Add docs

docs/reference/decorators/with_columns.rst

@@ -2,7 +2,19 @@
 with_columns
 =======================
 
-** Overview **
+Pandas
+--------------
+
+We have a ``with_columns`` option to run operations on columns of a Pandas dataframe and append the results as new columns.
+
+**Reference Documentation**
+
+.. autoclass:: hamilton.function_modifiers.with_columns
+   :special-members: __init__
+
+
+PySpark
+--------------
 
 This is part of the hamilton pyspark integration. To install, run:
 

examples/pandas/with_columns/README

@@ -0,0 +1,7 @@
+# Using with_columns with Pandas
+
+We show the ability to use the familiar `with_columns` from either `pyspark` or `polars` on a Pandas dataframe.
+
+To see the example look at the notebook.
+
+![image info](./dag.png)

examples/pandas/with_columns/my_functions.py

@@ -0,0 +1,38 @@
+import pandas as pd
+
+"""
+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!
+"""
+
+
+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