# Structured Sheets

## List structured sheets

`structured_sheets.list(StructuredSheetListParams**kwargs)  -> SyncCursorIDPage[StructuredSheetResponse]`

**get** `/v1/structured-sheets`

List all structured sheets conversions for the authenticated user. Results are paginated using cursor-based pagination.

### Parameters

- `after: Optional[str]`

  A cursor for pagination. Use the `last_id` from a previous response to fetch the next page of results.

- `limit: Optional[int]`

  Maximum number of results to return per page.

### Returns

- `class StructuredSheetResponse: …`

  Response representing a structured sheet conversion job.

  This is returned from POST (create), GET (retrieve), and list endpoints.

  - `id: str`

    The unique identifier for this structured sheet conversion.

  - `created_at: datetime`

    The timestamp when the conversion was started.

  - `file_id: str`

    The unique identifier for the source file.

  - `object: Literal["structured_sheet"]`

    The object type, which is always 'structured_sheet'.

    - `"structured_sheet"`

  - `status: Literal["queued", "in_progress", "completed", 2 more]`

    The current processing status.

    - `"queued"`

    - `"in_progress"`

    - `"completed"`

    - `"failed"`

    - `"cancelled"`

  - `updated_at: datetime`

    The timestamp when the conversion was last updated.

  - `last_error: Optional[LastError]`

    Error information when processing fails.

    - `code: str`

      A machine-readable error code.

    - `message: str`

      A human-readable description of the error.

  - `sheet_names: Optional[List[str]]`

    List of sheet names included in this conversion.

  - `table_count: Optional[int]`

    Number of tables extracted from the workbook. Only present when status is 'completed'.

### Example

```python
import os
from deeptable import DeepTable

client = DeepTable(
    api_key=os.environ.get("DEEPTABLE_API_KEY"),  # This is the default and can be omitted
)
page = client.structured_sheets.list()
page = page.data[0]
print(page.id)
```

#### Response

```json
{
  "data": [
    {
      "id": "ss_01kfxgjd94fn9stqm42nejb627",
      "created_at": "2024-01-15T10:30:00Z",
      "file_id": "file_01kfxgjd94fn9stqm414vjb0s8",
      "object": "structured_sheet",
      "status": "completed",
      "updated_at": "2024-01-15T10:35:00Z",
      "last_error": {
        "code": "invalid_file_format",
        "message": "The uploaded file is not a valid Excel spreadsheet."
      },
      "sheet_names": [
        "Sheet1",
        "Financials"
      ],
      "table_count": 6
    }
  ],
  "has_more": false,
  "object": "list",
  "first_id": "ss_01kfxgjd94fn9stqm42nejb627",
  "last_id": "ss_01kfxgjd94fn9stqm42nejb627"
}
```

## Create structured sheet

`structured_sheets.create(StructuredSheetCreateParams**kwargs)  -> StructuredSheetResponse`

**post** `/v1/structured-sheets`

Start converting a spreadsheet workbook into structured data. This initiates an asynchronous conversion process. Poll the returned resource using the `id` to check completion status.

### Parameters

- `file_id: str`

  The unique identifier of the file to convert.

- `sheet_names: Optional[SequenceNotStr[str]]`

  List of sheet names to convert. If None, all sheets will be converted.

### Returns

- `class StructuredSheetResponse: …`

  Response representing a structured sheet conversion job.

  This is returned from POST (create), GET (retrieve), and list endpoints.

  - `id: str`

    The unique identifier for this structured sheet conversion.

  - `created_at: datetime`

    The timestamp when the conversion was started.

  - `file_id: str`

    The unique identifier for the source file.

  - `object: Literal["structured_sheet"]`

    The object type, which is always 'structured_sheet'.

    - `"structured_sheet"`

  - `status: Literal["queued", "in_progress", "completed", 2 more]`

    The current processing status.

    - `"queued"`

    - `"in_progress"`

    - `"completed"`

    - `"failed"`

    - `"cancelled"`

  - `updated_at: datetime`

    The timestamp when the conversion was last updated.

  - `last_error: Optional[LastError]`

    Error information when processing fails.

    - `code: str`

      A machine-readable error code.

    - `message: str`

      A human-readable description of the error.

  - `sheet_names: Optional[List[str]]`

    List of sheet names included in this conversion.

  - `table_count: Optional[int]`

    Number of tables extracted from the workbook. Only present when status is 'completed'.

