Python Spark Cumulative Sum by Group Using Dataframe
How Do I Compute the Cumulative Sum per Group Specifically Using the Dataframe Abstraction; and in Pyspark? with an Example Dataset as Follows: Df =...
How do I compute the cumulative sum per group specifically using the DataFrame abstraction; and in PySpark?
With an example dataset as follows:
df = sqlContext.createDataFrame( [(1,2,"a"),(3,2,"a"),(1,3,"b"),(2,2,"a"),(2,3,"b")],
["time", "value", "class"] )
+----+-----+-----+
|time|value|class|
+----+-----+-----+
| 1| 2| a|
| 3| 2| a|
| 1| 3| b|
| 2| 2| a|
| 2| 3| b|
+----+-----+-----+
I would like to add a cumulative sum column of value for each class grouping over the (ordered) time variable.
4 Answers
This can be done using a combination of a window function and the Window.unboundedPreceding value in the window's range as follows:
from pyspark.sql import Window
from pyspark.sql import functions as F
windowval = (Window.partitionBy('class').orderBy('time')
.rangeBetween(Window.unboundedPreceding, 0))
df_w_cumsum = df.withColumn('cum_sum', F.sum('value').over(windowval))
df_w_cumsum.show()
+----+-----+-----+-------+
|time|value|class|cum_sum|
+----+-----+-----+-------+
| 1| 3| b| 3|
| 2| 3| b| 6|
| 1| 2| a| 2|
| 2| 2| a| 4|
| 3| 2| a| 6|
+----+-----+-----+-------+
To make an update from previous answers. The correct and precise way to do is :
from pyspark.sql import Window
from pyspark.sql import functions as F
windowval = (Window.partitionBy('class').orderBy('time')
.rowsBetween(Window.unboundedPreceding, 0))
df_w_cumsum = df.withColumn('cum_sum', F.sum('value').over(windowval))
df_w_cumsum.show()
I have tried this way and it worked for me.
from pyspark.sql import Window
from pyspark.sql import functions as f
import sys
cum_sum = DF.withColumn('cumsum', f.sum('value').over(Window.partitionBy('class').orderBy('time').rowsBetween(-sys.maxsize, 0)))
cum_sum.show()
I create this function in this link for my use: kolang/column_functions/cumulative_sum
def cumulative_sum(col: Union[Column, str],
on_col: Union[Column, str],
ascending: bool = True,
partition_by: Union[Column, str, List[Union[Column, str]]] = None) -> Column:
on_col = on_col if ascending else F.desc(on_col)
if partition_by is None:
w = Window.orderBy(on_col).rangeBetween(Window.unboundedPreceding, 0)
else:
w = Window.partitionBy(partition_by).orderBy(on_col).rangeBetween(Window.unboundedPreceding, 0)
return F.sum(col).over(w)
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