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spacex_dash_app2.py
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# Import required libraries
from json import loads
from dash_core_components.Graph import Graph
import pandas as pd
import dash
import dash_html_components as html
import dash_core_components as dcc
from dash.dependencies import Input, Output,State
import plotly.express as px
#from dash import no_update
# 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()
site_list = ['All','CCAFS LC-40', 'VAFB SLC-4E', 'KSC LC-39A', 'CCAFS SLC-40']
# Create a dash application
app = dash.Dash(__name__)
app.config.suppress_callback_exceptions = True
# Create an app layout
app.layout = html.Div(children=[
html.H3('SpaceX Launch Records Dashboard',
style={'textAlign': 'center',
'color': '#503D36',
'backgroundColor': "white",
'font-size': 23}),
# TASK 1: Add a dropdown list to enable Launch Site selection
# The default select value is for ALL sites
# dcc.Dropdown(id='site-dropdown',...)
# outer division
html.Div([
# inner division
html.Div([
# Create an division for adding dropdown helper text for report type
html.Div([
html.H3('Launching Site:',
style = {'margin-right': '2em'}),
]),
# create dropdown list or options for launch sites
dcc.Dropdown(id ='site-dropdown',
options = [{'label': i, 'value': i } for i in (site_list)
#{'label': 'All', 'value': 'All'},
#{'label': 'CCAFS LC-40', 'value': 'CCAFS LC-40'},
#{'label': 'VAFB SLC-4E', 'value': 'VAFB SLC-4E'},
#{'label': 'KSC LC-39A', 'value': 'KSC LC-39A'},
#{'label': 'CCAFS SLC-40', 'value': 'CCAFS SLC-40'}
],
value = 'All',
multi=False,
placeholder = 'Select a Launching site',
searchable = True,
style = {'width':'80%','padding':'3px', 'font-size':'20px',
'text-align-last':'center'}
),
],
style={'display':'flex'}
),
]),
html.Br(),
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')),
#dcc.Loading(id='loading-1', type='default',children=html.Div('success-pie-chart')),
html.Br(),
html.Br(),
# Update graph figures for success-pie-chart.
html.Div([ ], id='plot1'),
html.Div([ ], id='plot2'),
html.Div([ ], id='plot3'),
html.Div([ ], id='plot4'),
html.Div([ ], id='plot5'),
html.Div([ ], id='plot6'),
html.Div([ ], id='plot7'),
# style={'display': 'flex'}),
html.Div([
html.Div([
html.H4("Payload range (Kg):",
style = {'margin-right': '2em'}),
]),
# 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],
marks={0: {'label': '0', 'style': {'color': '#f50'}},
2500: {'label': '2500'},
5000: {'label': '5000'},
7500:{'label': '7500'},
10000: {'label': '10000', 'style': {'color': '#f50'}}
}
),
]),
# TASK 4: Add a scatter chart to show the correlation between payload and launch success
#html.Div(dcc.Graph(id='success-payload-scatter-chart')),
#dcc.Loading(id='loading-2', type='default',children=html.Div('success-payload-scatter-chart')),
html.Br(),
html.Br(),
# Update graph figures for success-payload-scatter-chart.
html.Div([ ], id='plot8'),
html.Div([ ], id='plot9'),
html.Div([ ], id='plot10'),
html.Div([ ], id='plot11'),
html.Div([ ], id='plot12')
#style={'display': 'flex'}),
])
# TASK 2:
# Add a callback function for `site-dropdown` as input, `success-pie-chart` as output
@app.callback([Output(component_id = 'plot1', component_property = 'children'),
Output(component_id = 'plot2', component_property = 'children'),
Output(component_id = 'plot3', component_property = 'children'),
Output(component_id = 'plot4', component_property = 'children'),
Output(component_id = 'plot5', component_property = 'children'),
Output(component_id = 'plot6', component_property = 'children'),
Output(component_id = 'plot7', component_property = 'children')
],
Input(component_id='site-dropdown', component_property='value'),
[State('plot1','children'), State('plot2','children'),
State('plot3','children'), State('plot4','children'),
State('plot5','children'), State('plot6','children'),
State('plot7','children')]
)
# define get_pie(site)
def get_graphs(site,c1,c2,c3,c4,c5,c6,c7):
# pie section
#check if site value passed to a function equalls to that in dataframe.
pie_data = spacex_df[spacex_df['Launch Site']== site]
#line_data = spacex_df.groupby(['Launch Site','class']).sum().reset_index()
#tree_data = spacex_df.groupby(['Launch Site','class']).size().reset_index()
if site == 'All':
# pie figure
pie_fig = px.pie(spacex_df, values='class', names='Launch Site',
title= "Total Success Launches by: {}".format(site) +
" Sites using pie.")
