let data =[ {“id”:”2018″, “name”:”test”, “total”:1200}, {“id”:”2019″, “name”:”wath”, “total”:1500}, {“id”:”2019″, “name”:”wath”, “total”:1800}, {“id”:”2020″, “name”:”zooi”, “total”:1000}, ]; let map = data.reduce((prev, next) =>{ if (next.id in prev) { prev[next.id].total += next.total; } else { prev[next.id] = next; } return prev; }, {}); let result = Object.keys(map).map(id => map[id]); console.log(result);
Collectors.partitioningBy: Map<Boolean, List<Employee>> partitioned = listOfEmployees.stream().collect( Collectors.partitioningBy(Employee::isActive)); The resulting map contains two lists, corresponding to whether or not the predicate was matched: List<Employee> activeEmployees = partitioned.get(true); List<Employee> formerEmployees = partitioned.get(false); There are a couple of reasons to use partitioningBy over groupingBy (as suggested by Juan Carlos Mendoza): Firstly, the parameter of groupingBy is a Function<Employee, … Read more
UPDATE Some of the commentators make a good point that the original array is being mutated in order to break early inside the .reduce() logic. Therefore, I’ve modified the answer slightly by adding a .slice(0) before calling a follow-on .reduce() step, yielding a copy of the original array. NOTE: Similar ops that accomplish the same … Read more
The reduce phase has 3 steps: shuffle, sort, reduce. Shuffle is where the data is collected by the reducer from each mapper. This can happen while mappers are generating data since it is only a data transfer. On the other hand, sort and reduce can only start once all the mappers are done. You can … Read more
The problem is that your accumulator values are promises – they’re return values of async functions. To get sequential evaluation (and all but the last iteration to be awaited at all), you need to use const data = await array.reduce(async (accumP, current, index) => { const accum = await accumP; … }, Promise.resolve(…)); That said, … Read more
reduce vs foldLeft A big big difference, not mentioned in any other stackoverflow answer relating to this topic clearly, is that reduce should be given a commutative monoid, i.e. an operation that is both commutative and associative. This means the operation can be parallelized. This distinction is very important for Big Data / MPP / … Read more
You can read about the changes in What’s New In Python 3.0. You should read it thoroughly when you move from 2.x to 3.x since a lot has been changed. The whole answer here are quotes from the documentation. Views And Iterators Instead Of Lists Some well-known APIs no longer return lists: […] map() and … Read more
A cleaner way to accomplish this is by providing an initial value as the second argument to reduce: var arr = [{x:1}, {x:2}, {x:4}]; var result = arr.reduce(function (acc, obj) { return acc + obj.x; }, 0); console.log(result); // 7 The first time the anonymous function is called, it gets called with (0, {x: 1}) … Read more
Rather than using a for loop and pushing values, you can directly use map and reduce: let objArr = [ {key: ‘Mon Sep 23 2013 00:00:00 GMT-0400’, val: 42}, {key: ‘Mon Sep 24 2013 00:00:00 GMT-0400’, val: 78}, {key: ‘Mon Sep 25 2013 00:00:00 GMT-0400’, val: 23}, {key: ‘Mon Sep 23 2013 00:00:00 GMT-0400’, val: … Read more
On Python 3.8+, with floats, you can use statistics.fmean as it’s faster with floats. On Python 3.4+, you can use statistics.mean: l = [15, 18, 2, 36, 12, 78, 5, 6, 9] import statistics statistics.mean(l) # = 20.11111111111111 On older versions of Python you can: sum(l) / len(l) On Python 2, you need to convert … Read more