BatchDataFrameOperations Class

DataFrame-oriented wrappers for batch record operations.

Provides create, update, and delete that accept pandas.DataFrame / pandas.Series inputs and convert them to standard dicts before enqueueing on the batch. This lets data-science callers feed DataFrames directly into a batch without manual conversion.

Accessed via batch.dataframe.

Example:


   import pandas as pd

   batch = client.batch.new()
   df = pd.DataFrame([
       {"name": "Contoso", "telephone1": "555-0100"},
       {"name": "Fabrikam", "telephone1": "555-0200"},
   ])
   batch.dataframe.create("account", df)
   result = batch.execute()

Constructor

BatchDataFrameOperations(batch: _BatchContext)

Parameters

Name	Description
batch Required

Methods

create

Enqueue record creates from a pandas DataFrame.

Each row becomes a record. All rows are bundled in a single CreateMultiple batch item (one HTTP request in the batch).

Example:


   df = pd.DataFrame([{"name": "Contoso"}, {"name": "Fabrikam"}])
   batch.dataframe.create("account", df)

delete

Enqueue record deletes from a pandas Series of GUIDs.

Example:


   ids_series = pd.Series(["guid-1", "guid-2", "guid-3"])
   batch.dataframe.delete("account", ids_series)

update

Enqueue record updates from a pandas DataFrame.

Each row represents an update. The id_column specifies which column contains the record GUIDs.

Example:


   df = pd.DataFrame([
       {"accountid": "guid-1", "telephone1": "555-0100"},
       {"accountid": "guid-2", "telephone1": "555-0200"},
   ])
   batch.dataframe.update("account", df, id_column="accountid")

create

Enqueue record creates from a pandas DataFrame.

Each row becomes a record. All rows are bundled in a single CreateMultiple batch item (one HTTP request in the batch).

Example:


   df = pd.DataFrame([{"name": "Contoso"}, {"name": "Fabrikam"}])
   batch.dataframe.create("account", df)

create(table: str, records: DataFrame) -> None

Parameters

Name	Description
table Required	str Table schema name (e.g. `"account"`).
records Required	Dataframe DataFrame where each row is a record to create.

Exceptions

Type	Description
TypeError	If `records` is not a pandas DataFrame.
ValueError	If `records` is empty or any row has no non-null values.

delete

Enqueue record deletes from a pandas Series of GUIDs.

Example:


   ids_series = pd.Series(["guid-1", "guid-2", "guid-3"])
   batch.dataframe.delete("account", ids_series)

delete(table: str, ids: Series, use_bulk_delete: bool = True) -> None

Parameters

Name	Description
table Required	str Table schema name (e.g. `"account"`).
ids Required	Series Series of record GUIDs to delete.
use_bulk_delete	bool When `True` (default) and `ids` has multiple values, use the `BulkDelete` action. Default value: True

Exceptions

Type	Description
TypeError	If `ids` is not a pandas Series.
ValueError	If `ids` contains invalid values.

update

Enqueue record updates from a pandas DataFrame.

Each row represents an update. The id_column specifies which column contains the record GUIDs.

Example:


   df = pd.DataFrame([
       {"accountid": "guid-1", "telephone1": "555-0100"},
       {"accountid": "guid-2", "telephone1": "555-0200"},
   ])
   batch.dataframe.update("account", df, id_column="accountid")

update(table: str, changes: DataFrame, id_column: str, clear_nulls: bool = False) -> None

Parameters

Name	Description
table Required	str Table schema name (e.g. `"account"`).
changes Required	Dataframe DataFrame where each row contains a record GUID and the fields to update.
id_column Required	str Name of the DataFrame column containing record GUIDs.
clear_nulls	bool When `False` (default), NaN/None values are skipped. When `True`, NaN/None sends `null` to clear the field. Default value: False

Exceptions

Type	Description
TypeError	If `changes` is not a pandas DataFrame.
ValueError	If `changes` is empty, `id_column` is missing, or IDs are invalid.