PySpark UDF: a fir transform example - pyspark

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|
+---+-------+-----+-----+