I have a pyspark dataframe. I need to randomize values taken from list for all rows within given condition. I did:

df = df.withColumn('rand_col', f.when(f.col('condition_col') == condition, random.choice(my_list)))

but the effect is, that it randomizes only one value and assigns it to all rows:

How can I randomize separately for each row?

CodePudding user response：

You can:

• use rand and floor from pyspark.sql.functions to create a random indexing column to index into your my_list
• create a column in which the my_list value is repeated
• index into that column using f.col

It would look something like this:

import pyspark.sql.functions as f

my_list = [1, 2, 30]
df = spark.createDataFrame(
    [
        (1, 0),
        (2, 1),
        (3, 1),
        (4, 0),
        (5, 1),
        (6, 1),
        (7, 0),
    ],
    ["id", "condition"]
)

df = df.withColumn('rand_index', f.when(f.col('condition') == 1, f.floor(f.rand() * len(my_list))))\
       .withColumn('my_list', f.array([f.lit(x) for x in my_list]))\
       .withColumn('rand_value', f.when(f.col('condition') == 1, f.col("my_list")[f.col("rand_index")]))

df.show()
 --- --------- ---------- ---------- ---------- 
| id|condition|rand_index|   my_list|rand_value|
 --- --------- ---------- ---------- ---------- 
|  1|        0|      null|[1, 2, 30]|      null|
|  2|        1|         0|[1, 2, 30]|         1|
|  3|        1|         2|[1, 2, 30]|        30|
|  4|        0|      null|[1, 2, 30]|      null|
|  5|        1|         1|[1, 2, 30]|         2|
|  6|        1|         2|[1, 2, 30]|        30|
|  7|        0|      null|[1, 2, 30]|      null|
 --- --------- ---------- ---------- ----------