Update ui.table and ui.aggrid after modifying the underlying pandas dataframe

[rohitsathish]

Question

It looks like .update does not work when the source dataframe of ui.table.from_pandas or ui.aggrid.from_pandas is changed. I even tried with the app.storage approach and that doesn't work either. Is there an alternative approach that I'm missing?

from nicegui import ui, app
import pandas as pd
    
@ui.page('/')
async def main_ui():
    controls = ui.row()

    df = pd.DataFrame({'a': [1,2], 'b': [3,4]})
    app.storage.user['df'] = df.to_dict()

    df_ui = ui.aggrid.from_pandas(df).classes('max-h-24')
    df_ui1 = ui.table.from_pandas(df)

    df_ui3 = ui.aggrid.from_pandas(pd.DataFrame(app.storage.user['df'])).classes('max-h-24')
    df_ui4 = ui.table.from_pandas(pd.DataFrame(app.storage.user['df']))

    async def df_update():
        nonlocal df
        df = df.iloc[0]
        app.storage.user['df'] = pd.DataFrame(app.storage.user['df']).iloc[0].to_dict()
        print(df), print(app.storage.user['df'])
        df_ui.update(), df_ui1.update(), df_ui3.update(), df_ui4.update()

    with controls:
        ui.button('Update', on_click=df_update)

ui.run(port=8082, storage_secret='test')

[falkoschindler]

Hi @rohitsathish,

This is currently not implemented. The from_pandas methods are only for creating new ui.table or ui.aggrid instances. We could add something like update_from_pandas though. I'll turn this discussion into a feature request.

[tmlmt]

Hi guys, I stumbled upon the same issue and would love this feature request to be worked on.
Even reloading the page doesn't force the update of the table, so it's rather annoying.
In the meantime, how can we update the tables initially generated with from_pandas?

• Shall we revert to defining tables manually with columns and rows?
• or shall we regenerate with from_pandas, and remove the old table and add the new one into the corresponding container?

[falkoschindler]

I guess you can take the source code and modify it to update an existing table (or AG Grid):

nicegui/nicegui/elements/table.py

Lines 89 to 135 in 7a3cfc2

	@classmethod
	def from_pandas(cls,
	df: 'pd.DataFrame',
	row_key: str = 'id',
	title: Optional[str] = None,
	selection: Optional[Literal['single', 'multiple']] = None,
	pagination: Optional[Union[int, dict]] = None,
	on_select: Optional[Callable[..., Any]] = None) -> Self:
	"""Create a table from a Pandas DataFrame.
	Note:
	If the DataFrame contains non-serializable columns of type `datetime64[ns]`, `timedelta64[ns]`, `complex128` or `period[M]`,
	they will be converted to strings.
	To use a different conversion, convert the DataFrame manually before passing it to this method.
	See `issue 1698 <https://github.com/zauberzeug/nicegui/issues/1698>`_ for more information.
	:param df: Pandas DataFrame
	:param row_key: name of the column containing unique data identifying the row (default: "id")
	:param title: title of the table
	:param selection: selection type ("single" or "multiple"; default: `None`)
	:param pagination: a dictionary correlating to a pagination object or number of rows per page (`None` hides the pagination, 0 means "infinite"; default: `None`).
	:param on_select: callback which is invoked when the selection changes
	:return: table element
	"""
	def is_special_dtype(dtype):
	return (pd.api.types.is_datetime64_any_dtype(dtype) or
	pd.api.types.is_timedelta64_dtype(dtype) or
	pd.api.types.is_complex_dtype(dtype) or
	isinstance(dtype, pd.PeriodDtype))
	special_cols = df.columns[df.dtypes.apply(is_special_dtype)]
	if not special_cols.empty:
	df = df.copy()
	df[special_cols] = df[special_cols].astype(str)
	if isinstance(df.columns, pd.MultiIndex):
	raise ValueError('MultiIndex columns are not supported. '
	'You can convert them to strings using something like '
	'`df.columns = ["_".join(col) for col in df.columns.values]`.')
	return cls(
	columns=[{'name': col, 'label': col, 'field': col} for col in df.columns],
	rows=df.to_dict('records'),
	row_key=row_key,
	title=title,
	selection=selection,
	pagination=pagination,
	on_select=on_select)

Apart from that, the best way to push this feature forward would be to create a pull request. We would be happy to review and add it to the next release. 🙂

[tmlmt]

I will give it a try 😊

In the meantime, as a workaround, I've used a container that I clear and regenerate from_pandas:

def update_table() -> None:
    table_container.clear()
    with table_container:
        ui.table.from_pandas(df)

<...>

with ui.element() as table_container:
    ui.table.from_pandas(df)

[falkoschindler]

The update_from_pandas() method has been implemented in #3525 and will be released shortly in version 2.0.0.