how can I rewrite the code below to reduce memory usage as much as possible in databricks? df_improvement_view = df_X_plus_5_status_quo.drop(F.col('VIEW'))

while df_improvements_X_plus_5.count() > 0:
  
  # view without duplicates
  df_improvements_X_plus_5_temp_view = df_improvements_X_plus_5 \
    .orderBy(['Unique_Concat_Value', 'IMPR_DATE']).dropDuplicates(subset=['Unique_Concat_Value'])
  
  # Merge improved view with status quo df
  df_X_plus_5_with_improvements = df_improvement_view \
    .join(df_improvements_X_plus_5_temp_view, on=['Unique_Concat_Value'], how='left')

  if df_X_plus_5_with_improvements.count() == df_improvement_view.count(): # Continue

    # check columns for improved values -> if value is entered, keep value, else take the one from the standard view
    temp = df_X_plus_5_with_improvements \
        .withColumn('ORGANIZATION_CODE',
            F.when((F.col('IMPR_Organization_Code').isNull()) | (F.isnan(F.col('IMPR_Organization_Code'))) | 
                (F.col('IMPR_Organization_Code') == 0) | (F.col('PERIOD') < F.col('IMPR_DATE')), F.col('ORGANIZATION_CODE'))
            .otherwise(F.col('IMPR_Organization_Code'))
        ) \
        .withColumn('TESTER_CODE',
            F.when((F.col('IMPR_TESTER_CODE').isNull()) | (F.col('PERIOD') < F.col('IMPR_DATE')), F.col('TESTER_CODE'))
            .otherwise(F.col('IMPR_TESTER_CODE'))
        ) \
        .withColumn('TESTER_DESCRIPTION',
            F.when((F.col('IMPR_TESTER_DESCRIPTION').isNull()) | (F.col('PERIOD') < F.col('IMPR_DATE')), F.col('TESTER_DESCRIPTION'))
            .otherwise(F.col('IMPR_TESTER_DESCRIPTION'))
        ) \
        .withColumn('RESOURCE_CODE',
            F.when((F.col('IMPR_RESOURCE_CODE').isNull()) | (F.col('PERIOD') < F.col('IMPR_DATE')), F.col('RESOURCE_CODE'))
            .otherwise(F.col('IMPR_RESOURCE_CODE'))
        ) \
        .withColumn('RESOURCE_DESCRIPTION',
            F.when((F.col('IMPR_RESOURCE_DESCRIPTION').isNull()) | (F.col('PERIOD') < F.col('IMPR_DATE')),
                F.col('RESOURCE_DESCRIPTION'))
            .otherwise(F.col('IMPR_RESOURCE_DESCRIPTION'))
        ) \
        .withColumn('PLANNING_YIELD',
            F.when((F.col('IMPR_PLANNING_YIELD').isNull()) | (F.col('PERIOD') < F.col('IMPR_DATE')), F.col('PLANNING_YIELD'))
            .otherwise(F.col('IMPR_PLANNING_YIELD'))
        ) \
        .withColumn('REV_CUM_PLANNING_YIELD',
            F.when((F.col('IMPR_REV_CUM_PLANNING_YIELD').isNull()) | (F.col('PERIOD') < F.col('IMPR_DATE')),
                F.col('REV_CUM_PLANNING_YIELD'))
            .otherwise(F.col('IMPR_REV_CUM_PLANNING_YIELD'))
        ) \
        .withColumn('EXEC_TIME',
            F.when((F.col('IMPR_EXEC_TIME').isNull()) | (F.col('PERIOD') < F.col('IMPR_DATE')), F.col('EXEC_TIME'))
            .otherwise(F.col('IMPR_EXEC_TIME'))
        ) \
        .withColumn('SETUP_TIME',
            F.when((F.col('IMPR_SETUP_TIME').isNull()) | (F.col('PERIOD') < F.col('IMPR_DATE')), F.col('SETUP_TIME'))
            .otherwise(F.col('IMPR_SETUP_TIME'))
        ) \
        .withColumn('INDEX_TIME',
            F.when((F.col('IMPR_INDEX_TIME').isNull()) | (F.col('PERIOD') < F.col('IMPR_DATE')), F.col('INDEX_TIME'))
            .otherwise(F.col('IMPR_INDEX_TIME'))
        ) \
        .withColumn('PARALLEL_FACTOR',
            F.when((F.col('IMPR_PARALLEL_FACTOR').isNull()) | (F.col('PERIOD') < F.col('IMPR_DATE')), F.col('PARALLEL_FACTOR'))
            .otherwise(F.col('IMPR_PARALLEL_FACTOR'))
        ) \
        .withColumn('RETEST_FACTOR',
            F.when((F.col('IMPR_RETEST_FACTOR').isNull()) | (F.col('PERIOD') < F.col('IMPR_DATE')), F.col('RETEST_FACTOR'))
            .otherwise(F.col('IMPR_RETEST_FACTOR'))
        ) \
        .withColumn('SAMPLE_RATE',
            F.when((F.col('IMPR_SAMPLE_RATE').isNull()) | (F.col('PERIOD') < F.col('IMPR_DATE')), F.col('SAMPLE_RATE'))
            .otherwise(F.col('IMPR_SAMPLE_RATE'))
        ) \
        .withColumn('PERF_FACTOR',
            F.when((F.col('IMPR_PERFORMANCE').isNull()) | (F.col('PERIOD') < F.col('IMPR_DATE')), F.col('PERF_FACTOR'))
            .otherwise(F.col('IMPR_PERFORMANCE'))
        )

