Adds stub of NarwhalsAdapter (#998)

The purpose of this adapter is to showcase how you can write
transforms that are agnostic of the dataframe type.

Assumptions for this plugin:

* you can only have one "backend"; you can't mix & match. That means you can't load some in pandas, and some in polars I don't think -- this is a narwhals limitation.
* This change uses the narwhals decorator. This assumes that non pandas/polars stuff would be left alone by it. If not, we could just skip adding it if we don't detect a type.
* This makes the user choose what the return result builder is and then requires them to nest it in the narwhals result builder that just converts the outputs to the backend that is being used.
* I think this is a good enough integration to get out -- we'll likely tweak/add more functionality as feedback comes in.



Squashed commits:

* Adds stub of NarwhalsAdapter

Assumptions narwhals has (I believe):
1. you can only have one "backend"; you can't mix & match.
That means you can't load some in pandas, and some in polars
I don't think.
2. This change uses the narwhals decorator. This assumes
that non pandas/polars stuff would be left alone by it.
If not, we could just skip adding it if we don't detect
a type.

Otherwise probably need a better example from narhwals.

* Adds one attempt at a result builder

This makes the user choose what the return type
is and then requires them to nest it in the
narwhals result builder that just converts
the outputs to the backend that is being used.

* Adds narwhals plugin v1

First version of narwhals support.

* Completes Narwhals example

Adds README and notebook so that people can run
this example easily.

Also adds circleci tests.

* Adds missing dependency

* Fixes polars test for polars 1.0+

* Adds narwhals to integration docs

.ci/test.sh

@@ -51,6 +51,13 @@ if [[ ${TASK} == "vaex" ]]; then
     exit 0
 fi
 
+if [[ ${TASK} == "narwhals" ]]; then
+    pip install -e .
+    pip install polars pandas narwhals
+    pytest plugin_tests/h_narwhals
+    exit 0
+fi
+
 if [[ ${TASK} == "tests" ]]; then
     pip install .
     pytest \

.circleci/config.yml

@@ -155,3 +155,21 @@ workflows:
           name: integrations-py312
           python-version: '3.12'
           task: integrations
+      - test:
+          requires:
+            - check_for_changes
+          name: narwhals-py39
+          python-version: '3.9'
+          task: narwhals
+      - test:
+          requires:
+            - check_for_changes
+          name: narwhals-py310
+          python-version: '3.10'
+          task: narwhals
+      - test:
+          requires:
+            - check_for_changes
+          name: narwhals-py311
+          python-version: '3.11'
+          task: narwhals

docs/integrations/index.rst

@@ -26,3 +26,4 @@ This section showcases how Hamilton integrates with popular frameworks.
     Slack <https://github.com/DAGWorks-Inc/hamilton/tree/main/examples/slack>
     Spark <https://github.com/DAGWorks-Inc/hamilton/tree/main/examples/spark>
     Vaex <https://github.com/DAGWorks-Inc/hamilton/tree/main/examples/vaex>
+    Narwhals <https://github.com/DAGWorks-Inc/hamilton/tree/main/examples/narwhals>

examples/narwhals/README.md

@@ -0,0 +1,28 @@
+# Narwhals
+
+[Narwhals](https://narwhals-dev.github.io/narwhals/) is a library that aims
+to unify expression across dataframe libraries. It is meant to be lightweight
+and focuses on python first dataframe libraries.
+
+This examples shows how you can write dataframe agnostic code
+and then load up a pandas or polars data to then use with it.
+
+## Running the example
+
+You can run the example doing:
+
+```bash
+# cd examples/narwhals/
+python example.py
+```
+This will run both variants one after the other.
+
+or running the notebook:
+
+```bash
+# cd examples/narwhals
+jupyter notebook   # pip install jupyter if you don't have it
+```
+Or you can open up the notebook in Colab:
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dagworks-inc/hamilton/blob/main/examples/narwhals/notebook.ipynb)

examples/narwhals/example.py

@@ -0,0 +1,70 @@
+import narwhals as nw
+import pandas as pd
+import polars as pl
+
+from hamilton.function_modifiers import config, tag
+
+
+@config.when(load="pandas")
+def df__pandas() -> nw.DataFrame:
+    return pd.DataFrame({"a": [1, 1, 2, 2, 3], "b": [4, 5, 6, 7, 8]})
+
+
+@config.when(load="pandas")
+def series__pandas() -> nw.Series:
+    return pd.Series([1, 3])
+
+
+@config.when(load="polars")
+def df__polars() -> nw.DataFrame:
+    return pl.DataFrame({"a": [1, 1, 2, 2, 3], "b": [4, 5, 6, 7, 8]})
+
+
+@config.when(load="polars")
+def series__polars() -> nw.Series:
+    return pl.Series([1, 3])
+
+
+@tag(nw_kwargs=["eager_only"])
+def example1(df: nw.DataFrame, series: nw.Series, col_name: str) -> int:
+    return df.filter(nw.col(col_name).is_in(series.to_numpy())).shape[0]
+
+
+def group_by_mean(df: nw.DataFrame) -> nw.DataFrame:
+    return df.group_by("a").agg(nw.col("b").mean()).sort("a")
+
+
+if __name__ == "__main__":
+    import __main__ as example
+
+    from hamilton import base, driver
+    from hamilton.plugins import h_narwhals, h_polars
+
+    # pandas
+    dr = (
+        driver.Builder()
+        .with_config({"load": "pandas"})
+        .with_modules(example)
+        .with_adapters(
+            h_narwhals.NarwhalsAdapter(),
+            h_narwhals.NarwhalsDataFrameResultBuilder(base.PandasDataFrameResult()),
+        )
+        .build()
+    )
+    r = dr.execute([example.group_by_mean, example.example1], inputs={"col_name": "a"})
+    print(r)
+
+    # polars
+    dr = (
+        driver.Builder()
+        .with_config({"load": "polars"})
+        .with_modules(example)
+        .with_adapters(
+            h_narwhals.NarwhalsAdapter(),
+            h_narwhals.NarwhalsDataFrameResultBuilder(h_polars.PolarsDataFrameResult()),
+        )
+        .build()
+    )
+    r = dr.execute([example.group_by_mean, example.example1], inputs={"col_name": "a"})
+    print(r)
+    dr.display_all_functions("example.png")