Data Visualization with Python | Course | IBM
| Task 1 → Completed Notebook |
|---|
| Task 2 → Completed Codes |
Task 1: Create visualizations using Matplotib, Seaborn and Folium
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Task 1.1 - Line chart
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Task 1.2 - Line chart
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Task 1.3 - Bar chart
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Task 1.4 - Sub plott
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Task 1.5 - Bubble plot
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Task 1.6 - Scatter plot
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Task 1.7 - Pie chart
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Task 1.8 - Pie chart
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Task 1.9 - Count plot
Task 2: Create Dashboard with Plotly and Dash
- Task 2.1 - Title
- Task 2.2 - Drop-downs
- Task 2.3 - Output Division
- Task 2.4 - Callback Function
- Task 2.5 - Recession Report Statistics
- Task 2.6 - Yearly Report Statistics
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Task 1.1 - Develop a Line chart using the functionality of pandas to show how automobile sales fluctuate from year to year (1 point)
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Task 1.2 - Plot different lines for categories of vehicle type and analyse the trend to answer the question "Is there a noticeable difference in sales trends between different vehicle types during recession periods?" (1 point)
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Task 1.3 - Use the functionality of Seaborn Library to create a visualization to compare the sales trend per vehicle type for a recession period with a non-recession period. (1 point)
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Task 1.4 - Use sub plotting to compare the variations in GDP during recession and non-recession period by developing line plots for each period. (2 points)
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Task 1.5 - Develop a Bubble plot for displaying the impact of seasonality on Automobile Sales. (1 point)
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Task 1.6 - Use the functionality of Matplotlib to develop a scatter plot to identify the correlation between average vehicle price relate to the sales volume during recessions. (1 point)
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Create a pie chart to display the portion of advertising expenditure of XYZAutomotives during recession and non-recession periods. (1 point)
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Task 1.8 - Develop a pie chart to display the total Advertisement expenditure for each vehicle type during recession period. (1 point)
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Task 1.9 - Develop a countplot to analyse the effect of the unemployment rate on vehicle type and sales during the Recession Period. (1 point)
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Task 2.1 - Create a Dash application and give it a meaningful title. (2 points)
html.H1("Automobile Sales Statistics Dashboard", style={'textAlign': 'center', 'color': '#503D36', 'font-size': '24px'})
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Task 2.2 - Add drop-downs to your dashboard with appropriate titles and options. (1 point)
html.Div([
#TASK 2.2: Add two dropdown menus
html.Label("Select Statistics:"),
dcc.Dropdown(
id='dropdown-statistics',
options= dropdown_options,
placeholder='Select a report type',
value='Select Statistics',
style={'textAlign': 'center', 'font-size': '20px', 'width': '80%', 'padding':'3px'},
)
]),
html.Div(dcc.Dropdown(
id='select-year',
options=[{'label': i, 'value': i} for i in year_list],
placeholder='Select Year',
)),
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Task 2.3 - Add a division for output display with appropriate 'id' and 'classname' property. (1 point)
html.Div([
#TASK 2.3: Add a division for output display
html.Div(id='output-container', className='chart-grid', style={'display':'flex'}),
]),
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Task 2.4 - Creating Callbacks; Define the callback function to update the input container. (5 points)
#TASK 2.4: Creating Callbacks
# Define the callback function to update the input container based on the selected statistics
@app.callback(
Output(component_id='select-year', component_property='disabled'),
Input(component_id='dropdown-statistics',component_property='value'))
