Change Sheet Data from a Select

Refresh your analysis with new data, configured through a Dash select component.

Motivation

1. You have a dropdown that allows users to select a specific dataset.

2. You want to then automatically load this data directly into the Mito spreadsheet.

To do so, you simply need to write to the data parameter of the Mito spreadsheet.

Valid types for data

The data parameter should be a list of dicts with strings as keys and values of type string, number, or boolean. For example, valid data might be:

# Passing the data
[
    {'Column 1': 1, 'Column 2': 4}, 
    {'Column 1': 2, 'Column 2': 5}, 
    {'Column 1': 3, 'Column 2': 6}
]
# Results in Mito displaying the dataframe
df = pd.DataFrame({'Column 1': [1, 2, 3], 'Column 2': [4, 5, 6]})

You can go from Pandas dataframe to data format with the following code:

df = pd.DataFrame({'Column 1': [1, 2, 3], 'Column 2': [4, 5, 6]})

df.to_dict('records') # Returns data in the correct format.

Note that Mito aims to accept the same data input as the Dash Data Table.

Examples

Change the data in the Mito spreadsheet based on a select

from dash import Dash, callback, Input, Output, html, dcc
from mitosheet.mito_dash.v1 import Spreadsheet, mito_callback, activate_mito
import pandas as pd

app = Dash(__name__)
activate_mito(app)

app.layout = html.Div([
    html.H1("Stock Analysis", style={'color': 'white'}),
    # A dropdown for selecting between two dataframes
    dcc.Dropdown(
        id='dropdown',
        options=[
            {'label': 'Stock Data', 'value': 'stock_data'},
            {'label': 'Stock Data 2', 'value': 'stock_data_2'},
        ],
        value='stock_data'
    ),
    Spreadsheet(id={'type': 'spreadsheet', 'id': 'sheet'}),
])

@callback(
    Output({'type': 'spreadsheet', 'id': 'sheet'}, 'data'),
    Input('dropdown', 'value')
)
def update_spreadsheet_data(dropdown_value):
    if dropdown_value == 'stock_data':
        df = pd.DataFrame({'First Data': [1, 2, 3], 'Second Data': [4, 5, 6]})
    else:
        df = pd.DataFrame({'First Data': [7, 8, 9], 'Second Data': [10, 11, 12]})

    return df.to_dict('records')

if __name__ == '__main__':
    app.run_server(debug=True)