Add Window Functions for use with function builder

.gitignore

@@ -4,6 +4,7 @@ target
 /docs/temp
 /docs/build
 .DS_Store
+.vscode
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
@@ -31,3 +32,6 @@ apache-rat-*.jar
 CHANGELOG.md.bak
 
 docs/mdbook/book
+
+.pyo3_build_config
+

docs/source/user-guide/common-operations/aggregations.rst

@@ -15,6 +15,8 @@
 .. specific language governing permissions and limitations
 .. under the License.
 
+.. _aggregation:
+
 Aggregation
 ============
 

docs/source/user-guide/common-operations/windows.rst

@@ -15,13 +15,16 @@
 .. specific language governing permissions and limitations
 .. under the License.
 
+.. _window_functions:
+
 Window Functions
 ================
 
-In this section you will learn about window functions. A window function utilizes values from one or multiple rows to
-produce a result for each individual row, unlike an aggregate function that provides a single value for multiple rows.
+In this section you will learn about window functions. A window function utilizes values from one or
+multiple rows to produce a result for each individual row, unlike an aggregate function that
+provides a single value for multiple rows.
 
-The functionality of window functions in DataFusion is supported by the dedicated :py:func:`~datafusion.functions.window` function.
+The window functions are availble in the :py:mod:`~datafusion.functions` module.
 
 We'll use the pokemon dataset (from Ritchie Vink) in the following examples.
 
@@ -40,54 +43,176 @@ We'll use the pokemon dataset (from Ritchie Vink) in the following examples.
     ctx = SessionContext()
     df = ctx.read_csv("pokemon.csv")
 
-Here is an example that shows how to compare each pokemons’s attack power with the average attack power in its ``"Type 1"``
+Here is an example that shows how you can compare each pokemon's speed to the speed of the
+previous row in the DataFrame.
 
 .. ipython:: python
 
     df.select(
         col('"Name"'),
-        col('"Attack"'),
-        f.alias(
-            f.window("avg", [col('"Attack"')], partition_by=[col('"Type 1"')]),
-            "Average Attack",
-        )
+        col('"Speed"'),
+        f.lag(col('"Speed"')).alias("Previous Speed")
     )
 
-You can also control the order in which rows are processed by window functions by providing
+Setting Parameters
+------------------
+
+
+Ordering
+^^^^^^^^
+
+You can control the order in which rows are processed by window functions by providing
 a list of ``order_by`` functions for the ``order_by`` parameter.
 
