PySpark - Struggling to arrange the data by a specific format - pyspark

Question

I am working on outputting total deduplicated counts from a pre-aggregated frame as follows.
I currently have a data frame that displays like so. It's the initial structure and the point that I have gotten to by filtering out unneeded columns.
ID
Source
101
Grape
101
Flower
102
Bee
103
Peach
105
Flower
We can see from the example above that 101 is found in both Grape and Flower. I would like to arrange the format so that the distinct string values from the "Source" column become their own sources, as from there I can perform a groupBy for a specific arrangement of yes's and no's as so.
ID
Grape
Flower
Bee
Peach
101
Yes
Yes
No
No
102
No
No
Yes
No
103
No
No
No
Yes
I agree that creating this manually via the above example is a good fit, but I am working with +100m rows and need something more susinct.
What I've extracted so far is a list of distinct Source values and arranged them into a list:
dedupeTableColumnNames = dedupeTable.select('SOURCE').distinct().collect()
dedupeTableColumnNamesCleaned = re.findall(r"'([^']*)'", str(dedupeTableColumnNames))

Answer 1

That's just a pivot :
df.groupBy("id").pivot("source").count().show()
+---+------+------+------+------+
| id|Bee |Flower|Grape |Peach |
+---+------+------+------+------+
|103| null| null| null| 1|
|105| null| 1| null| null|
|101| null| 1| 1| null|
|102| 1| null| null| null|
+---+------+------+------+------+