L6_spacex_dash_app.py

# 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
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()

# 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=[
                                                {'label':'All Sites', 'value':'ALL'}, {'label':'CCAFS LC-40', 'value':'CCAFS LC-40'},
                                                {'label':'CCAFS SLC-40','value':'CCAFS SLC-40'}, {'label':'KSC LC-39A','value':'KSC LC-39A'},
                                                {'label':'KSC LC-39A','value':'KSC LC-39A'},
                                            ], 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=0, max=10000, step=1000,
                                                marks={0: '0', 100: '100'},
                                                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):
    filtered_df = spacex_df
    if entered_site == 'ALL':
        fig = px.pie(filtered_df, values='class', 
        names='Launch Site', 
        title='Total Success Launches')
        return fig
    else:
        # return the outcomes piechart for a selected site
        filtered_df = filtered_df[filtered_df['Launch Site'] == entered_site]
        class_count = filtered_df['class'].value_counts().reset_index()
        
        fig = px.pie(class_count, values='class',
        names='index', 
        title=f"Success Launch Rate for {entered_site} 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, payload):
    filtered_df = spacex_df[spacex_df['Payload Mass (kg)'].between(payload[0],payload[1])]

    if entered_site == 'ALL':
        fig = px.scatter(filtered_df, 
                        x='Payload Mass (kg)', 
                        y='class', 
                        color='Booster Version Category')
        return fig
    else:
        # return the outcomes scatter plot for a selected site
        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')
        return fig


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