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__)

launch_sites=spacex_df['Launch Site'].unique()
launch_sites_options = [{'label': launch_sites[i], 'value': launch_sites[i]} for i in range(len(launch_sites))]
#print(launch_sites_options)
opt = [{'label': 'All Sites', 'value': 'ALL'}, *launch_sites_options]
#print(opt)

# 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=opt,
                                    value='ALL',
                                    placeholder='Select a Lauch 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,
                                    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
# add callback decorator
@app.callback( Output(component_id='success-pie-chart', component_property='figure'),
               Input(component_id='site-dropdown', component_property='value'))
# Add computation to callback function and return graph
def get_pie_chart(entered_site):
    if entered_site == 'ALL':
        fig = px.pie(spacex_df, values='class', 
        names='Launch Site',
        title='Total Success Launches by Site')
    else:
        # return the outcomes piechart for a selected site
        filtered_df = spacex_df[spacex_df['Launch Site']==entered_site]
        filtered_df['num_of_launches']=1
        fig = px.pie(filtered_df, values='num_of_launches', 
        names='class', 
        title=f'Total Success vs Failed Launches by 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, slider_range):

    if entered_site == 'ALL':
        filtered_df = spacex_df
    else:
        filtered_df = spacex_df[spacex_df['Launch Site']==entered_site]
        
    low, high = slider_range
    mask = (filtered_df['Payload Mass (kg)'] > low) & (filtered_df['Payload Mass (kg)'] < high)
    fig = px.scatter(filtered_df[mask], x="Payload Mass (kg)", y="class", color="Booster Version Category")
    return fig

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