使用回调没有为我的 plotly 仪表板显示图表
No Graph Displayed for My plotly dashboard using callback
这是 Coursera 上的一门课程的代码编写作业。任务是为一些数据创建一个包含各种图形的仪表板（可以从 URL 下载）。我已经完成了作业的编码部分，但似乎没有显示图表。我的猜测是回调函数发生了某些事情，但这只是一个大胆的猜测。我完全不知道问题出在哪里...
下面的代码块已经给出了，所以我怀疑这里有问题...
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
# from jupyter_dash import JupyterDash
import plotly.graph_objects as go
import plotly.express as px
from dash import no_update
# Create a dash application
app = dash.Dash(__name__)
# JupyterDash.infer_jupyter_proxy_config()
# REVIEW1: Clear the layout and do not display exception till callback gets executed
app.config.suppress_callback_exceptions = True
# Read the airline data into pandas dataframe
airline_data = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/airline_data.csv',
encoding = "ISO-8859-1",
dtype={'Div1Airport': str, 'Div1TailNum': str,
'Div2Airport': str, 'Div2TailNum': str})
# List of years
year_list = [i for i in range(2005, 2021, 1)]
"""Compute graph data for creating yearly airline performance report
Function that takes airline data as input and create 5 dataframes based on the grouping condition to be used for plottling charts and grphs.
Argument:
df: Filtered dataframe
Returns:
Dataframes to create graph.
"""
def compute_data_choice_1(df):
# Cancellation Category Count
bar_data = df.groupby(['Month','CancellationCode'])['Flights'].sum().reset_index()
# Average flight time by reporting airline
line_data = df.groupby(['Month','Reporting_Airline'])['AirTime'].mean().reset_index()
# Diverted Airport Landings
div_data = df[df['DivAirportLandings'] != 0.0]
# Source state count
map_data = df.groupby(['OriginState'])['Flights'].sum().reset_index()
# Destination state count
tree_data = df.groupby(['DestState', 'Reporting_Airline'])['Flights'].sum().reset_index()
return bar_data, line_data, div_data, map_data, tree_data
"""Compute graph data for creating yearly airline delay report
This function takes in airline data and selected year as an input and performs computation for creating charts and plots.
Arguments:
df: Input airline data.
Returns:
Computed average dataframes for carrier delay, weather delay, NAS delay, security delay, and late aircraft delay.
"""
def compute_data_choice_2(df):
# Compute delay averages
avg_car = df.groupby(['Month','Reporting_Airline'])['CarrierDelay'].mean().reset_index()
avg_weather = df.groupby(['Month','Reporting_Airline'])['WeatherDelay'].mean().reset_index()
avg_NAS = df.groupby(['Month','Reporting_Airline'])['NASDelay'].mean().reset_index()
avg_sec = df.groupby(['Month','Reporting_Airline'])['SecurityDelay'].mean().reset_index()
avg_late = df.groupby(['Month','Reporting_Airline'])['LateAircraftDelay'].mean().reset_index()
return avg_car, avg_weather, avg_NAS, avg_sec, avg_late
以下代码块是我的工作，无法弄清楚我哪里出错了。
# Application layout
app.layout = html.Div(children=[
# TODO1: Add title to the dashboard
html.H1('US Domestic Airline Flights Performance',
style={'textAlign': 'center', 'color': '#503D36',
'font-size': 24}),
# REVIEW2: Dropdown creation
# Create an outer division
html.Div([
# Add an division
html.Div([
# Create an division for adding dropdown helper text for report type
html.Div(
[
html.H2('Report Type:', style={'margin-right': '2em'}),
]
),
# TODO2: Add a dropdown
dcc.Dropdown(id = 'input-type',
options=[
{'label': 'Yearly Airline Performance Report', 'value': 'OPT1'},
{'label': 'Yearly Airline Delay Report', 'value': 'OPT2'}
],
placeholder = 'Select a report type',
style = {'width': '80%', 'padding': '3px', 'font-size': '20px', 'textAlign': 'center'})
# Place them next to each other using the division style
], style={'display':'flex'}),
# Add next division
html.Div([
# Create an division for adding dropdown helper text for choosing year
html.Div(
[
html.H2('Choose Year:', style={'margin-right': '2em'})
]
),
dcc.Dropdown(id='input-year',
# Update dropdown values using list comphrehension
options=[{'label': i, 'value': i} for i in year_list],
placeholder="Select a year",
style={'width':'80%', 'padding':'3px', 'font-size': '20px', 'text-align-last' : 'center'}),
# Place them next to each other using the division style
], style={'display': 'flex'}),
]),
# Add Computed graphs
# REVIEW3: Observe how we add an empty division and providing an id that will be updated during callback
html.Div([], id ='plot1'),
html.Div([
html.Div([], id ='plot2'),
html.Div([], id ='plot3')
