Is there a way to use ONE Plotly drop down button to interact with data as well as a plot?

Question

I am trying to showcase a scatter plot as well as the data for the the plot side by side in jupyter. I wish to add a plotly button (dropdown) that will show the filtered data as well as the corresponding scatter plot. Is this possible WITHOUT USING ipywidgets, using plotly dropdowns?

I was able to build two separate plots for data and scatter plot with dropdowns but cannot combine them together. Following is the code I tried. Here the dropdown only interacts with the table, the scatter plot is not updating.

import plotly.express as px
import plotly.graph_objects as go
import numpy as np
import pandas as pd

df = px.data.iris()
species = sorted(set(df['species']))
#fig=go.Figure()
default_species = "setosa"
df_default = df[df['species']==default_species]

fig = make_subplots(
    rows=1, cols=2,
    shared_xaxes=True,
    horizontal_spacing=0.02,
    specs=[[{"type": "scatter"},{"type": "table"}]]
)
fig.add_trace(
    go.Scatter(
        x=df_default["sepal_length"],
        y=df_default["sepal_width"],
        mode="markers"
    ),
    row=1, col=1
)
fig.add_trace(
    go.Table(
        header=dict(
            values=["sepal<br>length","sepal<br>length","petal<br>length","petal<br>width","species","species<br>id"],
            font=dict(size=10),
            align="left"
        ),
        cells=dict(
            values=[df_default[k].tolist() for k in df_default.columns],
            align = "left")
    ),
    row=1, col=2
)

buttons = []
for s in species:
    s_data = df[df['species']==s]
    buttons.append(dict(
        method='restyle',
        label=s,
        visible=True,
        args=[ 
              {'values':[["sepal<br>length","sepal<br>length","petal<br>length","petal<br>width","species","species<br>id"]],
               'cells':[dict(values=[s_data[k].tolist() for k in s_data.columns],align = "left")]},
              {'y':[s_data["sepal_width"]],
               'x':[s_data['sepal_length']],
               'type':'scatter',
               'mode':'markers'}
             ]           
        
        ))
    
#fig.update_layout(width=1500, height=500)
# some adjustments to the updatemenus
updatemenu = []
your_menu = dict()
updatemenu.append(your_menu)

updatemenu[0]['buttons'] = buttons
updatemenu[0]['direction'] = 'down'
updatemenu[0]['showactive'] = True
updatemenu[0]['active'] = species.index(default_species)
updatemenu[0]['x'] = 0
updatemenu[0]['xanchor'] = 'left'
updatemenu[0]['y'] = 1.2
updatemenu[0]['yanchor'] = 'top'

# add dropdown menus to the figure
fig.update_layout(showlegend=False, 
                  updatemenus=updatemenu,
                  xaxis_title="Sepal_Length",
                  yaxis_title="Sepal_Width"
                 )
fig.show()

I do not want to use ipywidgets.

The code that worked for me: Editing hope it helps some one

## Create Combination of Scatter Plot and Plotly Tables with interactive Static HTML
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np
import pandas as pd

df = px.data.iris()
species_list = sorted(set(df['species']))

# --- function ---

def make_multi_plot(df1, item_list):

    fig = make_subplots(rows=1, 
                        cols=2,
                        #shared_xaxes=True,
                        #vertical_spacing=0.2,
                        specs = [[{}, {"type": "table"}]]
                            
                       )

    for item_id in item_list:
            #print('item_id:', item_id)
            
        trace1 = go.Scatter(
                x=df1.loc[df1.species.isin([item_id])].sepal_length, 
                y=df1.loc[df1.species.isin([item_id])].sepal_width,
                mode='markers',
                name = str(item_id)
        )
        fig.append_trace(trace1, 1, 1)
            
            
        trace2 = go.Table(
                columnorder = [1,2,3,4,5,6],
                columnwidth = [2,2,2,2,4,2],
                header=dict(
                    values = ["sepal<br>length","sepal<br>length","petal<br>length","petal<br>width","species","species<br>id"],
                    font = dict(size=10),
                    align = "left"
                ),
                cells = dict(
                    values = [df1[df1.species.isin([item_id])][k].tolist() for k in df1[df1.species.isin([item_id])].columns[:]],
                    align = "left"
                )
            )
        fig.append_trace(trace2,1,2)
        
    Ld = len(fig.data)
    #print(Ld)
    Lc = len(item_list)
        
    for k in range(2, Ld):
        fig.update_traces(visible=False, selector=k)
    
    def create_layout_button(k, item_id):
            #print(k, item_id)
            
        visibility = [False]*2*Lc
        for tr in [2*k, 2*k+1]:
                visibility[tr] = True
                
            #print(visibility)
                
        return dict(label = item_id,
                        method = 'restyle',
                        args = [{'visible': visibility,
                                 'title': item_id
                                 }])    
    
    #updatemenu[0]['x'] = 0
    #updatemenu[0]['xanchor'] = 'left'
    #updatemenu[0]['y'] = 1.2
    #updatemenu[0]['yanchor'] = 'top'
    fig.update_layout(
           updatemenus=[go.layout.Updatemenu(
               active=0,
               buttons=[create_layout_button(k, item_id) for k, item_id in enumerate(item_list)],
               x=0.5,
               y=1.3
           )],
           #title='Model Raporu',
           #template='plotly_dark',
           #height=800
           xaxis_title="Sepal_Length",
           yaxis_title="Sepal_Width"
        )
    
    fig.show()

# --- main ---
make_multi_plot(df1=df, item_list=species_list)