Dash DataTable

Dash DataTable is an interactive table component designed for viewing, editing, and exploring large datasets.

This component was written from scratch in React.js specifically
for the Dash community. Its API was designed to be ergonomic
and its behavior is completely customizable through its properties.
DataTable is rendered with standard, semantic HTML <table> markup,
which makes it accessible, responsive, and easy to style.

For production Dash applications, DataTable is intended to be used with
Python data pipelines for ingesting
the table data and Design Kit for
DataTable styling.

Quickstart

pip install dash==2.1.0

The data and columns properties are the first two arguments of dash_table.DataTable.
You can set these with

from dash import Dash, Input, Output, callback, dash_table
import pandas as pd
import dash_bootstrap_components as dbc

df = pd.read_csv('https://git.io/Juf1t')

app = Dash(external_stylesheets=[dbc.themes.BOOTSTRAP])

app.layout = dbc.Container([
    dbc.Label('Click a cell in the table:'),
    dash_table.DataTable(df.to_dict('records'),[{"name": i, "id": i} for i in df.columns], id='tbl'),
    dbc.Alert(id='tbl_out'),
])

@callback(Output('tbl_out', 'children'), Input('tbl', 'active_cell'))
def update_graphs(active_cell):
    return str(active_cell) if active_cell else "Click the table"

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

User Guide

Reference

A comprehensive list of all of the DataTable properties.

DataTable Height

How to set the height of the DataTable. Examples include how to set the height with vertical scroll, pagination, virtualization, and fixed headers.

DataTable Width & Column Width

How to set the width of the table and the columns. Examples include how to handle word wrapping, cell clipping, horizontal scroll, fixed columns, and more.

Styling

The style of the DataTable is highly customizable. This chapter includes examples for:

Conditional Formatting

Several examples of how to highlight certain cells, rows, or columns based on their value or state.

Number Formatting

Several examples of how to format and localize numbers.

Sorting, Filtering, Selecting, and Paging Natively

The DataTable is interactive. This chapter demonstrates the interactive features of the table and how to wire up these interations to Python callbacks. These actions include:

DataTable Tooltips

Display tooltips on data and header rows, conditional tooltips, define tooltips for each cell, customize behavior.

Python-Driven Filtering, Paging, Sorting

In Part 3, the paging, sorting, and filtering was done entirely clientside (in the browser). This means that you need to load all of the data into the table up-front. If your data is large, then this can be prohibitively slow. In this chapter, you’ll learn how to write your own filtering, sorting, and paging backends in Python with Dash. We’ll do the data processing with Pandas but you could write your own routines with SQL or even generate the data on the fly!

Editable DataTable

The DataTable is editable. Like a spreadsheet, it can be used as an input for controlling models with a variable number of inputs. This chapter includes recipes for:

Typing and User Input Processing

In this chapter, you’ll learn how to configure the table to

Dropdowns Inside DataTable

Cells can be rendered as editable Dropdowns. This is our first stake in bringing a full typing system to the table. Rendering cells as dropdowns introduces some complexity in the markup and so there are a few limitations that you should be aware of.

Virtualization

Examples using DataTable virtualization.

Filtering Syntax

An explanation and examples of filtering syntax for both frontend and backend filtering in the DataTable.