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.
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Asked: 2022-10-21 11:00:00 +0000
Seen: 13 times
Last updated: Aug 01 '22
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