pyspark.sql.DataFrameReader.parquet¶
-
DataFrameReader.parquet(*paths, **options)[source]¶ Loads Parquet files, returning the result as a
DataFrame.New in version 1.4.0.
- Parameters
- pathsstr
- Other Parameters
- mergeSchemastr or bool, optional
sets whether we should merge schemas collected from all Parquet part-files. This will override
spark.sql.parquet.mergeSchema. The default value is specified inspark.sql.parquet.mergeSchema.- pathGlobFilterstr or bool, optional
an optional glob pattern to only include files with paths matching the pattern. The syntax follows org.apache.hadoop.fs.GlobFilter. It does not change the behavior of partition discovery. # noqa
- recursiveFileLookupstr or bool, optional
recursively scan a directory for files. Using this option disables partition discovery. # noqa
modification times occurring before the specified time. The provided timestamp must be in the following format: YYYY-MM-DDTHH:mm:ss (e.g. 2020-06-01T13:00:00)
- modifiedBefore (batch only)an optional timestamp to only include files with
modification times occurring before the specified time. The provided timestamp must be in the following format: YYYY-MM-DDTHH:mm:ss (e.g. 2020-06-01T13:00:00)
- modifiedAfter (batch only)an optional timestamp to only include files with
modification times occurring after the specified time. The provided timestamp must be in the following format: YYYY-MM-DDTHH:mm:ss (e.g. 2020-06-01T13:00:00)
Examples
>>> df = spark.read.parquet('python/test_support/sql/parquet_partitioned') >>> df.dtypes [('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')]