Python dash table

Python dash table DEFAULT

Notice

As of Dash 2, the development of Dash Table has been moved to the main Dash repo

This package exists for backward compatibility


An interactive for Dash.

Documentation

Quickstart

importdashimportdash_tableimportpandasaspddf=pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/solar.csv') app=dash.Dash(__name__) app.layout=dash_table.DataTable( id='table', columns=[{"name": i, "id": i} foriindf.columns], data=df.to_dict('records'), ) if__name__=='__main__': app.run_server(debug=True)

Interactive Dash DataTable

Background

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

DataTable is rendered with standard, semantic HTML markup, which makes it accessible, responsive, and easy to style.

This component was written from scratch in React.js and Typescript specifically for the Dash community. Its API was designed to be ergonomic and its behavior is completely customizable through its properties.

DataTable was designed with a featureset that allows that Dash users to create complex, spreadsheet driven applications with no compromises. We're excited to continue to work with users and companies that invest in DataTable's future.

Please subscribe to dash-table#207 and the CHANGELOG.md to stay up-to-date with any breaking changes. Note: DataTable is currently supported in Chrome, Firefox, Safari, Edge (version 15+), and Internet Explorer 11.

Share your DataTable Dash apps on the community forum!

Contributing

See CONTRIBUTING.md

Sours: https://github.com/plotly/dash-table

Creating Interactive Data Tables in Plotly Dash

Plotly Dash is an incredibly powerful framework that allows you to create fully functional data visualization dashboards. Using Dash, you can create a full front-end experience using only Python. The library does a great job of abstracting away from the complicated HTML, CSS, and JS associated with all of the different web app components.

Check out this great article from Neptune.ai for a comprehensive overview of all the different visualizations available in Plotly.

While this is very nice, itsometimes becomes confusing. For example, Dash provides the dash_table.DataTable component, which is a table that comes with lots of prebuilt, ready-to-use functionalities. However, some of these features are hard to find or understand, and so getting the table to work exactly as you imagined is very tricky. So, I decided to document two table features that are not very well advertised, but which I feel are extremely important to creating a truly dynamic table.

Adding click events to your data table is a great way to make your dashboard more interactive. Thankfully, this one actually isn’t too difficult.

Using the active_cell component of the DataTable, you can easily get the data of a cell the user clicks on.

A callback using active_cell would look something like this:

@app.callback(
Output('output_div', 'children'),
Input('data_table', 'active_cell'),
State('data_table', 'data')
)
def getActiveCell(active_cell, data): if active_cell:
col = active_cell['column_id']
row = active_cell['row']
cellData = data[row][col] return html.P(f'row: {row}, col: {col}, value: {cellData}') return html.P('no cell selected')

The input is the active_cell component, which means selecting a cell is what causes this callback to fire. Also, note how the callback is sensitive to the data of the table. Without this, you can only access the row and column of the selected cell and not the actual value inside.

Using this callback in a full app would look something like this:

app = dash.Dash(__name__)app.layout = html.Div([ dash_table.DataTable(
id='data_table',
columns=[{
'name': 'Column {}'.format(i),
'id': 'column-{}'.format(i),
} for i in range(1, 5)],
data=[
{'column-{}'.format(i): (j + (i-1)*5) for i in range(1, 5)}
for j in range(5)
]
),
html.Div(id='output_div')
])@app.callback(
Output('output_div', 'children'),
Input('data_table', 'active_cell'),
State('data_table', 'data')
)
def getActiveCell(active_cell, data):
if active_cell:
col = active_cell['column_id']
row = active_cell['row']
cellData = data[row][col] return html.P(f'row: {row}, col: {col}, value: {cellData}') return html.P('no cell selected')if __name__ == '__main__':
app.run_server(debug=True)

Here’s what the code looks like in action:

Example Data Table with clickable cells
Example Data Table with clickable cells

This option is great for dashboards that need to display information in real-time (i.e. a dashboard that monitors the usage of some service).

The live updates are divided into 2 main ideas.

Update Interval

In order to update the table periodically, you have to set up an interval for how often the data should change. This is done with the dcc.Intervalcomponent from Dash.

This component stores a value, n_intervals, which tracks how many intervals of some specified length have passed.

You can see a full example of using the Interval for updates in the Dash documentation.

The declaration for the dcc.Interval component looks like this:

dcc.Interval(
id='interval-component',
interval=1*1000, # in milliseconds
n_intervals=0
)

And the callback for the n_intervals component looks like this:

@app.callback(
Output('component-to-update', 'property'),
Input('interval-component', 'n_intervals')
)
def update(n_intervals):
if n_intervals > 0:
# update table data

Appending Data

Now that we have a way to periodically perform some action, we need to use this method to update the table data.

We can do this by modifying the callback above. All we have to do is change the output to the data attribute of the table, as well as add a state for data.

This new callback looks like this:

@app.callback(
Output('data_table', 'data'),
Input('interval-component', 'n_intervals'),
[State('data_table', 'data'), State('data_table', 'columns')]
)
def updateData(n, data, columns):
if n > 0:
data.append({c['id']: n for c in columns})
return data

This callback takes in the data already in the table and appends a new row.

Adding this callback to the app detailed above, along with adding the dcc.Interval component to the layout creates the following dashboard.

Note: It’s a good idea to have the interval controlled by a toggle or switch so that it doesn’t run forever. You can do this by updating the disabled attribute of the dcc.Interval in a callback.

In this short blog, we saw two methods of adding extra functionality to a Dash DataTable. Adding click events and live updates are both awesome ways to make your data table more interactive, more flexible, and more powerful.

Try out these features in your own dashboards to truly take your visualization to the next level.

If you liked this blog, let me know!

Sours: https://towardsdatascience.com/creating-interactive-data-tables-in-plotly-dash-6d371de0942b
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Dash DataTable

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:

  • 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.

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:

  • 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.

Sours: https://dash.plotly.com/datatable
DataTable (Dropdown) - Dash Plotly Python

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Table python dash

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Introduction to Dash Plotly - Data Visualization in Python

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