Function with arguments `cols_in` and `cols_out` defining column names having complex types that need to be transformed during input and output for GROUPED_MAP. In case of SCALAR, we are dealing with a series and thus transformation is done if `cols_in` or `cols_out` evaluates to `True...
('salary'),1)*100))#Where group starts make it 0, and rest compute increment .withColumn('incr_count',sum((col("%increase")>0).cast('int')).over(w))#Compute increment count .where(col("%increase")>20).drop('new_salary')#Filter where salary >20% and drop unwanted column )....
withColumn( "exploded", F.explode(longitudinal_addons.installed_addons) ) .select("exploded") # noqa: E501 - long lines .rdd.flatMap(lambda x: x) .distinct() .collect() ) logging.info( "Number of unique guids co-installed in sample: " + str(len(guid_set_unique)) ) restructured ...
您可以添加一个排序数组为src和dst的src_dst列,然后获取每个src_dst的权重之和,并删除src_dst的重复行: from pyspark.sql import functions as F, Windowdf2 = df.withColumn( 'src_dst', F.sort_array(F.array('src', 'dst'))).withColumn( 'weight', F.sum('weight').over(Window.partitionBy('src...