Another version of a hash with a key for each element in your array and value for the count of each element a = [ 1, 2, 3, 3, 4, 3] h = Hash.new(0) a.each { | v | h.store(v, h[v]+1) } # h = { 3=>3, 2=>1, 1=>1, 4=>1 }
Commonly, you’d express this as either SELECT COUNT(*) FROM employee WHERE name like ‘kaushik%’ AND rownum = 1 where the rownum = 1 predicate allows Oracle to stop looking as soon as it finds the first matching row or SELECT 1 FROM dual WHERE EXISTS( SELECT 1 FROM employee WHERE name like ‘kaushik%’ ) where … Read more
No, COUNT(*) is not a constant time operation. A COUNT(*) must return a count of rows that conform to the current scan predicate (ie. WHERE clause), so that alone would make the return of a metadata property invalid. But even if you have no predicates, the COUNT still has to satisfy the current transaction isolation … Read more
count doesn’t sum Trues, it only counts the number of non null values. To count the True values, you need to convert the conditions to 1 / 0 and then sum: import pyspark.sql.functions as F cnt_cond = lambda cond: F.sum(F.when(cond, 1).otherwise(0)) test.groupBy(‘x’).agg( cnt_cond(F.col(‘y’) > 12453).alias(‘y_cnt’), cnt_cond(F.col(‘z’) > 230).alias(‘z_cnt’) ).show() +—+—–+—–+ | x|y_cnt|z_cnt| +—+—–+—–+ | bn| … Read more
According to the documentation NUM_ROWS is the “Number of rows in the table”, so I can see how this might be confusing. There, however, is a major difference between these two methods. This query selects the number of rows in MY_TABLE from a system view. This is data that Oracle has previously collected and stored. … Read more
It might be easiest to turn your Series into a DataFrame and use Pandas’ groupby functionality (if you already have a DataFrame then skip straight to adding another column below). If your Series is called s, then turn it into a DataFrame like so: >>> df = pd.DataFrame({‘Timestamp’: s.index, ‘Category’: s.values}) >>> df Category Timestamp … Read more
What you need is the DataFrame aggregation function countDistinct: import sqlContext.implicits._ import org.apache.spark.sql.functions._ case class Log(page: String, visitor: String) val logs = data.map(p => Log(p._1,p._2)) .toDF() val result = logs.select(“page”,”visitor”) .groupBy(‘page) .agg(‘page, countDistinct(‘visitor)) result.foreach(println)
Yes, you can use GROUP BY: SELECT time, activities, COUNT(*) FROM table GROUP BY time, activities;