snowflake.snowpark.DataFrame.to_pandas_batches¶
- DataFrame.to_pandas_batches(*, statement_params: Dict[str, str] | None = None, block: bool = True, **kwargs: Dict[str, Any]) Iterator[pandas.DataFrame] [source]¶
- DataFrame.to_pandas_batches(*, statement_params: Dict[str, str] | None = None, block: bool = False, **kwargs: Dict[str, Any]) AsyncJob
Executes the query representing this DataFrame and returns an iterator of Pandas dataframes (containing a subset of rows) that you can use to retrieve the results.
Unlike
to_pandas()
, this method does not load all data into memory at once.Example:
>>> df = session.create_dataframe([[1, 2], [3, 4]], schema=["a", "b"]) >>> for pandas_df in df.to_pandas_batches(): ... print(pandas_df) A B 0 1 2 1 3 4
- Parameters:
statement_params – Dictionary of statement level parameters to be set while executing this action.
block – A bool value indicating whether this function will wait until the result is available. When it is
False
, this function executes the underlying queries of the dataframe asynchronously and returns anAsyncJob
.
Note
This method is only available if Pandas is installed and available.
2. If you use
Session.sql()
with this method, the input query ofSession.sql()
can only be a SELECT statement.