Python 3.x Python绘图划线类型错误：字符串索引必须是整数

我是python Dash的新手。我需要有关如何使用Dash核心组件clickData更新图表的帮助

我已经做了一张地图，点击它的标记，它应该会显示图表 特定的网络运营商。但是，每当我单击图形时，它都会 显示行的字符串索引必须是整数

    app=dash.Dash()

    def data_sort():
            Stats1='C:/Users/muzamal.pervez/Desktop/Python Scripts/Second Project/IBRoamers.csv'
            LOC1='C:/Users/muzamal.pervez/Desktop/Python Scripts/Second Project/mcc-mnc-table.csv'

            DF1=pd.read_csv(Stats1)
            Table=pd.read_csv(LOC1,encoding = "ISO-8859-1")

            DF2=DF1.groupby(['Start Time', 'MCC', 'MNC']).sum()
            DF2=DF2.reset_index()
            DF2.insert(loc=1, column='NE Name', value='NWD')
            DF=pd.concat([DF1,DF2])
            #DF['Start Time']=pd.to_datetime(DF['Start Time'])
            DF=DF.sort_values(by=['Start Time','MCC', 'MNC'], ascending=False)
            DF=DF.reset_index(drop=True)

            DF['2G_Attach_SR(%)']=(DF['Gb mode attach accept times per PLMN (times)']/DF['Gb mode attach request times per PLMN (times)'])*100
            DF['3G_Attach_SR(%)']=(DF['Iu mode attach accept times per PLMN (times)']/DF['Iu mode attach request times per PLMN (times)'])*100
            DF['LTE_Combined_Attach_SR(%)']=(DF['S1 Mode Combined Attach Success Times for EPS and non-EPS Services per PLMN (times)']/DF['S1 Mode Combined Attach Request Times per PLMN (times)'])*100
            DF['2G_PDP_SR(%)']=((DF['Gb mode MS init PDP context act request times per PLMN (times)']-DF['Gb mode MS init PDP context act fail times per PLMN (times)'])/DF['Gb mode MS init PDP context act request times per PLMN (times)'])*100
            DF['3G_PDP_SR(%)']=(DF['Iu mode MS init PDP context act success times per PLMN (times)']/DF['Iu mode MS init PDP context act request times per PLMN (times)'])*100
            DF['LTE_PDN_Connect_SR(%)']=(DF['S1 Mode PDN Connect Success Times per PLMN (times)']/DF['S1 Mode PDN Connect Request Times per PLMN (times)'])*100
            DF['2G_Att_Fail(times)']=DF['Gb mode attach request times per PLMN (times)']-DF['Gb mode attach accept times per PLMN (times)']
            DF['2G_PDP_Fail(times)']=DF['Gb mode MS init PDP context act fail times per PLMN (times)']
            DF['3G_Att_Fail(times)']=DF['Iu mode attach request times per PLMN (times)']-DF['Iu mode attach accept times per PLMN (times)']
            DF['3G_PDP_Fail(times)']=DF['Iu mode MS init PDP context act request times per PLMN (times)']-DF['Iu mode MS init PDP context act success times per PLMN (times)']
            DF['LTE_Att_Fail(times)']=DF['S1 Mode Combined Attach Request Times per PLMN (times)']-DF['S1 Mode Combined Attach Success Times for EPS and non-EPS Services per PLMN (times)']
            DF['LTE_PDP_Fail(times)']=DF['S1 Mode PDN Connect Request Times per PLMN (times)']-DF['S1 Mode PDN Connect Success Times per PLMN (times)']
            DF['Total_Att_Fail(times)']=DF['2G_Att_Fail(times)']+DF['3G_Att_Fail(times)']+DF['LTE_Att_Fail(times)']
            DF['Total_PDP_Fail(times)']=DF['2G_PDP_Fail(times)']+DF['3G_PDP_Fail(times)']+DF['LTE_PDP_Fail(times)']
            DF['Attached Users(2G,3G,LTE)']=DF['Iu mode attached Average user number per PLMN (number)']+DF['Gb mode attached Average user number per PLMN (number)']+DF['Gb mode attached Average user number per PLMN (number)']
            DF['MCCMNC']=(DF['MCC'].str[-3:]) + (DF['MNC'].str[-2:])
            Table['MCCMNC']=Table['MCCMNC'].astype(str)
            DF=DF.merge(Table, on='MCCMNC')
            DF['Start Time']=pd.to_datetime(DF['Start Time'])
            Latest=DF['Start Time'].max()
            DF.to_csv('DF.csv', index=False)
            DF3=DF[DF['Start Time']==Latest]
            DF3=DF3[DF3['NE Name']=='NWD']
            DF3=DF3.drop(DF3[DF3['Attached Users(2G,3G,LTE)']<50].index)
            DF3=DF3.drop(DF3[DF3['MCCMNC']=='41001'].index)
            DF3=DF3.drop(DF3[DF3['MCCMNC']=='41007'].index)
            DF3=DF3.reset_index(drop=True)
            DF3['Text']=DF3['MCCMNC'].astype(str)+' '+DF3['Country']+ ":"+DF3['Network']+ "Total IB roamers: "+DF3['Attached Users(2G,3G,LTE)'].astype(str)
            DF3.to_csv('DF3.csv', index=False)
            scale = 15

