DataTable Height

By default, the table's height will expand in order to render up to 250 rows. After 250 rows, the table will display a pagination UI which enables viewing of up to 250 rows at a time.

Setting Table Height with Pagination

If you are using pagination, you can control the height by displaying fewer rows at a time. Instead of 250 rows, you could display 10 rows at a time. By default and without wrapping, each row takes up 30px. So 10 rows with one header would set the table to be 330px tall. The pagination UI itself is around 60px tall.

using Dash 
using OrderedCollections, DataFrames   

data = OrderedDict(
      "Date" => ["2015-01-01", "2015-10-24", "2016-05-10", "2017-01-10", "2018-05-10", "2018-08-15"],
      "Region" => ["Montreal", "Toronto", "New York City", "Miami", "San Francisco", "London"],
      "Temperature" => [1, -20, 3.512, 4, 10423, -441.2],
      "Humidity" => [10, 20, 30, 40, 50, 60],
      "Pressure" => [2, 10924, 3912, -10, 3591.2, 15],
)

df = DataFrame(data)

app = dash()

app.layout = dash_datatable(
    data = map(eachrow(df)) do r
      Dict(names(r) .=> values(r))
    end,
    columns=[Dict("name" =>c, "id" => c) for c in names(df)],
    page_size=10
)

run_server(app, "0.0.0.0", debug=true)
Date
Region
Temperature
Humidity
Pressure
2015-01-01
Montreal
1
10
2
2015-10-24
Toronto
-20
20
10924
2016-05-10
New York City
3.512
30
3912
2017-01-10
Miami
4
40
-10
2018-05-10
San Francisco
10423
50
3591.2
2018-08-15
London
-441.2
60
15
2015-01-01
Montreal
1
10
2
2015-10-24
Toronto
-20
20
10924
2016-05-10
New York City
3.512
30
3912
2017-01-10
Miami
4
40
-10
1
/
6

In this example, the pagination is done natively in the browser: all of the data are sent up front to the browser and Dash renders new pages as you click on the buttons. You can also do pagination in the backend so that only 10 rows are sent to the browser at a time (lowering network costs and memory). This is a good strategy if you have more than 10,000-50,000 rows. Learn about backend pagination.

Setting Table Height with Vertical Scroll

If the table contains less than roughly 1000 rows, one option is to remove pagination, constrain the height, and display a vertical scrollbar.

using Dash 
using OrderedCollections, DataFrames   

data = OrderedDict(
      "Date" => ["2015-01-01", "2015-10-24", "2016-05-10", "2017-01-10", "2018-05-10", "2018-08-15"],
      "Region" => ["Montreal", "Toronto", "New York City", "Miami", "San Francisco", "London"],
      "Temperature" => [1, -20, 3.512, 4, 10423, -441.2],
      "Humidity" => [10, 20, 30, 40, 50, 60],
      "Pressure" => [2, 10924, 3912, -10, 3591.2, 15],
)

df = DataFrame(data) 

app = dash()

app.layout = dash_datatable(
    data = map(eachrow(df)) do r
      Dict(names(r) .=> values(r))
    end,
    columns=[Dict("name" =>c, "id" => c) for c in names(df)],
    page_action="none",
    style_table=Dict("height" => "300px", "overflowY" => "auto")
)

