Tabulator#

Open this notebook in Jupyterlite | Download this notebook from GitHub (right-click to download).

import datetime as dt
import numpy as np
import pandas as pd
import panel as pn

np.random.seed(7)
pn.extension('tabulator')

The Tabulator widget allows displaying and editing a pandas DataFrame. The Tabulator is a largely backward compatible replacement for the DataFrame widget and will eventually replace it. It is built on the version 6.3.0 of the Tabulator library, which provides for a wide range of features.


Discover more on using widgets to add interactivity to your applications in the how-to guides on interactivity. Alternatively, learn how to set up callbacks and (JS-)links between parameters or how to use them as part of declarative UIs with Param.




Parameters:#


For details on other options for customizing the component see the layout and styling how-to guides.




Core#


    

  • aggregators (dict): A dictionary mapping from index name to an aggregator to be used for hierarchical multi-indexes (valid aggregators include ‘min’, ‘max’, ‘mean’ and ‘sum’). If separate aggregators for different columns are required the dictionary may be nested as {index_name: {column_name: aggregator}}
    • 

  • buttons (dict): A dictionary of buttons to add to the table mapping from column name to the HTML contents of the button cell, e.g. {'print': '<i class="fa fa-print"></i>'}. Buttons are added after all data columns.
    • 

  • configuration (dict): A dictionary mapping used to specify Tabulator options not explicitly exposed by Panel.
    • 

  • editors (dict):  A dictionary mapping from column name to a bokeh CellEditor instance or Tabulator editor specification.
    • 

  • embed_content (boolean): Whether to embed the row_content or to dynamically fetch it when a row is expanded.
    • 

  • expanded (list): The currently expanded rows as a list of integer indexes.
    • 

  • filters (list): A list of client-side filter definitions that are applied to the table.
    • 

  • formatters (dict): A dictionary mapping from column name to a bokeh CellFormatter instance or Tabulator formatter specification.
    • 

  • frozen_columns (list or dict): Defines the frozen columns:
    

      

    • list
List of columns to freeze, preventing them from scrolling out of frame. Column can be specified by name or index.
      • 

    • dict
Dict of columns to freeze and the position in table ('left' or 'right') to freeze them in. Column names or index can be used as keys. If value does not match
left or right then the default behaviour is to not be frozen at all.
      • 

    

    • 

  • frozen_rows: (list): List of rows to freeze, preventing them from scrolling out of frame. Rows can be specified by positive or negative index.
    • 

  • groupby (list): Groups rows in the table by one or more columns.
    • 

  • header_align (dict or str): A mapping from column name to header alignment or a fixed header alignment, which should be one of 'left', 'center', 'right'.
    • 

  • header_filters (boolean/dict): A boolean enabling filters in the column headers or a dictionary providing filter definitions for specific columns.
    • 

  • header_tooltips (dict): Dictionary mapping from column name to a tooltip to show when hovering over the column header.
    • 

  • hidden_columns (list): List of columns to hide.
    • 

  • hierarchical (boolean, default=False): Whether to render multi-indexes as hierarchical index (note hierarchical must be enabled during instantiation and cannot be modified later)
    • 

  • initial_page_size (int, default=20): If pagination is enabled and page_size this determines the initial size of each page before rendering.
    • 

  • layout (str, default='fit_data_table'): Describes the column layout mode with one of the following options 'fit_columns', 'fit_data', 'fit_data_stretch', 'fit_data_fill', 'fit_data_table'.
    • 

  • page (int, default=1): Current page, if pagination is enabled.
    • 

  • page_size (int | None, default=None): Number of rows on each page, if pagination is enabled. By default the number of rows is automatically determined based on the number of rows that fit on screen. If None the initial amount of data is determined by the initial_page_size.
    • 

  • pagination (str, default=None):  Set to 'local or 'remote' to enable pagination; by default pagination is disabled with the value set to None.
    • 

  • row_content (callable): A function that receives the expanded row (pandas.Series) as input and should return a Panel object to render into the expanded region below the row.
    • 

  • selection (list): The currently selected rows as a list of integer indexes.
    • 

  • selectable (boolean or str or int, default=True): Defines the selection mode:
    

      

    • True
Selects rows on click. To select multiple use Ctrl-select, to select a range use Shift-select
      • 

    • False
Disables selection
      • 

    • 'checkbox'
Adds a column of checkboxes to toggle selections
      • 

    • 'checkbox-single'
Same as ‘checkbox’ but header does not allow select/deselect all
      • 

    • 'toggle'
Selection toggles when clicked
      • 

    • int
The maximum number of selectable rows.
      • 

    

