change doc examples

Author: anafalcao

aws_lambda_powertools/utilities/data_masking/base.py

@@ -95,6 +95,9 @@ def erase(self, data: tuple, fields: list[str]) -> tuple[str]: ...
     @overload
     def erase(self, data: dict, fields: list[str]) -> dict: ...
 
+    @overload
+    def erase(self, data: dict[Any, Any], *, masking_rules: dict[str, object]) -> dict[Any, Any]: ...
+
     @overload
     def erase(
         self,

docs/utilities/data_masking.md

@@ -119,22 +119,37 @@ Erasing will remove the original data and replace it with a `*****`. This means
 
 The `erase` method also supports additional flags for more advanced and flexible masking:
 
-| Flag | Behavior                                                    |
-| ---------------- | ----------------------------------------------------------|
-| `dynamic_mask`(bool)    | When set to `True`, this flag enables custom masking behavior. It activates the use of advanced masking techniques such as pattern-based or regex-based masking.|
-| `custom_mask`(str)    | Specifies a simple pattern for masking data. This pattern is applied directly to the input string, replacing all the original characters. For example, with a `custom_mask` of "XX-XX" applied to "12345", the result would be "XX-XX".|
-| `regex_pattern`(str)    | Defines a regular expression pattern used to identify parts of the input string that should be masked. This allows for more complex and flexible masking rules. It's used in conjunction with `mask_format`.|
-| `mask_format`(str)    | Specifies the format to use when replacing parts of the string matched by `regex_pattern`. It can include placeholders (like \1, \2) to refer to captured groups in the regex pattern, allowing some parts of the original string to be preserved.|
-| `masking_rules`(dict)    | Allows you to apply different masking rules (flags) for each data field.|
-
-=== "custom_data_masking.py"
-    ```python hl_lines="13 17 21 25 36"
+=== "dynamic_mask"
+
+    (bool) When set to `True`, this flag enables custom masking behavior. It activates the use of advanced masking techniques such as pattern-based or regex-based masking.
+
+    > Expression: `data_masker.erase(data, fields=["address.zip"], dynamic_mask=True)`
+
+    > Field result: `'street': '*** **** **'`
+
+=== "custom_mask"
+
+    (str) Specifies a simple pattern for masking data. This pattern is applied directly to the input string, replacing all the original characters. For example, with a `custom_mask` of "XX-XX" applied to "12345", the result would be "XX-XX".
+
+    > Expression: `data_masker.erase(data, fields=["address.zip"], custom_mask="XX")`
+
+    > Field result: `'zip': 'XX'`
+
+=== "regex_pattern & mask_format"
+
+    (str) `regex_pattern` defines a regular expression pattern used to identify parts of the input string that should be masked. This allows for more complex and flexible masking rules. It's used in conjunction with `mask_format`.
+    `mask_format` specifies the format to use when replacing parts of the string matched by `regex_pattern`. It can include placeholders (like \1, \2) to refer to captured groups in the regex pattern, allowing some parts of the original string to be preserved.
+
+    > Expression: `data_masker.erase(data, fields=["email"], regex_pattern=r"(.)(.*)(@.*)", mask_format=r"\1****\3")`
+
+    > Field result: `'email': 'j****@example.com'`
+
+=== "masking_rules"
+
+    (dict) Allows you to apply different masking rules (flags) for each data field.
+    ```python hl_lines="20"
     --8<-- "examples/data_masking/src/custom_data_masking.py"
     ```
-=== "generic_data_input.json"
-    ```json hl_lines="6 7 9 12"
-    --8<-- "examples/data_masking/src/generic_data_input.json"
-    ```
 
 ### Encrypting data
 

examples/data_masking/src/custom_data_masking.py

@@ -9,22 +9,6 @@
 def lambda_handler(event: dict, context: LambdaContext) -> dict:
     data: dict = event.get("body", {})
 
-    # Default erase (*****)
-    default_erased = data_masker.erase(data, fields=["address.zip"])
-    # 'street': '*****'
-
-    # dynamic_mask
-    dynamic_mask = data_masker.erase(data, fields=["address.zip"], dynamic_mask=True)
-    #'street': '*** **** **'
-
-    # custom_mask
-    custom_mask = data_masker.erase(data, fields=["address.zip"], custom_mask="XX")
-    #'zip': 'XX'
-
-    # regex_pattern and mask_format
-    regex_pattern = data_masker.erase(data, fields=["email"], regex_pattern=r"(.)(.*)(@.*)", mask_format=r"\1****\3")
-    #'email': 'j****@example.com'
-
     # Masking rules for each field
     masking_rules = {
         "email": {"regex_pattern": "(.)(.*)(@.*)", "mask_format": r"\1****\3"},
@@ -33,6 +17,6 @@ def lambda_handler(event: dict, context: LambdaContext) -> dict:
         "address.street": {"dynamic_mask": False},
     }
 
-    masking_rules_erase = data_masker.erase(data, masking_rules=masking_rules)
+    result = data_masker.erase(data, masking_rules=masking_rules)
 
-    return default_erased, dynamic_mask, custom_mask, regex_pattern, masking_rules_erase
+    return result