], style={'display': 'flex'}),
# TODO3: Add a division with two empty divisions inside. See above disvision for example.
html.Div([
html.Div([], id ='plot4'),
html.Div([], id ='plot5')
], style={'display': 'flex'}),
])
# Callback function definition
# TODO4: Add 5 ouput components
@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')],
[Input(component_id='input-type', component_property='value'),
Input(component_id='input-year', component_property='value')],
# REVIEW4: Holding output state till user enters all the form information. In this case, it will be chart type and year
[State("plot1", 'children'), State("plot2", "children"),
State("plot3", "children"), State("plot4", "children"),
State("plot5", "children")
])
# Add computation to callback function and return graph
def get_graph(chart, year, children1, children2, c3, c4, c5):
# Select data
df = airline_data[airline_data['Year']==int(year)]
if chart == 'OPT1':
# Compute required information for creating graph from the data
bar_data, line_data, div_data, map_data, tree_data = compute_data_choice_1(df)
# Number of flights under different cancellation categories
bar_fig = px.bar(bar_data, x='Month', y='Flights', color='CancellationCode', title='Monthly Flight Cancellation')
# TODO5: Average flight time by reporting airline
line_fig = px.line(line_data, x = 'Month', y = 'AirTime', color = 'Reporting_Airline', title = 'Average monthly flight time (minutes) by airline')
# Percentage of diverted airport landings per reporting airline
pie_fig = px.pie(div_data, values='Flights', names='Reporting_Airline', title='% of flights by reporting airline')
# REVIEW5: Number of flights flying from each state using choropleth
map_fig = px.choropleth(map_data, # Input data
locations='OriginState',
color='Flights',
hover_data=['OriginState', 'Flights'],
locationmode = 'USA-states', # Set to plot as US States
color_continuous_scale='GnBu',
range_color=[0, map_data['Flights'].max()])
map_fig.update_layout(
title_text = 'Number of flights from origin state',
geo_scope='usa') # Plot only the USA instead of globe
# TODO6: Number of flights flying to each state from each reporting airline
tree_fig = px.treemap(tree_data, path=['DestState', 'Reporting_Airline'],
values='Flights',
color='Flights',
color_continuous_scale='RdBu',
title='Flight count by airline to destination state'
)
# REVIEW6: Return dcc.Graph component to the empty division
return [dcc.Graph(figure=tree_fig),
dcc.Graph(figure=pie_fig),
dcc.Graph(figure=map_fig),
dcc.Graph(figure=bar_fig),
dcc.Graph(figure=line_fig)
]
else:
# REVIEW7: This covers chart type 2 and we have completed this exercise under Flight Delay Time Statistics Dashboard section
# Compute required information for creating graph from the data
avg_car, avg_weather, avg_NAS, avg_sec, avg_late = compute_data_choice_2(df)
# Create graph
carrier_fig = px.line(avg_car, x='Month', y='CarrierDelay', color='Reporting_Airline', title='Average carrrier delay time (minutes) by airline')
weather_fig = px.line(avg_weather, x='Month', y='WeatherDelay', color='Reporting_Airline', title='Average weather delay time (minutes) by airline')
nas_fig = px.line(avg_NAS, x='Month', y='NASDelay', color='Reporting_Airline', title='Average NAS delay time (minutes) by airline')
sec_fig = px.line(avg_sec, x='Month', y='SecurityDelay', color='Reporting_Airline', title='Average security delay time (minutes) by airline')
late_fig = px.line(avg_late, x='Month', y='LateAircraftDelay', color='Reporting_Airline', title='Average late aircraft delay time (minutes) by airline')
return[dcc.Graph(figure=carrier_fig),
dcc.Graph(figure=weather_fig),
dcc.Graph(figure=nas_fig),
dcc.Graph(figure=sec_fig),
dcc.Graph(figure=late_fig)]
if __name__ == '__main__':
app.run_server()
你的代码很长，缩进不一致。我建议您开始使用 https://black.readthedocs.io/en/stable/
我确实发现你的 dash 应用基本上可以工作，这只是一个例子
- 阻止，因为端口已被使用。重启所有内核解决
- 不打开浏览器window查看运行应用程序。下面的代码将为您执行此操作。
if __name__ == "__main__":
from threading import Timer
import webbrowser
Timer(10, webbrowser.open_new("http://127.0.0.1:8050/")).start()
app.run_server()
这是 Coursera 上的一门课程的代码编写作业。任务是为一些数据创建一个包含各种图形的仪表板（可以从 URL 下载）。我已经完成了作业的编码部分，但似乎没有显示图表。我的猜测是回调函数发生了某些事情，但这只是一个大胆的猜测。我完全不知道问题出在哪里...