    • 

  • selectable_rows (callable): A function that should return a list of integer indexes given a DataFrame indicating which rows may be selected.
    • 

  • show_index (boolean, default=True): Whether to show the index column.
    • 

  • sortable (bool | dict[str, bool], default=True): Whether the table is sortable or whether individual columns are sortable. If specified as a bool applies globally otherwise sorting can be enabled/disabled per column.
    • 

  • sorters (list): A list of sorter definitions mapping where each item should declare the column to sort on and the direction to sort, e.g. [{'field': 'column_name', 'dir': 'asc'}, {'field': 'another_column', 'dir': 'desc'}].
    • 

  • text_align (dict or str): A mapping from column name to alignment or a fixed column alignment, which should be one of 'left', 'center', 'right'.
    • 

  • theme (str, default='simple'): The CSS theme to apply (note that changing the theme will restyle all tables on the page), which should be one of 'default', 'site', 'simple', 'midnight', 'modern', 'bootstrap', 'bootstrap4', 'bootstrap5', 'materialize', 'bulma', 'semantic-ui', or 'fast'.
    • 

  • theme_classes (list[str]): List of extra CSS classes to apply to the Tabulator element to customize the theme.
    • 

  • title_formatters (dict): A dictionary mapping from column name to a Tabulator formatter specification.
    • 

  • titles (dict): A mapping from column name to a title to override the name with.
    • 

  • value (pd.DataFrame): The pandas DataFrame to display and edit
    • 

  • widths (dict): A dictionary mapping from column name to column width in the rendered table.
    • 







Display#


    

  • disabled (boolean): Whether the cells are editable
    • 







Properties#


    

  • current_view (DataFrame): The current view of the table that is displayed, i.e. after sorting and filtering are applied. current_view isn’t guaranteed to be in sync with the displayed current view when sorters are applied and values are edited, in which case current_view is sorted while the displayed table isn’t.
    • 

  • selected_dataframe (DataFrame): A DataFrame reflecting the currently selected rows.
    • 







Callbacks#


    

  • on_click: Allows registering callbacks which are given CellClickEvent objects containing the column, row and value of the clicked cell.
    • 

  • on_edit: Allows registering callbacks which are given TableEditEvent objects containing the column, row, value and old value of the edited cell.
    • 



In both these callbacks row is the index of the value DataFrame.




The Tabulator widget renders a DataFrame using an interactive grid, which allows directly editing the contents of the DataFrame in place, with any changes being synced with Python. The Tabulator will usually determine the appropriate formatter appropriately based on the type of the data:


df = pd.DataFrame({
    'int': [1, 2, 3],
    'float': [3.14, 6.28, 9.42],
    'str': ['A', 'B', 'C'],
    'bool': [True, False, True],
    'date': [dt.date(2019, 1, 1), dt.date(2020, 1, 1), dt.date(2020, 1, 10)],
    'datetime': [dt.datetime(2019, 1, 1, 10), dt.datetime(2020, 1, 1, 12), dt.datetime(2020, 1, 10, 13)]
}, index=[1, 2, 3])

df_widget = pn.widgets.Tabulator(df, buttons={'Print': "<i class='fa fa-print'></i>"})
df_widget







            
            






Formatters#


By default the widget will pick Bokeh CellFormatter and CellEditor types appropriate to the dtype of the column. These may be overridden by explicit dictionaries mapping from the column name to the editor or formatter instance. For example below we create a NumberFormatter to customize the formatting of the numbers in the float column and a BooleanFormatter instance to display the values in the bool column with tick crosses:


from bokeh.models.widgets.tables import NumberFormatter, BooleanFormatter

bokeh_formatters = {
    'float': NumberFormatter(format='0.00000'),
    'bool': BooleanFormatter(),
}

pn.widgets.Tabulator(df, formatters=bokeh_formatters)







            
            
The list of valid Bokeh formatters includes:



However in addition to the formatters exposed by Bokeh it is also possible to provide valid formatters built into the Tabulator library. These may be defined either as a string or as a dictionary declaring the type and other arguments, which are passed to Tabulator as the formatterParams:


tabulator_formatters = {
    'float': {'type': 'progress', 'max': 10},
    'bool': {'type': 'tickCross'}
}

pn.widgets.Tabulator(df, formatters=tabulator_formatters)







            
            
The list of valid Tabulator formatters can be found in the Tabulator documentation.


Note that the equivalent specification may also be applied for column titles using the title_formatters parameter (but does not support Bokeh CellFormatter types).






