spacex_dash_app.py

# 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("./spacex_launch_dash.csv")
max_payload = spacex_df['Payload Mass (kg)'].max()
min_payload = spacex_df['Payload Mass (kg)'].min()


launch_sites = spacex_df['Launch Site'].unique()
options = [{'label': site, 'value': site} for site in launch_sites.tolist()]
options = [{'label': 'All Sites', 'value': 'ALL'}] + options
# 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
                                # The default select value is for ALL sites
                                dcc.Dropdown(id='site-dropdown', options=options, value='ALL',
                                             placeholder='Select a Launch Site here', searchable=True),
                                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', min=min_payload, max=max_payload, step=1000,
                                                marks={0: '0', 2500: '2500', 5000: '5000', 7500: '7500', 10000: '10000'},
                                                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':
        fig = px.pie(spacex_df, values='class', names='Launch Site', title='Success vs. Failed count for all sites')
        return fig
    else:
        filtered_df = spacex_df[spacex_df['Launch Site'] == entered_site]
        fig = px.pie(filtered_df, names='class', title='Success vs. Failed count 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_chart(entered_site, payload_range):
    if entered_site == 'ALL':
        filtered_df = spacex_df[(spacex_df['Payload Mass (kg)'] >= payload_range[0]) & (spacex_df['Payload Mass (kg)'] <= payload_range[1])]
        fig = px.scatter(filtered_df, x='Payload Mass (kg)', y='class', color='Booster Version Category',
                        title='Payload vs. Outcome for all sites')
        return fig
    else:
        filtered_df = spacex_df[(spacex_df['Payload Mass (kg)'] >= payload_range[0]) & (spacex_df['Payload Mass (kg)'] <= payload_range[1])]
        filtered_df = filtered_df[filtered_df['Launch Site'] == entered_site]
        fig = px.scatter(filtered_df, x='Payload Mass (kg)', y='class', color='Booster Version Category',
                        title='Payload vs. Outcome for site ' + entered_site)
        return fig


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