下面的代码块已经给出了，所以我怀疑这里有问题...
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
# from jupyter_dash import JupyterDash
import plotly.graph_objects as go
import plotly.express as px
from dash import no_update
# Create a dash application
app = dash.Dash(__name__)
# JupyterDash.infer_jupyter_proxy_config()
# REVIEW1: Clear the layout and do not display exception till callback gets executed
app.config.suppress_callback_exceptions = True
# Read the airline data into pandas dataframe
airline_data = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/airline_data.csv',
encoding = "ISO-8859-1",
dtype={'Div1Airport': str, 'Div1TailNum': str,
'Div2Airport': str, 'Div2TailNum': str})
# List of years
year_list = [i for i in range(2005, 2021, 1)]
"""Compute graph data for creating yearly airline performance report
Function that takes airline data as input and create 5 dataframes based on the grouping condition to be used for plottling charts and grphs.
Argument:
df: Filtered dataframe
Returns:
Dataframes to create graph.
"""
def compute_data_choice_1(df):
# Cancellation Category Count
bar_data = df.groupby(['Month','CancellationCode'])['Flights'].sum().reset_index()
# Average flight time by reporting airline
line_data = df.groupby(['Month','Reporting_Airline'])['AirTime'].mean().reset_index()
# Diverted Airport Landings
div_data = df[df['DivAirportLandings'] != 0.0]
# Source state count
map_data = df.groupby(['OriginState'])['Flights'].sum().reset_index()
# Destination state count
tree_data = df.groupby(['DestState', 'Reporting_Airline'])['Flights'].sum().reset_index()
return bar_data, line_data, div_data, map_data, tree_data
"""Compute graph data for creating yearly airline delay report
This function takes in airline data and selected year as an input and performs computation for creating charts and plots.
Arguments:
df: Input airline data.
Returns:
Computed average dataframes for carrier delay, weather delay, NAS delay, security delay, and late aircraft delay.
"""
def compute_data_choice_2(df):
# Compute delay averages
avg_car = df.groupby(['Month','Reporting_Airline'])['CarrierDelay'].mean().reset_index()
avg_weather = df.groupby(['Month','Reporting_Airline'])['WeatherDelay'].mean().reset_index()
avg_NAS = df.groupby(['Month','Reporting_Airline'])['NASDelay'].mean().reset_index()
avg_sec = df.groupby(['Month','Reporting_Airline'])['SecurityDelay'].mean().reset_index()
avg_late = df.groupby(['Month','Reporting_Airline'])['LateAircraftDelay'].mean().reset_index()
return avg_car, avg_weather, avg_NAS, avg_sec, avg_late
以下代码块是我的工作，无法弄清楚我哪里出错了。
# Application layout
app.layout = html.Div(children=[
# TODO1: Add title to the dashboard
html.H1('US Domestic Airline Flights Performance',
style={'textAlign': 'center', 'color': '#503D36',
'font-size': 24}),
# REVIEW2: Dropdown creation
# Create an outer division
html.Div([
# Add an division
html.Div([
# Create an division for adding dropdown helper text for report type
html.Div(
[
html.H2('Report Type:', style={'margin-right': '2em'}),
]
),
# TODO2: Add a dropdown
dcc.Dropdown(id = 'input-type',
options=[
{'label': 'Yearly Airline Performance Report', 'value': 'OPT1'},
{'label': 'Yearly Airline Delay Report', 'value': 'OPT2'}
],
placeholder = 'Select a report type',
style = {'width': '80%', 'padding': '3px', 'font-size': '20px', 'textAlign': 'center'})
# Place them next to each other using the division style
], style={'display':'flex'}),
# Add next division
html.Div([
# Create an division for adding dropdown helper text for choosing year
html.Div(
[
html.H2('Choose Year:', style={'margin-right': '2em'})
]
),
dcc.Dropdown(id='input-year',
# Update dropdown values using list comphrehension
options=[{'label': i, 'value': i} for i in year_list],
placeholder="Select a year",
style={'width':'80%', 'padding':'3px', 'font-size': '20px', 'text-align-last' : 'center'}),
# Place them next to each other using the division style
], style={'display': 'flex'}),
]),
# Add Computed graphs
# REVIEW3: Observe how we add an empty division and providing an id that will be updated during callback
html.Div([], id ='plot1'),
html.Div([
html.Div([], id ='plot2'),
html.Div([], id ='plot3')