Editors/Editing#


Just like the formatters, the Tabulator will natively understand the Bokeh Editor types. However, in the background it will replace most of them with equivalent editors natively supported by the Tabulator library:


from bokeh.models.widgets.tables import CheckboxEditor, NumberEditor, SelectEditor

bokeh_editors = {
    'float': NumberEditor(),
    'bool': CheckboxEditor(),
    'str': SelectEditor(options=['A', 'B', 'C', 'D']),
}

pn.widgets.Tabulator(df[['float', 'bool', 'str']], editors=bokeh_editors)







            
            
Therefore it is often preferable to use one of the Tabulator editors directly. Setting the editor of a column to None makes that column non-editable. Note that in addition to the standard Tabulator editors the Tabulator widget also supports 'date' and 'datetime' editors:


tabulator_editors = {
    'int': None,
    'float': {'type': 'number', 'max': 10, 'step': 0.1},
    'bool': {'type': 'tickCross', 'tristate': True, 'indeterminateValue': None},
    'str': {'type': 'list', 'valuesLookup': True},
    'date': 'date',
    'datetime': 'datetime'
}

edit_table = pn.widgets.Tabulator(df, editors=tabulator_editors)

edit_table







            
            
When editing a cell the data stored on the Tabulator.value is updated and you can listen to any changes using the usual .param.watch(callback, 'value') mechanism. However if you need to know precisely which cell was changed you may also attach an on_edit callback which will be passed a TableEditEvent containing the:


    

  • column: Name of the edited column
    • 

  • row: Integer index of the edited row of the value DataFrame
    • 

  • old: Old cell value
    • 

  • value: New cell value
    • 



edit_table.on_edit(lambda e: print(e.column, e.row, e.old, e.value))







Nested editor#


Suppose you want an editor to depend on values in another cell. The nested type can be used. The nested type needs two arguments, options and lookup_order; the latter describes how the options should be looked up.


Let’s create a simple DataFrame with three columns, the 2 column now depends on the values in the 0 and 1 column. If the 0 is A, the 2 column should always be between 1 and 5. If the 0 column is B, the 2 column will now also depend on the 1 column.


options = {
    "A": ["A.1", "A.2", "A.3", "A.4", "A.5"],
    "B": {
        "1": ["B1.1", "B1.2", "B1.3"],
        "2": ["B2.1", "B2.2", "B2.3"],
        "3": ["B3.1", "B3.2", "B3.3"],
    },
}
tabulator_editors = {
    "0": {"type": "list", "values": ["A", "B"]},
    "1": {"type": "list", "values": [1, 2, 3]},
    "Nested Selection": {"type": "nested", "options": options, "lookup_order": ["0", "1"]},
}

nested_df = pd.DataFrame({"0": ["A", "B", "A"], "1": [1, 2, 3], "Nested Selection": [None, None, None]})
nested_table = pn.widgets.Tabulator(nested_df, editors=tabulator_editors, show_index=False)
nested_table







            
            
Some things to note about the nested editor:


    

  • Only string keys can be used in options dictionary.
    • 

  • Care must be taken so there is always a valid option for the nested editor.
    • 

  • No guarantee is made that the value shown is a nested editor is a valid option.
    • 



For the last point, you can use an on_edit callback, which either change the value or clear it. Below is an example of how to clear it.


def clear_nested_column(event):
    if event.column in ["0", "1"]:
        nested_table.patch({"2": [(event.row, None)]})

nested_table.on_edit(clear_nested_column)











Column layouts#


By default the DataFrame widget will adjust the sizes of both the columns and the table based on the contents, reflecting the default value of the parameter: layout="fit_data_table". Alternative modes allow manually specifying the widths of the columns, giving each column equal widths, or adjusting just the size of the columns.




Manual column widths#


To manually adjust column widths provide explicit widths for each of the columns:


custom_df = df.iloc[:3, :]

pn.widgets.Tabulator(custom_df, widths={'index': 70, 'A': 50, 'B': 50, 'C': 70, 'D': 130})







            
            
You can also declare a single width for all columns this way:


pn.widgets.Tabulator(custom_df, widths=130)







            
            
or even use percentage widths:


pn.widgets.Tabulator(custom_df, widths={'index': '5%', 'A': '15%', 'B': '15%', 'C': '25%', 'D': '40%'}, sizing_mode='stretch_width')







            
            




Autosize columns#


To automatically adjust the columns depending on their content set layout='fit_data':


pn.widgets.Tabulator(custom_df, layout='fit_data', width=400)







            
            
To ensure that the table fits all the data but also stretches to fill all the available space, set layout='fit_data_stretch':


pn.widgets.Tabulator(custom_df, layout='fit_data_stretch', width=400)







