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spacex_dash_app.py
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/
spacex_dash_app.py
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# Import required libraries
import pandas as pd
import dash
from dash import html
from dash import dcc
from dash.dependencies import Input, Output
import plotly.express as px
# Read the airline data into pandas dataframe
spacex_df = pd.read_csv(r"D:\Documents\Coursera\Capstone Project\spacex_launch_dash.csv")
max_payload = spacex_df['Payload Mass (kg)'].max()
min_payload = spacex_df['Payload Mass (kg)'].min()
# Create a dash application
app = dash.Dash(__name__)
# Create an app layout
app.layout = html.Div(children=[html.H1('SpaceX Launch Records Dashboard',
style={'textAlign': 'center', 'color': '#503D36',
'font-size': 40}),
# TASK 1: Add a dropdown list to enable Launch Site selection
dcc.Dropdown(id='site-dropdown',
options=[{'label': 'All Sites', 'value': 'ALL'}] + [
{'label':l, 'value':l}
for l in spacex_df['Launch Site'].unique().tolist()
],
value='ALL',
placeholder="Select a Launch Site here",
searchable=True
),
# The default select value is for ALL sites
# dcc.Dropdown(id='site-dropdown',...)
html.Br(),
# TASK 2: Add a pie chart to show the total successful launches count for all sites
# If a specific launch site was selected, show the Success vs. Failed counts for the site
html.Div(dcc.Graph(id='success-pie-chart')),
html.Br(),
html.P("Payload range (Kg):"),
# TASK 3: Add a slider to select payload range
#dcc.RangeSlider(id='payload-slider',...)
dcc.RangeSlider(id='payload-slider',
min=0, max=10000, step=1000,
value=[min_payload, max_payload]
),
# TASK 4: Add a scatter chart to show the correlation between payload and launch success
html.Div(dcc.Graph(id='success-payload-scatter-chart')),
])
# TASK 2:
# Add a callback function for `site-dropdown` as input, `success-pie-chart` as output
@app.callback(Output(component_id='success-pie-chart', component_property='figure'),
Input(component_id='site-dropdown', component_property='value'))
def get_pie_chart(entered_site):
if entered_site == 'ALL':
data = spacex_df
fig = px.pie(data, values='class', names='Launch Site',
title='Total Success Launches By Site')
return fig
else:
# return the outcomes piechart for a selected site
data = (spacex_df
.loc[spacex_df['Launch Site'] == entered_site]
.groupby('class')[['class']]
.count()
.rename({"class":"class_count"}, axis=1)
.reset_index()
)
fig = px.pie(data, values='class_count', names='class',
title=f'Total Success Launches for site "{entered_site}"')
return fig
# TASK 4:
# Add a callback function for `site-dropdown` and `payload-slider` as inputs, `success-payload-scatter-chart` as output
@app.callback(Output(component_id='success-payload-scatter-chart', component_property='figure'),
[
Input(component_id='site-dropdown', component_property='value'),
Input(component_id='payload-slider', component_property='value')
])
def get_scatter_plot(entered_site, entered_payload):
min_value = entered_payload[0]
max_value = entered_payload[1]
data = spacex_df.loc[
(spacex_df['Payload Mass (kg)'] >= min_value) &
(spacex_df['Payload Mass (kg)'] <= max_value)
]
if entered_site == 'ALL':
fig = px.scatter(data, x="Payload Mass (kg)", y="class", color="Booster Version Category",
title=f'Correlation between Payload and Success for all Sites')
return fig
else:
data = data.loc[data['Launch Site'] == entered_site]
fig = px.scatter(data, x="Payload Mass (kg)", y="class", color="Booster Version Category",
title=f'Correlation between Payload and Success for site "{entered_site}"')
return fig
# Run the app
if __name__ == '__main__':
app.run_server()