How to insert concatenated values from a data-frame into another data-frame in Pyspark?

Question

I'm creating a time_interval column and adding it to an existing Data-frame in Pyspark . Ideally the time_interval will be in the " HHmm " format with the minutes being rounded down to the nearest 15 minute mark (815, 830, 845, 900, etc).

I have the spark sql code that does the logic for me but how do I take that value that's concatenated as string column and insert that into an existing Data-frame?

time_interval = sqlContext.sql("select extract(hour from current_timestamp())||floor(extract(minute from current_timestamp())/15)*15")

time_interval.show()

+-------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|concat(CAST(hour(current_timestamp()) AS STRING), CAST((FLOOR((CAST(minute(current_timestamp()) AS DOUBLE) / CAST(15 AS DOUBLE))) * CAST(15 AS BIGINT)) AS STRING))|
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|                                                                                                                                                               1045|
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------+

baseDF = sqlContext.sql("select * from test_table")
newBase = baseDF.withColumn("time_interval", lit(str(time_interval)))

newBase.select("time_interval").show()

+--------------------+
|       time_interval|
+--------------------+
|DataFrame[concat(...|
|DataFrame[concat(...|
|DataFrame[concat(...|
|DataFrame[concat(...|
|DataFrame[concat(...|
|DataFrame[concat(...|
|DataFrame[concat(...|
|DataFrame[concat(...|
|DataFrame[concat(...|
|DataFrame[concat(...|
|DataFrame[concat(...|
|DataFrame[concat(...|
|DataFrame[concat(...|
|DataFrame[concat(...|
|DataFrame[concat(...|
|DataFrame[concat(...|
|DataFrame[concat(...|
|DataFrame[concat(...|
|DataFrame[concat(...|
|DataFrame[concat(...|
+--------------------+
only showing top 20 rows

So the actual expected results should be just showing the actual string value in the new column i'm creating rather than this concatenated value from a data-frame. Something like below:

newBase.select("time_interval").show(1)
+-------------+
|time_interval|
+-------------+
|    1045     |                                                                                                                                           
+-------------+

Answer 1

As time_interval is a dataframe type, for this case need to collect and extract the required value out from dataframe .

Try this way:

newBase = baseDF.withColumn("time_interval", lit(str(time_interval.collect()[0][0])))
newBase.show()

(or)

By using select(expr()) function:

newBase = baseDF.select("*",expr("string(extract(hour from current_timestamp())||floor(extract(minute from current_timestamp())/15)*15) AS time_interval"))

As pault mentioned in comments, using selectExpr() function:

newBase = baseDF.selectExpr("*","string(extract(hour from current_timestamp())||floor(extract(minute from current_timestamp())/15)*15) AS time_interval")

Example:

>>> from pyspark.sql.functions import *
>>> from pyspark.sql.types import IntegerType
>>> time_interval = spark.sql("select extract(hour from current_timestamp())||floor(extract(minute from current_timestamp())/15)*15")
>>> baseDF=spark.createDataFrame([1,2,3,4],IntegerType())
>>> newBase = baseDF.withColumn("time_interval", lit(str(time_interval.collect()[0][0])))
>>> newBase.show()
+-----+-------------+
|value|time_interval|
+-----+-------------+
|    1|         1245|
|    2|         1245|
|    3|         1245|
|    4|         1245|
+-----+-------------+