            
            
The 'fit_data_fill' option on the other hand won’t stretch the last column but still fill the space:


pn.widgets.Tabulator(custom_df, layout='fit_data_fill', width=400)







            
            
Perhaps the most useful of these options is layout='fit_data_table' (and therefore the default) since this will automatically size both the columns and the table:


pn.widgets.Tabulator(custom_df, layout='fit_data_table')







            
            




Equal size#


The simplest option is simply to allocate each column equal amount of size:


pn.widgets.Tabulator(custom_df, layout='fit_columns', width=650)







            
            






Alignment#


The content of a column or its header can be horizontally aligned with text_align and header_align. These two parameters accept either a string that globally defines the alignment or a dictionary that declares which particular columns are meant to be aligned and how.


pn.widgets.Tabulator(df.iloc[:, :2], header_align='center', text_align={'int': 'center', 'float': 'left'}, widths=150)







            
            




Styling#


The ability to style the contents of a table based on its content and other considerations is very important. Thankfully pandas provides a powerful styling API, which can be used in conjunction with the Tabulator widget. Specifically the Tabulator widget exposes a .style attribute just like a pandas.DataFrame which lets the user apply custom styling using methods like .apply and .applymap. For a detailed guide to styling see the Pandas documentation.


Here we will demonstrate with a simple example, starting with a basic table:


style_df = pd.DataFrame(np.random.randn(4, 5), columns=list('ABCDE'))
styled = pn.widgets.Tabulator(style_df)





Next we define two functions which apply styling cell-wise (color_negative_red) and column-wise (highlight_max), which we then apply to the Tabulator using the .style API and then display the styled table:


def color_negative_red(val):
    """
    Takes a scalar and returns a string with
    the css property `'color: red'` for negative
    strings, black otherwise.
    """
    color = 'red' if val < 0 else 'black'
    return 'color: %s' % color

def highlight_max(s):
    '''
    highlight the maximum in a Series yellow.
    '''
    is_max = s == s.max()
    return ['background-color: yellow' if v else '' for v in is_max]

styled.style.map(color_negative_red).apply(highlight_max)

styled







            
            
You can style your tables with gradients using the .text_gradient or .background_gradient methods, along with named Matplotlib color maps.


Note: Styling with gradients requires Matplotlib to be installed.


gradient_table = pn.widgets.Tabulator(style_df)
gradient_table.style.text_gradient(cmap="RdYlGn", subset=["B", "C"])
gradient_table.style.background_gradient(cmap="RdYlGn", subset=["D", "E"])
gradient_table







            
            




Theming#


The Tabulator library ships with a number of themes, which are defined as CSS stylesheets. For that reason changing the theme on one table will affect all tables on the page and it will usually be preferable to see the theme once at the class level like this:


pn.widgets.Tabulator.theme = 'default'





For a full list of themes see the Tabulator documentation, however the default themes include:


    

  • 'simple'
    • 

  • 'default'
    • 

  • 'midnight'
    • 

  • 'site'
    • 

  • 'modern'
    • 

  • 'bootstrap'
    • 

  • 'bootstrap4'
    • 

  • 'materialize'
    • 

  • 'semantic-ui'
    • 

  • 'bulma'
    • 



Additionally, you may provide additional theming classes as described here.


pn.widgets.Tabulator(df, theme='bootstrap5', theme_classes=['thead-dark', 'table-sm'])







            
            


Changing font-size#


Font-size may vary from theme to theme. E.g with ‘bootstrap’ it is 13px while with ‘bootstrap5’ it is 16px. Below is one way to overwrite the font-size value for theme ‘bootstrap5’ to 10px.


pn.widgets.Tabulator(df, theme='bootstrap5', stylesheets=[":host .tabulator {font-size: 10px;}"])







            
            






Selection/Click#


The selection parameter controls which rows in the table are selected and can be set from Python and updated by selecting rows on the frontend:


sel_df = pd.DataFrame(np.random.randn(3, 5), columns=list('ABCDE'))

select_table = pn.widgets.Tabulator(sel_df, selection=[0, 2])
select_table







            
            
Once initialized, the selection parameter will return the integer indexes of the selected rows, while the selected_dataframe property will return a new DataFrame containing just the selected rows:


select_table.selection = [1]

select_table.selected_dataframe






  
    

      
      A
      B
      C
      D
      E
    

  
  
    

      1
      -1.450679
      -0.405228
      -2.288315
      1.049397
      -0.416474
    

  



The selectable parameter declares how the selections work.


