Skip to main content

Table / Pagination / Pandas

Use a paginated table to display large (100k+ rows) tabular data using pandas dataframe.

import osfrom typing import Dict, Listfrom h2o_wave import main, app, Q, uiimport pandas as pd

all_issues_df = pd.DataFrame(    [[i + 1, 'Closed' if i % 2 == 0 else 'Open'] for i in range(100)],    columns=['text', 'status'])rows_per_page = 10total_rows = len(all_issues_df)

def df_to_table_rows(df: pd.DataFrame) -> List[ui.TableRow]:    return [ui.table_row(name=str(r[0]), cells=[str(r[0]), r[1]]) for r in df.itertuples(index=False)]

def get_df(base: pd.DataFrame, sort: Dict[str, bool] = None, search: Dict = None, filters: Dict[str, List[str]] = None) -> pd.DataFrame:    # Make a deep copy in order to not mutate the original df which serves as our baseline.    df = base.copy()
    if sort:        # Reverse values since default sort of Wave table is different from Pandas.        ascending = [not v for v in list(sort.values())]        df = df.sort_values(by=list(sort.keys()), ascending=ascending)    # Filter out all rows that do not contain searched string in `text` cell.    if search:        search_val = search['value'].lower()        # Filter dataframe by search value case insensitive.        df = df[df.apply(lambda r: any(search_val in str(r[col]).lower() for col in search['cols']), axis=1)]    # Filter out rows that do not contain filtered column value.    if filters:        # We want only rows that have no filters applied or their col value matches active filters.        query = ' & '.join([f'({not bool(filters)} | {col} in {filters})' for col, filters in filters.items()])        df = df.query(query)
    return df

@app('/demo')async def serve(q: Q):    if not q.client.initialized:['meta'] = ui.meta_card(box='')['form'] = ui.form_card(box='1 1 -1 -1', items=[            ui.table(                name='table',                columns=[                    ui.table_column(name='text', label='Text', sortable=True, searchable=True, link=False),                    ui.table_column(name='status', label='Status', filterable=True, filters=['Open', 'Closed']),                ],                rows=df_to_table_rows(get_df(all_issues_df)[0:rows_per_page]),                resettable=True,                downloadable=True,                pagination=ui.table_pagination(total_rows, rows_per_page),                # Make sure to register the necessary events for the feature you want to support, e.g. sorting.                # All the registered events have to be handled by the developer.                # `page_change` event is required to be handled for pagination to work.                events=['sort', 'filter', 'search', 'page_change', 'download', 'reset']            )        ])        q.client.initialized = True
    # Check if user triggered any table action and save it to local state for allowing multiple    # actions to be performed on the data at the same time, e.g. sort the filtered data etc.    if        table =['form'].items[0].table        if            q.client.sort =            q.client.page_offset = 0        if            q.client.filters =            q.client.page_offset = 0        if is not None:   =            q.client.page_offset = 0        if            q.client.page_offset ='offset', 0)        if   = None            q.client.sort = None            q.client.filters = None            q.client.page_offset = 0            table.pagination = ui.table_pagination(total_rows, rows_per_page)
        offset = q.client.page_offset or 0        df = get_df(all_issues_df, q.client.sort,, q.client.filters)
        if            # Create and upload a CSV file for downloads.            # For multi-user apps, the tmp file name should be unique for each user, not hardcoded.            df.to_csv('data_download.csv')            download_url, = await['data_download.csv'])            # Clean up.            os.remove('data_download.csv')  ['meta'].script = ui.inline_script(f'"{download_url}")')
        # Update table pagination according to the new row count.        if is not None or q.client.filters:            table.pagination = ui.table_pagination(len(df), rows_per_page)
        table.rows = df_to_table_rows(df[offset:offset + rows_per_page])

Tags: โ€‚form โ€‚pagination โ€‚pandas โ€‚table