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Updating of Spark Dataframe

I am looking for the equivalent of SQL’s update.
I have a data frame:
my_df = df.withColumn(my_col, lit(”)).withColumn(my_col, df.col(‘my_col’) + 1).drop([‘my_col’], 1)

where my_col is a column whose value I wish to increment each time the method is called.
What’s the easiest way to do this in a performant way? I am not sure how to track the data frame changes. I tried the following but the whole dataframe is converted to integers.
df.withColumn(my_col, lit(df.col(my_col) + 1))

Update
I am going to try to present the actual problem I am working on:
I have a simple spark DataFrame:
+—–+————+
|my_col|col_2 |
+—–+————+
| | |
| 0 | 0.3|
| 0 | 0.5|
| 0 | 0.9|
| | |
| 1 | 2.2|
| | 2.3|
| | 2.8|
+—–+————+

I am creating a new column col_1. I want col_1 to be something like ((col_2+col_2+col_2)/3). The problem is that I have to compute col_1 of old data frame and add the new computed value to all the
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