code: def parquet(self, *paths):
          
          return self._df(self._jreader.parquet(_to_seq(self._spark._sc, paths)))
docs: Loads Parquet files, returning the result as a :class:`DataFrame`.
  
          You can set the following Parquet-specific option(s) for reading Parquet files:
              * ``mergeSchema``: sets whether we should merge schemas collected from all \
                  Parquet part-files. This will override ``spark.sql.parquet.mergeSchema``. \
                  The default value is specified in ``spark.sql.parquet.mergeSchema``.
  
          >>> df = spark.read.parquet('python/test_support/sql/parquet_partitioned')
          >>> df.dtypes
          [('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')]