Adds stub of NarwhalsAdapter

.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")