Support string column identifiers for sort/aggregate/window and stricter Expr validation (#1221)

* refactor: improve DataFrame expression handling, type checking, and docs

- Refactor expression handling and `_simplify_expression` for stronger
  type checking and clearer error handling
- Improve type annotations for `file_sort_order` and `order_by` to
  support string inputs
- Refactor DataFrame `filter` method to better validate predicates
- Replace internal error message variable with public constant
- Clarify usage of `col()` and `column()` in DataFrame examples

* refactor: unify expression and sorting logic; improve docs and error handling

- Update `order_by` handling in Window class for better type support
- Improve type checking in DataFrame expression handling
- Replace `Expr`/`SortExpr` with `SortKey` in file_sort_order and
  related functions
- Simplify file_sort_order handling in SessionContext
- Rename `_EXPR_TYPE_ERROR` → `EXPR_TYPE_ERROR` for consistency
- Clarify usage of `col()` vs `column()` in DataFrame examples
- Enhance documentation for file_sort_order in SessionContext

* feat: add ensure_expr helper for validation; refine expression handling, sorting, and docs

- Introduce `ensure_expr` helper and improve internal expression
  validation
- Update error messages and tests to consistently use `EXPR_TYPE_ERROR`
- Refactor expression handling with `_to_raw_expr`, `_ensure_expr`, and
  `SortKey`
- Improve type safety and consistency in sort key definitions and file
  sort order
- Add parameterized parquet sorting tests
- Enhance DataFrame docstrings with clearer guidance and usage examples
- Fix minor typos and error message clarity

* Refactor and enhance expression handling, test coverage, and documentation

- Introduced `ensure_expr_list` to validate and flatten nested
  expressions, treating strings as atomic
- Updated expression utilities to improve consistency across aggregation
  and window functions
- Consolidated and expanded parameterized tests for string equivalence
  in ranking and window functions
- Exposed `EXPR_TYPE_ERROR` for consistent error messaging across
  modules and tests
- Improved internal sort logic using `expr_internal.SortExpr`
- Clarified expectations for `join_on` expressions in documentation
- Standardized imports and improved test clarity for maintainability

* refactor: update docstring for sort_or_default function to clarify its purpose

* fix Ruff errors

* refactor: update type hints to use typing.Union for better clarity and consistency

* fix Ruff errors

* refactor: simplify type hints by removing unnecessary imports for type checking

* refactor: update type hints for rex_type and types methods to improve clarity

* refactor: remove unnecessary type ignore comments from rex_type and types methods

* docs: update section title for clarity on DataFrame method arguments

* docs: clarify description of DataFrame methods accepting column names

* docs: add note to clarify function documentation reference for DataFrame methods

* docs: remove outdated information about predicate acceptance in DataFrame class

* refactor: simplify type hint for expr_list parameter in expr_list_to_raw_expr_list function

* docs: clarify usage of datafusion.col and datafusion.lit in DataFrame method documentation

* docs: clarify usage of col() and lit() in DataFrame filter examples

* Fix ruff errors

Author: kosiew

docs/source/user-guide/dataframe/index.rst

@@ -126,6 +126,56 @@ DataFusion's DataFrame API offers a wide range of operations:
     # Drop columns
     df = df.drop("temporary_column")
 