# bar figure
bar_fig = px.bar(spacex_df, x = 'Launch Site', y = 'class', color='Launch Site',
title= "Total Success Launches by: {}".format(site) + " Sites using bar.")
# treemap figure
tree_fig = px.treemap(spacex_df, path = ['class','Launch Site'],values = 'class', color = 'Launch Site',
color_continuous_scale = 'RdBu', title = "Total Success Launches by: {}".format(site) +
" Sites using treemap.")
# box figure
box_fig = px.box(spacex_df, x='class', y='Launch Site',orientation='h',color='Launch Site', notched=True,
title="Total Success Launches by: {}".format(site) + " Sites using box.")
# violin figure
violin_fig = px.violin(spacex_df, x='Launch Site',y = 'class',color = 'Launch Site', box=True, points='all',
title="Total Success Launches by: {}".format(site) + " Sites using violin." )
# histogram figure
hist_fig = px.histogram(spacex_df, x='class', y = 'Launch Site', color = 'Launch Site',
title="Total Success Launches by: {}".format(site) + " Sites using histogram." )
den_heatmap_fig = px.density_heatmap(spacex_df, x='Launch Site',y='class', hover_name='Launch Site',
color_continuous_scale=['red','green','blue','yellow','black','blue','gray','green'],
height=600, title="Total Success Launches by: {}".format(site) + " Sites using density heatmap.")
return [
dcc.Graph(figure=pie_fig),
dcc.Graph(figure=bar_fig),
dcc.Graph(figure=tree_fig),
dcc.Graph(figure=box_fig),
dcc.Graph(figure=violin_fig),
dcc.Graph(figure=hist_fig),
dcc.Graph(figure=den_heatmap_fig)
]
else:
group_data = pie_data.groupby(['Launch Site','class']).size().reset_index(name='class count')
# pie section
pie_fig = px.pie(group_data, values='class count', names='class',
title= "Total Success Launches by site: {}".format(site) + ' using pie.')
# bar section
bar_fig = px.bar(group_data, x='class count', y='Launch Site', color='class',
title= "Total Success Launches by site: {}".format(site) + ' using bar.')
# treemap section
tree_fig = px.treemap(group_data, values = 'class count', color = 'class', path = ['class','Launch Site'],
color_continuous_scale = 'RdBu', title = "Total Success Launches by: {}".format(site) +
' using treemap.')
# box section
box_fig = px.box(group_data, x='class count',y='Launch Site',orientation='h',color='Launch Site', notched=True,
title="Total Success Launches by: {}".format(site) + ' using box.')
# violin figure
violin_fig = px.violin(group_data, x='Launch Site', y = 'class',color = 'Launch Site', box=True, points='all',
title="Total Success Launches by: {}".format(site) +
' using violin.')
# histogram figure
hist_fig = px.histogram(group_data, x='class count', y = 'Launch Site', color = 'class',
title="Total Success Launches by: {}".format(site) +
' using histogram.')
# density_heatmap figure
den_heatmap_fig = px.density_heatmap(group_data, x='class count',y='Launch Site', hover_name='Launch Site',
color_continuous_scale=['red','green','blue','yellow','black'],
height=600, title="Total Success Launches by: {}".format(site) +
' using density_heatmap.')
