[Acronym] Approach docs cleanup

exercises/practice/acronym/.approaches/config.json

@@ -9,49 +9,56 @@
       "slug": "functools-reduce",
       "title": "Functools Reduce",
       "blurb": "Use functools.reduce() to form an acronym from text cleaned using str.replace().",
-      "authors": ["bethanyg"]
+      "authors": ["bethanyg"],
+      "contributors": ["yrahcaz7"]
     },
     {
       "uuid": "d568ea30-b839-46ad-9c9b-73321a274325",
       "slug": "generator-expression",
       "title": "Generator Expression",
       "blurb": "Use a generator expression with str.join() to form an acronym from text cleaned using str.replace().",
-      "authors": ["bethanyg"]
+      "authors": ["bethanyg"],
+      "contributors": ["yrahcaz7"]
     },
     {
       "uuid": "da53b1bc-35c7-47a7-88d5-56ebb9d3658d",
       "slug": "list-comprehension",
       "title": "List Comprehension",
       "blurb": "Use a list comprehension with str.join() to form an acronym from text cleaned using str.replace().",
-      "authors": ["bethanyg"]
+      "authors": ["bethanyg"],
+      "contributors": ["yrahcaz7"]
     },
     {
       "uuid": "abd51d7d-3743-448d-b8f1-49f484ae6b30",
       "slug": "loop",
       "title": "Loop",
       "blurb": "Use str.replace() to clean the input string and a loop with string concatenation to form the acronym.",
-      "authors": ["bethanyg"]
+      "authors": ["bethanyg"],
+      "contributors": ["yrahcaz7"]
     },
     {
       "uuid": "9eee8db9-80f8-4ee4-aaaf-e55b78221283",
       "slug": "map-function",
       "title": "Map Built-in",
       "blurb": "Use the built-in map() function to form an acronym after cleaning the input string with str.replace().",
-      "authors": ["bethanyg"]
+      "authors": ["bethanyg"],
+      "contributors": ["yrahcaz7"]
     },
     {
       "uuid": "8f4dc8ba-fd1c-4c85-bcc3-8ef9dca34c7f",
       "slug": "regex-join",
       "title": "Regex join",
       "blurb": "Use regex to clean the input string and form the acronym with str.join().",
-      "authors": ["bethanyg"]
+      "authors": ["bethanyg"],
+      "contributors": ["yrahcaz7"]
     },
     {
       "uuid": "8830be43-44c3-45ab-8311-f588f60dfc5f",
       "slug": "regex-sub",
       "title": "Regex Sub",
       "blurb": "Use re.sub() to clean the input string and create the acronym in one step.",
-      "authors": ["bethanyg"]
+      "authors": ["bethanyg"],
+      "contributors": ["yrahcaz7"]
     },
     {
       "uuid": "0ce3eaf7-da79-403d-a481-5dd8f476d286",

exercises/practice/acronym/.approaches/double-generator-expression/content.md

@@ -4,15 +4,15 @@
 from string import ascii_letters
 
 
-VALID_CHARS = {' ', '-'} | set(ascii_letters)
+VALID_CHARS = {" ", "-"} | set(ascii_letters)
 
 
 def abbreviate(to_abbreviate):
-    to_abbreviate = ''.join(' ' if char == '-' else char
+    to_abbreviate = "".join(" " if char == "-" else char
                             for char in to_abbreviate
                             if char in VALID_CHARS)
 
-    return ''.join(word[0] for word in to_abbreviate.split()).upper()
+    return "".join(word[0] for word in to_abbreviate.split()).upper()
 ```
 
 One way someone might try to increase performce is to use a single [generator expression][generator-expression] to clean the input, rather than using multiple calls to [`str.replace()`][str-replace].

exercises/practice/acronym/.approaches/double-generator-expression/snippet.txt

@@ -1,8 +1,8 @@
-VALID_CHARS = {' ', '-'} | set(ascii_letters)
+VALID_CHARS = {" ", "-"} | set(ascii_letters)
 
 def abbreviate(to_abbreviate):
-    to_abbreviate = ''.join(' ' if char == '-' else char
+    to_abbreviate = "".join(" " if char == "-" else char
                             for char in to_abbreviate
                             if char in VALID_CHARS)
 
-    return ''.join(word[0] for word in to_abbreviate.split()).upper()
+    return "".join(word[0] for word in to_abbreviate.split()).upper()

exercises/practice/acronym/.approaches/functools-reduce/content.md

@@ -1,46 +1,47 @@
-# Scrub with `replace()` and join via `functools.reduce()`
+# Clean with `replace()` and join via `functools.reduce()`
 
 
 ```python
 from functools import reduce
 
 
 def abbreviate(to_abbreviate):
-    phrase = to_abbreviate.replace("_", " ").replace("-", " ").upper().split()
+    phrase = to_abbreviate.replace("-", " ").replace("_", " ").upper().split()
 