            data = dict(
                    type = 'scattergeo',
                    autosize=False, 
                    width=2000,
                    height=2000,
                    locationmode = 'world',
                    lon = DF3['Long1'],
                    lat = DF3['Lat1'],
                    text = DF3['Text'],
                    customdata=DF3['MCCMNC'].astype(int),
                    marker = dict(
                        size = DF3['Attached Users(2G,3G,LTE)']/scale,
                         color = np.where(((DF3['3G_Attach_SR(%)'] < 70)|(DF3['2G_Attach_SR(%)'] < 70)|(DF3['3G_PDP_SR(%)'] < 70)|(DF3['2G_PDP_SR(%)'] < 70))&((DF3['2G_Att_Fail(times)']>500)|(DF3['2G_PDP_Fail(times)']>500)|(DF3['3G_Att_Fail(times)']>500)|(DF3['3G_PDP_Fail(times)']>500)), 'red', 'green'),
                        line=dict(color='#7FFF00'),
                        sizemode = 'area'
                    )
                ),
            layout = dict(
                    title = 'Jazz IB Roamers from around the Globe',
                    showlegend = False,
                    geo = dict(
                        resolution=50,
                        scope='world',
                        projection=dict( type='world' ),
                        showcoastlines=True,
                        coastlinecolor="#F4A460",
                        coastlinewidth=0.2,
                        showland = True,
                        showframe=False,
                        showocean=True,
                        showcountries=True,
                        showcountrycodes=True,
                        showcontinents=True,
                        showrivers=True,
                        rivercolor="#F4A460",
                        riverwidth=0.2,
                        showlakes=True,
                        lakecolor="#F4A460",
                        lakewidth=0.2,
                        landcolor = '#262626',
                        oceancolor='#000033',
                        showsubunit=True,
                        subunitwidth=0.2,
                        countrywidth=0.5,
                        subunitcolor="#F4A460",
                        countrycolor="#F4A460"
                    ),
                )


            fig = dict( data=data, layout=layout )
            return fig

        app.layout  = html.Div([
            dcc.Graph(id='Map1', figure=data_sort()),
              html.Div([
                dcc.Dropdown(
                id='my_dropdown',
                options=[
                    {'label': 'NWD', 'value': 'NWD'},
                    {'label': 'ISB', 'value': 'USN-ISB'},
                    {'label': 'KHI', 'value': 'USN-KHI'},
                    {'label': 'LHR', 'value': 'USN-LHR'}
                ],
                value='NWD')]),
            dcc.Interval(id='graph-update',interval=300000),
            dcc.Graph(id='graph1', animate=True,clickData={'points': [{'customdata': '42403'}]})
        ])

