Start on approach: filter for multiples

Author: MatthijsBlom

exercises/practice/sum-of-multiples/.approaches/config.json

@@ -0,0 +1,18 @@
+{
+    "introduction": {
+        "authors": [
+            "MatthijsBlom"
+        ]
+    },
+    "approaches": [
+        {
+            "uuid": "7dd85d5b-12bd-48a6-97fe-8eb7dd87af72",
+            "slug": "filter-for-multiples",
+            "title": "Filter for multiples",
+            "blurb": "Use the built-in filter function to select the numbers that are multiples, then sum these.",
+            "authors": [
+                "MatthijsBlom"
+            ]
+        }
+    ]
+}

exercises/practice/sum-of-multiples/.approaches/filter-for-multiples/content.md

@@ -0,0 +1,156 @@
+# `filter` for multiples
+
+```python
+def sum_of_multiples(limit, factors):
+    is_multiple = lambda n: any(n % f == 0 for f in factors if f != 0)
+    return sum(filter(is_multiple, range(limit)))
+```
+
+Probably the most straightforward way of solving this problem is to
+
+1. look at every individual integer between `0` and `limit`,
+2. check that it is a multiple of any of the given `factors`, and
+3. add it to the sum when it is.
+
+
+## Notable language features used in this solution
+
+### Built-in function: `sum`
+
+Adding all the numbers in a collection together is a very common operation.
+Therefore, Python provides the built-in function [`sum`][builtin-sum].
+
+`sum` takes one argument, and requires that it be **iterable**.
+A value is iterable whenever it makes sense to use it in a `for` loop like this:
+
+```python
+for _ in iterable_value:  # 👈
+    ...
+```
+
+The `list` is the most commonly used iterable data structure.
+Many other containers are also iterable, such as `set`s, `tuple`s, `range`s, and even `dict`s and `str`ings.
+Still other examples include iterators and generators, which are discuss below.
+
+When given such a collection of numbers, `sum` will look at the elements one by one and add them together.
+The result is a single number.
+
+```python
+numbers = range(1, 100 + 1)  # 1, 2, …, 100
+sum(numbers)
+# ⟹ 5050
+```
+
+Had the highlighted solution not used `sum`, it might have looked like this:
+
+```python
+def sum_of_multiples(limit, factors):
+    is_multiple = lambda n: any(n % f == 0 for f in factors if f != 0)
+    total = 0
+    for multiple in filter(is_multiple, range(limit)):
+        total += total
+    return total
+```
+
+
+### Built-in function: `filter`
+
+Selecting elements of a collection for having a certain property is also a very common operation.
+Therefore, Python provides the built-in function [`filter`][builtin-filter].
+
+`filter` takes two arguments.
+The first is a **predicate**.
+The second is the iterable the elements of which should be filtered.
+
+A predicate is a function that takes one argument (of any particular type) and returns a `bool`.
+Such functions are commonly used to encode properties of values.
+An example is `str.isupper`, which takes a `str` and returns `True` whenever it is uppercase:
+
+```python
+str.isupper("AAAAH! 😱")  # ⟹ True
+str.isupper("Eh? 😕")     # ⟹ False
+str.isupper("⬆️💼")        # ⟹ False
+```
+
+Thus, the function `str.isupper` represents the property of _being an uppercase string_.
+
+Contrary to what you might expect, `filter` does not return a data structure like the one given as an argument:
+
+```python
+filter(str.isupper, ["THUNDERBOLTS", "and", "LIGHTNING"])
+# ⟹ <filter object at 0x000002F46B107BE0>
+```
+
+Instead, it returns an **iterator**.
+
+An iterator is an object whose sole purpose is to guide iteration through some data structure.
+In particular, `filter` makes sure that elements that do not satisfy the predicate are skipped.
+It is a bit like a cursor that can move only to the right.
+
+The main differences between containers (such as `list`s) and iterators are
+
+- Containers can, depending on their contents, take up a lot of space in memory, but iterators are generally very small (regardless of how many elements they 'contain').
+- Containers can be iterated over multiple times, but iterators can be used only once.
+
+To illustrate the latter difference:
+
+```python
+is_even = lambda n: n % 2 == 0
+numbers = range(20)                      # 0, 1, …, 19
+even_numbers = filter(is_even, numbers)  # 0, 2, …, 18
+sum(numbers)       # ⟹ 190
+sum(numbers)       # ⟹ 190
+sum(even_numbers)  # ⟹ 90
+sum(even_numbers)  # ⟹ 0
+```
+
+Here, `sum` iterates over both `numbers` and `even_numbers` twice.
+
+In the case of `numbers` everything is fine.
+Even after looping through the whole of `numbers`, all its elements are still there, and so `sum` can ask to see them again without problem.
+
+The situation with `even_numbers` is move involved.
+To use the _cursor_ analogy: after going through all of `even_number`'s 'elements' &ndash; actually elements of `numbers` &ndash; the cursor has moved all the way to the right.
+It cannot move backwards, so if you wish to iterate over all even numbers then you need a new cursor.
+We say the the `even_numbers` iterator is _exhausted_. When `sum` asks for its elements again, `even_numbers` comes up empty and so `sum` returns `0`.
+
+Had the highlighted solution not used `filter`, it might have looked like this:
+
+```python
+def sum_of_multiples(limit, factors):
+    is_multiple = lambda n: any(n % f == 0 for f in factors if f != 0)
+    multiples = [candidate for candidate in range(limit) if is_multiple(candidate)]
+    return sum(multiples)
+```
+
+This variant stores all the multiples in a `list` before summing them.
+Such a list can potentially be very big.
+For example, if `limit = 1_000_000_000` and `factors = [1]` then `multiples` will be a list 8 gigabytes large!
+It is to avoid unnecessarily creating such large intermediate data structures that iterators are often used.
+
+
+### A function expression: `lambda`
+
+...
+
+
+### Built-in function: `any`
+
+...
+
+
+### A generator expression
+
+...
+
+
+## Reflections on this approach
+
+An important advantage of this approach is that it is very easy to understand.
+However, it suffers from potentially performing a lot of unnecessary work, for example when all `factors` are large, or when there are no `factors` at all.
+
+<!-- TODO elaborate -->
+
+
+[builtin-sum]: https://docs.python.org/3/library/functions.html#sum "Built-in Functions: sum"
+[builtin-filter]: https://docs.python.org/3/library/functions.html#filter "Built-in Functions: filter"

