Dash table update values for column/ row based on callback/user input from another column

Dash Python
6

Hi @AnnMarieW -

Thanks for the background and pointing out the distinction between rows.

My initial DataFrame is generated and rendered from the callback, unlike the example here:https://dash.plotly.com/datatable/editable. I do the calculations in the callback function as well.
When the user updates an editable column, I’d like to update the values in another column in same data-table.

Here’s a minimal reproducible example:

import dash
from dash.dependencies import Input, Output, State
import dash_table
import dash_html_components as html

app = dash.Dash(__name__)

app.layout = html.Div([

    dbc.Button("Run", id="dt-button", size="lg"),

    dash_table.DataTable(
        id='table1',
        columns=[
            {'name': 'Year', 'id': 'year'},
            {'name': 'Income', 'id': 'income'},
            {'name': 'Expenses', 'id': 'expense'},
            {'name': 'Cash flow', 'id': 'cash flow'}
        ],
      
        editable=True,
    ),
])


# Callback

@app.callback(Output("table1", "data"),
                     [

                        Input("dt-button", "n_clicks"),
                        Input("table1", "data_timestamp")
                       
                    ],
                    [
                       State("table1", "data"),
                   ]
                  )
def table(n_clicks, timestamp, rows):

         if n_clicks:
            # Construct Pandas DataFrame 
             df = pd.DataFrame({
                                           "Year": ['2018','2019','2020'],
                                           "Income": ['200','300','400'],
                                           "Expense": np.nan, # Editable column, user inputs expenses
                                           "Cash Flow": np.nan #Income - expense
                                        })

            # Update columns
            for row in rows:
                 try:
                      row['cash flow'] = (row['income'] - row['expense']) # Expense is entered by the user
                except:
                      row['cash flow'] = row['income']

            df_table = df.to_dict("records")

            return (df_table)

         else:

           return (no_update)