Table of Contents

This script processes time records and generates daily attendance statistics. For each employee and day, it determines the earliest entry (clock-in) and the latest entry (clock-out) and writes the results to a separate statistics table.

You need two tables:

  • Time records: Contains columns Name, Date, and Time with the raw clocking data.
  • Statistics: Contains columns Name, Date, Clock in, and Clock out for the results.
from seatable_api import Base, context

base = Base(context.api_token, context.server_url)
base.auth()

TIME_RECORDS_TABLE = "Time records"
STATISTICS_TABLE = "Statistics"

# Clear statistics table to avoid duplicates on re-run
for row in base.list_rows(STATISTICS_TABLE):
    base.delete_row(STATISTICS_TABLE, row['_id'])

# Group time records by date and name
rows = base.list_rows(TIME_RECORDS_TABLE)
groups = {}
for row in rows:
    name = row.get('Name', '')
    date = row.get('Date', '')
    time = row.get('Time', '')
    if not name or not date or not time:
        continue
    key = f"{date}_{name}"
    if key not in groups:
        groups[key] = {'name': name, 'date': date, 'times': []}
    groups[key]['times'].append(time)

for key, group in groups.items():
    times = sorted(group['times'])
    clock_in = times[0]
    clock_out = times[-1]
    base.append_row(STATISTICS_TABLE, {
        'Name': group['name'],
        'Date': group['date'],
        'Clock in': clock_in,
        'Clock out': clock_out
    })
    print(f"{group['name']} ({group['date']}): {clock_in} - {clock_out}")

print(f"---\n{len(groups)} entries created.")

Adjust the table and column names to match your setup. The script clears the statistics table before each run, so it can be executed repeatedly without creating duplicates.

For the complete function reference, visit the SeaTable Developer Manual .