What does a Dash Table component look like? dash-table-experiments
is a package of alpha-level explorations in a Dash Table
component. Everything is subject to change. See the CHANGELOG.md for recent changes.
The Dash Table component will likely be merged into the dash-core-components
once it stabilizes.
For updates and more, please see the dash community discussion on tables.
If your organization or company is interested in sponsoring enhancements to this project, please reach out.
Example from usage-editable.py
# Install
$ pip install dash-table-experiments
Per this Dash community answer, to use callbacks with dash-table-experiments
there are two key steps (for a full working example see usage-callback.py):
# 1. Declare the table in app.layout
dt.DataTable(
rows=[{}], # initialise the rows
row_selectable=True,
filterable=True,
sortable=True,
selected_row_indices=[],
id='datatable'
)
# 2. Update rows in a callback
@app.callback(Output('datatable', 'rows'), [Input('field-dropdown', 'value')])
def update_datatable(user_selection):
"""
For user selections, return the relevant table
"""
if user_selection == 'Summary':
return DATA.to_dict('records')
else:
return SOMEOTHERDATA.to_dict('records')
This example demonstrates the user's ability to select data points either in the table which updates the plot, or in the reverse, select points on the graph which updates the selection on the table. For a full working example see usage.py.
Enable edits to a table which updates other objects e.g. a graph. For a full working example see usage-editable.py