run_server(app, "0.0.0.0", debug=true)
Date
Region
Temperature
Humidity
Pressure
2015-01-01
Montreal
1
10
2
2015-10-24
Toronto
-20
20
10924
2016-05-10
New York City
3.512
30
3912
2017-01-10
Miami
4
40
-10
2018-05-10
San Francisco
10423
50
3591.2
2018-08-15
London
-441.2
60
15
2015-01-01
Montreal
1
10
2
2015-10-24
Toronto
-20
20
10924
2016-05-10
New York City
3.512
30
3912
2017-01-10
Miami
4
40
-10
2018-05-10
San Francisco
10423
50
3591.2
2018-08-15
London
-441.2
60
15
2015-01-01
Montreal
1
10
2
2015-10-24
Toronto
-20
20
10924
2016-05-10
New York City
3.512
30
3912
2017-01-10
Miami
4
40
-10
2018-05-10
San Francisco
10423
50
3591.2
2018-08-15
London
-441.2
60
15
2015-01-01
Montreal
1
10
2
2015-10-24
Toronto
-20
20
10924
2016-05-10
New York City
3.512
30
3912
2017-01-10
Miami
4
40
-10
2018-05-10
San Francisco
10423
50
3591.2
2018-08-15
London
-441.2
60
15
2015-01-01
Montreal
1
10
2
2015-10-24
Toronto
-20
20
10924
2016-05-10
New York City
3.512
30
3912
2017-01-10
Miami
4
40
-10
2018-05-10
San Francisco
10423
50
3591.2
2018-08-15
London
-441.2
60
15
2015-01-01
Montreal
1
10
2
2015-10-24
Toronto
-20
20
10924
2016-05-10
New York City
3.512
30
3912
2017-01-10
Miami
4
40
-10
2018-05-10
San Francisco
10423
50
3591.2
2018-08-15
London
-441.2
60
15
2015-01-01
Montreal
1
10
2
2015-10-24
Toronto
-20
20
10924
2016-05-10
New York City
3.512
30
3912
2017-01-10
Miami
4
40
-10
2018-05-10
San Francisco
10423
50
3591.2
2018-08-15
London
-441.2
60
15
2015-01-01
Montreal
1
10
2
2015-10-24
Toronto
-20
20
10924
2016-05-10
New York City
3.512
30
3912
2017-01-10
Miami
4
40
-10
2018-05-10
San Francisco
10423
50
3591.2
2018-08-15
London
-441.2
60
15
2015-01-01
Montreal
1
10
2
2015-10-24
Toronto
-20
20
10924
2016-05-10
New York City
3.512
30
3912
2017-01-10
Miami
4
40
-10
2018-05-10
San Francisco
10423
50
3591.2
2018-08-15
London
-441.2
60
15
2015-01-01
Montreal
1
10
2
2015-10-24
Toronto
-20
20
10924
2016-05-10
New York City
3.512
30
3912
2017-01-10
Miami
4
40
-10
2018-05-10
San Francisco
10423
50
3591.2
2018-08-15
London
-441.2
60
15

Limitations

If you have more than 1000 rows, then the browser will slow down when trying to render the table. With more than 1000 rows, we recommend switching to browser or server pagination (as above) or virtualization (as below).

Vertical Scroll With Pagination

If you have more than ~1000 rows, then you could keep pagination at the bottom of the table, constrain the height, and display a vertical scrollbar.

using Dash 
using OrderedCollections, DataFrames   

data = OrderedDict(
      "Date" => ["2015-01-01", "2015-10-24", "2016-05-10", "2017-01-10", "2018-05-10", "2018-08-15"],
      "Region" => ["Montreal", "Toronto", "New York City", "Miami", "San Francisco", "London"],
      "Temperature" => [1, -20, 3.512, 4, 10423, -441.2],
      "Humidity" => [10, 20, 30, 40, 50, 60],
      "Pressure" => [2, 10924, 3912, -10, 3591.2, 15],
)

df = DataFrame(data)

app = dash()

app.layout = dash_datatable(
    data = map(eachrow(df)) do r
      Dict(names(r) .=> values(r))
    end,
    columns=[Dict("name" =>c, "id" => c) for c in names(df)],
    page_size=20, # we have less data in this example, so setting to 20
    style_table=Dict("height" => "300px", "overflowY" => "auto")
)

run_server(app, "0.0.0.0", debug=true)
Date
Region
Temperature
Humidity
Pressure
2015-01-01
Montreal
1
10
2
2015-10-24
Toronto
-20
20
10924
2016-05-10
New York City
3.512
30
3912
2017-01-10
Miami
4
40
-10
2018-05-10
San Francisco
10423
50
3591.2
2018-08-15
London
-441.2
60
15
2015-01-01
Montreal
1
10
2
2015-10-24
Toronto
-20
20
10924
2016-05-10
New York City
3.512
30
3912
2017-01-10
Miami
4
40
-10
2018-05-10
San Francisco
10423
50
3591.2
2018-08-15
London
-441.2
60
15
2015-01-01
Montreal
1
10
2
2015-10-24
Toronto
-20
20
10924
2016-05-10
New York City
3.512
30
3912
2017-01-10
Miami
4
40
-10
2018-05-10
San Francisco
10423
50
3591.2
2018-08-15
London
-441.2
60
15
2015-01-01
Montreal
1
10
2
2015-10-24
Toronto
-20
20
10924
1
/
3

Vertical Scroll With Fixed Headers

You can also fix headers so that the table content scrolls but the headers remain visible.