+Column Names as Function Arguments
+----------------------------------
+
+Some ``DataFrame`` methods accept column names when an argument refers to an
+existing column. These include:
+
+* :py:meth:`~datafusion.DataFrame.select`
+* :py:meth:`~datafusion.DataFrame.sort`
+* :py:meth:`~datafusion.DataFrame.drop`
+* :py:meth:`~datafusion.DataFrame.join` (``on`` argument)
+* :py:meth:`~datafusion.DataFrame.aggregate` (grouping columns)
+
+See the full function documentation for details on any specific function.
+
+Note that :py:meth:`~datafusion.DataFrame.join_on` expects ``col()``/``column()`` expressions rather than plain strings.
+
+For such methods, you can pass column names directly:
+
+.. code-block:: python
+
+    from datafusion import col, functions as f
+
+    df.sort('id')
+    df.aggregate('id', [f.count(col('value'))])
+
+The same operation can also be written with explicit column expressions, using either ``col()`` or ``column()``:
+
+.. code-block:: python
+
+    from datafusion import col, column, functions as f
+
+    df.sort(col('id'))
+    df.aggregate(column('id'), [f.count(col('value'))])
+
+Note that ``column()`` is an alias of ``col()``, so you can use either name; the example above shows both in action.
+
+Whenever an argument represents an expression—such as in
+:py:meth:`~datafusion.DataFrame.filter` or
+:py:meth:`~datafusion.DataFrame.with_column`—use ``col()`` to reference
+columns. The comparison and arithmetic operators on ``Expr`` will automatically
+convert any non-``Expr`` value into a literal expression, so writing
+
+.. code-block:: python
+
+    from datafusion import col
+    df.filter(col("age") > 21)
+
+is equivalent to using ``lit(21)`` explicitly. Use ``lit()`` (also available
+as ``literal()``) when you need to construct a literal expression directly.
+
 Terminal Operations
 -------------------
 

python/datafusion/context.py

@@ -22,29 +22,31 @@
 import warnings
 from typing import TYPE_CHECKING, Any, Protocol
 
-import pyarrow as pa
-
 try:
     from warnings import deprecated  # Python 3.13+
 except ImportError:
     from typing_extensions import deprecated  # Python 3.12
 
+import pyarrow as pa
+
 from datafusion.catalog import Catalog, CatalogProvider, Table
 from datafusion.dataframe import DataFrame
-from datafusion.expr import Expr, SortExpr, sort_list_to_raw_sort_list
+from datafusion.expr import SortKey, sort_list_to_raw_sort_list
 from datafusion.record_batch import RecordBatchStream
 from datafusion.user_defined import AggregateUDF, ScalarUDF, TableFunction, WindowUDF
 
 from ._internal import RuntimeEnvBuilder as RuntimeEnvBuilderInternal
 from ._internal import SessionConfig as SessionConfigInternal
 from ._internal import SessionContext as SessionContextInternal
 from ._internal import SQLOptions as SQLOptionsInternal
+from ._internal import expr as expr_internal
 
 if TYPE_CHECKING:
     import pathlib
+    from collections.abc import Sequence
 
     import pandas as pd
-    import polars as pl
+    import polars as pl  # type: ignore[import]
 
     from datafusion.plan import ExecutionPlan, LogicalPlan
 
@@ -553,7 +555,7 @@ def register_listing_table(
         table_partition_cols: list[tuple[str, str | pa.DataType]] | None = None,
         file_extension: str = ".parquet",
         schema: pa.Schema | None = None,
-        file_sort_order: list[list[Expr | SortExpr]] | None = None,
+        file_sort_order: Sequence[Sequence[SortKey]] | None = None,
     ) -> None:
         """Register multiple files as a single table.
 
@@ -567,23 +569,20 @@ def register_listing_table(
             table_partition_cols: Partition columns.
             file_extension: File extension of the provided table.
             schema: The data source schema.
-            file_sort_order: Sort order for the file.
+            file_sort_order: Sort order for the file. Each sort key can be
+                specified as a column name (``str``), an expression
+                (``Expr``), or a ``SortExpr``.
         """
         if table_partition_cols is None:
             table_partition_cols = []
         table_partition_cols = self._convert_table_partition_cols(table_partition_cols)
-        file_sort_order_raw = (
-            [sort_list_to_raw_sort_list(f) for f in file_sort_order]
-            if file_sort_order is not None
-            else None
-        )
         self.ctx.register_listing_table(
             name,
             str(path),
             table_partition_cols,
             file_extension,
             schema,
-            file_sort_order_raw,
+            self._convert_file_sort_order(file_sort_order),
         )
 
     def sql(self, query: str, options: SQLOptions | None = None) -> DataFrame:
@@ -808,7 +807,7 @@ def register_parquet(
         file_extension: str = ".parquet",
         skip_metadata: bool = True,
         schema: pa.Schema | None = None,
-        file_sort_order: list[list[SortExpr]] | None = None,
+        file_sort_order: Sequence[Sequence[SortKey]] | None = None,
     ) -> None:
         """Register a Parquet file as a table.
 
