Dropdown per column in dynamically added columns in dash datatable

Dash Python
1

i am adding sentiment_classification column dynamically and want to have dropdown in it.

@app.callback(
[Output(‘datatable-paging’, “data”),
Output(‘datatable-interactivity-container’, “children”),
Output(‘datatable-filter-container’, “children”),
Output(‘datatable-paging’, “columns”),
Output(‘datatable-paging’, “dropdown”),

],
[Input('datatable-paging', "page_current"),
 Input('datatable-paging', "page_size"),
 Input('datatable-paging', "sort_by"),
 Input('datatable-paging', "filter_query"),
    Input(component_id='drop_column_dropdown', component_property='value'),
 Input("classification_column_dropdown", "value"),
Input("sentiment_classification_btn", "n_clicks")])

def update_table(page_current, page_size, sort_by, filter,drop_columns,classification_column,sentiment_clicked):
filtering_expressions = filter.split(’ && ')
dff = load(os.path.join(UPLOAD_DIRECTORY, upload_pickle))
#dff[(dff[‘region’].isin(town)]
print(“columns to drop”,drop_columns)
if drop_columns is not None:
dff.drop(drop_columns, axis = 1,inplace=True)

for filter_part in filtering_expressions:
    filter_part = str(filter_part)+"|\?"
    col_name, operator, filter_value = split_filter_part(filter_part)

    if operator in ('eq', 'ne', 'lt', 'le', 'gt', 'ge'):
        # these operators match pandas series operator method names
        dff = dff.loc[getattr(dff[col_name], operator)(filter_value)]
    elif operator == 'contains':
        dff = dff.loc[dff[col_name].astype(str).str.contains(filter_value)]
    elif operator == 'datestartswith':
        # this is a simplification of the front-end filtering logic,
        # only works with complete fields in standard format
        dff = dff.loc[dff[col_name].astype(str).str.startswith(filter_value)]

if len(sort_by):
    dff = dff.sort_values(
        [col['column_id'] for col in sort_by],
        ascending=[
            col['direction'] == 'asc'
            for col in sort_by
        ],
        inplace=False
    )
column_dropdown = {}
if sentiment_clicked is not None:
    dff["Sentiment_Classification"] = ["Negative","Positive"]*(int(len(dff)/2))
    column_dropdown = {
        'Sentiment_Classification': {
            'options': [
                {'label': i, 'value': i}
                `for i in dff['Sentiment_Classification'].unique()`
            ]
        }}
    
    
columns=[{'name': i, 'id': i,} for i in dff.columns]
return dff.iloc[ 
    page_current*page_size: (page_current + 1)*page_size
].to_dict('records'),f"Total rows:{dff.shape[0]} ,Total columns: {dff.shape[1]}",f"Filter(s) Applied : {filter}" ,columns,column_dropdown`Preformatted text`