To perform a cumulative sum by group in a PySpark DataFrame, we can use the `Window` function along with `cumsum()`. This allows us to partition the data by a specific group and then perform the cumulative sum within each group. Below is an example to demonstrate how this can be done in PySpark.
Step-by-Step Process
1. Setup and Sample Data
First, let’s import the necessary libraries and create a sample DataFrame.
from pyspark.sql import SparkSession
from pyspark.sql import functions as F
from pyspark.sql.window import Window
# Initialize Spark session
spark = SparkSession.builder.appName("Cumulative Sum by Group").getOrCreate()
# Create sample data
data = [
("A", 1),
("A", 2),
("A", 3),
("B", 2),
("B", 3),
("B", 4)
]
# Create DataFrame
df = spark.createDataFrame(data, ["Group", "Value"])
df.show()
+-----+-----+
|Group|Value|
+-----+-----+
| A| 1|
| A| 2|
| A| 3|
| B| 2|
| B| 3|
| B| 4|
+-----+-----+
2. Define the Window Spec
Next, we define a window specification to partition the DataFrame by the “Group” column and order by some other column if necessary.
# Define the window spec
window_spec = Window.partitionBy("Group").orderBy("Value").rowsBetween(Window.unboundedPreceding, Window.currentRow)
3. Apply Cumulative Sum Function
We then use the `F.sum()` function over the defined window to calculate the cumulative sum by group.
# Apply cumulative sum function
df_with_cumsum = df.withColumn("Cumulative_Sum", F.sum("Value").over(window_spec))
df_with_cumsum.show()
+-----+-----+--------------+
|Group|Value|Cumulative_Sum|
+-----+-----+--------------+
| A| 1| 1|
| A| 2| 3|
| A| 3| 6|
| B| 2| 2|
| B| 3| 5|
| B| 4| 9|
+-----+-----+--------------+
Explanation
1. Window.partitionBy("Group").orderBy("Value")
: Creates a window specification that partitions the DataFrame by the “Group” column and orders the rows within each group by the “Value” column.
2. F.sum("Value").over(window_spec)
: Calculates the cumulative sum of the “Value” column over the specified window.
3. withColumn("Cumulative_Sum", ...)
: Adds the result as a new column called “Cumulative_Sum” to the DataFrame.
This approach ensures that the cumulative sum is calculated correctly for each group individually, based on the order of values within the group.