exercises/practice/sum-of-multiples/.approaches/filter-for-multiples/snippet.txt

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+def sum_of_multiples(limit, factors):
+    is_multiple = lambda n: any([n % f == 0 for f in factors if f != 0])
+    return sum(filter(is_multiple, range(limit)))

exercises/practice/sum-of-multiples/.approaches/introduction.md

@@ -19,9 +19,16 @@ def sum_of_multiples(limit, factors):
     return sum(filter(is_multiple, range(limit)))
 ```
 
-Egregious performance when multiples are few.
+Probably the most straightforward way of solving this problem is to
 
-...
+1. look at every individual integer between `0` and `limit`,
+2. check that it is a multiple of any of the given `factors`, and
+3. add it to the sum when it is.
+
+An important advantage of this approach is that it is very easy to understand.
+However, it suffers from potentially performing a lot of unnecessary work, for example when all `factors` are large, or when there are no `factors` at all.
+
+[Read more about this approach][filter-for-multiples].
 
 
 <!-- TODO improve section title -->
@@ -107,3 +114,7 @@ This approach saves on a lot of iteration, but is still vulnerable to excessive
 Fortunately it can be combined with the generator merging approach.
 
 ...
+
+
+
+[filter-for-multiples]: https://exercism.org/tracks/python/exercises/sum-of-multiples/approaches/filter-for-multiples "Approach: filter for multiples"