ipyvuetable

Fast and customizable table widget for the Jupyter ecosystem build on ipyvuetify and Polars.

ipyvuetable can sort, filter, edit large polars.LazyFrame in a paginated way. You can easily customize you table widget, add actions, hide columns, add special visualisation for some columns and benefit from all the ipyvuetify customization

from ipyvuetable import EditingTable, Table
import polars as pl
df = (
    pl.LazyFrame({
        'id': range(6), 
        'name': ['Tom', 'Joseph', 'Krish', 'John', 'Alice', 'Bod'],
        'birthday': ['01-03-1995', '27-01-1999', '24-07-1977', '27-12-1970', '17-07-2005', '19-09-2001'],
        'score': [3.5, 4.0, 7.5, 1.0, 6.5, 8.2],
        'bool': [True, True, False, True, False, True]
    })
    .with_columns(pl.col('birthday').str.strptime(pl.Datetime, "%d-%m-%Y"))
)

name_custom_repr = pl.LazyFrame({
    'name' : ['Tom', 'Joseph', 'Krish', 'John', 'Alice', 'Bod'],
    'name__repr' : ['Tom - 🐬', 'Joseph - 🐟', 'Krish - 🐠 ', 'John - 🦐', 'Alice - 🦞', 'Bob - 🐌']
})

EditingTable(
    df = df, 
    title = 'My table', 
    
    show_actions= True,
    show_filters=True,
    columns_to_hide = ['id'],
    
    # all ipyvuetify options
    show_select = True,
    
    columns_repr = {'name' : name_custom_repr}
)

Installation

Install the latest ipyvuetable version with:

pip install ipyvuetable

Benefit from keyboard events with:

pip install ipyvuetable[ipyevents]