reduce

I have a data frame containing a time series with two time stamp columns, d$day and d$time , and say, for simplicity, one measured variable d$val1 . Suppose I want to examine the situation at the close of each day's experiment, i.e. the last measurement, if it exists. (Not every day has a measurement, and measurements can be taken at different times each day.) I would like to be able to aggregate by day and use some sort of last() or tail() function on time to pull back the corresponding val . I've tried variations like this with not much success (one issue is that tail requires an argument,

I have code similar to follows: var temp=[{"name":"Agency","y":32,"drilldown":{"name":"Agency","categories":["APPS & SI","ERS"],"data":[24,8]}},{"name":"ER","y":60,"drilldown":{"name":"ER","categories":["APPS & SI","ERS"],"data":[7,53]}},{"name":"Direct","y":60,"drilldown":{"name":"Direct","categories":["APPS & SI","ERS"],"data":[31,29]}}]; var reduced=temp.reduce(function (a,b) { return a.y + b.y; }); console.log(reduced) // returns NAN You could use a start value and the add only one value from the array. var temp=[{"name":"Agency","y":32,"drilldown":{"name":"Agency","categories":["APPS & SI

I have a PySpark DataFrame named DF with (K,V) pairs. I would like to apply multiple functions with ReduceByKey. For example, I have following three simple functions: def sumFunc(a,b): return a+b def maxFunc(a,b): return max(a,b) def minFunc(a,b): return min(a,b) When I apply only one function, e.g,, following three work: DF.reduceByKey(sumFunc) #works DF.reduceByKey(maxFunc) #works DF.reduceByKey(minFunc) #works But, when I apply more than one function, it does not work, e.g., followings do not work. DF.reduceByKey(sumFunc, maxfunc, minFunc) #it does not work DF.reduceByKey(sumFunc, maxfunc)

I wrote this code to reduce a list of words to a long count of how many words start with an 'A'. I'm just writing it to learn Java 8, so I'd like to understand it a little better [Disclaimer: I realize this is probably not the best way to write this code; it's just for practice!] . Long countOfAWords = results.stream().reduce( 0L, (a, b) -> b.charAt(0) == 'A' ? a + 1 : a, Long::sum); The middle parameter/lambda (called the accumulator) would seem to be capable of reducing the full list without the final 'Combiner' parameter. In fact, the Javadoc actually says: The {@code accumulator} function

In Java 8, Stream has a method reduce: T reduce(T identity, BinaryOperator<T> accumulator); Is the accumulator operator allowed to modify either of its arguments? I presume not since the JavaDoc says the accumulator should be NonInterfering, though all examples talk of modifying the collection, rather than modifying the elements of the collection. So, for a concrete example, if we have integers.reduce(0, Integer::sum); and suppose for a moment that Integer was mutable, would sum be allowed to modify its first parameter by adding to it (in place) the value of its second parameter? I presume not

I think the title is already explaining my question. I would like to change key (tab space) value into key;value in all output files the reducers are generating from the output of mappers. I could not find good documentation on this using google. Can anyone please give a fraction of code on how to achieve this? Set the configuration property mapred.textoutputformat.separator to ";" In lack of better documentation, here's what I've collected: setTextOutputFormatSeparator(final Job job, final String separator){ final Configuration conf = job.getConfiguration(); //ensure accurate config ref conf