[DOC] Fix min_max_scale docstrings rendering (#1123)

* DOC: Fix `min_max_scale` docstrings rendering

* Hide these methods docstrings rendering

* Use mkdoc admonition

from https://squidfunk.github.io/mkdocs-material/reference/admonitions/

* " -> `

* Drop `:`

* Update CHANGELOG.md

* FIx git comment prefixes

* Fix git comment prefixes

Author: Zeroto521

CHANGELOG.md

@@ -7,8 +7,8 @@
 -   [ENH] New decorator `deprecated_kwargs` for breaking API. #1103 @Zeroto521
 -   [ENH] Extend select_columns to support non-string columns. Also allow selection on MultiIndex columns via level parameter. #1105 @samukweku
 -   [ENH] Performance improvement for groupby_topk. #1093 @samukweku
--   [EHN] `min_max_scale` drop `old_min` and `old_max` to fit sklearn's method API. Issue #1068 @Zeroto521
--   [EHN] Add `jointly` option for `min_max_scale` support to transform each column values or entire values. Default transform each column, similar behavior to `sklearn.preprocessing.MinMaxScaler`. Issue #1067 @Zeroto521
+-   [ENH] `min_max_scale` drop `old_min` and `old_max` to fit sklearn's method API. Issue #1068 @Zeroto521
+-   [ENH] Add `jointly` option for `min_max_scale` support to transform each column values or entire values. Default transform each column, similar behavior to `sklearn.preprocessing.MinMaxScaler`. (Issue #1067, PR #1112, PR #1123) @Zeroto521
 
 ## [v0.23.1] - 2022-05-03
 
@@ -82,7 +82,7 @@
 -   [DOC] Updated various documentation sources to reflect pyjanitor-dev ownership. @loganthomas
 -   [INF] Fix `isort` automatic checks. Issue #845. @loganthomas
 -   [ENH] `complete` function now uses variable args (\*args) - @samukweku
--   [EHN] Set `expand_column`'s `sep` default is `"|"`, same to `pandas.Series.str.get_dummies`. Issue #876. @Zeroto521
+-   [ENH] Set `expand_column`'s `sep` default is `"|"`, same to `pandas.Series.str.get_dummies`. Issue #876. @Zeroto521
 -   [ENH] Deprecate `limit` from fill_direction. fill_direction now uses kwargs. @samukweku
 -   [ENH] Added `conditional_join` function that supports joins on non-equi operators. @samukweku
 -   [INF] Speed up pytest via `-n` (pytest-xdist) option. Issue #881. @Zeroto521

janitor/functions/min_max_scale.py

@@ -96,6 +96,12 @@ def min_max_scale(
     1 to 14. Hence, 3 gets scaled not to 0 but approx. 0.15 instead, while 10
     gets scaled to approx. 0.69 instead.
 
+    !!! summary "Version Changed"
+
+        - 0.24.0
+            - Deleted `old_min`, `old_max`, `new_min`, and `new_max` options.
+            - Added `feature_range`, and `jointly` options.
+
     :param df: A pandas DataFrame.
     :param feature_range: (optional) Desired range of transformed data.
     :param column_name: (optional) The column on which to perform scaling.
@@ -105,10 +111,6 @@ def min_max_scale(
     :raises ValueError: if the length of `feature_range` isn't equal to two.
     :raises ValueError: if the element of `feature_range` isn't number type.
     :raises ValueError: if `feature_range[1]` <= `feature_range[0]`.
-
-    Changed in version 0.24.0: Deleted "old_min", "old_max", "new_min", and
-    "new_max" options.
-    Changed in version 0.24.0: Added "feature_range", and "jointly" options.
     """
 
     if not (
@@ -125,24 +127,24 @@ def min_max_scale(
     if column_name is not None:
         df = df.copy()  # Avoid to change the original DataFrame.
 
-        old_feature_range = df[column_name].pipe(min_max_value, jointly)
+        old_feature_range = df[column_name].pipe(_min_max_value, jointly)
         df[column_name] = df[column_name].pipe(
-            apply_min_max,
+            _apply_min_max,
             *old_feature_range,
             *feature_range,
         )
     else:
-        old_feature_range = df.pipe(min_max_value, jointly)
+        old_feature_range = df.pipe(_min_max_value, jointly)
         df = df.pipe(
-            apply_min_max,
+            _apply_min_max,
             *old_feature_range,
             *feature_range,
         )
 
     return df
 
 
-def min_max_value(df: pd.DataFrame, jointly: bool) -> tuple:
+def _min_max_value(df: pd.DataFrame, jointly: bool) -> tuple:
     """
     Return the minimum and maximum of DataFrame.
 
@@ -162,7 +164,7 @@ def min_max_value(df: pd.DataFrame, jointly: bool) -> tuple:
     return mmin, mmax
 
 
-def apply_min_max(
+def _apply_min_max(
     df: pd.DataFrame,
     old_min: int | float | pd.Series,
     old_max: int | float | pd.Series,