Coursework_Capstone_SpaceX_Predictions/7 Dashboard.py at main · dmarks84/Coursework_Capstone_SpaceX_Predictions · GitHub

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# 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()
dropdown_options = [{'label': 'All Sites', 'value': 'ALL'}]
for location in list(spacex_df['Launch Site'].unique()):
    dropdown_options.append({'label': location, 'value': location})
marks = {}
for step in range(0,10001,2500):
    marks[int(step)] = str(step)

# 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',...)
                                dcc.Dropdown(id='site-dropdown',
                                options=dropdown_options,
                                value='ALL',
                                placeholder='Select a 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')),
                                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=marks,
                                value=[min_payload, max_payload]),
                                html.Br(),

                                # 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
# Function decorator to specify function input and output
@app.callback(Output(component_id='success-pie-chart', component_property='figure'),
              Input(component_id='site-dropdown', component_property='value'))
def get_pie_chart(site):
    if site == 'ALL':
        data = spacex_df.groupby("Launch Site", as_index=False).sum("class").reset_index()
        fig = px.pie(data_frame=data,
        values='class',
        names='Launch Site',
        title='Total Successful Launches by Site')
    else:
        data = spacex_df[spacex_df['Launch Site'] == site].groupby('class').count().reset_index()
        fig = px.pie(data_frame=data,
        values='Launch Site',
        names='class',
        title='Total Successful Launches for Site '+site)
    return fig

# TASK 3:
# 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_scatterplot(site,payload_range):
    min_pay = payload_range[0]
    max_pay = payload_range[1]
    data = spacex_df[spacex_df['Payload Mass (kg)'] >= min_pay]
    data = data[data['Payload Mass (kg)'] <= max_pay]
    if site == 'ALL':
        fig = px.scatter(data_frame=data,
        x="Payload Mass (kg)",
        y='class',
        color='Booster Version Category',
        title='Successful Launches vs. Payload Mass (kg) for all Sites')
    else:
        data = data[data['Launch Site'] == site]
        fig = px.scatter(data_frame=data,
        x='Payload Mass (kg)',
        y='class',
        color='Booster Version Category',
        title=f'Successful Launches for Site {site} vs. Payload Mass (kg)')
    return fig

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