def update_input_container(selected_statistics):
if selected_statistics =='Yearly Statistics':
return False
else:
return True
#Callback for plotting
# Define the callback function to update the input container based on the selected statistics
@app.callback(
Output(component_id='output-container', component_property='children'),
[Input(component_id='select-year', component_property='value'), Input(component_id='dropdown-statistics', component_property='value')])
def update_output_container(input_year, selected_statistics):
if selected_statistics == 'Recession Period Statistics':
# Filter the data for recession periods
recession_data = data[data['Recession'] == 1]
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Task 2.5 - Create and display graphs for Recession Report Statistics. (3 points)
#TASK 2.5: Create and display graphs for Recession Report Statistics
#Plot 1 Automobile sales fluctuate over Recession Period (year wise)
# use groupby to create relevant data for plotting
yearly_rec=recession_data.groupby('Year')['Automobile_Sales'].mean().reset_index()
R_chart1 = dcc.Graph(
figure=px.line(yearly_rec,
x='Year',
y='Automobile_Sales',
title="Average Automobile Sales Fluctuation Over Recession Period"))
#Plot 2 Calculate the average number of vehicles sold by vehicle type
# use groupby to create relevant data for plotting
average_sales = recession_data.groupby('Vehicle_Type')['Automobile_Sales'].mean().reset_index()
R_chart2 = dcc.Graph(
figure=px.bar(average_sales,
x='Vehicle_Type',
y='Automobile_Sales',
title="Average Number Of Vehicles Sold By Vehicle Type"))
# Plot 3 Pie chart for total expenditure share by vehicle type during recessions
# use groupby to create relevant data for plotting
exp_rec= recession_data.groupby('Vehicle_Type')['Advertising_Expenditure'].sum().reset_index()
R_chart3 = dcc.Graph(
figure=px.pie(exp_rec,
values='Advertising_Expenditure',
names='Vehicle_Type',
title="Total Expenditure Share By Vehicle Type During Recessions"
)
)
# Plot 4 bar chart for the effect of unemployment rate on vehicle type and sales
average_sales = recession_data.groupby(['unemployment_rate','Vehicle_Type'])['Automobile_Sales'].mean().reset_index()
R_chart4 = dcc.Graph(
figure=px.bar(average_sales,
x='unemployment_rate',
y='Automobile_Sales',
color = 'Vehicle_Type',
title="The Effect Of Unemployment Rate On Vehicle Type And Sales Over Recession Period"))
return [
html.Div(className='chart-item', children=[html.Div(children=R_chart1),html.Div(children=R_chart2)]),
html.Div(className='chart-item', children=[html.Div(children=R_chart3),html.Div(children=R_chart4)])
]
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Task 2.6 - Create and display graphs for Yearly Report Statistics. (2 points)
# TASK 2.6: Create and display graphs for Yearly Report Statistics
# Yearly Statistic Report Plots
elif (input_year and selected_statistics=='Yearly Statistics') :
yearly_data = data[data['Year'] == input_year]
#plot 1 Yearly Automobile sales using line chart for the whole period.
yas= data.groupby('Year')['Automobile_Sales'].mean().reset_index()
Y_chart1 = dcc.Graph(
figure=px.line(yas,
x='Year',
y='Automobile_Sales',
title="Average Automobile Sales Fluctuation Over Time"))
# Plot 2 Total Monthly Automobile sales using line chart.
mas= yearly_data.groupby('Month')['Automobile_Sales'].sum().reset_index()
Y_chart2 = dcc.Graph(
figure=px.line(mas,
x='Month',
y='Automobile_Sales',
title="Total Monthly Automobile Sales in the year {}" .format(input_year)))
# Plot bar chart for average number of vehicles sold during the given year
avr_vdata=yearly_data.groupby('Vehicle_Type')['Automobile_Sales'].mean().reset_index()
Y_chart3 = dcc.Graph(
figure=px.bar(avr_vdata,
x='Vehicle_Type',
y='Automobile_Sales',
title='Average Vehicles Sold by Vehicle Type in the year {}'.format(input_year)))
# Total Advertisement Expenditure for each vehicle using pie chart
exp_data=yearly_data.groupby('Vehicle_Type')['Advertising_Expenditure'].sum().reset_index()
Y_chart4 = dcc.Graph(
figure=px.pie(exp_data,
values='Advertising_Expenditure',
names='Vehicle_Type',
title='Average Vehicles Sold by Vehicle Type in the year {}'.format(input_year)))
#TASK 2.6: Returning the graphs for displaying Yearly data
return [
html.Div(className='chart-item', children=[html.Div(children=Y_chart1),html.Div(children=Y_chart2)]),
html.Div(className='chart-item', children=[html.Div(children=Y_chart3),html.Div(children=Y_chart4)])
]
else:
return None
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