When dealing with arrays in PySpark, a common requirement is to prepend an element at the beginning of an array, effectively creating a new array that includes the new element as well as all elements from the source array. PySpark, doesn’t have a built-in function for prepending. However, you can achieve this by using a combination of existing PySpark functions. This article guides you through this process with a working example.
Creating the DataFrame
Let’s first create a PySpark DataFrame with an array column to use in the demonstration:
Creating the DataFrame
Let’s first create a PySpark DataFrame with an array column to use in the demonstration:
from pyspark.sql import SparkSession
from pyspark.sql.functions import array
# Initialize a SparkSession
spark = SparkSession.builder.getOrCreate()
# Create a DataFrame
data = [("fruits", ["apple", "banana", "cherry", "date", "elderberry"]),
("numbers", ["one", "two", "three", "four", "five"]),
("colors", ["red", "blue", "green", "yellow", "pink"])]
df = spark.createDataFrame(data, ["Category", "Items"])
df.show()+--------+-----------------------------------------+
|Category|Items |
+--------+-----------------------------------------+
|fruits |[apple, banana, cherry, date, elderberry]|
|numbers |[one, two, three, four, five] |
|colors |[red, blue, green, yellow, pink] |
+--------+-----------------------------------------+Prepending an Element to an Array
The approach to prepending an element to an array in PySpark involves combining the array() and concat() functions. We will create a new array with the element to prepend and concatenate it with the original array:
from pyspark.sql.functions import array, concat
# Element to prepend
element = "zero"
# Prepend the element
df = df.withColumn("Items", concat(array(lit(element)), df["Items"]))
df.show(20,False)
This code creates a new column “Items” by concatenating a new array containing the element to prepend (“zero”) with the existing “Items” array.
The lit() function is used to create a column of literal value. The array() function is used to create an array with the literal value, and the concat() function is used to concatenate two arrays.
This results in a new DataFrame where “zero” is prepended to each array in the “Items” column.
While PySpark doesn’t provide a built-in function for prepending an element to an array, we can achieve the same result by creatively using the functions available. We walked through an example of how to prepend an element to an array in a PySpark DataFrame. This method highlights the flexibility of PySpark and how it can handle a variety of data manipulation tasks by combining its available functions.
+--------+-----------------------------------------------+
|Category|Items |
+--------+-----------------------------------------------+
|fruits |[zero, apple, banana, cherry, date, elderberry]|
|numbers |[zero, one, two, three, four, five] |
|colors |[zero, red, blue, green, yellow, pink] |
+--------+-----------------------------------------------+
Spark important urls to refer
