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 - ๐ฌ', 'Joseph - ๐', 'Krish - ๐ ', 'John - ๐ฆ', 'Alice - ๐ฆ', 'Bob - ๐']
})
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}
)
Install the latest ipyvuetable version with:
pip install ipyvuetable
Benefit from keyboard events with:
pip install ipyvuetable[ipyevents]