Add h_polars.with_columns

Supporting now using with_columns for both polars eager and lazy
execution.

Inherits from with_columns_base. Some functionality is shared with
h_pandas.with_columns -- TODO: refactor out common logic when
appropriate.

examples/polars/with_columns/README

@@ -0,0 +1,8 @@
+# Using with_columns with Polars
+
+We show the ability to use the familiar `with_columns` from `polars`. Supported for both `pl.DataFrame` and `pl.LazyFrame`.
+
+To see the example look at the notebook.
+
+![image info](./DAG_DataFrame.png)
+![image info](./DAG_lazy.png)

examples/polars/with_columns/my_functions.py

@@ -0,0 +1,51 @@
+import polars as pl
+
+from hamilton.function_modifiers import config
+
+"""
+Notes:
+  1. This file is used for all the [ray|dask|spark]/hello_world examples.
+  2. It therefore show cases how you can write something once and not only scale it, but port it
+     to different frameworks with ease!
+"""
+
+
+@config.when(case="millions")
+def avg_3wk_spend__millions(spend: pl.Series) -> pl.Series:
+    """Rolling 3 week average spend."""
+    return (
+        spend.to_frame("spend").select(pl.col("spend").rolling_mean(window_size=3) / 1e6)
+    ).to_series(0)
+
+
+@config.when(case="thousands")
+def avg_3wk_spend__thousands(spend: pl.Series) -> pl.Series:
+    """Rolling 3 week average spend."""
+    return (
+        spend.to_frame("spend").select(pl.col("spend").rolling_mean(window_size=3) / 1e3)
+    ).to_series(0)
+
+
+def spend_per_signup(spend: pl.Series, signups: pl.Series) -> pl.Series:
+    """The cost per signup in relation to spend."""
+    return spend / signups
+
+
+def spend_mean(spend: pl.Series) -> float:
+    """Shows function creating a scalar. In this case it computes the mean of the entire column."""
+    return spend.mean()
+
+
+def spend_zero_mean(spend: pl.Series, spend_mean: float) -> pl.Series:
+    """Shows function that takes a scalar. In this case to zero mean spend."""
+    return spend - spend_mean
+
+
+def spend_std_dev(spend: pl.Series) -> float:
+    """Function that computes the standard deviation of the spend column."""
+    return spend.std()
+
+
+def spend_zero_mean_unit_variance(spend_zero_mean: pl.Series, spend_std_dev: float) -> pl.Series:
+    """Function showing one way to make spend have zero mean and unit variance."""
+    return spend_zero_mean / spend_std_dev

examples/polars/with_columns/my_functions_lazy.py

@@ -0,0 +1,47 @@
+import polars as pl
+
+from hamilton.function_modifiers import config
+
+"""
+Notes:
+  1. This file is used for all the [ray|dask|spark]/hello_world examples.
+  2. It therefore show cases how you can write something once and not only scale it, but port it
+     to different frameworks with ease!
+"""
+
+
+@config.when(case="millions")
+def avg_3wk_spend__millions(spend: pl.Expr) -> pl.Expr:
+    """Rolling 3 week average spend."""
+    return spend.rolling_mean(window_size=3) / 1e6
+
+
+@config.when(case="thousands")
+def avg_3wk_spend__thousands(spend: pl.Expr) -> pl.Expr:
+    """Rolling 3 week average spend."""
+    return spend.rolling_mean(window_size=3) / 1e3
+
+
+def spend_per_signup(spend: pl.Expr, signups: pl.Expr) -> pl.Expr:
+    """The cost per signup in relation to spend."""
+    return spend / signups
+
+
+def spend_mean(spend: pl.Expr) -> float:
+    """Shows function creating a scalar. In this case it computes the mean of the entire column."""
+    return spend.mean()
+
+
+def spend_zero_mean(spend: pl.Expr, spend_mean: float) -> pl.Expr:
+    """Shows function that takes a scalar. In this case to zero mean spend."""
+    return spend - spend_mean
+
+
+def spend_std_dev(spend: pl.Expr) -> float:
+    """Function that computes the standard deviation of the spend column."""
+    return spend.std()
+
+
+def spend_zero_mean_unit_variance(spend_zero_mean: pl.Expr, spend_std_dev: float) -> pl.Expr:
+    """Function showing one way to make spend have zero mean and unit variance."""
+    return spend_zero_mean / spend_std_dev

hamilton/plugins/h_polars.py

@@ -1,8 +1,27 @@
-from typing import Any, Dict, Type, Union
+import sys
+from types import ModuleType
+from typing import Any, Callable, Collection, Dict, List, Tuple, Type, Union, get_type_hints
 