### Example

```python
import os
from deeptable import DeepTable

client = DeepTable(
    api_key=os.environ.get("DEEPTABLE_API_KEY"),  # This is the default and can be omitted
)
structured_sheet_response = client.structured_sheets.create(
    file_id="file_01h45ytscbebyvny4gc8cr8ma2",
)
print(structured_sheet_response.id)
```

#### Response

```json
{
  "id": "ss_01kfxgjd94fn9stqm42nejb627",
  "created_at": "2024-01-15T10:30:00Z",
  "file_id": "file_01kfxgjd94fn9stqm414vjb0s8",
  "object": "structured_sheet",
  "status": "completed",
  "updated_at": "2024-01-15T10:35:00Z",
  "last_error": {
    "code": "invalid_file_format",
    "message": "The uploaded file is not a valid Excel spreadsheet."
  },
  "sheet_names": [
    "Sheet1",
    "Financials"
  ],
  "table_count": 6
}
```

## Delete structured sheet

`structured_sheets.delete(strstructured_sheet_id)  -> StructuredSheetDeleteResponse`

**delete** `/v1/structured-sheets/{structured_sheet_id}`

Delete a structured sheet conversion and its associated exports. This action cannot be undone.

### Parameters

- `structured_sheet_id: str`

  The unique identifier of the structured sheet conversion.

### Returns

- `class StructuredSheetDeleteResponse: …`

  Response from deleting a structured sheet.

  Following the OpenAI API convention for delete responses.

  - `id: str`

    The unique identifier of the deleted structured sheet.

  - `deleted: Literal[true]`

    Whether the structured sheet was successfully deleted.

    - `true`

  - `object: Literal["structured_sheet"]`

    The object type, which is always 'structured_sheet'.

    - `"structured_sheet"`

### Example

```python
import os
from deeptable import DeepTable

client = DeepTable(
    api_key=os.environ.get("DEEPTABLE_API_KEY"),  # This is the default and can be omitted
)
structured_sheet = client.structured_sheets.delete(
    "ss_01kfxgjd94fn9stqm42nejb627",
)
print(structured_sheet.id)
```

#### Response

```json
{
  "id": "ss_01kfxgjd94fn9stqm42nejb627",
  "deleted": true,
  "object": "structured_sheet"
}
```

## Retrieve structured sheet

`structured_sheets.retrieve(strstructured_sheet_id)  -> StructuredSheetResponse`

**get** `/v1/structured-sheets/{structured_sheet_id}`

Get the status and details of a structured sheet conversion.

### Parameters

- `structured_sheet_id: str`

  The unique identifier of the structured sheet conversion.

### Returns

- `class StructuredSheetResponse: …`

  Response representing a structured sheet conversion job.

  This is returned from POST (create), GET (retrieve), and list endpoints.

  - `id: str`

    The unique identifier for this structured sheet conversion.

  - `created_at: datetime`

    The timestamp when the conversion was started.

  - `file_id: str`

    The unique identifier for the source file.

  - `object: Literal["structured_sheet"]`

    The object type, which is always 'structured_sheet'.

    - `"structured_sheet"`

  - `status: Literal["queued", "in_progress", "completed", 2 more]`

    The current processing status.

    - `"queued"`

    - `"in_progress"`

    - `"completed"`

    - `"failed"`

    - `"cancelled"`

  - `updated_at: datetime`

    The timestamp when the conversion was last updated.

  - `last_error: Optional[LastError]`

    Error information when processing fails.

    - `code: str`

      A machine-readable error code.

    - `message: str`

      A human-readable description of the error.

  - `sheet_names: Optional[List[str]]`

    List of sheet names included in this conversion.

  - `table_count: Optional[int]`

    Number of tables extracted from the workbook. Only present when status is 'completed'.

### Example

```python
import os
from deeptable import DeepTable

client = DeepTable(
    api_key=os.environ.get("DEEPTABLE_API_KEY"),  # This is the default and can be omitted
)
structured_sheet_response = client.structured_sheets.retrieve(
    "ss_01kfxgjd94fn9stqm42nejb627",
)
print(structured_sheet_response.id)
```

#### Response

```json
{
  "id": "ss_01kfxgjd94fn9stqm42nejb627",
  "created_at": "2024-01-15T10:30:00Z",
  "file_id": "file_01kfxgjd94fn9stqm414vjb0s8",
  "object": "structured_sheet",
  "status": "completed",
  "updated_at": "2024-01-15T10:35:00Z",
  "last_error": {
    "code": "invalid_file_format",
    "message": "The uploaded file is not a valid Excel spreadsheet."
  },
  "sheet_names": [
    "Sheet1",
    "Financials"
  ],
  "table_count": 6
}
```

## Cancel structured sheet processing

`structured_sheets.cancel(strstructured_sheet_id)  -> StructuredSheetResponse`

**post** `/v1/structured-sheets/{structured_sheet_id}/cancel`

Cancel a structured sheet conversion that is in progress. Only jobs with status 'queued' or 'in_progress' can be cancelled.

