bigframes.pandas.read_parquet#

bigframes.pandas.read_parquet(path:str|IO[bytes],*,engine:str='auto',write_engine:Literal['default','bigquery_inline','bigquery_load','bigquery_streaming','bigquery_write','_deferred']='default')→DataFrame[source]#

Load a Parquet object from the file path (local or Cloud Storage), returning a DataFrame.

Note

This method will not guarantee the same ordering as the file.Instead, set a serialized index column as the index and sort bythat in the resulting DataFrame.

Note

For non-“bigquery” engine, data is inlined in the query SQL if it issmall enough (roughly 5MB or less in memory). Larger size data isloaded to a BigQuery table instead.

Examples:

>>>importbigframes.pandasasbpd

>>>gcs_path="gs://cloud-samples-data/bigquery/us-states/us-states.parquet">>>df=bpd.read_parquet(path=gcs_path,engine="bigquery")

Parameters:

• path (str) – Local or Cloud Storage path to Parquet file.

• engine (str) – One of'auto','pyarrow','fastparquet', or'bigquery'.Parquet library to parse the file. If set to'bigquery',order is not preserved. Default,'auto'.

Returns:

A BigQuery DataFrames.

Return type:

bigframes.pandas.DataFrame