 import polars as pl
 
-from hamilton import base
+_sys_version_info = sys.version_info
+_version_tuple = (_sys_version_info.major, _sys_version_info.minor, _sys_version_info.micro)
+
+if _version_tuple < (3, 11, 0):
+    pass
+else:
+    pass
+
+# Copied this over from function_graph
+# TODO -- determine the best place to put this code
+from hamilton import base, node, registry
+from hamilton.function_modifiers.expanders import extract_columns
+from hamilton.function_modifiers.recursive import (
+    _default_inject_parameter,
+    subdag,
+    with_columns_base,
+)
+from hamilton.plugins.polars_extensions import DATAFRAME_TYPE
 
 
 class PolarsDataFrameResult(base.ResultMixin):
@@ -54,3 +73,216 @@ def build_result(
 
     def output_type(self) -> Type:
         return pl.DataFrame
+
+
+# Do we need this here?
+class with_columns(with_columns_base):
+    """Initializes a with_columns decorator for polars.
+
+    This allows you to efficiently run groups of map operations on a dataframe. We support
+    both eager and lazy mode in polars. In case of using eager mode the type should be
+    pl.DataFrame and the subsequent operations run on columns with type pl.Series.
+
+    Here's an example of calling in eager mode -- if you've seen ``@subdag``, you should be familiar with
+    the concepts:
+
+    .. code-block:: python
+
+        # my_module.py
+        def a_b_average(a: pl.Series, b: pl.Series) -> pl.Series:
+            return (a + b) / 2
+
+
+    .. code-block:: python
+
+        # with_columns_module.py
+        def a_plus_b(a: pl.Series, b: pl.Series) -> pl.Series:
+            return a + b
+
+
+        # the with_columns call
+        @with_columns(
+            *[my_module], # Load from any module
+            *[a_plus_b], # or list operations directly
+            columns_to_pass=["a", "b"], # The columns to pass from the dataframe to
+            # the subdag
+            select=["a_plus_b", "a_b_average"], # The columns to append to the dataframe
+        )
+        def final_df(initial_df: pl.DataFrame) -> pl.DataFrame:
+            # process, or just return unprocessed
+            ...
+
+    In this instance the ``initial_df`` would get two columns added: ``a_plus_b`` and ``a_b_average``.
+
+    Note that the operation is "append", meaning that the columns that are selected are appended
+    onto the dataframe.
+
+    If the function takes multiple dataframes, the dataframe input to process will always be
+    the first argument. This will be passed to the subdag, transformed, and passed back to the function.
+    This follows the hamilton rule of reference by parameter name. To demonstarte this, in the code
+    above, the dataframe that is passed to the subdag is `initial_df`. That is transformed
+    by the subdag, and then returned as the final dataframe.
+
+    You can read it as:
+
+    "final_df is a function that transforms the upstream dataframe initial_df, running the transformations
+    from my_module. It starts with the columns a_from_df and b_from_df, and then adds the columns
+    a, b, and a_plus_b to the dataframe. It then returns the dataframe, and does some processing on it."
+
+    In case you need more flexibility you can alternatively use ``on_input``, for example,
+
+    .. code-block:: python
+
+        # with_columns_module.py
+        def a_from_df() -> pl.Expr:
+            return pl.col(a).alias("a") / 100
+
+        def b_from_df() -> pl.Expr:
+            return pl.col(b).alias("b") / 100
+
+
+        # the with_columns call
+        @with_columns(
+            *[my_module],
+            on_input="initial_df",
+            select=["a_from_df", "b_from_df", "a_plus_b", "a_b_average"],
+        )
+        def final_df(initial_df: pl.DataFrame) -> pl.DataFrame:
+            # process, or just return unprocessed
+            ...
+
+    the above would output a dataframe where the two columns ``a`` and ``b`` get
+    overwritten.
+    """
+
+    def __init__(
+        self,
+        *load_from: Union[Callable, ModuleType],
+        columns_to_pass: List[str] = None,
+        pass_dataframe_as: str = None,
+        on_input: str = None,
+        select: List[str] = None,
+        namespace: str = None,
+        config_required: List[str] = None,
+    ):
+        """Instantiates a ``@with_columns`` decorator.
+
+        :param load_from: The functions or modules that will be used to generate the group of map operations.
+        :param columns_to_pass: The initial schema of the dataframe. This is used to determine which
+            upstream inputs should be taken from the dataframe, and which shouldn't. Note that, if this is
+            left empty (and external_inputs is as well), we will assume that all dependencies come
+            from the dataframe. This cannot be used in conjunction with on_input.
+        :param on_input: The name of the dataframe that we're modifying, as known to the subdag.