    # Recalculate below
    temp = temp \
        .withColumn('CALC_TEST_TIME_DEVICE', 
            (F.col('SETUP_TIME') + F.col('LEAD_TIME_LOT_SIZE') * (1 + F.col('RETEST_FACTOR')) * F.col('SAMPLE_RATE') * 
            ((F.col('EXEC_TIME') + F.col('INDEX_TIME'))/F.col('PARALLEL_FACTOR')))/F.col('LEAD_TIME_LOT_SIZE')
        ) \
        .withColumn('CALC_TEST_TIME_LOT', 
            (F.col('SETUP_TIME') + F.col('LEAD_TIME_LOT_SIZE') * (1 + F.col('RETEST_FACTOR')) * F.col('SAMPLE_RATE') * 
            ((F.col('EXEC_TIME') + F.col('INDEX_TIME'))/F.col('PARALLEL_FACTOR')))/3600
        ) \
        .withColumn('CALC_THROUGHPUT', 3600 / F.col('CALC_TEST_TIME_DEVICE')) \
        .withColumn('CALC_TEST_TIME_DEVICE_PERF', F.col('CALC_TEST_TIME_DEVICE')/F.col('PERF_FACTOR')) \
        .withColumn('CALC_TEST_TIME_LOT_PERF', F.col('CALC_TEST_TIME_LOT')/F.col('PERF_FACTOR')) \
        .withColumn('CALC_THROUGHPUT_PERF', F.col('CALC_THROUGHPUT') * F.col('PERF_FACTOR'))

    # when resoure code changes from BACKEND to HANDLER, the resource group needs to be updated as well
    df_X_plus_5_with_improvements = temp \
        .withColumn('RESOURCE_GROUP',
            F.when((F.col('IMPR_RESOURCE_CODE').isNull()) | (F.col('PERIOD') < F.col('IMPR_DATE')), F.col('RESOURCE_GROUP'))
            .when((F.col('IMPR_RESOURCE_CODE').substr(0,2) == 'P-') & (F.col('PERIOD') >= F.col('IMPR_DATE')), F.lit('PROBER'))
            .when((F.col('IMPR_RESOURCE_CODE').substr(0,2) == 'B-') & (F.col('PERIOD') >= F.col('IMPR_DATE')), F.lit('BACKEND'))
            .when((F.col('IMPR_RESOURCE_CODE').substr(0,2) == 'C-') & (F.col('PERIOD') >= F.col('IMPR_DATE')), F.lit('COMBI FT'))
            .when((F.col('IMPR_RESOURCE_CODE').substr(0,2) == 'H-') & (F.col('PERIOD') >= F.col('IMPR_DATE')), F.lit('HANDLER'))
            .when((F.col('IMPR_RESOURCE_CODE').substr(0,2) == 'M-') & (F.col('PERIOD') >= F.col('IMPR_DATE')), F.lit('HANDLER'))
            .otherwise(F.col('RESOURCE_GROUP'))
        )

    # Add column with the view (Status Quo vs Improvements)
    df_improvement_view = df_X_plus_5_with_improvements.withColumn('VIEW', F.lit('Improvements'))    
    
    # drop the columns that were joined from the IMPALA view as the columns will be added again if there are duplicates 
    # (naming should remain the same to have a succesfull loop)
    col_to_drop = ('IMPR_Organization_Code', 'IMPR_RESOURCE_CODE', 'IMPR_RESOURCE_DESCRIPTION', 'IMPR_TESTER_CODE', 
      'IMPR_TESTER_DESCRIPTION', 'IMPR_PLANNING_YIELD', 'IMPR_REV_CUM_PLANNING_YIELD', 'IMPR_EXEC_TIME', 'IMPR_SETUP_TIME', 
      'IMPR_INDEX_TIME', 'IMPR_PARALLEL_FACTOR', 'IMPR_RETEST_FACTOR', 'IMPR_SAMPLE_RATE', 'IMPR_PERFORMANCE', 'IMPR_DATE', 
      'IMPR_REFERENCE', 'IMPR_STATUS', 'COMMENT')

    df_improvement_view = df_improvement_view.drop(*col_to_drop)
    
  else:
    raise Exception("ERROR in merge. Qty of lines after the merge is not equal the Qty of lines from before the merge")
  
  # view with duplicates ONLY!
  df_improvements_X_plus_5 = df_improvements_X_plus_5.subtract(df_improvements_X_plus_5_temp_view)