], style={'display': 'flex'}),
# TODO3: Add a division with two empty divisions inside. See above disvision for example.
html.Div([
html.Div([], id ='plot4'),
html.Div([], id ='plot5')
], style={'display': 'flex'}),
])
# Callback function definition
# TODO4: Add 5 ouput components
@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')],
[Input(component_id='input-type', component_property='value'),
Input(component_id='input-year', component_property='value')],
# REVIEW4: Holding output state till user enters all the form information. In this case, it will be chart type and year
[State("plot1", 'children'), State("plot2", "children"),
State("plot3", "children"), State("plot4", "children"),
State("plot5", "children")
])
# Add computation to callback function and return graph
def get_graph(chart, year, children1, children2, c3, c4, c5):
# Select data
df = airline_data[airline_data['Year']==int(year)]
if chart == 'OPT1':
# Compute required information for creating graph from the data
bar_data, line_data, div_data, map_data, tree_data = compute_data_choice_1(df)
# Number of flights under different cancellation categories
bar_fig = px.bar(bar_data, x='Month', y='Flights', color='CancellationCode', title='Monthly Flight Cancellation')
# TODO5: Average flight time by reporting airline
line_fig = px.line(line_data, x = 'Month', y = 'AirTime', color = 'Reporting_Airline', title = 'Average monthly flight time (minutes) by airline')
# Percentage of diverted airport landings per reporting airline
pie_fig = px.pie(div_data, values='Flights', names='Reporting_Airline', title='% of flights by reporting airline')
# REVIEW5: Number of flights flying from each state using choropleth
map_fig = px.choropleth(map_data, # Input data
locations='OriginState',
color='Flights',
hover_data=['OriginState', 'Flights'],
locationmode = 'USA-states', # Set to plot as US States
color_continuous_scale='GnBu',
range_color=[0, map_data['Flights'].max()])
map_fig.update_layout(
title_text = 'Number of flights from origin state',
geo_scope='usa') # Plot only the USA instead of globe
# TODO6: Number of flights flying to each state from each reporting airline
tree_fig = px.treemap(tree_data, path=['DestState', 'Reporting_Airline'],
values='Flights',
color='Flights',
color_continuous_scale='RdBu',
title='Flight count by airline to destination state'
)
# REVIEW6: Return dcc.Graph component to the empty division
return [dcc.Graph(figure=tree_fig),
dcc.Graph(figure=pie_fig),
dcc.Graph(figure=map_fig),
dcc.Graph(figure=bar_fig),
dcc.Graph(figure=line_fig)
]
else:
# REVIEW7: This covers chart type 2 and we have completed this exercise under Flight Delay Time Statistics Dashboard section
# Compute required information for creating graph from the data
avg_car, avg_weather, avg_NAS, avg_sec, avg_late = compute_data_choice_2(df)
# Create graph
carrier_fig = px.line(avg_car, x='Month', y='CarrierDelay', color='Reporting_Airline', title='Average carrrier delay time (minutes) by airline')
weather_fig = px.line(avg_weather, x='Month', y='WeatherDelay', color='Reporting_Airline', title='Average weather delay time (minutes) by airline')
nas_fig = px.line(avg_NAS, x='Month', y='NASDelay', color='Reporting_Airline', title='Average NAS delay time (minutes) by airline')
sec_fig = px.line(avg_sec, x='Month', y='SecurityDelay', color='Reporting_Airline', title='Average security delay time (minutes) by airline')
late_fig = px.line(avg_late, x='Month', y='LateAircraftDelay', color='Reporting_Airline', title='Average late aircraft delay time (minutes) by airline')
return[dcc.Graph(figure=carrier_fig),
dcc.Graph(figure=weather_fig),
dcc.Graph(figure=nas_fig),
dcc.Graph(figure=sec_fig),
dcc.Graph(figure=late_fig)]
if __name__ == '__main__':
app.run_server()
你的代码很长，缩进不一致。我建议您开始使用 https://black.readthedocs.io/en/stable/
我确实发现你的 dash 应用基本上可以工作，这只是一个例子
- 阻止，因为端口已被使用。重启所有内核解决
- 不打开浏览器window查看运行应用程序。下面的代码将为您执行此操作。
if __name__ == "__main__":
from threading import Timer
import webbrowser
Timer(10, webbrowser.open_new("http://127.0.0.1:8050/")).start()
app.run_server()