Its main task is to transform and improve the user’s SQL or DataFrame operation to generate an efficient physical execution plan tailored to the specific query and dataset characteristics. Describe how to implement custom aggregations in PySpark. To implement custom aggregations in PySpark, we can ...
F1 = udf(lambda x: ‘-1’ if x in not_found_cat else x, StringType()) After registering the “bus” as the table using “registerAsTable” operation, we apply the SQL queries on the “bus_table” for selecting the “P_ID”; the SQL query result will be the DataFrame. We have ...