04. Interactive Visual Analytics - Plotly Dash dashboard_ 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("4. Data Visualization - Plotly Dash data: spacex_launch_dash.csv")

#Minimum and maximum Payload masses for the range slider
max_payload = spacex_df['Payload Mass (kg)'].max()
min_payload = spacex_df['Payload Mass (kg)'].min()

# Create a dash application
app = dash.Dash(__name__)


#Get the launch sites from the spacex_df dataframe to use in the dropdown list
launch_sites = []
launch_sites.append({'label': 'All Sites', 'value': 'All Sites'})
for launch_site in spacex_df['Launch Site'].unique().tolist():
    launch_sites.append({'label': launch_site, 'value': launch_site})





# 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 = launch_sites,
                                            value='All Sites',
                                            placeholder="Launch Site",
                                            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')), #this id will be used in the callback function to change the 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', 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):
    filtered_df = spacex_df[spacex_df['Launch Site'] == entered_site]
    if entered_site == 'All Sites':
        fig = px.pie(spacex_df, values='class', names='Launch Site', title='Total Success Launches by Site')
        return fig
    else:
        # return the outcomes pie chart for a selected site
        site_df = filtered_df.groupby(['Launch Site', 'class']).size().reset_index(name='class count')
        fig = px.pie(site_df,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')]) #note the 2 inputs, so they are in a list


def get_scatter_chart(entered_site, payload_slider):
    low, high = payload_slider
    slide=(spacex_df['Payload Mass (kg)'] > low) & (spacex_df['Payload Mass (kg)'] < high)
    dropdown_scatter=spacex_df[slide]

    #If All sites are selected, render a scatter plot to display all values for variables Payload Mass (kg) and class.
    #Point colour is set to the booster version category
    if entered_site == 'All Sites':
        fig = px.scatter(
            dropdown_scatter, x='Payload Mass (kg)', y='class',
            hover_data=['Booster Version'],
            color='Booster Version Category',
            title='Correlation between Payload and Success for all Sites')
        return fig
    else:
    #If a specific site is selected, filter the spacex_df dataframe first, then render a scatter plot to display the same as for all sites.
        dropdown_scatter = dropdown_scatter[spacex_df['Launch Site'] == entered_site]
        fig=px.scatter(
            dropdown_scatter,x='Payload Mass (kg)', y='class', 
            hover_data=['Booster Version'],
            color='Booster Version Category',
            title = f'Success by Payload Size for site {entered_site}')
        return fig

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