Dash DataTable

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Introduction to Dash DataTable – Growing a Spreadsheet into an Application


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


Table with click callback


Styling & Fommatting


height with vertical scroll, pagination, virtualization, and fixed headers.

cf. Backend Paging


  • style_cell : data cells & the header cells.
  • style_header : header styles
  • style_data : data cell styles


The style of the DataTable is highly customizable.

  • Displaying multiple rows of headers
  • Text alignment
  • Styling the table as a list view
  • Changing the colors (including a dark theme!)

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.

config를 활용한 Styling


Interactive DataTable

Sorting, Filtering, Selecting, and Paging Natively

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

  • Paging
  • Selecting Rows
  • Sorting Columns
  • Filtering Data

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:

  • Determining which cell has changed
  • Filtering out null values
  • Adding or removing columns
  • Adding or removing rows
  • Ensuring that a minimum set of rows are visible
  • Running Python computations on certain columns or cells

Typing and User Input Processing

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

  • assign the column type
  • change the data presentation
  • change the data formatting
  • validate or coerce user data input
  • apply default behavior for valid and invalid data

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.Dash Python > Dash DataTable

Categories: dash


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