    

  • True: Selects rows on click. To select multiple use Ctrl-select, to select a range use Shift-select
    • 

  • False: Disables selection
    • 

  • 'checkbox': Adds a column of checkboxes to toggle selections
    • 

  • 'checkbox-single': Same as 'checkbox' but disables (de)select-all in the header
    • 

  • 'toggle': Selection toggles when clicked
    • 

  • Any positive int: A number that sets the maximum number of selectable rows
    • 



pn.widgets.Tabulator(sel_df, selection=[0, 2], selectable='checkbox')







            
            
Additionally we can also disable selection for specific rows by providing a selectable_rows function. The function must accept a DataFrame and return a list of integer indexes indicating which rows are selectable, e.g. here we disable selection for every second row:


select_table = pn.widgets.Tabulator(sel_df, selectable_rows=lambda df: list(range(0, len(df), 2)))
select_table







            
            
To trigger events based on an exact cell that was clicked you may also register an on_click callback which is called whenever a cell is clicked.


def click(event):
    print(f'Clicked cell in {event.column!r} column, row {event.row!r} with value {event.value!r}')

select_table.on_click(click) 
# Optionally we can also limit the callback to a specific column
# select_table.on_click(click, column='A') 







Freezing rows and columns#


Sometimes your table will be larger than can be displayed in a single viewport, in which case scroll bars will be enabled. In such cases, you might want to make sure that certain information is always visible. This is where the frozen_columns and frozen_rows options come in.




Frozen columns#


When you have a large number of columns and can’t fit them all on the screen you might still want to make sure that certain columns do not scroll out of view. The frozen_columns option makes this possible by specifying a list of columns that should be frozen, e.g. frozen_columns=['index'] will freeze the index column:


pn.widgets.Tabulator(df, frozen_columns=['index'], width=400)







            
            
By default, columns given in the list format are frozen to the left hand side of the table. If you want to customize where columns are frozen to on the table, you can specify this with a dictionary:


pn.widgets.Tabulator(df, frozen_columns={'index': 'left', 'float': 'right'}, width=400)







            
            
The ‘index’ column will be frozen on the left side of the table, and the ‘float’ on the right. Non-frozen columns will scroll between these two.






Frozen rows#


Another common scenario is when you have certain rows with special meaning, e.g. aggregates that summarize the information in the rest of the table. In this case you may want to freeze those rows so they do not scroll out of view. You can achieve this by setting a list of frozen_rows by integer index (which can be positive or negative, where negative values are relative to the end of the table):


date_df = df.set_index('date').iloc[:5, :2]
agg_df = pd.concat([date_df, date_df.median().to_frame('Median').T, date_df.mean().to_frame('Mean').T])
agg_df.index= agg_df.index.map(str)

pn.widgets.Tabulator(agg_df, frozen_rows=[-2, -1], height=200)







            
            








Row contents#


A table can only display so much information without becoming difficult to scan. We may want to render additional information to a table row to provide additional context. To make this possible you can provide a row_content function which is given the table row as an argument (a pandas.Series object) and should return a panel object that will be rendered into an expanding region below the row. By default the contents are fetched dynamically whenever a row is expanded, however using the embed_content parameter we can embed all the content.


Below we create a periodic table of elements where the Wikipedia page for each element will be rendered into the expanded region:


from bokeh.sampledata.periodic_table import elements

periodic_df = elements[['atomic number', 'name', 'atomic mass', 'metal', 'year discovered']].set_index('atomic number')

content_fn = lambda row: pn.pane.HTML(
    f'<iframe src="https://en.wikipedia.org/wiki/{row["name"]}?printable=yes" width="100%" height="200px"></iframe>',
    sizing_mode='stretch_width'
)

periodic_table = pn.widgets.Tabulator(
    periodic_df, height=350, layout='fit_columns', sizing_mode='stretch_width',
    row_content=content_fn, embed_content=True
)

periodic_table







            
            
The currently expanded rows can be accessed and set on the expanded parameter:


periodic_table.expanded





[]






Grouping#


Another useful option is the ability to group specific rows together, which can be achieved using groups parameter. The groups parameter should be composed of a dictionary mapping from the group titles to the column names:


pn.widgets.Tabulator(date_df.iloc[:3], groups={'Group 1': ['A', 'B'], 'Group 2': ['C', 'D']})







            
            




Groupby#


In addition to grouping columns we can also group rows by the values along one or more columns:


from bokeh.sampledata.autompg import autompg

pn.widgets.Tabulator(autompg, groupby=['yr', 'origin'], height=240)







            
            


Hierarchical Multi-index#


The Tabulator widget can also render a hierarchical multi-index and aggregate over specific categories. If a DataFrame with a hierarchical multi-index is supplied and the hierarchical is enabled the widget will group data by the categories in the order they are defined in. Additionally for each group in the multi-index an aggregator may be provided which will aggregate over the values in that category.


