> For the complete documentation index, see [llms.txt](https://docs.trymito.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.trymito.io/mito-for-dash/common-design-patterns/change-sheet-data-from-a-select.md).

# 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:

```python
# 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:

```python
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*](https://dash.plotly.com/datatable/reference)*.*

## Examples

#### Change the data in the Mito spreadsheet based on a select

```python
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)
```


---

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