### Parameters

- `structured_sheet_id: str`

  The unique identifier of the structured sheet conversion.

### Returns

- `class StructuredSheetResponse: …`

  Response representing a structured sheet conversion job.

  This is returned from POST (create), GET (retrieve), and list endpoints.

  - `id: str`

    The unique identifier for this structured sheet conversion.

  - `created_at: datetime`

    The timestamp when the conversion was started.

  - `file_id: str`

    The unique identifier for the source file.

  - `object: Literal["structured_sheet"]`

    The object type, which is always 'structured_sheet'.

    - `"structured_sheet"`

  - `status: Literal["queued", "in_progress", "completed", 2 more]`

    The current processing status.

    - `"queued"`

    - `"in_progress"`

    - `"completed"`

    - `"failed"`

    - `"cancelled"`

  - `updated_at: datetime`

    The timestamp when the conversion was last updated.

  - `last_error: Optional[LastError]`

    Error information when processing fails.

    - `code: str`

      A machine-readable error code.

    - `message: str`

      A human-readable description of the error.

  - `sheet_names: Optional[List[str]]`

    List of sheet names included in this conversion.

  - `table_count: Optional[int]`

    Number of tables extracted from the workbook. Only present when status is 'completed'.

### Example

```python
import os
from deeptable import DeepTable

client = DeepTable(
    api_key=os.environ.get("DEEPTABLE_API_KEY"),  # This is the default and can be omitted
)
structured_sheet_response = client.structured_sheets.cancel(
    "ss_01kfxgjd94fn9stqm42nejb627",
)
print(structured_sheet_response.id)
```

#### Response

```json
{
  "id": "ss_01kfxgjd94fn9stqm42nejb627",
  "created_at": "2024-01-15T10:30:00Z",
  "file_id": "file_01kfxgjd94fn9stqm414vjb0s8",
  "object": "structured_sheet",
  "status": "completed",
  "updated_at": "2024-01-15T10:35:00Z",
  "last_error": {
    "code": "invalid_file_format",
    "message": "The uploaded file is not a valid Excel spreadsheet."
  },
  "sheet_names": [
    "Sheet1",
    "Financials"
  ],
  "table_count": 6
}
```

## Download structured sheet export

`structured_sheets.download(strstructured_sheet_id, StructuredSheetDownloadParams**kwargs)  -> BinaryResponseContent`

**get** `/v1/structured-sheets/{structured_sheet_id}/download`

Download the structured data in the specified format. Only available when conversion status is 'completed'.

Available formats:

- `sqlite`: SQLite database containing all extracted tables
- `cell_labels`: CSV file with cell-level semantic labels

### Parameters

- `structured_sheet_id: str`

  The unique identifier of the structured sheet conversion.

- `format: Optional[Literal["sqlite", "cell_labels"]]`

  The export format to download.

  - `"sqlite"`

  - `"cell_labels"`

### Returns

- `BinaryResponseContent`

### Example

```python
import os
from deeptable import DeepTable

client = DeepTable(
    api_key=os.environ.get("DEEPTABLE_API_KEY"),  # This is the default and can be omitted
)
response = client.structured_sheets.download(
    structured_sheet_id="ss_01kfxgjd94fn9stqm42nejb627",
)
print(response)
content = response.read()
print(content)
```

## Domain Types

### Structured Sheet Response

- `class StructuredSheetResponse: …`

  Response representing a structured sheet conversion job.

  This is returned from POST (create), GET (retrieve), and list endpoints.

  - `id: str`

    The unique identifier for this structured sheet conversion.

  - `created_at: datetime`

    The timestamp when the conversion was started.

  - `file_id: str`

    The unique identifier for the source file.

  - `object: Literal["structured_sheet"]`

    The object type, which is always 'structured_sheet'.