We will use the Automobile Mileage dataset for various car models from the 1970s and 1980s around the world, broken down by regions, model years and manufacturers. The dataset includes details on car characteristics and performance metrics.


from bokeh.sampledata.autompg import autompg_clean as autompg_df

autompg_df = autompg_df.set_index(["origin", "yr", "mfr"])
autompg_df.head(3)






  
    

      
      
      
      mpg
      cyl
      displ
      hp
      weight
      accel
      name
    

    

      origin
      yr
      mfr
      
      
      
      
      
      
      
    

  
  
    

      North America
      70
      chevrolet
      18.0
      8
      307.0
      130
      3504
      12.0
      chevrolet chevelle malibu
    

    

      buick
      15.0
      8
      350.0
      165
      3693
      11.5
      buick skylark 320
    

    

      plymouth
      18.0
      8
      318.0
      150
      3436
      11.0
      plymouth satellite
    

  



If we specify aggregators over the ‘origin’ (region) and ‘yr’ (model year) indexes, we can see the aggregated values for each of those groups. Note that if no aggregators are specified to an outer index level, it will be aggregated with the default method of sum.


pn.widgets.Tabulator(value=autompg_df, hierarchical=True, aggregators={"origin": "mean", "yr": "mean"}, height=200)







            
            
Separate aggregators for different columns are also supported. You can specify the aggregators as a nested dictionary as {index_name: {column_name: aggregator}}


Applied to the same dataset, we can aggregate the data in the mpg (miles per galon) and hp columns differently, with mean and max, respectively.


nested_aggs = {"origin": {"mpg": "mean", "hp": "max"}, "yr": {"mpg": "mean", "hp": "max"}}
pn.widgets.Tabulator(value=autompg_df[["mpg", "hp"]], hierarchical=True, aggregators=nested_aggs, height=200)







            
            






Pagination#


When working with large tables it is generally not advisible to display the whole table at once. In these scenarios we can enable either 'local' or 'remote' pagination, which will render only a single page of data at the same time. In the case of 'remote' pagination only the currently viewed data is actually transferred from the backend server to the frontend and new data is fetched dynamically when we switch the page or filter and sort the data. Note that Panel will automatically enable 'local' pagination for tables larger than 200 rows and enable 'remote' pagination for tables larger than 10 000 rows. This protection may be overridden by explicitly setting the pagination parameter.


The pagination setting may be enabled by setting pagination='remote' or pagination='local' and the size of each page can be set using the page_size option:


large_df = pd.DataFrame({'A': np.random.rand(10000)})
pn.widgets.Tabulator(large_df, pagination='remote', page_size=3)







            
            
Note that the default page_size is None, which means it will measure the height of the rows and try to fit the appropriate number of rows into the available space. To override the number of rows sent to the frontend before the measurement has taken place set the initial_page_size.


Contrary to the 'remote' option, 'local' pagination transfers all of the data but still allows to display it on multiple pages:


medium_df = pd.DataFrame({'A': np.random.rand(1000)})
pn.widgets.Tabulator(medium_df, pagination='local', page_size=3)







            
            




Filtering#


A very common scenario is that you want to attach a number of filters to a table in order to view just a subset of the data. You can achieve this through callbacks or other reactive approaches but the  .add_filter method makes it much easier.




Constant and Widget filters#


The simplest approach to filtering is to select along a column with a constant or dynamic value. The .add_filter method allows passing in constant values, widgets and Param Parameters. If a widget or Parameter is provided the table will watch the object for changes in the value and update the data in response. The filtering will depend on the type of the constant or dynamic value:


    

  • scalar: Filters by checking for equality
    • 

  • tuple: A tuple will be interpreted as range, the start and end bounds being both included in the range. Setting one of the bounds to None create an open-ended bound.
    • 

  • list/set: A list or set will be interpreted as a set of discrete scalars and the filter will check if the values in the column match any of the items in the list.
    • 



As an example we will create a DataFrame with some data of mixed types:


filter_table = pn.widgets.Tabulator(df)
filter_table







            
            
Now we will start adding filters one-by-one, e.g. to start with we add a filter for the 'A' column, selecting a range from 0 to 3:


filter_table.add_filter((1, 2), 'int')





