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Dash DataFrames ​

Source rows ​

source, pinnedTopSource, and pinnedBottomSource accept lists of JSON-serializable row objects:

python
rows = [
    {"id": 1, "product": "Keyboard", "price": 95.0},
    {"id": 2, "product": "Mouse", "price": 35.5},
]

Values that cross the Dash boundary should be strings, finite numbers, booleans, None, lists, or dictionaries containing those values. Convert datetime, Decimal, NumPy scalar, UUID, and other Python-specific values before assigning a prop.

See Data & Rows for the Core row model and Data Source Loading and Syncing for source ownership patterns.

pandas DataFrames ​

For a DataFrame containing only JSON-native values, records are enough:

python
rows = frame.to_dict("records")
grid = RevoGrid(columns=columns, source=rows, style={"height": 420})

For dates, missing values, NumPy values, or other pandas-specific types, use a JSON round trip to normalize the records:

python
import json

rows = json.loads(
    frame.to_json(
        orient="records",
        date_format="iso",
    )
)

This converts timestamps to ISO strings and missing values to JSON null, which arrives in Python callbacks as None.

Python-driven updates ​

Return a new source when the application intentionally loads a different dataset:

python
from dash import Input, Output, callback


@callback(
    Output("orders-grid", "source"),
    Input("reload-orders", "n_clicks"),
    prevent_initial_call=True,
)
def reload_orders(_clicks):
    return load_orders_from_database()

Full source replacement is appropriate for reloads, searches, pagination, or dataset changes. For a normal cell edit, prefer the compact afteredit event described in Edit synchronization.