如何修复Python美国航空航班数据Dash看板代码的缩进错误

你遇到的缩进报错是多个语法错误共同导致的，所有问题点和修复后的完整代码如下：

核心错误点 #

• compute_data_choice_1函数定义末尾缺少冒号，且整个函数体未缩进
• compute_data_choice_2函数定义后整个函数体未缩进
• get_graph回调函数、函数内的if/else代码块全部未正确缩进
• 特殊变量拼写错误：name应为__name__、'main'应为'__main__'
• 装饰器@app.callback和对应的函数定义之间不能有空行
• 初始化Dash应用时的参数应为__name__而非name

修复后完整代码 #

# 导入所需库
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.graph_objects as go
import plotly.express as px
from dash import no_update

# 创建Dash应用
app = dash.Dash(__name__)

# 清空布局，回调执行前不显示异常
app.config.suppress_callback_exceptions = True

# 读取航空公司数据集到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}
)

# 年份列表
year_list = [i for i in range(2005, 2021, 1)]

"""
计算年度航空公司绩效报告所需的图表数据
接收筛选后的dataframe作为输入，返回5个分组后的dataframe用于绘图
参数：
df: 筛选后的dataframe
返回：
绘图所需的dataframe
"""
def compute_data_choice_1(df):
    # 取消航班分类统计
    bar_data = df.groupby(['Month','CancellationCode'])['Flights'].sum().reset_index()
    # 各航司平均飞行时长
    line_data = df.groupby(['Month','Reporting_Airline'])['AirTime'].mean().reset_index()
    # 改道降落航班
    div_data = df[df['DivAirportLandings'] != 0.0]
    # 出发州航班数量统计
    map_data = df.groupby(['OriginState'])['Flights'].sum().reset_index()
    # 目的州航班数量统计
    tree_data = df.groupby(['DestState', 'Reporting_Airline'])['Flights'].sum().reset_index()
    return bar_data, line_data, div_data, map_data, tree_data

"""
计算年度航空公司延误报告所需的图表数据
接收航空公司数据和选中年份作为输入，返回各类延误的平均数据用于绘图
参数：
df: 输入的航空公司数据
返回：
承运人延误、天气延误、NAS延误、安保延误、晚到飞机延误的平均dataframe
"""
def compute_data_choice_2(df):
    # 计算各类延误平均值
    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

# 应用布局
app.layout = html.Div(children=[
    # 看板标题
    html.H2('美国国内航班运营绩效',style={'textAlign':'center','color':'#503d36','font-size':25}),
    # 下拉选择区域
    html.Div([
        html.Div([
            html.Div(
                [
                    html.H2('报告类型:', style={'margin-right': '2em'}),
                ]
            ),
            # 报告类型下拉框
            dcc.Dropdown(id='input-type',
                         options=[{'label': '年度航司绩效报告', 'value':'OPT1'},{'label':'年度航司延误报告','value':'OPT2'}],
                         placeholder="请选择报告类型",
                         style={'width':'80%', 'padding':'3px', 'font-size': '20px', 'text-align-last' : 'center'}),
        ],
        style={'display':'flex'}),

        html.Div([
            html.Div(
                [
                    html.H2('选择年份:', style={'margin-right': '2em'})
                ]
            ),
            # 年份下拉框
            dcc.Dropdown(id='input-year',
                         options=[{'label': i, 'value': i} for i in year_list],
                         placeholder="请选择年份",
                         style={'width':'80%', 'padding':'3px', 'font-size': '20px', 'text-align-last' : 'center'}),
        ], style={'display': 'flex'}),
    ]),

    # 图表渲染区域
    html.Div([ ], id='plot1'),

    html.Div([
        html.Div([ ], id='plot2'),
        html.Div([ ], id='plot3')
    ], style={'display': 'flex'}),

    html.Div([
        html.Div([ ], id='plot4'),
        html.Div([ ], id='plot5')
    ], style={'display': 'flex'})

])

# 回调函数定义
@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')],
    [State("plot1", 'children'), State("plot2", "children"),
     State("plot3", "children"), State("plot4", "children"),
     State("plot5", "children")
     ])
def get_graph(chart, year, children1, children2, c3, c4, c5):
    # 筛选对应年份数据
    df =  airline_data[airline_data['Year']==int(year)]

    if chart == 'OPT1':
        # 计算绩效报告所需数据
        bar_data, line_data, div_data, map_data,tree_data = compute_data_choice_1(df)

        # 不同取消原因的月度航班数量柱状图
        bar_fig = px.bar(bar_data, x='Month', y='Flights', color='CancellationCode', title='月度航班取消情况')

        # 各航司月度平均飞行时长折线图
        line_fig = px.line(line_data, x='Month', y='AirTime', color='Reporting_Airline', title='各航司月度平均飞行时长（分钟）')

        # 各航司改道降落航班占比饼图
        pie_fig = px.pie(div_data, values='Flights', names='Reporting_Airline', title='各航司改道降落航班占比')

        # 各州出发航班数量热力图
        map_fig = px.choropleth(map_data,
                                locations='OriginState',
                                color='Flights',
                                hover_data=['OriginState', 'Flights'],
                                locationmode = 'USA-states',
                                color_continuous_scale='GnBu',
                                range_color=[0, map_data['Flights'].max()])
        map_fig.update_layout(
            title_text = '各州出发航班数量',
            geo_scope='usa')

        # 各目的州对应航司航班数量树形图
        tree_fig = px.treemap(tree_data,path=['DestState', 'Reporting_Airline'],values='Flights',color='Flights',color_continuous_scale='RdBu',title='各航司飞往目的州航班数量')

        # 返回图表组件
        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:
        # 计算延误报告所需数据
        avg_car, avg_weather, avg_NAS, avg_sec, avg_late = compute_data_choice_2(df)

        # 各类延误折线图
        carrier_fig = px.line(avg_car, x='Month', y='CarrierDelay', color='Reporting_Airline', title='各航司平均承运人延误时长（分钟）')
        weather_fig = px.line(avg_weather, x='Month', y='WeatherDelay', color='Reporting_Airline', title='各航司平均天气延误时长（分钟）')
        nas_fig = px.line(avg_NAS, x='Month', y='NASDelay', color='Reporting_Airline', title='各航司平均NAS延误时长（分钟）')
        sec_fig = px.line(avg_sec, x='Month', y='SecurityDelay', color='Reporting_Airline', title='各航司平均安保延误时长（分钟）')
        late_fig = px.line(avg_late, x='Month', y='LateAircraftDelay', color='Reporting_Airline', title='各航司平均晚到飞机延误时长（分钟）')

        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()

内容的提问来源于stack exchange，提问作者Ratnesh Pathak