# ipyvuetable
Fast and customizable table widget for the Jupyter ecosystem build on [ipyvuetify](https://github.com/widgetti/ipyvuetify) and [Polars](https://github.com/pola-rs/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
```python
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_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:
```sh
pip install ipyvuetable
```
Benefit from keyboard events with:
```sh
pip install ipyvuetable[ipyevents]
```
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