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Table Management

This guide covers how to manage tables in Teable.

Basic Table Management

Getting Tables

from teable import TeableClient, TeableConfig

# Initialize the client
client = TeableClient(TeableConfig(
    api_url="https://your-teable-instance.com/api",
    api_key="your-api-key"
))

# Get a specific table
table = client.tables.get("table_id")

# Get all tables in a space
tables = client.tables.get_all(space_id="space123")

# Process tables
for table in tables:
    print(f"Table ID: {table.table_id}")

Updating Tables

# Update table properties
table = client.tables.update(
    table_id="table_id",
    name="New Table Name",
    description="Updated description"
)

Field Management

Managing Fields

from teable import FieldType

# Get all fields
fields = table.fields

# Create new field
new_field = client.fields.create(
    table_id=table.table_id,
    name="Status",
    field_type=FieldType.SINGLE_SELECT,
    options={
        "choices": ["Active", "Inactive"]
    }
)

# Update field
field = client.fields.update(
    field_id="field_id",
    name="New Name"
)

# Delete field
client.fields.delete(field_id="field_id")

Managing Views

from teable import FilterOperator, SortDirection

# Get all views
views = table.views

# Create new view
new_view = client.views.create(
    table_id=table.table_id,
    name="Custom View",
    type="grid",
    filter={
        "operator": "and",
        "conditions": [
            {
                "field": "Status",
                "operator": FilterOperator.EQUALS,
                "value": "Active"
            }
        ]
    }
)

# Update view
view = client.views.update(
    view_id="view_id",
    name="Updated View",
    sort=[
        {
            "field": "CreatedTime",
            "direction": SortDirection.DESCENDING
        }
    ]
)

# Delete view
client.views.delete(view_id="view_id")

Data Management

Record Operations

from teable import FilterOperator

# Get records with filtering
records = client.records.get_records(
    table_id=table.table_id,
    query={
        "filter": {
            "operator": "and",
            "conditions": [
                {
                    "field": "Status",
                    "operator": FilterOperator.EQUALS,
                    "value": "Active"
                }
            ]
        }
    }
)

Batch Operations

# Batch create records
records = [
    {"Name": f"Record {i}"} for i in range(100)
]
result = client.records.batch_create_records(
    table_id=table.table_id,
    records=records
)

# Batch update records
updates = [
    {
        "record_id": f"rec{i}",
        "fields": {"Status": "Updated"}
    }
    for i in range(100)
]
result = client.records.batch_update_records(
    table_id=table.table_id,
    records=updates
)

# Batch delete records
record_ids = [f"rec{i}" for i in range(100)]
result = client.records.batch_delete_records(
    table_id=table.table_id,
    record_ids=record_ids
)

Best Practices

  1. Table Design
  2. Plan table structure carefully
  3. Use appropriate field types
  4. Consider relationships

  5. Document table purpose

  6. Performance

  7. Use batch operations
  8. Implement pagination
  9. Optimize queries

  10. Monitor table size

  11. Data Integrity

  12. Validate data before operations
  13. Regular data cleanup
  14. Backup important data

  15. Handle errors appropriately

  16. Organization

  17. Use clear naming conventions
  18. Group related tables
  19. Maintain documentation
  20. Regular maintenance

Next Steps