Stream: Grouping & Partitioning

`groupingBy(Employee::getDepartment)` sorts employees into one bucket per department.

`groupingBy(dept, counting())` gives the head count per department as a Map<String, Long>.

`groupingBy(dept, averagingDouble(getSalary))` gives the mean salary per department.

`groupingBy(dept, collectingAndThen(maxBy(by salary), Optional::get))` gives the top earner per department.

`groupingBy(dept, mapping(Employee::getName, toList()))` groups by department but stores only the names, giving a `Map<String, List<String>>`.

`groupingBy(dept, groupingBy(gender))` builds a two-level map: department, then gender, then the list of people.

`partitioningBy(e -> e.getSalary().compareTo(FIFTY_K) > 0)` always yields a two-key map, `true` and `false`, never missing either side.

`groupingBy(dept, reducing(BigDecimal.ZERO, Employee::getSalary, BigDecimal::add))` totals each department's salaries exactly, with no floating-point error.

`averagingDouble` gives a fast mean as a `double`; for money, sum `BigDecimal` values and divide by the count with an explicit scale and rounding mode.

groupingBy(e -> e.getJoinDate().getYear()) buckets employees by the year they joined.

groupingBy(dept, summingDouble(salary)) produces a department-to-total-salary map in a single pass.

Pass a lambda that converts age into a band label as the groupingBy classifier to bucket employees by range.

Group by department, sort each group by salary descending, skip(1).findFirst() to get the runner-up employee.

groupingBy(dept, partitioningBy(isMale)) first buckets by department, then splits each bucket into true (male) and false (female).

groupingBy(e -> e.getName().charAt(0)) groups employees by the first letter of their name, building a phone-book-style index.

`partitioningBy(pred, counting())` yields two keys, true and false, each mapped to the count of elements on that side, in a single pass.

groupingBy(String::length) partitions strings by their character count into a Map where each key is a length and each value is the list of strings of that length.

groupingBy(String::length, counting()) buckets strings by length and counts the members of each bucket, producing a Map<Integer, Long> histogram.

groupingBy(s -> s.charAt(0)) groups strings by their first character, producing a Map<Character, List<String>> for alphabetical directory navigation.

`groupingBy(dept, groupingBy(city))` builds a two-level department-then-city map in a single collect pass.

Collectors.partitioningBy(n -> n % 2 == 0) splits a stream into even (true) and odd (false) lists, always producing both keys.

Count each integer with groupingBy(n -> n, counting()), then filter entries with count greater than 1 to find the values that appear more than once.