Spark add new column to dataframe with value from previous row

Question

I\'m wondering how I can achieve the following in Spark (Pyspark)

Initial Dataframe:

+--+---+
|id|num|
+--+---+
|4 |9.0|
+--+---+
|3 |7.0|
+--+---+
|         


        

Answer 1

You can use lag window function as follows

from pyspark.sql.functions import lag, col
from pyspark.sql.window import Window

df = sc.parallelize([(4, 9.0), (3, 7.0), (2, 3.0), (1, 5.0)]).toDF(["id", "num"])
w = Window().partitionBy().orderBy(col("id"))
df.select("*", lag("num").over(w).alias("new_col")).na.drop().show()

## +---+---+-------+
## | id|num|new_col|
## +---+---+-------|
## |  2|3.0|    5.0|
## |  3|7.0|    3.0|
## |  4|9.0|    7.0|
## +---+---+-------+

but there some important issues:

1. if you need a global operation (not partitioned by some other column / columns) it is extremely inefficient.
2. you need a natural way to order your data.

While the second issue is almost never a problem the first one can be a deal-breaker. If this is the case you should simply convert your DataFrame to RDD and compute lag manually. See for example:

• How to transform data with sliding window over time series data in Pyspark
• Apache Spark Moving Average (written in Scala, but can be adjusted for PySpark. Be sure to read the comments first).

Other useful links:

• https://github.com/UrbanInstitute/pyspark-tutorials/blob/master/05_moving-average-imputation.ipynb
• Spark Window Functions - rangeBetween dates

Answer 2

   val df = sc.parallelize(Seq((4, 9.0), (3, 7.0), (2, 3.0), (1, 5.0))).toDF("id", "num")
df.show
+---+---+
| id|num|
+---+---+
|  4|9.0|
|  3|7.0|
|  2|3.0|
|  1|5.0|
+---+---+
df.withColumn("new_column", lag("num", 1, 0).over(w)).show
+---+---+----------+
| id|num|new_column|
+---+---+----------+
|  1|5.0|       0.0|
|  2|3.0|       5.0|
|  3|7.0|       3.0|
|  4|9.0|       7.0|
+---+---+----------+