bigframes.pandas.read_pandas#

bigframes.pandas.read_pandas(pandas_dataframe:DataFrame,*,write_engine:constants.WriteEngineType='default')→bigframes.dataframe.DataFrame[source]#

bigframes.pandas.read_pandas(pandas_dataframe:Series,*,write_engine:constants.WriteEngineType='default')→bigframes.series.Series

bigframes.pandas.read_pandas(pandas_dataframe:Index,*,write_engine:constants.WriteEngineType='default')→bigframes.core.indexes.Index

Loads DataFrame from a pandas DataFrame.

The pandas DataFrame will be persisted as a temporary BigQuery table, which can beautomatically recycled after the Session is closed.

Note

Data is inlined in the query SQL if it is small enough (roughly 5MBor less in memory). Larger size data is loaded to a BigQuery tableinstead.

Examples:

>>>d={'col1':[1,2],'col2':[3,4]}>>>pandas_df=pd.DataFrame(data=d)>>>df=bpd.read_pandas(pandas_df)>>>df   col1  col20     1     31     2     4[2 rows x 2 columns]

Parameters:

• pandas_dataframe (pandas.DataFrame,pandas.Series, orpandas.Index) – a pandas DataFrame/Series/Index object to be loaded.

• write_engine (str) –

  How data should be written to BigQuery (if at all). Supportedvalues:

  • ”default”:(Recommended) Select an appropriate mechanism to write datato BigQuery. Depends on data size and supported data types.

  • ”bigquery_inline”:Inline data in BigQuery SQL. Use this when you know the datais small enough to fit within BigQuery’s 1 MB query text sizelimit.

  • ”bigquery_load”:Use a BigQuery load job. Use this for larger data sizes.

  • ”bigquery_streaming”:Use the BigQuery streaming JSON API. Use this if yourworkload is such that you exhaust the BigQuery load jobquota and your data cannot be embedded in SQL due to size ordata type limitations.

  • ”bigquery_write”:[Preview] Use the BigQuery Storage Write API. This featureis in public preview.

Returns:

An equivalent bigframes.pandas.(DataFrame/Series/Index) object

Raises:

ValueError – When the object is not a Pandas DataFrame.