HyukjinKwon commented on a change in pull request #23414: [Spark
26449][PYSPARK] add a transform method to the Dataframe class
URL: https://github.com/apache/spark/pull/23414#discussion_r244604853
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File path: python/pyspark/sql/dataframe.py
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@@ -2046,6 +2046,40 @@ def toDF(self, *cols):
jdf = self._jdf.toDF(self._jseq(cols))
return DataFrame(jdf, self.sql_ctx)
+ @since(3.0)
+ def transform(self, func):
+ """Returns a new class:`DataFrame` according to a user-defined custom
transform method.
+ This allows chaining transformations rather than using nested or
temporary variables.
+
+ :param func: a user-defined custom transform function
+ This is equiavalent to a nested call:
+ actual_df = with_something(with_greeting(source_df), "crazy"))
+
+ credit to:
https://medium.com/@mrpowers/chaining-custom-pyspark-transformations-4f38a8c7ae55
+
+ A more concrete example::
+ >>> sc = pyspark.SparkContext(master='local')
+ >>> spark = pyspark.sql.SparkSession(sparkContext=sc)
+ >>> from pyspark.sql.functions import lit
+ >>> def with_greeting(df):
+ ... return df.withColumn("greeting", lit("hi"))
+ >>> def with_something(df, something):
+ ... return df.withColumn("something", lit(something))
+ >>> data = [("jose", 1), ("li", 2), ("liz", 3)]
+ >>> source_df = spark.createDataFrame(data, ["name", "age"])
+ >>> actual_df = source_df.transform(with_greeting).transform(lambda x:
with_something(x, "crazy"))
Review comment:
I also wouldn't add with prefix
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