PySpark UDF: a fir transform example

Question

I am really new to PySpark and am trying to translate some python code into pyspark. I start with a panda, convert to a document - term matrix and then apply PCA.

The UDF:

    class MultiLabelCounter():
        def __init__(self, classes=None):
            self.classes_ = classes

        def fit(self,y):
            self.classes_ = 
    sorted(set(itertools.chain.from_iterable(y)))
            self.mapping = dict(zip(self.classes_,
                                         
    range(len(self.classes_))))
            return self

    def transform(self,y):
        yt = []
        for labels in y:
            data = [0]*len(self.classes_)
            for label in labels:
                data[self.mapping[label]] +=1
            yt.append(data)
        return yt

    def fit_transform(self,y):
        return self.fit(y).transform(y)

    mlb = MultiLabelCounter()

    df_grouped = 
    df_grouped.withColumnRenamed("collect_list(full)","full")

    udf_mlb = udf(lambda x: mlb.fit_transform(x),IntegerType())

    mlb_fitted = df_grouped.withColumn('full',udf_mlb(col("full")))

I am of course getting NULL results.

I am using spark 2.4.4 version.

EDIT

Adding sample input and output as per request

Input:

|id|val|
|--|---|
|1|[hello,world]|
|2|[goodbye, world]|
|3|[hello,hello]|

Output:

|id|hello|goodbye|world|
|--|-----|-------|-----|
|1|1|0|1|
|2|0|1|1|
|3|2|0|0|

Answer 1

Based upon input data shared, I tried replicating your output and it works. Please see below -

Input Data

df = spark.createDataFrame(data=[(1, ['hello', 'world']), (2, ['goodbye', 'world']), (3, ['hello', 'hello'])], schema=['id', 'vals'])

df.show()

+---+----------------+
| id|            vals|
+---+----------------+
|  1|  [hello, world]|
|  2|[goodbye, world]|
|  3|  [hello, hello]|
+---+----------------+

Now, using explode to create separate rows out of vals list items. Thereafter, using pivot and count will calculate the frequency. Finally, replacing null values with 0 using fillna(0). See below -

from pyspark.sql.functions import *

df1 = df.select(['id', explode(col('vals'))]).groupBy("id").pivot("col").agg(count(col("col")))

df1.fillna(0).orderBy("id").show()

Output

+---+-------+-----+-----+
| id|goodbye|hello|world|
+---+-------+-----+-----+
|  1|      0|    1|    1|
|  2|      1|    0|    1|
|  3|      0|    2|    0|
+---+-------+-----+-----+