{"action":"created","comment":{"url":"https://api.github.com/repos/sparklingpandas/sparklingpandas/pulls/comments/36046864","id":36046864,"diff_hunk":"@@ -141,22 +151,57 @@ def csv_rows(partition_number, rows):\n \n     def parquetFile(self, *paths):\n         \"\"\"Loads a Parquet file, returning the result as a L{Dataframe}.\n+\n+        Parameters\n+        ----------\n+        paths: string, variable length\n+             The path(s) of the parquet files to load. Should be Hadoop style\n+             paths (e.g. hdfs://..., file://... etc.).\n+        Returns\n+        -------\n+        A L{Dataframe} of the contents of the parquet files.\n         \"\"\"\n-        return self.from_spark_rdd(self.sql_ctx.parquetFile(paths),\n-                                   self.sql_ctx)\n+        return self.from_spark_rdd(self.sql_ctx.parquetFile(paths))\n \n     def jsonFile(self, path, schema=None, sampling_ratio=1.0):\n         \"\"\"Loads a text file storing one JSON object per line as a\n         L{Dataframe}.\n+        Parameters\n+        ----------\n+        path: string\n+             The path of the json files to load. Should be Hadoop style\n+             paths (e.g. hdfs://..., file://... etc.).\n+        schema: StructType, optional\n+             If you know the schema of your input data you can specify it. The\n+             schema is specified using Spark SQL's schema information. 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