use lambda function

Author: dpprdan

docs/user-guide/preprocessing.qmd

@@ -55,16 +55,12 @@ less than 10 characters. We can't directly use the `~~Validate.col_vals_lt()` va
 that column because it is meant to be used with a column of numeric values. Let's just give that
 method what it needs and create a column with string lengths!
 
-The target table is a Polars DataFrame so we'll provide a function that uses the Polars API to add
-in that numeric column:
+The target table is a Polars DataFrame so we'll provide a lambda function that uses the Polars API
+to add in that numeric column:
 
 ```{python}
 import polars as pl
 
-# Define a preprocessing function that gets string lengths from column `b`
-def add_string_length_column(df):
-    return df.with_columns(string_lengths=pl.col("b").str.len_chars())
-
 (
     pb.Validate(
         data=pb.load_dataset(dataset="small_table", tbl_type="polars"),
@@ -79,8 +75,8 @@ def add_string_length_column(df):
         # The string length value to be less than ---
         value=10,
 
-        # The preprocessing function that modifies the table ---
-        pre=add_string_length_column
+        # Polars expression that modifies the table ---
+        pre=lambda df: df.with_columns(string_lengths=pl.col("b").str.len_chars())
     )
     .interrogate()
 )
@@ -213,7 +209,7 @@ def get_median_columns_c_and_a(df):
         columns="c",
         value=pb.col("a"),
 
-        # Using Narwhals to modify the table; generates table with columns `c` and `a` ---
+        # Custom function to modify the table; generates table with columns `c` and `a` ---
         pre=get_median_columns_c_and_a
     )
     .interrogate()