+            If you pass this in, you are responsible for extracting columns out. If not provided, you have
+            to pass columns_to_pass in, and we will extract the columns out on the first parameter for you.
+        :param select: The end nodes that represent columns to be appended to the original dataframe
+            via with_columns. Existing columns will be overridden. The selected nodes need to have the
+            corresponding column type, in this case pl.Series, to be appended to the original dataframe.
+        :param namespace: The namespace of the nodes, so they don't clash with the global namespace
+            and so this can be reused. If its left out, there will be no namespace (in which case you'll want
+            to be careful about repeating it/reusing the nodes in other parts of the DAG.)
+        :param config_required: the list of config keys that are required to resolve any functions. Pass in None\
+            if you want the functions/modules to have access to all possible config.
+        """
+
+        if pass_dataframe_as is not None:
+            raise NotImplementedError(
+                "We currently do not support pass_dataframe_as for pandas. Please reach out if you need this "
+                "functionality."
+            )
+
+        super().__init__(
+            *load_from,
+            columns_to_pass=columns_to_pass,
+            on_input=on_input,
+            select=select,
+            namespace=namespace,
+            config_required=config_required,
+            dataframe_type=DATAFRAME_TYPE,
+        )
+
+    def _create_column_nodes(
+        self, fn: Callable, inject_parameter: str, params: Dict[str, Type[Type]]
+    ) -> List[node.Node]:
+        output_type = params[inject_parameter]
+
+        def temp_fn(**kwargs) -> Any:
+            return kwargs[inject_parameter]
+
+        # We recreate the df node to use extract columns
+        temp_node = node.Node(
+            name=inject_parameter,
+            typ=output_type,
+            callabl=temp_fn,
+            input_types={inject_parameter: output_type},
+        )
+
+        extract_columns_decorator = extract_columns(*self.initial_schema)
+
+        out_nodes = extract_columns_decorator.transform_node(temp_node, config={}, fn=temp_fn)
+        return out_nodes[1:]
+
+    def get_initial_nodes(
+        self, fn: Callable, params: Dict[str, Type[Type]]
+    ) -> Tuple[str, Collection[node.Node]]:
+        """Selects the correct dataframe and optionally extracts out columns."""
+        inject_parameter = _default_inject_parameter(fn=fn, target_dataframe=self.target_dataframe)
+        with_columns_base.validate_dataframe(
+            fn=fn,
+            inject_parameter=inject_parameter,
+            params=params,
+            required_type=self.dataframe_type,
+        )
+
+        initial_nodes = (
+            []
+            if self.target_dataframe is not None
+            else self._create_column_nodes(fn=fn, inject_parameter=inject_parameter, params=params)
+        )
+
+        return inject_parameter, initial_nodes
+
+    def get_subdag_nodes(self, fn: Callable, config: Dict[str, Any]) -> Collection[node.Node]:
+        return subdag.collect_nodes(config, self.subdag_functions)
+
+    def chain_subdag_nodes(
+        self, fn: Callable, inject_parameter: str, generated_nodes: Collection[node.Node]
+    ) -> node.Node:
+        "Node that adds to / overrides columns for the original dataframe based on selected output."
+
+        if self.select is None:
+            self.select = [
+                sink_node.name
+                for sink_node in generated_nodes
+                if sink_node.type == registry.get_column_type_from_df_type(self.dataframe_type)
+            ]
+
+        def new_callable(**kwargs) -> Any:
+            df = kwargs[inject_parameter]
+            columns_to_append = {}
+            for column in self.select:
+                columns_to_append[column] = kwargs[column]
+
+            return df.with_columns(**columns_to_append)
+
+        column_type = registry.get_column_type_from_df_type(self.dataframe_type)
+        input_map = {column: column_type for column in self.select}
+        input_map[inject_parameter] = self.dataframe_type
+        merge_node = node.Node(
+            name="__append",
+            typ=self.dataframe_type,
+            callabl=new_callable,
+            input_types=input_map,
+        )
+        output_nodes = generated_nodes + [merge_node]
+        return output_nodes, merge_node.name
+
+    def validate(self, fn: Callable):
+        inject_parameter = _default_inject_parameter(fn=fn, target_dataframe=self.target_dataframe)
+        params = get_type_hints(fn)
+        with_columns_base.validate_dataframe(
+            fn=fn,
+            inject_parameter=inject_parameter,
+            params=params,
+            required_type=self.dataframe_type,
+        )