return [dcc.Graph(figure=pie_fig),
dcc.Graph(figure=bar_fig),
dcc.Graph(figure=tree_fig),
dcc.Graph(figure=box_fig),
dcc.Graph(figure=violin_fig),
dcc.Graph(figure=hist_fig),
dcc.Graph(figure=den_heatmap_fig)
]
# TASK 4:
# Add a callback function for `site-dropdown` and `payload-slider` as inputs, `success-payload-scatter-chart` as output
#def get_scatter(site,slider):
@app.callback([Output(component_id = 'plot8', component_property='children'),
Output(component_id = 'plot9', component_property = 'children'),
Output(component_id = 'plot10', component_property = 'children'),
Output(component_id = 'plot11', component_property = 'children'),
Output(component_id = 'plot12', component_property = 'children')],
[Input(component_id='site-dropdown', component_property='value'),
Input(component_id='payload-slider', component_property='value')],
[State('plot8','children'),State('plot9','children'),
State('plot10','children'),State('plot11','children'),
State('plot12','children')],
)
# function for
def other_graphs(site,slider_range,c8,c9,c10,c11,c12):
low,high= slider_range
low = min(spacex_df['Payload Mass (kg)'])
high = max(spacex_df['Payload Mass (kg)'])
sliding = (spacex_df['Payload Mass (kg)'] >=low) & (spacex_df['Payload Mass (kg)'] <= high)
scatter_df = spacex_df[sliding]
if site == 'All':
# scatter figure
scat_fig = px.scatter(scatter_df,x='Payload Mass (kg)',y='class',color='Booster Version Category',
title='Correlation between Payload and Success for {}'.format(site) +
' Sites using scatter.')
scat_modify_fig = px.scatter(scatter_df,x='Payload Mass (kg)',y='class',color='Booster Version Category',
size_max=50,marginal_y ='box',height = 500, marginal_x='box',
trendline='ols', template='plotly_white', hover_name='Launch Site',
title='Correlation between Payload and Success for {}'.format(site) +
' Sites using tuned scatter.')
scatter_matric_fig = px.scatter_matrix(scatter_df, dimensions=['Payload Mass (kg)','class'],color='Booster Version Category',
title='Correlation between Payload and Success for {}'.format(site) +
' Sites using scatter_matrix.')
par_coord_fig = px.parallel_coordinates(scatter_df, dimensions=['Payload Mass (kg)', 'class' ],
color ='class', color_continuous_scale= ['red','green','blue'], height=600,
title='Correlation between Payload and Success for {}'.format(site) +
' Sites using parallel_coordinates.')
par_cat_fig = px.parallel_categories(scatter_df, dimensions=['Payload Mass (kg)', 'class'],
color ='class', color_continuous_scale= ['red','green','blue'],
height=600, dimensions_max_cardinality=50,
title='Correlation between Payload and Success for {}'.format(site) +
' Sites using parallel_categories.')
return [dcc.Graph(figure=scat_fig),
dcc.Graph(figure=scat_modify_fig),
dcc.Graph(figure=scatter_matric_fig),
dcc.Graph(figure=par_coord_fig),
dcc.Graph(figure=par_cat_fig)
]
else:
#check if site value passed to a function equalls to that in dataframe.
scatter_data = scatter_df[scatter_df['Launch Site'] == site]
scat_fig = px.scatter(scatter_data,x='Payload Mass (kg)',y='class',color='Booster Version Category',
title='Correlation between Payload and Success for {}'.format(site) +
' using scatter.')
scat_modify_fig = px.scatter(scatter_data, x='Payload Mass (kg)', y='class', color="Booster Version Category",
size_max=50, marginal_y ='box', height = 500, marginal_x='box',
trendline='ols', template='plotly_white', hover_name='Launch Site',
title='Correlation between Payload and Success for site: {}'.format(site) +
' using tuned scatter.')
scat_matric_fig = px.scatter_matrix(scatter_data, dimensions = ['Payload Mass (kg)','class'],
color='Booster Version Category', hover_name='Booster Version Category',
opacity= 0.5, title='Correlation between Payload and Success for {}'.format(site) +
'using scatter_matrix.')
par_coord_fig = px.parallel_coordinates(scatter_data, dimensions=['Payload Mass (kg)', 'class'],
color ='class', color_continuous_scale= ['red','green','blue'],
height=600, title='Correlation between Payload and Success for {}'.format(site) +
' using parallel_coordinates.')
par_cat_fig = px.parallel_categories(scatter_data, dimensions=['Payload Mass (kg)', 'class'],
color ='class', color_continuous_scale= ['red','green','blue'],
height=600, dimensions_max_cardinality=50,
title='Correlation between Payload and Success for {}'.format(site) +
' using parallel_coordinates.')
return [dcc.Graph(figure=scat_fig),
dcc.Graph(figure=scat_modify_fig),
dcc.Graph(figure=scat_matric_fig),
dcc.Graph(figure=par_coord_fig),
dcc.Graph(figure=par_cat_fig)
]
# Run the app
if __name__ == '__main__':
app.run_server(debug=True)