<file_sep># 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()
launch_sites = spacex_df['Launch Site']

# 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':'VAFB SLC-4E', 'value':'VAFB SLC-4E'},
                                ],
                                value='ALL',
                                placeholder="place holder 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',...)
                                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 pie(site_dropdown):
    if site_dropdown == 'ALL':
        title_pie = f"Sucess Launches for site {site_dropdown}"
        fig = px.pie(spacex_df, values='class', name='Launch Site', title=title_pie)
        return fig
    else:
        filtered_DD = spacex_df[spacex_df['Launch Site'] == site_dropdown]
        filtered_LS = filtered_DD.groupby(['Launch Site', 'class']).size().reset_index(name='class count')
        title_pie = f"Sucess Launches for site {site_dropdown}"
        fig = px.pie(filtered_LS,values='class count', names='class', title=title_pie)
        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 scatter (site_dropdown,payload_slider):
    low,high=(payload_slider)
    mask=(spacex_df['Payload Mass (kg)']>low)&(spacex_df['Payload Mass (kg)']<high)
    filtered_df=spacex_df[mask]
    if site_dropdown == 'ALL':
        fig = px.scatter(spacex_df, x='Payload Mass (kg)', y='class',
                            color='Booster Version Category',
                            title='Payload vs. Outcome for All Sites')
        fig=px.scatter(spacex_df, x='Payload Mass (kg)', y='class', color='Booster Version Category',
                        title="Payload vs. Outcome for All Sites")
        return fig
    else:
        filtered_df1 = filtered_df[filtered_df['Launch Site']==site_dropdown]
        fig = px.scatter (filtered_df1, x='Payload Mass (kg)', y='class',
                            color='Booster Version Category',
                            title='Payload and Booster Versions for site')
        filtered_df1=filtered_df[filtered_df['Launch Site']==site_dropdown]
        fig=px.scatter(filtered_df1,x='Payload Mass (kg)',y='class',color='Booster Version Category',
                        title="Payload and Booster Versions for site")
        return fig


# Run the app
if __name__ == '__main__':
    app.run_server()
<file_sep># IBM_Applied_DataScience_Capstone
To submit final Assignment Capstone for the course Applied data science capstone
#just a Transcript file
