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One way to accomplish this is to use PySpark's array functions along with the when and otherwise clauses to split the array based on a specific value and update the corresponding column.
For example, if we have a PySpark DataFrame with a column my_array that contains an array of integers, and we want to split the array based on the value 5, we could use the following code:
from pyspark.sql.functions import array, when, otherwise
split_array = array([when(x == 5, None).otherwise(x) for x in df['my_array']])
# Update the corresponding column with the split array
df = df.withColumn('split_array', split_array)
In this code, we first use PySpark's array function to create a new array from the result of the when and otherwise clauses. The when clause checks if the current array element is equal to 5, and if so, replaces it with None, effectively splitting the array at that position. The otherwise clause simply uses the original array element if it is not equal to 5.
We then use this newly created split_array column to update the corresponding column in our PySpark DataFrame.