        @app.callback(
            dash.dependencies.Output('graph1','figure'),
            [dash.dependencies.Input('my_dropdown', 'value'),
            dash.dependencies.Input('Map1', 'clickData')],
            events=[dash.dependencies.Event('graph-update', 'interval')]
            )
        def Graph1(clickData, USN):
            data_sort()
            address2 = 'C:/Users/muzamal.pervez/Desktop/Python Scripts/DF.csv'
            DF6=pd.read_csv(address2)
            DF6=DF6.loc[DF6['NE Name'] == USN]
            mccmnc = clickData['points']['customdata']
            DF6=DF6.loc[DF6['MCCMNC'] == mccmnc]
            return {
                'data': [{
                        'x': DF6['Start Time'],
                        'y': DF6['2G_Attach_SR(%)'],
                        'name':'{} ::2G Roaming Attach SR'.format(USN),
                            'line' : {
                                'color':'#00CD00'
                            }

                    }, 
                    {
                    'x': DF6['Start Time'],
                    'y': DF6['3G_Attach_SR(%)'],
                        'name':'{} ::3G Roaming Attach SR'.format(USN),
                         'line' : {
                                'color':'#4876FF'
                            }

                    }
                    ],
                    'layout' : go.Layout(
                      title='Live Throughput Trend(Mbps)',
                      titlefont=dict(
                      size=36,
                      color='#212121'
                       ),
                      xaxis=dict(
                      title='Start Time',
                      titlefont=dict(
                      family='Courier New, monospace',
                      size=24,
                      color='#030303'
                    )
                ),
                    yaxis=dict(
                    title='Throughput(Mbps)',
                    titlefont=dict(
                    family='Courier New, monospace',
                    size=24,
                    color='#030303'
                )
            )
                    )
            }

        if __name__ == '__main__':
            app.run_server(debug=False)

