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Table / Pagination / Pandas

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

import os
from typing import Dict, List
from h2o_wave import main, app, Q, ui
import 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 = 10
total_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:
q.page['meta'] = ui.meta_card(box='')
q.page['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 q.events.table:
table = q.page['form'].items[0].table
if q.events.table.sort:
q.client.sort = q.events.table.sort
q.client.page_offset = 0
if q.events.table.filter:
q.client.filters = q.events.table.filter
q.client.page_offset = 0
if q.events.table.search is not None:
q.client.search = q.events.table.search
q.client.page_offset = 0
if q.events.table.page_change:
q.client.page_offset = q.events.table.page_change.get('offset', 0)
if q.events.table.reset:
q.client.search = 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.search, q.client.filters)

if q.events.table.download:
# 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 q.site.upload(['data_download.csv'])
# Clean up.
os.remove('data_download.csv')
q.page['meta'].script = ui.inline_script(f'window.open("{download_url}")')

# Update table pagination according to the new row count.
if q.client.search 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])

await q.page.save()

Tags:  formpaginationpandastable