Next we add dynamic widget based filter, a RangeSlider which allows us to further narrow down the data along the 'A' column:


slider = pn.widgets.RangeSlider(start=0, end=3, name='A Filter')
filter_table.add_filter(slider, 'int')





Lastly we will add a MultiSelect filter along the 'C' column:


select = pn.widgets.MultiSelect(options=list('ABC'), name='str Filter')
filter_table.add_filter(select, 'str')





Now let’s display the table alongside the widget based filters:


pn.Row(
    pn.Column(slider, select),
    filter_table
)







            
            
After filtering (and sorting) you can inspect the current view with the current_view property:


select.value = ['A', 'B']
filter_table.current_view






  
    

      
      int
      float
      str
      bool
      date
      datetime
    

  
  
    

      1
      1
      3.14
      A
      True
      2019-01-01
      2019-01-01 10:00:00
    

    

      2
      2
      6.28
      B
      False
      2020-01-01
      2020-01-01 12:00:00
    

  







Function based filtering#


For more complex filtering tasks you can supply a function that should accept the DataFrame to be filtered as the first argument and must return a filtered copy of the data. Let’s start by loading some data.


import sqlite3

from bokeh.sampledata.movies_data import movie_path

con = sqlite3.Connection(movie_path)
movies_df = pd.read_sql('SELECT Title, Year, Genre, Director, Writer, Rating, imdbRating from omdb', con)
movies_df = movies_df[~movies_df.Director.isna()]

movies_table = pn.widgets.Tabulator(movies_df, pagination='remote', page_size=4)





By using the pn.bind function, which binds widget and Parameter values to a function, complex filtering can be achieved. E.g. here we will add a filter function that tests whether the string or regex is contained in the ‘Director’ column of a listing of thousands of movies:


director_filter = pn.widgets.TextInput(name='Director filter', value='Chaplin')

def contains_filter(df, pattern, column):
    if not pattern:
        return df
    return df[df[column].str.contains(pattern)]
    
movies_table.add_filter(pn.bind(contains_filter, pattern=director_filter, column='Director'))    

pn.Row(director_filter, movies_table)







            
            




Client-side filtering#


In addition to the Python API the Tabulator widget also offers a client-side filtering API, which can be exposed through header_filters or by manually setting filters in the rendered table. The API for declaring header filters is almost identical to the API for defining Editors. The header_filters can either be enabled by setting it to True or by manually supplying filter types for each column. The types of filters supports all the same options as the editors, in fact if you do not declare explicit header_filters the Tabulator widget will simply use the defined editors to determine the correct filter type:


tabulator_editors = {
    'float': {'type': 'number', 'max': 10, 'step': 0.1},
    'bool': {'type': 'tickCross', 'tristate': True, 'indeterminateValue': None},
    'str': {'type': 'list', 'valuesLookup': True},
}

header_filter_table = pn.widgets.Tabulator(
    df[['float', 'bool', 'str']], height=140, width=400, layout='fit_columns',
    editors=tabulator_editors, header_filters=True
)
header_filter_table







            
            
When a filter is applied client-side the filters parameter is synced with Python. The definition of filters looks something like this:


[{'field': 'Director', 'type': '=', 'value': 'Steven Spielberg'}]





Try applying a filter and then inspect the filters parameter:


For all supported filtering types see the Tabulator Filtering documentation.


If we want to change the filter type for the header_filters we can do so in the definition by supplying a dictionary indexed by the column names and then either providing a dictionary which may define the 'type', a comparison 'func', a 'placeholder' and any additional keywords supported by the particular filter type.


movie_filters = {
    'Title': {'type': 'input', 'func': 'like', 'placeholder': 'Enter title'},
    'Year': {'placeholder': 'Enter year'},
    'Genre': {'type': 'input', 'func': 'like', 'placeholder': 'Enter genre'},
    'Director': {'type': 'input', 'func': 'like', 'placeholder': 'Enter director'},
    'Writer': {'type': 'input', 'func': 'like', 'placeholder': 'Enter writer'},
    'Rating': {'type': 'list', 'func': 'in', 'valuesLookup': True, 'sort': 'asc', 'multiselect': True},
    'imdbRating': {'type': 'number', 'func': '>=', 'placeholder': 'Enter minimum rating'},
}

filter_table = pn.widgets.Tabulator(
    movies_df.iloc[:200], pagination='local', layout='fit_columns', page_size=4, sizing_mode='stretch_width',
    header_filters=movie_filters
)
filter_table







            
            






Downloading#


The Tabulator widget also supports triggering a download of the data as a CSV or JSON file depending on the filename. The download can be triggered with the .download() method, which optionally accepts the filename as the first argument.


