Is it possible to upload a csv file in Dash and also store it as a pandas DataFrame?

I am developing a dashboard in Dash with Python and in one of the core components I am trying to upload a csv file and display it in a datatable format (see below). That works well (see picture), I followed this example: https://dash.plotly.com/dash-core-components/upload

However, I would also like to use the table as a pandas DataFrame later in the code. Since I upload the csv file after I’ve run the code for the dashboard, I could not find a way to return the csv contents as a DataFrame. Any way in which this can be done? My code is below.

Dash app output

Thank you in advance!

###############################################################################
# Upload files
# https://dash.plotly.com/dash-core-components/upload
###############################################################################
    
def parse_contents(contents, filename, date):
    content_type, content_string = contents.split(',')

    decoded = base64.b64decode(content_string)
    try:
        if 'csv' in filename:
        # Assume that the user uploaded a CSV file
            df = pd.read_csv(
                io.StringIO(decoded.decode('utf-8')))
        elif 'xls' in filename:
        # Assume that the user uploaded an excel file
            df = pd.read_excel(io.BytesIO(decoded))
    except Exception as e:
        print(e)
        return html.Div([
            'There was an error processing this file.'
        ])
    
    trade_upload = pd.DataFrame(df)
    return dbc.Table.from_dataframe(trade_upload)

@app.callback(Output('output-data-upload', 'children'),
              [Input('upload-data', 'contents')],
              [State('upload-data', 'filename'),
               State('upload-data', 'last_modified')])
def update_output(list_of_contents, list_of_names, list_of_dates):
    if list_of_contents is not None:
        children = [
            parse_contents(c, n, d) for c, n, d in
            zip(list_of_contents, list_of_names, list_of_dates)]
        return children

if __name__ == '__main__':
    app.run_server(port=8051, debug=False)

Answer

When you define the parse_contents function, you can simply return df:

def parse_contents(contents, filename):
    content_type, content_string = contents.split(',')

    decoded = base64.b64decode(content_string)
    try:
        if 'csv' in filename:
        # Assume that the user uploaded a CSV file
            df = pd.read_csv(
                io.StringIO(decoded.decode('utf-8')))
        elif 'xls' in filename:
        # Assume that the user uploaded an excel file
            df = pd.read_excel(io.BytesIO(decoded))
    except Exception as e:
        print(e)
        return html.Div([
            'There was an error processing this file.'
        ])
    
    return df   

Then, you can call parse_contents in your following callbacks and generate a pandas dataframe:

@app.callback(
    Output('table-container', 'data'),
    [Input('file_upload', 'contents')],
    State('file_upload', 'filename'))
def filter_df(content, name):
    if content is not None:
    # Return all the rows on initial load/no country selected.
        df = parse_contents(content, name)
        dff = df.to_json()
        dff_pandas = pd.Dataframe(dff)

    else:
        df = parse_contents(content, name)
        dff = df.to_json()
        dff_pandas = pd.Dataframe(dff)
        dff_pandas_filtered = dff_pandas.query('column_A == 012345')