    - `"structured_sheet"`

  - `status: Literal["queued", "in_progress", "completed", 2 more]`

    The current processing status.

    - `"queued"`

    - `"in_progress"`

    - `"completed"`

    - `"failed"`

    - `"cancelled"`

  - `updated_at: datetime`

    The timestamp when the conversion was last updated.

  - `last_error: Optional[LastError]`

    Error information when processing fails.

    - `code: str`

      A machine-readable error code.

    - `message: str`

      A human-readable description of the error.

  - `sheet_names: Optional[List[str]]`

    List of sheet names included in this conversion.

  - `table_count: Optional[int]`

    Number of tables extracted from the workbook. Only present when status is 'completed'.

# Tables

## List tables in structured sheet

`structured_sheets.tables.list(strstructured_sheet_id, TableListParams**kwargs)  -> SyncCursorIDPage[TableResponse]`

**get** `/v1/structured-sheets/{structured_sheet_id}/tables`

List all tables extracted from the structured sheet. Only available when conversion status is 'completed'.

### Parameters

- `structured_sheet_id: str`

  The unique identifier of the structured sheet conversion.

- `after: Optional[str]`

  A cursor for pagination. Use the `last_id` from a previous response to fetch the next page of results.

- `limit: Optional[int]`

  Maximum number of tables to return per page.

### Returns

- `class TableResponse: …`

  Response representing a table extracted from a structured sheet.

  This is returned from GET (retrieve) and list table endpoints.
  Table names use a composite format: {normalized_sheet_name}__{table_name}.

  - `id: str`

    The unique identifier for this table.

  - `created_at: datetime`

    The timestamp when this table was created.

  - `name: str`

    Composite table name: {normalized_sheet_name}__{table_name}. Uses lowercase snake_case. Aggregation tables end with '__aggregations'. Two special metadata tables exist per structured sheet: '__deeptable_workbook_metadata' (workbook provenance info) and '__deeptable_table_overview' (summary of all tables). Example: 'staffing__head_count' or 'staffing__head_count__aggregations'.

  - `object: Literal["table"]`

    The object type, which is always 'table'.

    - `"table"`

  - `sheet_name: str`

    The original Excel sheet name this table came from.

  - `structured_sheet_id: str`

    The ID of the structured sheet this table belongs to.

  - `type: Literal["relational", "aggregation", "tableless", "metadata"]`

    The type of table (relational, aggregation, tableless, or metadata).

    - `"relational"`

    - `"aggregation"`

    - `"tableless"`

    - `"metadata"`

### Example

```python
import os
from deeptable import DeepTable

client = DeepTable(
    api_key=os.environ.get("DEEPTABLE_API_KEY"),  # This is the default and can be omitted
)
page = client.structured_sheets.tables.list(
    structured_sheet_id="ss_01kfxgjd94fn9stqm42nejb627",
)
page = page.data[0]
print(page.id)
```

#### Response

```json
{
  "data": [
    {
      "id": "tbl_01kfxgjd94fn9stqm45rqr2pnz",
      "created_at": "2026-01-15T10:35:00Z",
      "name": "staffing__head_count",
      "object": "table",
      "sheet_name": "Staffing",
      "structured_sheet_id": "ss_01kfxgjd94fn9stqm42nejb627",
      "type": "relational"
    },
    {
      "id": "tbl_02abc2def3ghjkmnpqrs4uvwxz",
      "created_at": "2026-01-15T10:35:00Z",
      "name": "staffing__head_count__aggregations",
      "object": "table",
      "sheet_name": "Staffing",
      "structured_sheet_id": "ss_01kfxgjd94fn9stqm42nejb627",
      "type": "aggregation"
    }
  ],
  "has_more": false,
  "object": "list",
  "first_id": "tbl_01kfxgjd94fn9stqm45rqr2pnz",
  "last_id": "tbl_02abc2def3ghjkmnpqrs4uvwxz"
}
```

## Retrieve a table

`structured_sheets.tables.retrieve(strtable_id, TableRetrieveParams**kwargs)  -> TableResponse`

**get** `/v1/structured-sheets/{structured_sheet_id}/tables/{table_id}`

Get details of a specific table extracted from the structured sheet. Only available when conversion status is 'completed'.

### Parameters

- `structured_sheet_id: str`

  The unique identifier of the structured sheet conversion.

- `table_id: str`

  The unique identifier of the table.

### Returns

- `class TableResponse: …`

  Response representing a table extracted from a structured sheet.