@@ -827,7 +826,9 @@ def register_parquet(
                 that may be in the file schema. This can help avoid schema
                 conflicts due to metadata.
             schema: The data source schema.
-            file_sort_order: Sort order for the file.
+            file_sort_order: Sort order for the file. Each sort key can be
+                specified as a column name (``str``), an expression
+                (``Expr``), or a ``SortExpr``.
         """
         if table_partition_cols is None:
             table_partition_cols = []
@@ -840,9 +841,7 @@ def register_parquet(
             file_extension,
             skip_metadata,
             schema,
-            [sort_list_to_raw_sort_list(exprs) for exprs in file_sort_order]
-            if file_sort_order is not None
-            else None,
+            self._convert_file_sort_order(file_sort_order),
         )
 
     def register_csv(
@@ -1099,7 +1098,7 @@ def read_parquet(
         file_extension: str = ".parquet",
         skip_metadata: bool = True,
         schema: pa.Schema | None = None,
-        file_sort_order: list[list[Expr | SortExpr]] | None = None,
+        file_sort_order: Sequence[Sequence[SortKey]] | None = None,
     ) -> DataFrame:
         """Read a Parquet source into a :py:class:`~datafusion.dataframe.Dataframe`.
 
@@ -1116,19 +1115,17 @@ def read_parquet(
             schema: An optional schema representing the parquet files. If None,
                 the parquet reader will try to infer it based on data in the
                 file.
-            file_sort_order: Sort order for the file.
+            file_sort_order: Sort order for the file. Each sort key can be
+                specified as a column name (``str``), an expression
+                (``Expr``), or a ``SortExpr``.
 
         Returns:
             DataFrame representation of the read Parquet files
         """
         if table_partition_cols is None:
             table_partition_cols = []
         table_partition_cols = self._convert_table_partition_cols(table_partition_cols)
-        file_sort_order = (
-            [sort_list_to_raw_sort_list(f) for f in file_sort_order]
-            if file_sort_order is not None
-            else None
-        )
+        file_sort_order = self._convert_file_sort_order(file_sort_order)
         return DataFrame(
             self.ctx.read_parquet(
                 str(path),
@@ -1179,6 +1176,24 @@ def execute(self, plan: ExecutionPlan, partitions: int) -> RecordBatchStream:
         """Execute the ``plan`` and return the results."""
         return RecordBatchStream(self.ctx.execute(plan._raw_plan, partitions))
 
+    @staticmethod
+    def _convert_file_sort_order(
+        file_sort_order: Sequence[Sequence[SortKey]] | None,
+    ) -> list[list[expr_internal.SortExpr]] | None:
+        """Convert nested ``SortKey`` sequences into raw sort expressions.
+
+        Each ``SortKey`` can be a column name string, an ``Expr``, or a
+        ``SortExpr`` and will be converted using
+        :func:`datafusion.expr.sort_list_to_raw_sort_list`.
+        """
+        # Convert each ``SortKey`` in the provided sort order to the low-level
+        # representation expected by the Rust bindings.
+        return (
+            [sort_list_to_raw_sort_list(f) for f in file_sort_order]
+            if file_sort_order is not None
+            else None
+        )
+
     @staticmethod
     def _convert_table_partition_cols(
         table_partition_cols: list[tuple[str, str | pa.DataType]],