def create_wage_plots(df, industry_list, industry_name):
    subsetJ['County, State'] = subsetJ['county_state']
    subsetJ['date'] = pd.PeriodIndex(year=subsetJ['Year'], quarter=subsetJ['Qtr']).to_timestamp()
    industry = subsetJ[(subsetJ['Industry'] == industry_name)]
    industry = industry.sort_values('date')
    fig = px.line(industry, x='date', y='Average Weekly Wage', labels={'Average Weekly Wage': 'Average Weekly Wage ($)', 'date': 'Date'}, color='County, State', title=f'{industry_name}: Average Weekly Wage').for_each_trace((lambda t: t.update(name=t.name.split('=')[(- 1)])))
    fig.update_layout(legend=dict(orientation='h', yanchor='bottom', y=1.02, xanchor='right', x=1), xaxis=dict(tickmode='array', tick0=1, dtick=1, tickvals=[2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020], ticktext=['2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020']))
    fig.write_image('fig1.png')
    jobs_json = fig.to_json()
    return jobs_json
def create_wage_plots(df, industry_list, industry_name):
    subsetJ['County, State'] = subsetJ['county_state']
    subsetJ['date'] = pd.PeriodIndex(year=subsetJ['Year'], quarter=subsetJ['Qtr']).to_timestamp()
    industry = subsetJ[(subsetJ['Industry'] == industry_name)]
    industry = industry.sort_values('date')
    fig = px.line(industry, x='date', y='Average Weekly Wage', labels={'Average Weekly Wage': 'Average Weekly Wage ($)', 'date': 'Date'}, color='County, State', title=f'{industry_name}: Average Weekly Wage').for_each_trace((lambda t: t.update(name=t.name.split('=')[(- 1)])))
    fig.update_layout(legend=dict(orientation='h', yanchor='bottom', y=1.02, xanchor='right', x=1), xaxis=dict(tickmode='array', tick0=1, dtick=1, tickvals=[2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020], ticktext=['2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020']))
    fig.write_image('fig1.png')
    jobs_json = fig.to_json()
    return jobs_json