I am a newbie at using Spark dataframes. I am trying to use the pivot method with Spark (Spark version 2.x) and running into the following error:
Py4JError: An error occurred while calling o387.pivot. Trace: py4j.Py4JException: Method pivot([class java.lang.String, class java.lang.String]) does not exist
Even though I have the agg function as first here, I really do not need to apply any aggregation.
My dataframe looks like this:
+-----+-----+----------+-----+
| name|value| date| time|
+-----+-----+----------+-----+
|name1|100.0|2017-12-01|00:00|
|name1|255.5|2017-12-01|00:15|
|name1|333.3|2017-12-01|00:30|
Expected:
+-----+----------+-----+-----+-----+
| name| date|00:00|00:15|00:30|
+-----+----------+-----+-----+-----+
|name1|2017-12-01|100.0|255.5|333.3|
The way I am trying:
df = df.groupBy(["name","date"]).pivot(pivot_col="time",values="value").agg(first("value")).show
What is my mistake here?
The problem is the values="value" parameter in the pivot function. This should be used for a list of actual values to pivot on, not a column name. From the documentation:
values – List of values that will be translated to columns in the output DataFrame.
and an example:
df4.groupBy("year").pivot("course", ["dotNET", "Java"]).sum("earnings").collect() [Row(year=2012, dotNET=15000, Java=20000), Row(year=2013, dotNET=48000, Java=30000)]
For the example in the question values should be set to ["00:00","00:15", "00:30"]. However, the values argument is often not necessary (but will make the pivot more efficient), so you can simply change to:
df = df.groupBy(["name","date"]).pivot("time").agg(first("value"))
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