using Dash 
using CSV, DataFrames   
using OrderedCollections, DataFrames   

data = OrderedDict(
      "Date" => ["2015-01-01", "2015-10-24", "2016-05-10", "2017-01-10", "2018-05-10", "2018-08-15"],
      "Region" => ["Montreal", "Toronto", "New York City", "Miami", "San Francisco", "London"],
      "Temperature" => [1, -20, 3.512, 4, 10423, -441.2],
      "Humidity" => [10, 20, 30, 40, 50, 60],
      "Pressure" => [2, 10924, 3912, -10, 3591.2, 15],
)

app = dash()

app.layout = dash_datatable(
    data = map(eachrow(df)) do r
      Dict(names(r) .=> values(r))
    end,
    columns=[Dict("name" =>c, "id" => c) for c in names(df)],
    fixed_rows=Dict("headers" => true),
    style_table=Dict("height" => 400)  # defaults to 500
)

run_server(app, "0.0.0.0", debug=true)
Date
Region
Temperature
Humidity
Pressure
2015-01-01
Montreal
1
10
2
2015-10-24
Toronto
-20
20
10924
2016-05-10
New York City
3.512
30
3912
2017-01-10
Miami
4
40
-10
2018-05-10
San Francisco
10423
50
3591.2
2018-08-15
London
-441.2
60
15
2015-01-01
Montreal
1
10
2
2015-10-24
Toronto
-20
20
10924
2016-05-10
New York City
3.512
30
3912
2017-01-10
Miami
4
40
-10
2018-05-10
San Francisco
10423
50
3591.2
2018-08-15
London
-441.2
60
15
2015-01-01
Montreal
1
10
2
2015-10-24
Toronto
-20
20
10924
2016-05-10
New York City
3.512
30
3912
2017-01-10
Miami
4
40
-10
2018-05-10
San Francisco
10423
50
3591.2
2018-08-15
London
-441.2
60
15
2015-01-01
Montreal
1
10
2
2015-10-24
Toronto
-20
20
10924
2016-05-10
New York City
3.512
30
3912
2017-01-10
Miami
4
40
-10
2018-05-10
San Francisco
10423
50
3591.2
2018-08-15
London
-441.2
60
15
2015-01-01
Montreal
1
10
2
2015-10-24
Toronto
-20
20
10924
2016-05-10
New York City
3.512
30
3912
2017-01-10
Miami
4
40
-10
2018-05-10
San Francisco
10423
50
3591.2
2018-08-15
London
-441.2
60
15
2015-01-01
Montreal
1
10
2
2015-10-24
Toronto
-20
20
10924
2016-05-10
New York City
3.512
30
3912
2017-01-10
Miami
4
40
-10
2018-05-10
San Francisco
10423
50
3591.2
2018-08-15
London
-441.2
60
15
2015-01-01
Montreal
1
10
2
2015-10-24
Toronto
-20
20
10924
2016-05-10
New York City
3.512
30
3912
2017-01-10
Miami
4
40
-10
2018-05-10
San Francisco
10423
50
3591.2
2018-08-15
London
-441.2
60
15
2015-01-01
Montreal
1
10
2
2015-10-24
Toronto
-20
20
10924
2016-05-10
New York City
3.512
30
3912
2017-01-10
Miami
4
40
-10
2018-05-10
San Francisco
10423
50
3591.2
2018-08-15
London
-441.2
60
15
2015-01-01
Montreal
1
10
2
2015-10-24
Toronto
-20
20
10924
2016-05-10
New York City
3.512
30
3912
2017-01-10
Miami
4
40
-10
2018-05-10
San Francisco
10423
50
3591.2
2018-08-15
London
-441.2
60
15
2015-01-01
Montreal
1
10
2
2015-10-24
Toronto
-20
20
10924
2016-05-10
New York City
3.512
30
3912
2017-01-10
Miami
4
40
-10
2018-05-10
San Francisco
10423
50
3591.2
2018-08-15
London
-441.2
60
15

Limitations

  1. Percentage-based column widths is not available with fixed_rows & table-layout: fixed. See plotly/dash-table#745
  2. Percentage-based widths with fixed_rows and without table-layout: fixed has some issues when resizing the window. See plotly/dash-table#747
  3. If filtering is enabled, then horizontal scroll does not work with wide tables. plotly/dash-table#746
  4. If a column header is wider than the data within that column and the table's container isn't wide enough to display the headers, then the column will be as wide as the data and the header text will either be truncated (most browsers) or overflow onto the next column (Firefox). This is a bug (plotly/dash-table#432). The current workaround is to hide the overflow or fix the width of the columns in pixels. When using this workaround, you may run into a few of these issues:
    1. In those scenarios where the header is cut off, it is not possible to set ellipses within the header. For updates, see plotly/dash-table#735
    2. When the text is truncated, it is useful to display tooltips displaying the entire text. It is not yet possible to add tooltips to headers. For updates, see plotly/dash-table#295
    3. If the header text is truncated, then the header overflow is visible. The current workaround is to hide the overflow with overflow: 'hidden'.

