to be installed.
Correct
Question 10
What is the correct tile style for maps that are high contrast black and white, that are perfect for data mashups and exploring river meanders and coastal zones?
Mapbox Control RoomStamen Watercolor- Stamen Toner
Stamen Terrain
Correct
Question 10-1
Which of the followings are true regarding the Stamen Terrain tile style for Folium maps?
- Features natural vegetation colors.
- Features hill shading.
- Showcases advanced labeling and linework generalization of dual-carriageway roads.
Is perfect for data mashups and exploring river meanders and coastal zones.
Correct
Question 11
Plotly visualizations can be displayed in which of the following ways
- Displayed in Jupyter notebook
- Saved to HTML files
- Served as a pure python-build applications using Dash
- All of the above
Correct
Question 12
Dash components are
- HTML
CSS- Core
Correct