     return reduce(lambda start, word: start + word[0], phrase, "")
 ```
 
-
--  This approach begins by using  [`str.replace()`][str-replace] to "scrub" (_remove_) non-letter characters such as `'`,`-`,`_`, and white space from `to_abbreviate`.
-- The phrase is then upper-cased by calling [`str.upper()`][str-upper],
+- This approach begins by using [`str.replace()`][str-replace] on `to_abbreviate` to convert non-letter characters such as `-` and `_` into spaces.
+- The phrase is then upper-cased by calling [`str.upper()`][str-upper].
 - Finally, the phrase is turned into a `list` of words by calling [`str.split()`][str-split].
 
-The three methods above are all [chained][chaining] together, with the output of one method serving as the input to the next method in the "chain".
-This works because both `replace()` and `upper()` return strings, and both `upper()` and `split()` take strings as arguments.
-However, if `split()` were called first, `replace()` and `upper()` would fail, since neither method will take a `list` as input.
+The three methods above are all [chained][chaining] together, with each method operating on the output of the method before it in the "chain".
+This works because both `replace()` and `upper()` _operate on_ strings (as they are `str` methods) and _return_ strings.
+If `split()` was called first, `replace()` and `upper()` would fail, since they cannot operate on the `list` returned by `split()`.
 
 ~~~~exercism/note
-`re.findall()` or `re.finditer()` can also be used to "scrub" `to_abbreviate`.
-These two methods from the `re` module will return a `list` or a lazy `iterator` of results, respectively.
-As of this writing, both of these methods benchmark slower than using `str.replace()` for scrubbing.
+`re.findall()` or `re.finditer()` can also be used to clean `to_abbreviate`.
+These two methods from the [`re` module][re-module] will return a `list` or a lazy `iterator` of results, respectively.
+As of this writing, both of these methods benchmark slower than using `str.replace()` for cleaning.
+
+[re-module]: https://docs.python.org/3/library/re.html
 ~~~~
 
 
-Once the phrase is scrubbed and turned into a word `list`, the acronym is created via `reduce()`.
+Once the phrase is cleaned and turned into a word `list`, the acronym is created via `reduce()`.
 `reduce()` is a method from the [`functools`][functools] module, which provides support for higher-order functions and functional programming in Python.
 
 
 [`functools.reduce()`][reduce] applies an anonymous two-argument function (_the [lambda][python lambdas] in the code example_) to the items of an iterable.
- The application of the function travels from left to right, so that the iterable becomes a single value (_it is "reduced" to a single value_).
+The application of the function travels from left to right, so that the iterable becomes a single value (_it is "reduced" to a single value_).
 
 
- Using code from the example above, `reduce(lambda start, word: start + word[0], ['GNU', 'IMAGE', 'MANIPULATION', 'PROGRAM'])` would calculate `((('GNU'[0] + 'IMAGE'[0])+'MANIPULATION'[0])+'PROGRAM'[0])`, or `GIMP`.
-  The left argument, `start`, is the _accumulated value_ and the right argument, `word`, is the value from the iterable that is used to update the accumulated 'total'.
-  The optional 'initializer' value '' is used here, and is placed ahead/before the items of the iterable in the calculation, and serves as a default if the iterable that is passed is empty.
+Using code from the example above, `reduce(lambda start, word: start + word[0], ["GNU", "IMAGE", "MANIPULATION", "PROGRAM"])` would calculate `((("GNU"[0] + "IMAGE"[0]) + "MANIPULATION"[0]) + "PROGRAM"[0])`, or `GIMP`.
+The left argument, `start`, is the _accumulated value_ and the right argument, `word`, is the value from the iterable that is used to update the accumulated 'total'.
+The optional 'initializer' value `""` is used here, and is placed before the items of the iterable in the calculation, and serves as a default if the iterable that is passed is empty.
 
 
-Since using `reduce()` is fairly succinct, it is put directly on the `return` line to produce the acronym rather than assigning and returning an intermediate variable.
+Since using `reduce()` is fairly succinct, it is put directly on the `return` line to produce the acronym, rather than assigning and returning an intermediate variable.
 
 
 In benchmarks, this solution performed about as well as both the `loops` and the `list-comprehension` solutions.

exercises/practice/acronym/.approaches/functools-reduce/snippet.txt

@@ -1,6 +1,6 @@
 from functools import reduce
 
 def abbreviate(to_abbreviate):
-    phrase = to_abbreviate.replace("_", " ").replace("-", " ").upper().split()
+    phrase = to_abbreviate.replace("-", " ").replace("_", " ").upper().split()
 
     return reduce(lambda start, word: start + word[0], phrase, "")

exercises/practice/acronym/.approaches/generator-expression/content.md

@@ -1,43 +1,44 @@
-# Scrub with `replace()` and join via `generator-expression`
+# Clean with `replace()` and join via `generator-expression`
 
 
 ```python
 def abbreviate(to_abbreviate):
-    phrase = to_abbreviate.replace('-', ' ').replace('_', ' ').upper().split()
+    phrase = to_abbreviate.replace("-", " ").replace("_", " ").upper().split()
 