Example of the wide-header limitation

If the headers are wider than the cells and the table's container isn't wide enough to display all of the headers, then the column headers will be truncated on most browsers or will overflow on Firefox.

using Dash 
using OrderedCollections, DataFrames   

data = OrderedDict([ ("Column $i", collect(1:30)) for i in 1:15 ])

df = DataFrame(data)

app = dash()

app.layout = dash_datatable(
    data = map(eachrow(df)) do r
      Dict(names(r) .=> values(r))
    end,
    columns=[Dict("name" =>c, "id" => c) for c in names(df)],
    fixed_rows=Dict("headers" => true)
)

run_server(app, "0.0.0.0", debug=true)
Column 1
Column 2
Column 3
Column 4
Column 5
Column 6
Column 7
Column 8
Column 9
Column 10
Column 11
Column 12
Column 13
Column 14
Column 15
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
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Workaround Option 1: Hiding the header overflow for Firefox users

(If you are not on Firefox, then this example will look the same as above)

using Dash 
using OrderedCollections, DataFrames   

data = OrderedDict([ ("Column $i", collect(1:30)) for i in 1:15 ])

df = DataFrame(data)

app = dash()

app.layout = dash_datatable(
    data = map(eachrow(df)) do r
      Dict(names(r) .=> values(r))
    end,
    columns=[Dict("name" =>c, "id" => c) for c in names(df)],
    fixed_rows=Dict("headers" => true),
    style_header=Dict(
      "overflow" => "hidden",
      "textOverflow" => "ellipsis",
      "maxWidth" => 0,
  )
)

run_server(app, "0.0.0.0", debug=true)
Column 1
Column 2
Column 3
Column 4
Column 5
Column 6
Column 7
Column 8
Column 9
Column 10
Column 11
Column 12
Column 13
Column 14
Column 15
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
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Workaround Option 2: Fixing the width of the columns

using Dash 
using OrderedCollections, DataFrames   

data = OrderedDict([ ("Column $i", collect(1:30)) for i in 1:15 ])

df = DataFrame(data)

app = dash()

app.layout = dash_datatable(
    data = map(eachrow(df)) do r
      Dict(names(r) .=> values(r))
    end,
    columns=[Dict("name" =>c, "id" => c) for c in names(df)],
    fixed_rows=Dict("headers" => true),
    style_cell=Dict(
        "minWidth" =>  95, "maxWidth" =>  95, "width" =>  95
    )
)

run_server(app, "0.0.0.0", debug=true)
Column 1
Column 2
Column 3
Column 4
Column 5
Column 6
Column 7
Column 8
Column 9
Column 10
Column 11
Column 12
Column 13
Column 14
Column 15
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Vertical Scroll with Virtualization

As mentioned above, the browser has difficulty rendering thousands of rows in a table. By rendering rows on the fly as you scroll, virtualization works around rendering performance issues inherent with the web browser.

All of the data for your table will still be sent over the network to the browser, so if you are displaying more than 10,000-100,000 rows you may consider using backend pagination to reduce the volume of data that is transferred over the network and associated memory usage.

using Dash 
using OrderedCollections, DataFrames   

data = OrderedDict([ ("Column $i", collect(1:30)) for i in 1:15 ])

df = DataFrame(data)

app = dash()

app.layout = dash_datatable(
    data = map(eachrow(df)) do r
      Dict(names(r) .=> values(r))
    end,
    columns=[Dict("name" =>c, "id" => c) for c in names(df)],
    virtualization=true,
    fixed_rows=Dict("headers" => true),
    style_cell=Dict(
        "minWidth" =>  95, "maxWidth" =>  95, "width" =>  95
    ),
    style_table=Dict("height" => 300)  # default is 500
)

run_server(app, "0.0.0.0", debug=true)
Column 1
Column 2
Column 3
Column 4
Column 5
Column 6
Column 7
Column 8
Column 9
Column 10
Column 11
Column 12
Column 13
Column 14
Column 15
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Limitations

  1. With virtualization, the browser doesn't know the width of the columns in advance; it can only determine the width of the columns when they are rendered. So, your columns may change size as you scroll unless you fix the column widths.
  2. Since, with virtualization, we're rendering rows on the fly as we scroll, the rendering performance will be slower than the browser-optimized native vertical scrolling. If you scroll quickly, you may notice that table appears momentarily blank until rendering has completed.
  3. The same fixed_rows limitations exist as mentioned above.