group by column and get top-3 most frequent values from another column as comma-separated string
There is a dataframe with the columns district, crime_type, date, month
group by column and get top-3 most frequent values from another column as comma-separated string
There is a dataframe with the columns district, crime_type, date, month
how to run pySpark
I am new in Python and trying to run the code below in VS. But I am keep getting SyntaxError: invalid syntax
. How to get around with this ?
write.csv command is creating a folder and not a .csv file in pyspark
I am working through a book chapter in pyspark and the write.csv command is creating a folder, rather than a .csv file.
write.csv command is creating a folder and not a .csv file in pyspark
I am working through a book chapter in pyspark and the write.csv command is creating a folder, rather than a .csv file.
Unioning to PySpark Dataframes but ignoring nested columns
I have the two PySpark dataframes. Below a reproduction of the problem is shown. I want to union the two PySpark dataframes but gives the obvious following error: [INCOMPATIBLE_COLUMN_TYPE] UNION can only be performed on tables with compatible column types.
Handle diffrent levels/hierarchies in data using collect_list – PySpark
In the data below, for each id2, I want to collect a list of the id1 that is above them in hierarchy/level.
Collect list inside window function with condition, pyspark
I want to collect a list of all the values of id2 for each id1 that has the same or lower level within a group.
pyspark unpivot or reduce
I have the following dataframe:
How to read / restore a checkpointed Dataframe – across batches
I need to “checkpoint” certain information during my batch processing with pyspark that are needed in the next batches.