Built-in Functions - map — Python 3.13.2 documentation
map(function, iterable) -> returns an iterator that applies function to each item of iterable.
In [1]: map(int, ["1", "2", "3"])
Out[1]: <map at 0x10351f040>
In [2]: list(map(int, ["1", "2", "3"]))
Out[2]: [1, 2, 3]
In [3]: list(map(lambda x: x * 2, [1, 2, 3]))
Out[3]: [2, 4, 6]I always use this function to convert stdin strings -> numbers in competitive programming.
functools - reduce - … - Python 3.13.2 documentation
functools.reduce(function, iterable) -> function(function(function(element 1, element 2), element 3), element 4) … (repeat)
In [1]: import functools
In [2]: functools.reduce(lambda x, y: x + y, [1, 2, 3, 4])
Out[2]: 10
In [3]: functools.reduce(lambda x, y: x + y, ["1", "2", "3", "4"])
Out[3]: '1234'I rarely use this function, but I think I should be aware of this.
MapReduce is a common framework for batch processing, especially for large-scale distributed systems. Although there are some conceptual similarities, it seems to be better to consider them completely different.
Mapping: On each node, the map function processes the data it receivesm converting the data into key/value pairs.
Reducing: Reduce functions process the key/value pairs that the mappers emit.
The reduce step aggregates results across distributed nodes, whereas Python reduce() combines values sequentially.
TODO: