AttributeError: 'DataFrame' object has no attribute 'pivot' - dataframe

Question

I have PySpark dataframe:
user_id
item_id
last_watch_dt
total_dur
watched_pct
1
1
2021-05-11
4250
72
1
2
2021-05-11
80
99
2
3
2021-05-11
1000
80
2
4
2021-05-11
5000
40
I used this code:
df_new = df.pivot(index='user_id', columns='item_id', values='watched_pct')
To get this:
1
2
3
4
1
72
99
0
0
2
0
0
80
40
But I got an error:
AttributeError: 'DataFrame' object has no attribute 'pivot'
What did I do wrong?

Answer 1

You can only do .pivot on objects having pivot attribute (method or property). You tried to do df.pivot, so it would only work if df had such attribute. You can inspect all the attributes of df (it's an object of pyspark.sql.DataFrame class) here. You see many attributes there, but none of them is called pivot. That's why you get an attribute error.
pivot is a method of pyspark.sql.GroupedData object. It means, in order to use it, you must somehow create pyspark.sql.GroupedData object from your pyspark.sql.DataFrame object. In your case, it's by using .groupBy():
df.groupBy("user_id").pivot("item_id")
This creates yet another pyspark.sql.GroupedData object. In order to make a dataframe out of it you would want to use one of the methods of GroupedData class. agg is the method that you need. Inside it, you will have to provide Spark's aggregation function which you will use for all the grouped elements (e.g. sum, first, etc.).
df.groupBy("user_id").pivot("item_id").agg(F.sum("watched_pct"))
Full example:
from pyspark.sql import functions as F
df = spark.createDataFrame(
[(1, 1, '2021-05-11', 4250, 72),
(1, 2, '2021-05-11', 80, 99),
(2, 3, '2021-05-11', 1000, 80),
(2, 4, '2021-05-11', 5000, 40)],
['user_id', 'item_id', 'last_watch_dt', 'total_dur', 'watched_pct'])
df = df.groupBy("user_id").pivot("item_id").agg(F.sum("watched_pct"))
df.show()
# +-------+----+----+----+----+
# |user_id| 1| 2| 3| 4|
# +-------+----+----+----+----+
# | 1| 72| 99|null|null|
# | 2|null|null| 80| 40|
# +-------+----+----+----+----+
If you want to replace nulls with 0, use fillna of pyspark.sql.DataFrame class.
df = df.fillna(0)
df.show()
# +-------+---+---+---+---+
# |user_id| 1| 2| 3| 4|
# +-------+---+---+---+---+
# | 1| 72| 99| 0| 0|
# | 2| 0| 0| 80| 40|
# +-------+---+---+---+---+