https://shiny.posit.co/py/api/core/render.table.html#shiny.render.table

UX : ui.output_data_frame
SRVR: @ render.data_frame
– render.DataGrid
– render.DataTable

UX : ui.output_data_frame
SRVR : render.table

날짜가 숫자로 나오는 문제:

df[‘Date’] = df[‘Date’].dt.strftime(‘%Y-%m-%d’)

from shiny import (App, render, ui, Session, Inputs, Outputs, reactive, module)
import pandas as pd

df = pd.read_csv('./zzz/shiny/mtcars.csv')

app_ui = ui.page_fillable(
    ui.layout_columns(
        ui.output_data_frame("sixx_data_frame"),
        ui.output_data_frame("sixx_DataGrid"),
        ui.output_data_frame("sixx_DataTable"),
        ui.output_table("sixx_table"),
        col_widths= [6, 6, 6, 6]
    )
)

def server(input: Inputs, output: Outputs, session: Session):
    @render.data_frame
    def sixx_data_frame():
        return df
    
    @render.data_frame
    def sixx_DataGrid():
        return render.DataGrid(df, row_selection_mode='single')
       
    @render.data_frame
    def sixx_DataTable():
        return render.DataTable(df, 
            width='fit-content',height='200px',
            filters=False, row_selection_mode='multiple',
            #editable=True,
            summary=False,)    

    @render.table
    def sixx_table():
        return df   

app = App(app_ui, server)

Function	Description		특징
render.data_frame	DataFrame 형태로 출력
==> render.DataGrid	그리드 형태로 출력	데이터탐색을 위한 중간크기의 datasets	Offers sorting and filtering capabilities for interactive data exploration.
==>render.DataTable	HTML 테이블로 변환하여 출력.	interactive기능이 있는 대형 datasets 적합.	advanced features like pagination, searching, sorting, and filtering.

Function	Description		특징
render.table	HTML 테이블로 변환하여 출력	interactive기능이 없는 small datasets	No additional features like sorting, filtering, or pagination. 가장 기본적인 테이블 출력 기능을 제공. dataframe을 Simple and straightforward 형태로 렌더링할 때 사용. 고급 기능 없음 (정렬, 페이징, 검색)

    @output
    @render.table
    def ehist_df_excel_1():
        area = input.ehist_select_area()
        print("--------------------------------------->", area)
        
        #df = pd.read_excel(f"{DATA_DIR}/process/{area}_daily.csv")
        df = pd.DataFrame()
        return df
        if area is not None:
            
            #ddf = ddf.fillna('') 
            ddf['분류'] = ddf['분류'].fillna('')
            ddf['반송건수'] = ddf['반송건수'].fillna(0).astype(int)
            # return dd류
            return (
                ddf.style.set_table_attributes(
                    'class="dataframe shiny-table table w-auto"'
                )
                .hide(axis="index")
                .format(
                    {
                        '반송건수': "{:.0f}",#"{:,.0f}", 
                        # "Total": "{0:0.1f}",
                        # "Tip": "{0:0.2f}",
                        # "Total_bill": "{0:0.3f}",
                        # "Tip_rate": "{0:0.2%}",
                    }
                )
                .set_table_styles(
                    [dict(selector="th", props=[("text-align", "right")])]
                )
            )
        else:
            return None

itable

https://mwouts.github.io/itables/quick_start.html

ipydatagrid

https://github.com/bloomberg/ipydatagrid