HyukjinKwon commented on code in PR #37233:
URL: https://github.com/apache/spark/pull/37233#discussion_r929810416
##########
python/pyspark/sql/dataframe.py:
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@@ -1422,6 +1422,61 @@ def colRegex(self, colName: str) -> Column:
jc = self._jdf.colRegex(colName)
return Column(jc)
+ def asSchema(self, schema: StructType) -> "DataFrame":
+ """
+ Returns a new :class:`DataFrame` where each row is reconciled to match
the specified
+ schema.
+
+ Spark will:
+
+ 1, Reorder columns and/or inner fields by name to match the specified
schema.
+
+ 2, Project away columns and/or inner fields that are not needed by the
specified schema.
+ Missing columns and/or inner fields (present in the specified schema
but not input
+ DataFrame) lead to failures.
+
+ 3, Cast the columns and/or inner fields to match the data types in the
specified schema,
+ if the types are compatible, e.g., numeric to numeric (error if
overflows), but not string
+ to int.
+
+ 4, Carry over the metadata from the specified schema, while the
columns and/or inner fields
+ still keep their own metadata if not overwritten by the specified
schema.
+
+ 5, Fail if the nullability are not compatible. For example, the column
and/or inner field
+ is nullable but the specified schema requires them to be not nullable.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ schema : :class:`StructType`
+ Specified schema.
+
+ Examples
+ --------
+ >>> df = spark.createDataFrame([("a", 1)], ["i", "j"])
+ >>> df.schema
+ StructType([StructField('i', StringType(), True), StructField('j',
LongType(), True)])
+ >>> schema = StructType([StructField("j", StringType()),
StructField("i", StringType())])
+ >>> df2 = df.asSchema(schema)
+ >>> df2.schema
+ StructType([StructField('j', StringType(), True), StructField('i',
StringType(), True)])
+ >>> df2.show()
+ +---+---+
+ | j| i|
+ +---+---+
+ | 1| a|
+ +---+---+
+ """
+ assert schema is not None
+ sc = self.sparkSession._sc
+ assert sc is not None and sc._jvm is not None
+ _struct_type = getattr(
+ getattr(sc._jvm.org.apache.spark.sql.types, "StructType$"),
"MODULE$"
+ )
+ jschema = _struct_type.fromString(schema.json())
Review Comment:
Yeah, probably using `df._jdf.sparkSession().parseDataType` would be more
consistent and simplier.
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