app=dash.dash（）
def data_sort（）：
Stats1='C:/Users/muzamal.pervez/Desktop/Python Scripts/Second Project/IBRoamers.csv'
LOC1='C:/Users/muzamal.pervez/Desktop/Python Scripts/Second Project/mcc mnc table.csv'
DF1=pd.read\U csv（Stats1）
表=pd.read_csv（LOC1，编码=“ISO-8859-1”）
DF2=DF1.groupby（['Start Time'，'MCC'，'MNC']）.sum（）
DF2=DF2.reset_index（）
DF2.insert（loc=1，column='NE Name'，value='NWD'）
DF=pd.concat（[DF1，DF2]）
#DF['Start Time']=pd.to_datetime（DF['Start Time']）
DF=DF.sort_值（按=['Start Time'，'MCC'，'MNC']，升序=False）
DF=DF.reset_索引（drop=True）
DF['2G_-Attach_-SR（%）']=（DF['Gb-mode-Attach-accept-times per-PLMN（times）]/DF['Gb-mode-Attach-request-times per-PLMN（times）]*100
DF['3G_-Attach_-SR（%）']=（DF['Iu-mode-Attach-accept-times per-PLMN（times）]/DF['Iu-mode-Attach-request-times per-PLMN（times）]*100
DF['LTE\U组合连接\U SR（%）']=（DF['S1模式每PLMN EPS和非EPS服务组合连接成功次数（次）]/DF['S1模式每PLMN组合连接请求次数（次）]*100
DF['2G_PDP_SR（%）']=（（DF['Gb模式MS初始PDP上下文动作请求次数/PLMN（次）]-DF['Gb模式MS初始PDP上下文动作失败次数/PLMN（次）]）/DF['Gb模式MS初始PDP上下文动作请求次数/PLMN（次）]*100
DF['3G_-PDP_-SR（%）']=（DF['Iu-mode-MS-init-PDP-context-act-success times per-PLMN（times）]/DF['Iu-mode-MS-init-PDP-context-act-request-times per-PLMN（times）]*100
DF['LTE_PDN_Connect_SR（%）']=（DF['S1 Mode PDN Connect Success Times per PLMN（Times）]/DF['S1 Mode PDN Connect Request Times per PLMN（Times）]*100
DF['2G_Att_Fail（times）]=DF['Gb模式连接请求次数/PLMN（times）]-DF['Gb模式连接接受次数/PLMN（times）]
DF['2G_PDP_失败（次数）]=DF['Gb模式MS初始PDP上下文行为每PLMN失败次数（次数）]
DF['3G_Att_Fail（times）]=DF['Iu mode attach request times per PLMN（times）]-DF['Iu mode attach accept times per PLMN（times）]
DF['3G\u-PDP\u-Fail（次）]=DF['Iu-mode-MS-init-PDP-context-act-request-times/PLMN（次）]-DF['Iu-mode-MS-init-PDP-context-act-success-times/PLMN（次）]
DF['LTE\U Att\U Fail（次）]=DF['S1模式每PLMN组合连接请求次数（次）]-DF['S1模式每PLMN EPS和非EPS服务组合连接成功次数（次）]
DF['LTE\U PDP\U Fail（次）]=DF['S1模式PDN连接请求次数/PLMN（次）]-DF['S1模式PDN连接成功次数/PLMN（次）]
DF['Total_Att_Fail（times）]=DF['2G_Att_Fail（times）]+DF['3G_Att_Fail（times）]+DF['LTE_Att_Fail（times）]
DF['Total_PDP_Fail（times）]=DF['2G_PDP_Fail（times）]+DF['3G_PDP_Fail（times）]+DF['LTE_PDP_Fail（times）]
DF[‘附加用户（2G、3G、LTE）’]=DF[‘Iu模式附加平均每个PLMN用户数（数字）’]+DF[‘Gb模式附加平均每个PLMN用户数（数字）’]+DF[‘Gb模式附加平均每个PLMN用户数（数字）’]
DF['MCCMNC']=（DF['MCC'].str[-3:]）+（DF['MNC'].str[-2:]）
表['MCCMNC']=表['MCCMNC'].aType（str）
DF=DF.merge（表，on='MCCMNC'）
DF['Start Time']=pd.to_datetime（DF['Start Time']）
Latest=DF[‘开始时间’].max（）
DF.to_csv（'DF.csv'，index=False）
DF3=DF[DF['开始时间]==最新]
DF3=DF3[DF3['NE Name']=='NWD']
DF3=DF3.丢弃（DF3[DF3[‘连接用户（2G，3G，LTE）’]500）；（DF3[‘2G_PDP_失败（次数）’>500）；（DF3[‘3G_连接失败（次数）’>500）；（DF3[‘3G_PDP_失败（次数）’>500）；‘红色’、‘绿色’，
行=dict（颜色='#7FFF00'），
sizemode='area'
)
),
布局=记录(
title=‘来自全球的爵士IB漫游者’，
showlegend=False，
地理=口述(
分辨率=50，
scope='world'，
投影=dict（type='world'），
显示海岸线=正确，
COLOR=“#F4A460”，
宽度=0.2，
showland=True，
showframe=False，
showocean=True，
showcountries=正确，
showcountrycodes=True，
显示大陆=正确，
showrives=True，
rivercolor=“#F4A460”，
河流宽度=0.2，
showlakes=True，
湖色=“#F4A460”，
湖面宽度=0.2，
landcolor='#262626'，
oceancolor='#000033'，
showsubunit=True，
子单元宽度=0.2，
countrywidth=0.5，
子单元颜色=“#F4A460”，
countrycolor=“#F4A460”
),
)
图=dict（数据=数据，布局=布局）
返回图
app.layout=html.Div([
dcc.Graph（id='Map1'，figure=data\u sort（）），
html.Div([
dcc.下拉列表(
id='my_下拉列表'，
选择权=[
{'label'：'NWD'，'value'：'NWD'}，
{'label'：'ISB'，'value'：'USN-ISB'}，
{'label'：'KHI'，'value'：
@app.callback(
        dash.dependencies.Output('graph1','figure'),
        [dash.dependencies.Input('my_dropdown', 'value'),
        dash.dependencies.Input('Map1', 'clickData')],
        events=[dash.dependencies.Event('graph-update', 'interval')]
        )
    def Graph1(clickData, USN)

@app.callback(
        dash.dependencies.Output('graph1','figure'),
        [dash.dependencies.Input('my_dropdown', 'value'),
        dash.dependencies.Input('Map1', 'clickData')],
        events=[dash.dependencies.Event('graph-update', 'interval')]
        )
    def Graph1(USN, clickData)