 .. ipython:: python
 
     df.select(
         col('"Name"'),
         col('"Attack"'),
-        f.alias(
-            f.window(
-                "rank",
-                [],
-                partition_by=[col('"Type 1"')],
-                order_by=[f.order_by(col('"Attack"'))],
-            ),
-            "rank",
-        ),
+        col('"Type 1"'),
+        f.rank(
+            partition_by=[col('"Type 1"')],
+            order_by=[col('"Attack"').sort(ascending=True)],
+        ).alias("rank"),
+    ).sort(col('"Type 1"'), col('"Attack"'))
+
+Partitions
+^^^^^^^^^^
+
+A window function can take a list of ``partition_by`` columns similar to an
+:ref:`Aggregation Function<aggregation>`. This will cause the window values to be evaluated
+independently for each of the partitions. In the example above, we found the rank of each
+Pokemon per ``Type 1`` partitions. We can see the first couple of each partition if we do
+the following:
+
+.. ipython:: python
+
+    df.select(
+        col('"Name"'),
+        col('"Attack"'),
+        col('"Type 1"'),
+        f.rank(
+            partition_by=[col('"Type 1"')],
+            order_by=[col('"Attack"').sort(ascending=True)],
+        ).alias("rank"),
+    ).filter(col("rank") < lit(3)).sort(col('"Type 1"'), col("rank"))
+
+Window Frame
+^^^^^^^^^^^^
+
+When using aggregate functions, the Window Frame of defines the rows over which it operates.
+If you do not specify a Window Frame, the frame will be set depending on the following
+criteria.
+
+* If an ``order_by`` clause is set, the default window frame is defined as the rows between
+  unbounded preceeding and the current row.
+* If an ``order_by`` is not set, the default frame is defined as the rows betwene unbounded
+  and unbounded following (the entire partition).
+
+Window Frames are defined by three parameters: unit type, starting bound, and ending bound.
+
+The unit types available are:
+
+* Rows: The starting and ending boundaries are defined by the number of rows relative to the
+  current row.
+* Range: When using Range, the ``order_by`` clause must have exactly one term. The boundaries
+  are defined bow how close the rows are to the value of the expression in the ``order_by``
+  parameter.
+* Groups: A "group" is the set of all rows that have equivalent values for all terms in the
+  ``order_by`` clause.
+
+In this example we perform a "rolling average" of the speed of the current Pokemon and the
+two preceeding rows.
+
+.. ipython:: python
+
+    from datafusion.expr import WindowFrame
+
+    df.select(
+        col('"Name"'),
+        col('"Speed"'),
+        f.window("avg",
+            [col('"Speed"')],
+            order_by=[col('"Speed"')],
+            window_frame=WindowFrame("rows", 2, 0)
+        ).alias("Previous Speed")
+    )
+
+Null Treatment
+^^^^^^^^^^^^^^
+
+When using aggregate functions as window functions, it is often useful to specify how null values
+should be treated. In order to do this you need to use the builder function. In future releases
+we expect this to be simplified in the interface.
+
+One common usage for handling nulls is the case where you want to find the last value up to the
+current row. In the following example we demonstrate how setting the null treatment to ignore
+nulls will fill in with the value of the most recent non-null row. To do this, we also will set
+the window frame so that we only process up to the current row.
+
+In this example, we filter down to one specific type of Pokemon that does have some entries in
+it's ``Type 2`` column that are null.
+
+.. ipython:: python
+
+    from datafusion.common import NullTreatment
+
+    df.filter(col('"Type 1"') ==  lit("Bug")).select(
+        '"Name"',
+        '"Type 2"',
+        f.window("last_value", [col('"Type 2"')])
+            .window_frame(WindowFrame("rows", None, 0))
+            .order_by(col('"Speed"'))
+            .null_treatment(NullTreatment.IGNORE_NULLS)
+            .build()
+            .alias("last_wo_null"),
+        f.window("last_value", [col('"Type 2"')])
+            .window_frame(WindowFrame("rows", None, 0))
+            .order_by(col('"Speed"'))
+            .null_treatment(NullTreatment.RESPECT_NULLS)
+            .build()
+            .alias("last_with_null")
+    )
+
+Aggregate Functions
+-------------------
+
+You can use any :ref:`Aggregation Function<aggregation>` as a window function. Currently
+aggregate functions must use the deprecated
+:py:func:`datafusion.functions.window` API but this should be resolved in
+DataFusion 42.0 (`Issue Link <https://github.com/apache/datafusion-python/issues/833>`_). Here
+is an example that shows how to compare each pokemons’s attack power with the average attack
+power in its ``"Type 1"`` using the :py:func:`datafusion.functions.avg` function.
+
+.. ipython:: python
+    :okwarning:
+
+    df.select(
+        col('"Name"'),
+        col('"Attack"'),
+        col('"Type 1"'),
+        f.window("avg", [col('"Attack"')])
+            .partition_by(col('"Type 1"'))
+            .build()
+            .alias("Average Attack"),
     )
 
+Available Functions
+-------------------
+
 The possible window functions are:
 
 1. Rank Functions
-    - rank
-    - dense_rank
-    - row_number
-    - ntile
+    - :py:func:`datafusion.functions.rank`
+    - :py:func:`datafusion.functions.dense_rank`
+    - :py:func:`datafusion.functions.ntile`
+    - :py:func:`datafusion.functions.row_number`
 
 2. Analytical Functions
-    - cume_dist
-    - percent_rank
-    - lag
-    - lead
-    - first_value
-    - last_value
-    - nth_value
+    - :py:func:`datafusion.functions.cume_dist`
+    - :py:func:`datafusion.functions.percent_rank`
+    - :py:func:`datafusion.functions.lag`
+    - :py:func:`datafusion.functions.lead`
 
 3. Aggregate Functions
-    - All aggregate functions can be used as window functions.
+    - All :ref:`Aggregation Functions<aggregation>` can be used as window functions.

python/datafusion/dataframe.py

@@ -123,11 +123,10 @@ def select(self, *exprs: Expr | str) -> DataFrame:
             df = df.select("a", col("b"), col("a").alias("alternate_a"))
 
         """
-        exprs = [
-            arg.expr if isinstance(arg, Expr) else Expr.column(arg).expr
-            for arg in exprs
+        exprs_internal = [
+            Expr.column(arg).expr if isinstance(arg, str) else arg.expr for arg in exprs
         ]
-        return DataFrame(self.df.select(*exprs))
+        return DataFrame(self.df.select(*exprs_internal))
 
     def filter(self, *predicates: Expr) -> DataFrame:
         """Return a DataFrame for which ``predicate`` evaluates to ``True``.