To trigger the download client-side (i.e. without involving the server) you can use the .download_menu method which creates a TextInput and Button widget, which allow setting the filename and triggering the download respectively:


download_df = pd.DataFrame(np.random.randn(4, 5), columns=list('ABCDE'))

download_table = pn.widgets.Tabulator(download_df)

filename, button = download_table.download_menu(
    text_kwargs={'name': 'Enter filename', 'value': 'default.csv'},
    button_kwargs={'name': 'Download table'}
)

pn.Row(
    pn.Column(filename, button),
    download_table
)







            
            
Note that when pagination='remote' is enabled the download feature will only include the current page for technical reasons. If you want to support downloading all the data use the FileDownload widget.






Buttons#


If you want to trigger custom actions by clicking on a table cell you may declare a set of buttons that are rendered in columns after all the data columns. To respond to button clicks you can register a callback using the general on_click method:


button_table = pn.widgets.Tabulator(df, buttons={
    'print': '<i class="fa fa-print"></i>',
    'check': '<i class="fa fa-check"></i>'
})

string = pn.widgets.StaticText()

button_table.on_click(
    lambda e: string.param.update(value=f'Clicked {e.column!r} on row {e.row}')
)

pn.Row(button_table, string)







            
            
Please note, that in a server context you will have to include the font awesome css file to get the button icons rendered, i.e. use


pn.extension("tabulator", ..., css_files=["https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/all.min.css"])









Streaming#


When we are monitoring some source of data that updates over time, we may want to update the table with the newly arriving data. However, we do not want to transmit the entire dataset each time. To handle efficient transfer of just the latest data, we can use the .stream method on the Tabulator object:


stream_df = pd.DataFrame(np.random.randn(5, 5), columns=list('ABCDE'))

stream_table = pn.widgets.Tabulator(stream_df, layout='fit_columns', width=450, height=400)
stream_table







            
            
As example, we will schedule a periodic callback that streams new data every 1000ms (i.e. 1s) five times in a row:


def stream_data(follow=True):
    stream_df = pd.DataFrame(np.random.randn(5, 5), columns=list('ABCDE'))
    stream_table.stream(stream_df, follow=follow)

pn.state.add_periodic_callback(stream_data, period=1000, count=5);





If you are viewing this example with a live Python kernel you will be able to watch the table update and scroll along. If we want to disable the scrolling behavior, we can set follow=False:


stream_data(follow=False)









Patching#


In certain cases we don’t want to update the table with new data but just patch existing data.


patch_table = pn.widgets.Tabulator(df[['int', 'float', 'str', 'bool']].copy())
patch_table







            
            
The easiest way to patch the data is by supplying a dictionary as the patch value. The dictionary should have the following structure:


{
    column: [
        (index: int or slice, value),
        ...
    ],
    ...
}





As an example, below we will patch the 'bool' and 'int' columns. On the 'bool' column we will replace the 0th and 2nd row and on the 'int' column we replace the first two rows:


patch_table.patch({
    'bool': [
        (0, False),
        (2, False)
    ],
    'int': [
        (slice(0, 2), [3, 2])
    ]
}, as_index=False)









Static Configuration#


Panel does not expose all options available from Tabulator, if a desired option is not natively supported, it can be set via the configuration argument.

This dictionary can be seen as a base dictionary which the tabulator object fills and passes to the Tabulator javascript-library.


As an example, we can enable clipboard functionality and set the rowHeight options. columnDefaults takes a dictionary used to configure the columns specifically, in this example we disable header sorting with headerSort.


df = pd.DataFrame({
    'int': [1, 2, 3],
    'float': [3.14, 6.28, 9.42],
    'str': ['A', 'B', 'C'],
    'bool': [True, False, True],
    'date': [dt.date(2019, 1, 1), dt.date(2020, 1, 1), dt.date(2020, 1, 10)]
}, index=[1, 2, 3])

pn.widgets.Tabulator(df, configuration={
    'clipboard': True,
    'rowHeight': 50,
    'columnDefaults': {
        'headerSort': False,
    },
})







            
            
These and other available Tabulator options are listed at http://tabulator.info/docs/6.3.0/options.


Obviously not all options will work though, especially any settable callbacks and options which are set by the internal Panel tabulator module.
Additionally it should be noted that the configuration parameter is not responsive so it can only be set at instantiation time.




Open this notebook in Jupyterlite | Download this notebook from GitHub (right-click to download).







                

              
              
              
              
              
                
              
            

            
            
              
                



  

    
        Support us with a star on GitHub
    



  




  
    launch jupyterlite
  






  



     On this page