  This is returned from GET (retrieve) and list table endpoints.
  Table names use a composite format: {normalized_sheet_name}__{table_name}.

  - `id: str`

    The unique identifier for this table.

  - `created_at: datetime`

    The timestamp when this table was created.

  - `name: str`

    Composite table name: {normalized_sheet_name}__{table_name}. Uses lowercase snake_case. Aggregation tables end with '__aggregations'. Two special metadata tables exist per structured sheet: '__deeptable_workbook_metadata' (workbook provenance info) and '__deeptable_table_overview' (summary of all tables). Example: 'staffing__head_count' or 'staffing__head_count__aggregations'.

  - `object: Literal["table"]`

    The object type, which is always 'table'.

    - `"table"`

  - `sheet_name: str`

    The original Excel sheet name this table came from.

  - `structured_sheet_id: str`

    The ID of the structured sheet this table belongs to.

  - `type: Literal["relational", "aggregation", "tableless", "metadata"]`

    The type of table (relational, aggregation, tableless, or metadata).

    - `"relational"`

    - `"aggregation"`

    - `"tableless"`

    - `"metadata"`

### Example

```python
import os
from deeptable import DeepTable

client = DeepTable(
    api_key=os.environ.get("DEEPTABLE_API_KEY"),  # This is the default and can be omitted
)
table_response = client.structured_sheets.tables.retrieve(
    table_id="tbl_01kfxgjd94fn9stqm45rqr2pnz",
    structured_sheet_id="ss_01kfxgjd94fn9stqm42nejb627",
)
print(table_response.id)
```

#### Response

```json
{
  "id": "tbl_01kfxgjd94fn9stqm45rqr2pnz",
  "created_at": "2026-01-15T10:35:00Z",
  "name": "staffing__head_count",
  "object": "table",
  "sheet_name": "Staffing",
  "structured_sheet_id": "ss_01kfxgjd94fn9stqm42nejb627",
  "type": "relational"
}
```

## Download table data

`structured_sheets.tables.download(strtable_id, TableDownloadParams**kwargs)  -> BinaryResponseContent`

**get** `/v1/structured-sheets/{structured_sheet_id}/tables/{table_id}/download`

Download the table data in the specified format.

Available formats:

- `parquet`: Apache Parquet columnar format (recommended for data analysis)
- `csv`: Comma-separated values (compatible with any spreadsheet application)

### Parameters

- `structured_sheet_id: str`

  The unique identifier of the structured sheet conversion.

- `table_id: str`

  The unique identifier of the table.

- `format: Literal["parquet", "csv"]`

  The format to download the table data in.

  - `"parquet"`

  - `"csv"`

### Returns

- `BinaryResponseContent`

### Example

```python
import os
from deeptable import DeepTable

client = DeepTable(
    api_key=os.environ.get("DEEPTABLE_API_KEY"),  # This is the default and can be omitted
)
response = client.structured_sheets.tables.download(
    table_id="tbl_01kfxgjd94fn9stqm45rqr2pnz",
    structured_sheet_id="ss_01kfxgjd94fn9stqm42nejb627",
    format="parquet",
)
print(response)
content = response.read()
print(content)
```

## Domain Types

### Table Response

- `class TableResponse: …`

  Response representing a table extracted from a structured sheet.

  This is returned from GET (retrieve) and list table endpoints.
  Table names use a composite format: {normalized_sheet_name}__{table_name}.

  - `id: str`

    The unique identifier for this table.

  - `created_at: datetime`

    The timestamp when this table was created.

  - `name: str`

    Composite table name: {normalized_sheet_name}__{table_name}. Uses lowercase snake_case. Aggregation tables end with '__aggregations'. Two special metadata tables exist per structured sheet: '__deeptable_workbook_metadata' (workbook provenance info) and '__deeptable_table_overview' (summary of all tables). Example: 'staffing__head_count' or 'staffing__head_count__aggregations'.

  - `object: Literal["table"]`

    The object type, which is always 'table'.

    - `"table"`

  - `sheet_name: str`

    The original Excel sheet name this table came from.

  - `structured_sheet_id: str`

    The ID of the structured sheet this table belongs to.

  - `type: Literal["relational", "aggregation", "tableless", "metadata"]`

    The type of table (relational, aggregation, tableless, or metadata).

    - `"relational"`

    - `"aggregation"`

    - `"tableless"`

    - `"metadata"`