Adds hello_world contribution

To show a basic dataflow and have people pull it in and start with it.

contrib/hamilton/contrib/user/skrawcz/author.md

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+# skrawcz
+
+Hi I'm Stefan Krawczyk. I am on of the co-creators of Hamilton.
+
+# Github
+https://github.com/skrawcz
+# Linkedin
+https://www.linkedin.com/in/skrawczyk
+# X (Twitter)
+https://www.twitter.com/stefkrawczyk

contrib/hamilton/contrib/user/skrawcz/hello_world/README.md

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+# Purpose of this module
+
+This module implements a hello world dataflow to show transforming columns from a dataframe.
+
+You can pass in your own values for spend & signups, or use the default.
+
+# Configuration Options
+This module can be configured with the following options:
+ - {"default":  "True"} to run it without any inputs.
+ - {} to have to pass in `spend` and `signups`.
+
+# Limitations
+
+None to really note.

contrib/hamilton/contrib/user/skrawcz/hello_world/__init__.py

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+import logging
+
+logger = logging.getLogger(__name__)
+
+from hamilton import contrib
+
+with contrib.catch_import_errors(__name__, __file__, logger):
+    import pandas as pd
+
+from hamilton.function_modifiers import config, extract_columns
+
+
+@extract_columns("spend", "signups")
+@config.when(default="True")
+def default_data() -> pd.DataFrame:
+    return pd.DataFrame(
+        {
+            "signups": pd.Series([1, 10, 50, 100, 200, 400]),
+            "spend": pd.Series([10, 10, 20, 40, 40, 50]),
+        }
+    )
+
+
+def avg_3wk_spend(spend: pd.Series) -> pd.Series:
+    """Rolling 3 week average spend."""
+    return spend.rolling(3).mean()
+
+
+def spend_per_signup(spend: pd.Series, signups: pd.Series) -> pd.Series:
+    """The cost per signup in relation to spend."""
+    return spend / signups
+
+
+def spend_mean(spend: pd.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: pd.Series, spend_mean: float) -> pd.Series:
+    """Shows function that takes a scalar. In this case to zero mean spend."""
+    return spend - spend_mean
+
+
+def spend_std_dev(spend: pd.Series) -> float:
+    """Function that computes the standard deviation of the spend column."""
+    return spend.std()
+
+
+def spend_zero_mean_unit_variance(spend_zero_mean: pd.Series, spend_std_dev: float) -> pd.Series:
+    """Function showing one way to make spend have zero mean and unit variance."""
+    return spend_zero_mean / spend_std_dev
+
+
+if __name__ == "__main__":
+    # run as a script to test Hamilton's execution
+    import __init__ as hello_world
+
+    from hamilton import base, driver
+
+    dr = driver.Driver(
+        {"default": "True"},
+        hello_world,
+        adapter=base.DefaultAdapter(),
+    )
+    # create the DAG image
+    dr.display_all_functions("dag", {"format": "png", "view": False})

contrib/hamilton/contrib/user/skrawcz/hello_world/requirements.txt

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+pandas

contrib/hamilton/contrib/user/skrawcz/hello_world/tags.json

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+{
+  "schema": "1.0",
+  "use_case_tags": ["example", "transformation"],
+  "secondary_tags": {
+    "dataframe-library": "pandas"
+  }
+}

contrib/hamilton/contrib/user/skrawcz/hello_world/valid_configs.jsonl

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+{"description": "Default way to run the dataflow.", "name": "default", "config": {"default":  "True"}}
+{"description": "Bring your own spend & signup values.", "name": "bring-your-own", "config": {}}