ClassicTable

Introduction

ClassicTable is a widget in Supervisely that is used for displaying and manipulating data in a table format. It is similar to the Table widget but with fewer customization options and functionalities.

Note, that this is a legacy version. It is recommended to use the newer Table widget instead.

Function signature

classic_table = ClassicTable()
classic_table.read_pandas(pd.DataFrame(data=data, index=b, columns=a))

or

classic_table = ClassicTable(
    data=pd.DataFrame(data=data, index=b, columns=a),
    fixed_columns_num=1,
    widget_id=None
)

Parameters

Parameters	Type	Description
data	pd.DataFrame() or dict	Data of table
columns	list	List of columns names
fixed_columns_num	int	Number of fixed columns (left to right)
widget_id	str	ID of the widget

data

Data of table in different formats:

1. Pandas Dataframe:

pd.DataFrame(data=data, columns=columns)

1. Python dict with structure:

 {
    "columns_names": ["col_name_1", "col_name_2", ...],
    "values_by_rows": [
        ["row_1_column_1", "row_1_column_2", ...],
        ["row_2_column_1", "row_2_column_2", ...],
        ...
    ]
}

# prepare data for table
a = list(range(1, 11))
b = list(range(1, 5))

data = []
for row in b:
    temp = [round(row * number, 1) for number in a]
    data.append(temp)

a = [str(i) for i in a]
b = [str(i) for i in b]

data = pd.DataFrame(data=data, index=b, columns=a)

classic_table = ClassicTable(data=df)py

columns

List of columns names.

type: list

default value: None

columns = [f"Col#{i}" for i in range(1, 11)]
data = pd.DataFrame(data=data, index=b, columns=columns)

classic_table = ClassicTable(data=df)

fixed_columns_num

Number of fixed colums (left to right).

type: int

default value: None

classic_table = ClassicTable(data=df, fixed_columns_num=2)

widget_id

ID of the widget.

type: str

default value: None

Methods and attributes

Attributes and Methods	Description
fixed_columns_num	Get or set number of fixed columns (left to right) property.
to_json()	Convert table data to json.
to_pandas()	Convert table data to pandas dataframe.
read_json(value: dict)	Read and set table data from json.
read_pandas(value: pd.DataFrame)	Read and set table data from pandas dataframe.
insert_row(index=-1)	Insert new row in table.
pop_row(value: pd.DataFrame)	Remove row from table by index.
get_selected_cell(state)	Get selected table cell info.

Mini App Example

You can find this example in our Github repository:

ui-widgets-demos/tables/002_classic_table/src/main.py

Import libraries

import os

import pandas as pd
import supervisely as sly
from dotenv import load_dotenv
from supervisely.app.widgets import Card, ClassicTable, Container

Init API client

First, we load environment variables with credentials and init API for communicating with Supervisely Instance:

load_dotenv("local.env")
load_dotenv(os.path.expanduser("~/supervisely.env"))

api = sly.Api()

Prepare function that creates example pandas table

def multiplication_table():
    a = list(range(1, 11))
    b = list(range(1, 6))

    data = []
    for row in b:
        temp = [round(row * number, 1) for number in a]
        temp[-1] = sly.app.widgets.Table.create_button("Delete row")
        data.append(temp)

    a = [f"Col#{str(i)}" for i in a]
    b = [str(i) for i in b]
    return pd.DataFrame(data=data, index=b, columns=a)

Create data for table.

df = multiplication_table()

Initialize ClassicTable widget

classic_table = ClassicTable(data=df)

or you can initialize empty table and set data later.

classic_table = ClassicTable()
classic_table.read_pandas(df)

Create app layout

Prepare a layout for app using Card widget with the content parameter and place widget that we've just created in the Container widget.

card = Card(
    title="Classic Table",
    content=classic_table,
)
layout = Container(widgets=[card])

Create app using layout

Create an app object with layout parameter.

app = sly.Application(layout=layout)