-    # note the lack of square brackets around the comprehension.
-    return ''.join(word[0] for word in phrase)
+    # Note the lack of square brackets around the comprehension.
+    return "".join(word[0] for word in phrase)
 ```
 
-
--  This approach begins by using  [`str.replace()`][str-replace] to "scrub" (_remove_) non-letter characters such as `'`,`-`,`_`, and white space from `to_abbreviate`.
-- The phrase is then upper-cased by calling [`str.upper()`][str-upper],
+- This approach begins by using [`str.replace()`][str-replace] on `to_abbreviate` to convert non-letter characters such as `-` and `_` into spaces.
+- The phrase is then upper-cased by calling [`str.upper()`][str-upper].
 - Finally, the phrase is turned into a `list` of words by calling [`str.split()`][str-split].
 
-The three methods above are all [chained][chaining] together, with the output of one method serving as the input to the next method in the "chain".
-This works because both `replace()` and `upper()` return strings, and both `upper()` and `split()` take strings as arguments.
-However, if `split()` were called first, `replace()` and `upper()` would fail, since neither method will take a `list` as input.
+The three methods above are all [chained][chaining] together, with each method operating on the output of the method before it in the "chain".
+This works because both `replace()` and `upper()` _operate on_ strings (as they are `str` methods) and _return_ strings.
+If `split()` was called first, `replace()` and `upper()` would fail, since they cannot operate on the `list` returned by `split()`.
 
 ~~~~exercism/note
-`re.findall()` or `re.finditer()` can also be used to "scrub" `to_abbreviate`.
-These two methods from the `re` module will return a `list` or a lazy `iterator` of results, respectively.
-As of this writing, both of these methods benchmark slower than using `str.replace()` for scrubbing.
+`re.findall()` or `re.finditer()` can also be used to clean `to_abbreviate`.
+These two methods from the [`re` module][re-module] will return a `list` or a lazy `iterator` of results, respectively.
+As of this writing, both of these methods benchmark slower than using `str.replace()` for cleaning.
+
+[re-module]: https://docs.python.org/3/library/re.html
 ~~~~
 
 
 A [`generator-expression`][generator-expression] is then used to iterate through the phrase and select the first letters of each word via [`bracket notation`][subscript notation].
 
 
-Generator expressions are short-form [generators][generators] - lazy iterators that produce their values _on demand_, instead of saving them to memory.
+Generator expressions are short-form [generators][generators] — lazy iterators that produce their values _on demand_, instead of saving them to memory.
 This generator expression is consumed by [`str.join()`][str-join], which joins the generated letters together using an empty string.
-Other "separator" strings can be used with `str.join()` - see [concept:python/string-methods]() for some additional examples.
-Since the generator expression and `join()` are fairly succinct, they are put directly on the `return` line rather than assigning and returning an intermediate variable for the acronym.
+Other "separator" strings can be used with `str.join()` — see [concept:python/string-methods]() for some additional examples.
+Since the generator expression and `join()` are fairly succinct, they are put directly on the `return` line, rather than assigning and returning an intermediate variable for the acronym.
 
 
 In benchmarks, this solution was surprisingly slower than the `list comprehension` version.
-[This article][Oscar Alsing] from Oscar Alsing briefly explains why.
+[This article][Oscar-Alsing-article] from Oscar Alsing briefly explains why.
 
-[Oscar Alsing]: https://www.oscaralsing.com/list-comprehension-vs-generator-expression/#:~:text=List%20comprehensions%20are%20usually%20faster,difference%20is%20often%20quite%20small.
+[Oscar-Alsing-article]: https://www.oscaralsing.com/list-comprehension-vs-generator-expression/#:~:text=List%20comprehensions%20are%20usually%20faster,difference%20is%20often%20quite%20small.
 [chaining]: https://pyneng.readthedocs.io/en/latest/book/04_data_structures/method_chaining.html
 [generator-expression]: https://dbader.org/blog/python-generator-expressions
 [generators]: https://dbader.org/blog/python-generators

exercises/practice/acronym/.approaches/generator-expression/snippet.txt

@@ -1,5 +1,5 @@
 def abbreviate(to_abbreviate):
-    phrase = to_abbreviate.replace('-', ' ').replace('_', ' ').upper().split()
+    phrase = to_abbreviate.replace("-", " ").replace("_", " ").upper().split()
 
-    # note the lack of square brackets around the comprehension.
-    return ''.join(word[0] for word in phrase)
+    # Note the lack of square brackets around the comprehension.
+    return "".join(word[0] for word in phrase)