Align module-docstring examples with SKILL.md idioms

Drop the redundant lit() in the dataframe.py module-docstring filter
example and use a plain string group key in the aggregate() doctest, so
both examples model the style SKILL.md recommends. Also document the
sort("a") string form and sort_by() shortcut in SKILL.md's sorting
section.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Author: timsaucer

SKILL.md

@@ -128,14 +128,22 @@ aggregate.
 ### Sorting
 
 ```python
-df.sort(col("a"))                            # ascending (default)
+df.sort("a")                                 # ascending (plain name, preferred)
+df.sort(col("a"))                            # ascending via col()
 df.sort(col("a").sort(ascending=False))      # descending
 df.sort(col("a").sort(nulls_first=False))    # override null placement
+
+df.sort_by("a", "b")                         # ascending-only shortcut
 ```
 
-A plain expression passed to `sort()` is already treated as ascending. Only
-reach for `col(...).sort(...)` when you need to override a default (descending
-order or null placement). Writing `col("a").sort(ascending=True)` is redundant.
+As with `select()` and `aggregate()`, bare column references can be passed as
+plain name strings. A plain expression passed to `sort()` is already treated
+as ascending, so reach for `col(...).sort(...)` only when you need to override
+a default (descending order or null placement). Writing
+`col("a").sort(ascending=True)` is redundant.
+
+For ascending-only sorts with no null-placement override, `df.sort_by(...)` is
+a shorter alias for `df.sort(...)`.
 
 ### Joining
 

python/datafusion/dataframe.py

@@ -35,7 +35,7 @@
 Examples:
     >>> ctx = dfn.SessionContext()
     >>> df = ctx.from_pydict({"a": [1, 2, 3], "b": [10, 20, 30]})
-    >>> df.filter(col("a") > lit(1)).select("b").to_pydict()
+    >>> df.filter(col("a") > 1).select("b").to_pydict()
     {'b': [20, 30]}
 
 See :ref:`user_guide_concepts` in the online documentation for a high-level
@@ -812,7 +812,7 @@ def aggregate(
             Group by a column and produce one row per group:
 
             >>> df.aggregate(
-            ...     [col("team")], [F.sum(col("score")).alias("total")]
+            ...     ["team"], [F.sum(col("score")).alias("total")]
             ... ).sort("team").to_pydict()
             {'team': ['x', 'y'], 'total': [3, 5]}
         """