Consider the following DataFrame:
#+------+---+
#|letter|rpt|
#+------+---+
#| X| 3|
#| Y| 1|
#| Z| 2|
#+------+---+
which can be created using the following code:
df = spark.createDataFrame([("X", 3),("Y", 1),("Z", 2)], ["letter", "rpt"])
Suppose I wanted to repeat each row the number of times specified in the column rpt
, just like in this question.
One way would be to replicate my solution to that question using the following pyspark-sql
query:
query = """
SELECT *
FROM
(SELECT DISTINCT *,
posexplode(split(repeat(",", rpt), ",")) AS (index, col)
FROM df) AS a
WHERE index > 0
"""
query = query.replace("
", " ") # replace newlines with spaces, avoid EOF error
spark.sql(query).drop("col").sort('letter', 'index').show()
#+------+---+-----+
#|letter|rpt|index|
#+------+---+-----+
#| X| 3| 1|
#| X| 3| 2|
#| X| 3| 3|
#| Y| 1| 1|
#| Z| 2| 1|
#| Z| 2| 2|
#+------+---+-----+
This works and produces the correct answer. However, I am unable to replicate this behavior using the DataFrame API functions.
I tried:
import pyspark.sql.functions as f
df.select(
f.posexplode(f.split(f.repeat(",", f.col("rpt")), ",")).alias("index", "col")
).show()
But this results in:
TypeError: 'Column' object is not callable
Why am I able to pass the column as an input to repeat
within the query, but not from the API? Is there a way to replicate this behavior using the spark DataFrame functions?
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