pyspark.pandas.read_parquet#

pyspark.pandas.read_parquet(path, columns=None, index_col=None, pandas_metadata=False, **options)[source]#

Load a parquet object from the file path, returning a DataFrame.

Parameters:

pathstring: File path
columnslist, default=None: If not None, only these columns will be read from the file.
index_colstr or list of str, optional, default: None: Index column of table in Spark.
pandas_metadatabool, default: False: If True, try to respect the metadata if the Parquet file is written from pandas.
optionsdict: All other options passed directly into Spark’s data source.

Returns:

DataFrame

See also

DataFrame.to_parquet
DataFrame.read_table
DataFrame.read_delta
DataFrame.read_spark_io

Examples

>>> ps.range(1).to_parquet('%s/read_spark_io/data.parquet' % path)
>>> ps.read_parquet('%s/read_spark_io/data.parquet' % path, columns=['id'])
   id
0   0

You can preserve the index in the roundtrip as below.

>>> ps.range(1).to_parquet('%s/read_spark_io/data.parquet' % path, index_col="index")
>>> ps.read_parquet('%s/read_spark_io/data.parquet' % path, columns=['id'], index_col="index")
...
       id
index
0       0