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functional programming
Map, Filter, Reduce
Map, filter, and reduce are the three core operations for transforming collections in functional programming — all without mutating the original data. Think of them like stages on a conveyor belt: map reshapes each item, filter removes items that don't belong, and reduce combines everything into a final result. Together they replace imperative for-loops with declarative, composable, and self-documenting transformations.
Use Cases
- •Transforming API response data into UI-ready view models with map
- •Selecting relevant records from a dataset based on business rules with filter
- •Aggregating totals, averages, or grouped summaries from collections with reduce
- •Building ETL (extract-transform-load) pipelines that chain map, filter, and reduce stages
Implementation
Output
Click "Run Code" to see output...