PySpark, create line graph from a dataframe without a "category" on databricks

Question

I'm running the following code on databricks:

dataToShow = jDataJoined.\
withColumn('id', monotonically_increasing_id()).\
filter( 
  (jDataJoined.containerNumber == 'SUDU8108536')).\
select(col('id'), col('returnTemperature'), col('supplyTemperature'))

This will give me tabular data like

Now I would like to display a line graph with this returnTemperature and supplyTemperature as categories.

As far as I understood, the method display in databricks wants as second argument the category, so basically what I should have is something like

id - temperatureCategory - value
1 - returnTemperature - 25.0
1 - supplyTemperature - 27.0
2 - returnTemperature - 24.0
2 - supplyTemperature - 28.0

How can I transform the dataframe in this way?

cronoik · Accepted Answer · 2019-03-07 01:35:05Z

I don't know if your format is what the display method is expecting, but you can do this transformation with the sql functions create_map and explode:

#creates a example df
from pyspark.sql import functions as F
l1 = [(1,25.0,27.0),(2,24.0,28.0)]
df = spark.createDataFrame(l1,['id','returnTemperature','supplyTemperature'])

#creates a map column which contains the values of the returnTemperature and supplyTemperature
df = df.withColumn('mapCol', F.create_map(
                                    F.lit('returnTemperature'),df.returnTemperature
                                    ,F.lit('supplyTemperature'),df.supplyTemperature
                                   ) 
                  )
#The explode function creates a new row for each element of the map
df = df.select('id',F.explode(df.mapCol).alias('temperatureCategory','value'))
df.show()

Output:

+---+-------------------+-----+ 
| id|temperatureCategory|value| 
+---+-------------------+-----+ 
| 1 |  returnTemperature| 25.0| 
| 1 |  supplyTemperature| 27.0| 
| 2 |  returnTemperature| 24.0| 
| 2 |  supplyTemperature| 28.0| 
+---+-------------------+-----+

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