Add with_columns for pandas hello_world example

examples/pandas/with_columns/README

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+# 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

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+import pandas as pd
+
+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: pd.Series) -> pd.Series:
+    """Rolling 3 week average spend."""
+    return spend.rolling(3).mean() / 1e6
+
+
+@config.when(case="thousands")
+def avg_3wk_spend__thousands(spend: pd.Series) -> pd.Series:
+    """Rolling 3 week average spend."""
+    return spend.rolling(3).mean() / 1e3
+
+
+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