Python Plotly中龙卷风图的x轴自定义

如何设置两个镜像轴的单个起点？我已经用‘make_subploy’功能构建了一个图形.但我在中间有个空位.我使用的是来自Tableau data collection的‘Sample Superstore’数据集.

我的代码是:

df_oct = df.loc[(df['order_date'] > '2017-09-30') & (df['order_date'] < '2017-11-01') & (df['region'] == 'Central')]
df_sep = df.loc[(df['order_date'] > '2017-08-31') & (df['order_date'] < '2017-10-01') & (df['region'] == 'Central')]

states = pd.DataFrame(
            df.loc[(df['order_date'] > '2017-08-31') & (df['order_date'] < '2017-11-01') & (df['region'] == 'Central'), 'state'].unique()
            ,columns=['state'])

df_oct = states.merge(df_oct.groupby(by='state', as_index=False)['sales'].sum(), how='left', on='state')
df_sep = states.merge(df_sep.groupby(by='state', as_index=False)['sales'].sum(), how='left', on='state')

df_oct['month'] = 'October'
df_sep['month'] = 'September'
tornado_df = pd.concat([df_oct, df_sep])
tornado_df.fillna(0, inplace=True)

fig = make_subplots(
        rows=1
        ,cols=2
        ,vertical_spacing=0
)

fig_add = fig.add_trace(
            go.Histogram(
                x=tornado_df.loc[tornado_df['month'] == 'September', 'sales']
                ,y=tornado_df.loc[tornado_df['month'] == 'September', 'state']
                ,histfunc='sum'
                ,orientation='h'
                ,marker_color='#D95002'
                ,name='September'
                ,opacity=0.6)
            ,row=1
            ,col=1)

fig_add = fig.add_trace(
            go.Histogram(
                x=tornado_df.loc[tornado_df['month'] == 'October', 'sales']
                ,y=tornado_df.loc[tornado_df['month'] == 'October', 'state']
                ,histfunc='sum'
                ,orientation='h'
                ,marker_color='#523E89'
                ,name='October'
                ,opacity=0.6)
            ,row=1
            ,col=2)    

fig_add = fig.update_xaxes(
            autorange="reversed"
            ,row=1
            ,col=1)

fig_add = fig.update_yaxes(
            visible=False
            ,row=1
            ,col=2)

fig_add.show()

My chart: