snowflake.snowpark.DataFrame.to_pandas¶

DataFrame.to_pandas(*, statement_params: Optional[Dict[str, str]] = None, block: bool = True, _emit_ast: bool = True, **kwargs: Dict[str, Any]) → pandas.DataFrame[source]¶

DataFrame.to_pandas(*, statement_params: Optional[Dict[str, str]] = None, block: bool = False, _emit_ast: bool = True, **kwargs: Dict[str, Any]) → AsyncJob

Executes the query representing this DataFrame and returns the result as a pandas DataFrame.

When the data is too large to fit into memory, you can use to_pandas_batches().

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 an AsyncJob.

Note

This method is only available if pandas is installed and available.

2. If you use Session.sql() with this method, the input query of Session.sql() can only be a SELECT statement.

3. For TIMESTAMP columns: - TIMESTAMP_LTZ and TIMESTAMP_TZ are both converted to datetime64[ns, tz] in pandas, as pandas cannot distinguish between the two. - TIMESTAMP_NTZ is converted to datetime64[ns] (without timezone).